ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards

Rui Gao; Alfonso F. Torres-Rua; Hector Nieto; Einara Zahn; Lawrence Hipps; William P. Kustas; Maria Mar Alsina; Nicolas Bambach; Sebastian J. Castro; John H. Prueger; Joseph Alfieri; Lynn G. McKee; William A. White; Feng Gao; Andrew J. McElrone; Martha Anderson; Kyle Knipper; Calvin Coopmans; Ian Gowing; Nurit Agam; Luis Sanchez; Nick Dokoozlian

doi:10.3390/rs15030756

ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards

Gao, Rui;Torres-Rua, Alfonso F.;Nieto, Hector;Zahn, Einara;Hipps, Lawrence;Kustas, William P.;Alsina, Maria Mar;Bambach, Nicolas;Castro, Sebastian J.;Prueger, John H.;Alfieri, Joseph;McKee, Lynn G.;White, William A.;Gao, Feng;McElrone, Andrew J.;Anderson, Martha;Knipper, Kyle;Coopmans, Calvin;Gowing, Ian;Agam, Nurit;Sanchez, Luis;Dokoozlian, Nick 2023-01-28 00:00:00 remote sensing Article ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards 1, 1 2 3 4 5 Rui Gao * , Alfonso F. Torres-Rua , Hector Nieto , Einara Zahn , Lawrence Hipps , William P. Kustas , 6 7 7 8 5 5 Maria Mar Alsina , Nicolas Bambach , Sebastian J. Castro , John H. Prueger , Joseph Alfieri , Lynn G. McKee , 5 5 7,9 5 10 11 William A. White , Feng Gao , Andrew J. McElrone , Martha Anderson , Kyle Knipper , Calvin Coopmans , 1 12 6 6 Ian Gowing , Nurit Agam , Luis Sanchez and Nick Dokoozlian Civil and Environmental Engineering Department, Utah State University, Old Main Hill, Logan, UT 84322, USA Institute of Agricultural Sciences—CSIC, 28006 Madrid, Spain Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA Plants, Soils, and Climate Department, Utah State University, Old Main Hill, Logan, UT 84322, USA U.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA E & J Gallo Winegrowing Research, Modesto, CA 95354, USA Department of Land, Air, and Water Resources, University of California, Davis, CA 95616, USA U.S. Department of Agriculture, Agricultural Research Service, National Laboratory for Agriculture and the Environment, Ames, IA 50011, USA USDA-Agricultural Research Service, Davis, CA 95616, USA Sustainable Agriculture Water Systems Research Unit, USDA ARS, Davis, CA 95616, USA Electrical and Computer Engineering Department, Utah State University, Old Main Hill, Logan, UT 84322, USA Blaustein Institutes for Desert Research, Sede-Boqer Campus, Ben-Gurion University of the Negev, Be’er Sheva 84990, Israel * Correspondence: [email protected] or [email protected] Abstract: Evapotranspiration (ET) is a crucial part of commercial grapevine production in California, and the partitioning of this quantity allows the separate assessment of soil and vine water and energy Citation: Gao, R.; Torres-Rua, A.F.; ﬂuxes. This partitioning has an important role in agriculture since it is related to grapevine stress, Nieto, H.; Zahn, E.; Hipps, L.; Kustas, yield quality, irrigation efﬁciency, and growth. Satellite remote sensing-based methods provide an W.P.; Alsina, M.M.; Bambach, N.; opportunity for ET partitioning at a subﬁeld scale. However, medium-resolution satellite imagery Castro, S.J.; Prueger, J.H.; et al. ET Partitioning Assessment Using the from platforms such as Landsat is often insufﬁcient for precision agricultural management at the TSEB Model and sUAS Information plant scale. Small, unmanned aerial systems (sUAS) such as the AggieAir platform from Utah across California Central Valley State University enable ET estimation and its partitioning over vineyards via the two-source energy Vineyards. Remote Sens. 2023, 15, 756. balance (TSEB) model. This study explores the assessment of ET and ET partitioning (i.e., soil water https://doi.org/10.3390/rs15030756 evaporation and plant transpiration), considering three different resistance models using ground- based information and aerial high-resolution imagery from the Grape Remote sensing Atmospheric Academic Editor: Gabriel Senay Proﬁle and Evapotranspiration eXperiment (GRAPEX). We developed a new method for temperature Received: 4 January 2023 partitioning that incorporated a quantile technique separation (QTS) and high-resolution sUAS Revised: 20 January 2023 information. This new method, coupled with the TSEB model (called TSEB-2T ), improved sensible Accepted: 24 January 2023 heat ﬂux (H) estimation, regarding the bias, with around 61% and 35% compared with the H from Published: 28 January 2023 the TSEB-PT and TSEB-2T, respectively. Comparisons among ET partitioning estimates from three different methods (Modiﬁed Relaxed Eddy Accumulation—MREA; Flux Variance Similarity—FVS; and Conditional Eddy Covariance—CEC) based on EC ﬂux tower data show that the transpiration Copyright: © 2023 by the authors. estimates obtained from the FVS method are statistically different from the estimates from the MREA Licensee MDPI, Basel, Switzerland. and the CEC methods, but the transpiration from the MREA and CEC methods are statistically This article is an open access article the same. By using the transpiration from the CEC method to compare with the transpiration distributed under the terms and modeled by different TSEB models, the TSEB-2T shows better agreement with the transpiration conditions of the Creative Commons obtained via the CEC method. Additionally, the transpiration estimation from TSEB-2T coupled Attribution (CC BY) license (https:// with different resistance models resulted in insigniﬁcant differences. This comparison is one of the creativecommons.org/licenses/by/ 4.0/). Remote Sens. 2023, 15, 756. https://doi.org/10.3390/rs15030756 https://www.mdpi.com/journal/remotesensing Remote Sens. 2023, 15, 756 2 of 20 ﬁrst for evaluating ET partitioning estimation from sUAS imagery based on eddy covariance-based partitioning methods. Keywords: temperature separation; ET partitioning; transpiration; transpiration ratio; TSEB-PT; TSEB-2T; energy closure; sUAS; California vineyards 1. Introduction Climate change and water scarcity are elevating the importance of sustainable irri- gation management, as agriculture accounts for approximately 70% of the worldwide freshwater demands [1]. Precision irrigation management can improve crop growing status and yield production [2] and prevent soil erosion, while also balancing the rela- tionship between urban and agricultural water distribution. The accurate estimation of evapotranspiration (ET) and its component ﬂuxes transpiration (T) and soil evaporation (E), along with detailed corresponding spatial information at the sub-ﬁeld (plant) scale, is of particular interest for supporting site-speciﬁc, precision irrigation management [3,4]. Quantiﬁcation of the percentage of ET arising from T aids the understanding of changes in carbon assimilation and water cycling in a changing environment [5]; however, obtaining estimates of ET partitioning at spatiotemporal scales pertinent to management activities remains challenging. Remote sensing techniques provide a path for ET mapping and monitoring at the ﬁeld scale using satellite and airborne imagery [6–10]. Although satellites can generate useful timeseries imagery for research and ﬁeld-scale management over broad areas, the relatively coarse spatial resolution of these images, especially the satellite thermal infrared (TIR) resolution used in surface energy balance models, limits its application to ﬁeld and sub-ﬁeld scales with their utility for precision applications [11,12]. Conversely, sUAS, a type of platform equipped with high-resolution sensors, can potentially provide high-resolution data to meet precision agricultural requirements [9]. UAVs are not only a cost-effective tool for obtaining high-resolution data, but also a ﬂexible platform that users can equip with sensors and schedule ﬂight times based on their requirements [13–15]. High-resolution aerial images and ground measurements collected by the Grape Remote sensing Atmospheric Proﬁling and Evapotranspiration eXperiment (GRAPEX) program [16] provide a unique opportunity for ET monitoring and mapping over California vineyards at the plant scale. A two-source energy balance (TSEB) model [17,18] has been used to connect those two types of data, upscaling the spatial scale from a single vine scale to the vineyard scale. With the corresponding eddy-covariance ﬂux tower monitoring ET on the ground, these sites provide an excellent comparison for ET modeling [19–26] and transpiration partitioning via TSEB models at the plant scale. Two versions of the TSEB model have been designed to accommodate the resolution of input surface temperature data. For coarser resolution imagery that does not allow for the direct separation of soil and canopy temperatures, Norman et al., 1995 [17] developed a method to retrieve soil and canopy temperatures by using a single observation of the bulk directional radiometric temperature. This method iteratively adjusts a Priestley–Taylor coefﬁcient controlling the transpiration ﬂux to ﬁnd the realistic solution, and is referred to here as the TSEB-PT model. A second method has been developed to use higher resolution (sub-meter) land-surface temperature (LST) imagery supporting the separation of soil and canopy temperatures, known as TSEB-2T [27]. Leaf area index (LAI) and LST are two key inputs used by both TSEB versions to partition evaporative ﬂuxes between the soil and the canopy—or, in vineyards, between the grape vine and interrow soil or cover crop [28,29]. Although Gao et al., 2022 [30] used machine learning techniques to generate a robust approach to estimate LAI at the plant scale for vineyards across California, challenges related to modeling and evaluating TSEB partitioning remain. Remote Sens. 2023, 15, 756 3 of 20 One such challenge arises because separated canopy and soil temperatures signiﬁ- cantly affect available energy partitioning and the sensible heat ﬂuxes from the soil and canopy components and, thus, ultimately the soil evaporation and plant transpiration [31]. Errors in the soil and canopy temperatures can result in overestimation or underestimation of soil evaporation and plant transpiration [9]. Previous research has used the relation- ship between the normalized difference vegetation index (NDVI) and the corresponding temperature value to obtain separated temperatures [21,26,32]. However, errors resulting from shadows [33,34], image quality, etc., can affect the relationship between NDVI and temperature, which in turn can affect TSEB modeling results. Another challenge is determining the optimal TSEB model framework for ET partition- ing. Several previous studies have shown that TSEB-2T can estimate ET more accurately than TSEB-PT [27]. However, another study for a vineyard in Israel using ground-based LST observations with ground-based measurements of soil E, and eddy covariance (EC) based ET to derive T (T = ET E), suggested TSEB-2T does poorly in partitioning ET compared to TSEB-PT [3]. In that study, they found improvements were needed in soil heat ﬂux estimation, a better algorithm for radiation partitioning, and accounting for vine canopy structure to improve the partitioning using TSEB-PT. The ﬁnal challenge is how to verify the TSEB estimated E and T. While high-frequency EC ﬂux monitoring data are useful for the model validation of total ET [35,36], E and T are not directly measured by the EC ﬂux tower. Fortunately, several techniques have been developed to partition EC water and carbon dioxide ﬂuxes into ground and plant components [37], including Modiﬁed Relaxed Eddy Accumulation, MREA; Flux-Variance Similarity, FVS; and Conditional Eddy-Covariance, CEC. This potentially provides a method for comparison with remote sensing-based estimates aggregated over the EC tower footprint [38], and Nassar et al., 2020 [22] and Gao et al., 2021 [39,40] discussed the footprint calculation for the EC tower in California vineyards. The objectives of this research are (1) to improve the method for temperature separation based on high-resolution LST imagery; (2) to evaluate the performance of different TSEB models coupled with different aerodynamic resistance models in comparison with energy components measured by the EC flux tower; and (3) to quantify the performance of ET partitioning via TSEB models. The modeling and measurement approaches are first described in the Materials and Methods section, and then they are intercompared toward identifying an optimal configuration in assessing ET and ET partitioning in vineyard systems. 2. Materials and Methods 2.1. Study Area This study is part of the ongoing GRAPEX project started in 2013, which seeks to im- prove water-use efﬁciency through the modeling of ET and plant stress in vineyards [41,42]. Vineyard blocks included in this study were located in three different climatic regions in Cal- ifornia. Vineyard blocks equipped with EC ﬂux towers BAR012 and BAR007 were furthest north, in Sonoma County, approximately 6 km south of Cloverdale, CA; EC ﬂux towers SLM001 and SLM002 were located in Sacramento County, approximately 20 km northeast of Lodi, CA; and block RIP 720 equipped with four different EC ﬂux towers (RIP 720-1, RIP 720-2, RIP 720-3, and RIP 720-4) in the same vineyard block and EC ﬂux tower RIP 760 were located in Madera County, about 30 km west of Fresno, CA. The four EC ﬂux towers in block RIP 720 were intended to monitor the ﬂux from the corresponding sub-blocks with different amounts of irrigation applied to cause variations in vine stress, as it was a variable rate deﬁcit irrigation (VRDI) study site. Figure 1 shows the geographical location of each set of vineyard blocks. The position and name of the EC ﬂux towers are labeled with a red cross symbol and white font, respectively, in Figure 1, and the study-site geographic information is presented in Table A1. Remote Sens. 2022, 14, x FOR PEER REVIEW 4 of 21 2. Materials and Methods 2.1. Study Area This study is part of the ongoing GRAPEX project started in 2013, which seeks to improve water-use efficiency through the modeling of ET and plant stress in vineyards [41,42]. Vineyard blocks included in this study were located in three different climatic regions in California. Vineyard blocks equipped with EC flux towers BAR012 and BAR007 were furthest north, in Sonoma County, approximately 6 km south of Cloverdale, CA; EC flux towers SLM001 and SLM002 were located in Sacramento County, approximately 20 km northeast of Lodi, CA; and block RIP 720 equipped with four different EC flux towers (RIP 720-1, RIP 720-2, RIP 720-3, and RIP 720-4) in the same vineyard block and EC flux tower RIP 760 were located in Madera County, about 30 km west of Fresno, CA. The four EC flux towers in block RIP 720 were intended to monitor the flux from the corresponding sub-blocks with different amounts of irrigation applied to cause variations in vine stress, as it was a variable rate deficit irrigation (VRDI) study site. Figure 1 shows the geograph- ical location of each set of vineyard blocks. The position and name of the EC flux towers Remote Sens. 2023, 15, 756 4 of 20 are labeled with a red cross symbol and white font, respectively, in Figure 1, and the study-site geographic information is presented in Table A1. Figure 1. Study areas in California and the position of EC flux towers at each research site. The Figure 1. Study areas in California and the position of EC ﬂux towers at each research site. The position of each EC flux tower within the respective research sites is marked by a red cross and the position of each EC ﬂux tower within the respective research sites is marked by a red cross and the corresponding tower name in white font. corresponding tower name in white font. 2.2. Data 2.2. Data 2.2.1. sUAS Platform Collection 2.2.1. sUAS Platform Collection Remote sensing data gathered via the AggieAir sUAS platform Remote sensing data gathered via the AggieAir sUAS platform (https://uwrl.usu. (https://uwrl.usu.edu/aggieair/, accessed on 10 January 2020) between 2014 and 2019 were edu/aggieair/, accessed on 10 January 2020) between 2014 and 2019 were used in this study. used in this study. Details of the data are presented in Nassar et al., 2021 [23] and in Table Details of the data are presented in Nassar et al., 2021 [23] and in Table A2. These data A2. These data include 4-band spectral images (B, G, R, and NIR) at 10 × 10 cm resolution, include 4-band spectral images (B, G, R, and NIR) at 10 10 cm resolution, digital surface 2 2 2 digital surface model (DSM) data at 10 × 10 cm resolution, and thermal imagery (Tr) at 60 model (DSM) data at 10 10 cm resolution, and thermal imagery (Tr) at 60 60 cm × 60 cm resolution [43]. Images of 6 bands collected via the AggieAir sUAS platform are resolution [43]. Images of 6 bands collected via the AggieAir sUAS platform are included included as an example, and can be seen in Gao et al. 2022 [30]. as an example, and can be seen in Gao et al. 2022 [30]. 2.2.2. Eddy-Covariance Flux Tower Data High-frequency eddy covariance (EC) ﬂux data were also collected in conjunction with intensive observation periods (IOPs) at the tower sites identiﬁed in Figure 1. Tower measurements of net radiation (Rn, Wm ), latent heat ﬂux (or evapotranspiration rate, 2 2 2 LE, Wm ), sensible heat ﬂux (H, Wm ), and soil surface heat ﬂux (G, Wm ) are used in this study to assess the TSEB-PT and TSEB-2T output. More information about the EC ﬂux tower can be found in Kustas et al., 2018 [16] and Bambach et al., 2022 [44], while details about energy closure and ET partitioning for the EC tower data are provided in Section 2.3.3. 2.3. Methodology Figure 2 shows a ﬂowchart of the process for comparing ET rate (LE converted to mass units of mm d ) and ET partitioning between the EC ﬂux tower monitored data and the TSEB modeling results within the corresponding footprint area. The top 5 boxes, along with surface temperature in the second row, are the inputs for the TSEB models. Canopy height, the ratio of canopy width and height, and fractional cover are obtained with a python program [45]; LAI is obtained from the products of recent studies [30,46], using Remote Sens. 2022, 14, x FOR PEER REVIEW 5 of 21 2.2.2. Eddy-Covariance Flux Tower Data High-frequency eddy covariance (EC) flux data were also collected in conjunction with intensive observation periods (IOPs) at the tower sites identified in Figure 1. Tower −2 measurements of net radiation (Rn, Wm ), latent heat flux (or evapotranspiration rate, −2 −2 −2 LE, Wm ), sensible heat flux (H, Wm ), and soil surface heat flux (G, Wm ) are used in this study to assess the TSEB-PT and TSEB-2T output. More information about the EC flux tower can be found in Kustas et al., 2018 [16] and Bambach et al., 2022 [44], while details about energy closure and ET partitioning for the EC tower data are provided in Section 2.3.3. 2.3. Methodology Figure 2 shows a flowchart of the process for comparing ET rate (LE converted to −1 mass units of mm d ) and ET partitioning between the EC flux tower monitored data and the TSEB modeling results within the corresponding footprint area. The top 5 boxes, along Remote Sens. 2023, 15, 756 with surface temperature in the second row, are the inputs for the TSEB models. Canopy 5 of 20 height, the ratio of canopy width and height, and fractional cover are obtained with a python program [45] ; LAI is obtained from the products of recent studies [30,46], using sUAS information and ground-based LAI measurements via machine learning approach. sUAS information and ground-based LAI measurements via machine learning approach. In this study, the weather data are obtained from the flux tower instrumentation. The In this study, the weather data are obtained from the ﬂux tower instrumentation. The TSEB-2T model requires partitioned temperature input (canopy and soil temperature), but TSEB-2T model requires partitioned temperature input (canopy and soil temperature), but other inputs to the two model formulations are the same. other inputs to the two model formulations are the same. Figure 2. Flowchart showing the process of comparing ET rate and ET partitioning from TSEB models Figure 2. Flowchart showing the process of comparing ET rate and ET partitioning from TSEB mod- within the footprint area. The top 5 boxes, along with surface temperature in the second row, are the els within the footprint area. The top 5 boxes, along with surface temperature in the second row, are the inputs for the TSEB models. The ET rate and T/ET were extracted within the corresponding inputs for the TSEB models. The ET rate and T/ET were extracted within the corresponding footprint footprint area and then compared with the EC flux tower monitored data. area and then compared with the EC ﬂux tower monitored data. A A p python-pr ython-pro ogram gram tool tool de developed veloped b by y G Gao ao eet t al. al., , 20 2021 21 [3 [35 5] ] was was used used to ex to extract tract T TSEB SEB modeling modeling re results sultson on L LEEand and ET p ET partitioning artitioning w within ithin the thfootprint e footprin ar t a earea around around each eatower ch tower for comparison with EC ﬂux tower measurements using the approach by Kljun et al., 2015 [38]. for comparison with EC flux tower measurements using the approach by Kljun et al., 2015 [38]. 2.3.1. Temperature Separation This study uses the normalized difference vegetation index (NDVI) as an indicator to separate the total surface radiometric temperature, gridded at 3.6 m resolution, into representative canopy and soil temperature grids at 3.6 m resolution. This method is based on work from prior studies [21,22,26,27,30,34]. In this study, we also included a framework to remove shadow effects in the temperature partitioning process (Figure 3). The removal process is divided into 4 steps. (1) Shadow pixels are identiﬁed geometrically at the time of satellite overpass based on DSM data at 0.15 m pixel level. (2) Shadow pixels are aggregated from 0.15 m to 0.60 m pixel scale, with any 0.60 m pixel containing at least one 0.15 m shadow pixel recognized as a shadow pixel. The reason for choosing 0.60 m is because the coarse resolution for the thermal images is 0.6 m. (3) NDVI is generated based on the 0.15 m optical image and then aggregated to the 0.60 m pixel level. (4) Within each 3.6 m pixel in the ﬁnal modeling domain, the 0.6 m temperature, NDVI, and shadow data are aligned. Any 0.6 m temperature and NDVI pixels that are collocated with a shadow pixel are ignored in building the temperature-NDVI relationship used in the temperature partitioning, as described below. Remote Sens. 2022, 14, x FOR PEER REVIEW 6 of 21 2.3.1. Temperature Separation This study uses the normalized difference vegetation index (NDVI) as an indicator to separate the total surface radiometric temperature, gridded at 3.6 m resolution, into representative canopy and soil temperature grids at 3.6 m resolution. This method is based on work from prior studies [21,22,26,27,30,34]. In this study, we also included a frame- work to remove shadow effects in the temperature partitioning process (Figure 3). The removal process is divided into 4 steps. (1) Shadow pixels are identified geometrically at the time of satellite overpass based on DSM data at 0.15 m pixel level. (2) Shadow pixels are aggregated from 0.15 m to 0.60 m pixel scale, with any 0.60 m pixel containing at least one 0.15 m shadow pixel recognized as a shadow pixel. The reason for choosing 0.60 m is because the coarse resolution for the thermal images is 0.6 m. (3) NDVI is generated based on the 0.15 m optical image and then aggregated to the 0.60 m pixel level. (4) Within each 3.6 m pixel in the final modeling domain, the 0.6 m temperature, NDVI, and shadow data are aligned. Any 0.6 m temperature and NDVI pixels that are collocated with a shadow pixel are ignored in building the temperature-NDVI relationship used in the temperature Remote Sens. 2023, 15, 756 6 of 20 partitioning, as described below. Figure 3. Flowchart showing the ideal temperature separation process for a single TSEB model pixel Figure 3. Flowchart showing the ideal temperature separation process for a single TSEB model pixel (3.6 m resolution). (3.6 m resolution). In previous research, NDVI thresholds were created to identify the category of each In previous research, NDVI thresholds were created to identify the category of each pixel in the model pixeldomain. in the mo For del example, domain. For in ex the am 1:1 plplot e, in th shown e 1:1 p in lot Figur shown e 3 i , n NDVI Figur= e 3, 0.3 Nis DVI = 0.3 is recognized as recogni the threshold zed as the thre to identify shold whether to identi or fy not whethe the pixel r or not (point) the pix isesenescent l (point) is senescent cover cover crop stubble (interrow); the pixel is identified as an interrow pixel when the NDVI value crop stubble (interrow); the pixel is identiﬁed as an interrow pixel when the NDVI value is lower than 0.30. Likewise, the pixel is identified as a vegetation pixel when the NDVI is lower than 0.30. Likewise, the pixel is identiﬁed as a vegetation pixel when the NDVI value is higher than 0.65. The corresponding soil and vegetation temperature are normally value is higher than 0.65. The corresponding soil and vegetation temperature are normally averaged based on the temperature values within the corresponding zone, with NDVI < averaged based on the temperature values within the corresponding zone, with NDVI < 0.3 0.3 for soil zone and NDVI > 0.65 for vegetation zone, respectively. for soil zone and NDVI > 0.65 for vegetation zone, respectively. In some cases, the plot of temperature vs. NDVI shows low correlation, with signifi- In some cases, the plot of temperature vs. NDVI shows low correlation, with signiﬁcant cant scatter. Figure 4 is one such example, showing the temperature separation process scatter. Figure 4 is one such example, showing the temperature separation process for for one 3.6 m modeling pixel at SLM (9 August 2014, 10:41 am, the air temperature is one 3.6 m modeling pixel at SLM (9 August 2014, 10:41 am, the air temperature is around 27.7 C). Figure 4a–c displays an 0.15 m resolution spectral image of the modeling pixel, the corresponding 0.6 m resolution temperature image, and the 0.6 m resolution NDVI image, respectively. The three pixels highlighted by black dashed boxes in Figure 4b,c contain shadows, and the 0.15 m resolution shadows are represented by the red squares in Figure 4c. The solid trend line in Figure 4d is generated based on all (36) points, and the corresponding slope and intercept are shown on the ﬁgure. According to previous research experience, the separated soil temperature is calculated based on the trend line at the soil NDVI threshold (e.g., NDVI = 0.4), and the separated vegetation temperature is averaged based on the pixel temperature within the pure vegetation zone. In this case, the separated soil temperature is potentially underestimated, and the separated vegetation temperature is overestimated due to the large spread in values in the pure vegetation zone. The maximum spread vegetation temperatures is around 5 C; however, relatively small changes in the assumed canopy temperature will impact TSEB-2T [3], so it is important to better constrain temperature samples considered in determining the endpoint pure soil and vegetation temperatures. Remote Sens. 2022, 14, x FOR PEER REVIEW 7 of 21 around 27.7 °C). Figure 4a–c displays an 0.15 m resolution spectral image of the modeling pixel, the corresponding 0.6 m resolution temperature image, and the 0.6 m resolution NDVI image, respectively. The three pixels highlighted by black dashed boxes in Figure 4b,c contain shadows, and the 0.15 m resolution shadows are represented by the red squares in Figure 4c. The solid trend line in Figure 4d is generated based on all (36) points, and the corresponding slope and intercept are shown on the figure. According to previous research experience, the separated soil temperature is calculated based on the trend line at the soil NDVI threshold (e.g., NDVI = 0.4), and the separated vegetation temperature is averaged based on the pixel temperature within the pure vegetation zone. In this case, the separated soil temperature is potentially underestimated, and the separated vegetation temperature is overestimated due to the large spread in values in the pure vegetation zone. The maximum spread vegetation temperatures is around 5 °C; however, relatively small changes in the assumed canopy temperature will impact TSEB-2T [3], so it is im- Remote Sens. 2023, 15, 756 7 of 20 portant to better constrain temperature samples considered in determining the endpoint pure soil and vegetation temperatures. Figure 4. One example showing the performance of the method in one TSEB modeling pixel (3.6 m Figure 4. One example showing the performance of the method in one TSEB modeling pixel (3.6 m resolution grid) to separate the temperature as canopy and soil temperature. (a) Spectral image at resolution grid) to separate the temperature as canopy and soil temperature. (a) Spectral image at 0.15 m resolution, along with (b) co-collected temperature image and (c) generated NDVI image at 0.15 m resolution, along with (b) co-collected temperature image and (c) generated NDVI image at 0.6 m resolution. Pixels highlighted with the dashed line in (b) and (c) represent the locations of 0.6 m resolution. Pixels highlighted with the dashed line in (b,c) represent the locations of shadow at shadow at 0.6 m pixel scale, and the 0.15 m red pixels in (b) represent shadow locations at 0.15 m 0.6 m pixel scale, and the 0.15 m red pixels in (b) represent shadow locations at 0.15 m pixel scale; pixel scale; (d) linear relationship between temperature and NDVI considering 36 pairs of pixels (d) linear relationship between temperature and NDVI considering 36 pairs of pixels within the 3.6 m within the 3.6 m grid. The red points highlighted by dashed lines represent the temperatures from grid. The red points highlighted by dashed lines represent the temperatures from the shadow pixels. the shadow pixels. The pure vegetation zone whose x-axis value is higher than 0.70 and the pure soil zone whose x-axis value is lower than 0.40 are displayed at each side of the x-axis; (e) Within The pure vegetation zone whose x-axis value is higher than 0.70 and the pure soil zone whose x-axis the pure vegetation zone, pixels with temperatures higher than its 75th percentile temperature are value is lower than 0.40 are displayed at each side of the x-axis; (e) Within the pure vegetation zone, pixels with temperatures higher than its 75th percentile temperature are highlighted by dash-lined boxes; (f) pixel locations where the temperature is higher than its 75th percentile temperature are highlighted on the temperature image; (g) box plots for soil region, NDVI 2 [0, 0.40], vegetation region, NDVI 2 [0.70, 1], and the middle part region, NDVI 2 (0.4, 0.7). The 50th and 75th percentile temperatures within the pure vegetation zone are shown on the right side; (h) linear relationship between temperature and NDVI obtained by eliminating vegetation-temperature pixels above the 75th percentile temperature, highlighted by the red dashed-line box. The reason for the high variation of vegetation and soil temperature within a TSEB modeling pixel is potentially coming from the data collection and data processing. The imagery collection process is ﬁnished based on multiple spectral sensors, and the pixels of each sensor do not perfectly align with each other. During the imagery processing, the image–pixel alignment issue is still difﬁcult to address. Therefore, it potentially results in a high variation of temperature in a TSEB modeling pixel. However, this issue can be addressed by upgrading the sensor in the future work, or ﬂying the sensor at a lower Remote Sens. 2023, 15, 756 8 of 20 elevation. Another reason for the high variation of vegetation temperature is because of the vegetation type within the TSEB modeling pixel. The interrow pixel is a mixture of bare soil and senescent cover crop stubble, and the senescent cover crop stubble is short and not well irrigated. When upscaling NDVI from 0.15 m to 0.6 m pixels, most interrow pixels are recognized as healthy vegetation pixels. Therefore, the temperature of the interrow vegetation pixel is higher than the temperature of the vine vegetation, which is well irrigated compared with the senescent cover crop stubble. Quartile tests were performed to optimize the removal of contaminated pure vegeta- tion pixel temperatures. For example, the averaged vegetation temperature, considering all vegetation pixels, is around 32.4 C. If the pixel with a corresponding temperature higher than the 50th (75th) percentile of all vegetation–pixel temperatures is eliminated, the corresponding vegetation temperature is around 31.3 C (32.9 C) (Figure 4e–g). Based on extensive testing, the 75th percentile of the vegetation temperature was identiﬁed as the threshold to eliminate the high-vegetation temperature effect on the vegetation temperature estimation, based on further data analysis. This temperature-separation method is named quantile temperature separation (QTS). Another modiﬁcation in this QTS method relates to the linear relationship between the NDVI and temperature. Typically, pixel temperature decreases with increasing NDVI within a TSEB modeling pixel (e.g., 3.6 m resolution pixel). After the elimination of high vegetation temperatures in the pure vegetation zone, some high points in the middle region (NDVI 2 [0.40, 0.70]) still remain (Figure 4h). These anomalous pixels can affect the linear relationship between the temperature and NDVI [27]. Therefore, a tool called RANSACRegressor (Scikit-learn developers) from the “sklearn.linear_model” is used in this study. This tool is an iterative method for the robust estimation of parameters from a subset of inliers from the complete dataset. The three points highlighted by the red dash-line box (Figure 4h), for example, were eliminated by the tool and then the linear relationship was obtained based on the remaining red points. At the end, a soil temperature was estimated based on the linear relationship at 0.40 (NDVI value). If there was at least one soil pixel within the TSEB modeling pixel, the soil temperature was calculated as an average value based on the temperature value on the soil pixels. The canopy temperature was calculated as the average temperature of pixels above 0.70 NDVI and within the lower 75th percentile of temperature in that vegetation zone. If there were no vegetation pixels found in that TSEB modeling pixel, an “NAN” value was used to represent the canopy temperature. 2.3.2. TSEB Model The two-source energy balance (TSEB) model has been widely used for ET estimation over agricultural lands (e.g., corn, soybeans, cotton, grapevines, almonds, pastures and grazing lands) based on ground, aerial and satellite remote sensing data. A schematic diagram from Kustas et al., 2018 [16] shows the TSEB model resistance network for the sensible heat ﬂux, and lists the set of equations used to obtain the iterative solution. The soil and canopy temperatures constrain the sensible heat ﬂuxes, net radiation, and soil heat ﬂux with the added initial estimate of canopy latent heat ﬂux based on the Priestley–Taylor (PT). This version of the TSEB model is called TSEB-PT [17]. For applications using higher resolution (e.g., sUAS), thermal imagery of the soil and canopy temperatures are derived using the methods described in Section 2.3.1. This version of the model is referred to as TSEB-2T [27]. It is also noted that an earlier study by Kustas and Norman., 1997 [47], using radiometric temperatures at signiﬁcantly different viewing angles, could estimate soil and canopy temperatures. In the TSEB, net radiation, including soil and canopy net radiation, is estimated based on a set of land surface parameters (e.g., longwave emissions from soil, canopy, and sky, solar transmittance through the canopy; canopy and soil albedo). The ground heat flux, G, is estimated as a fraction of the soil net radiation (R ). Nieto et al., 2019 [27] show the empirical nS Remote Sens. 2023, 15, 756 9 of 20 G/R curve fit as a function of time of the day. Considering that all sUAS images were nS collected between 10 am to 4 pm, a constant G-ratio value (0.33) is used in this research. Equation (1) shows the sensible heat ﬂux calculation—the difference between TSEB-PT and TSEB-2T lies in the approach to obtaining T and T . In addition to these component C S temperatures, the aerodynamic resistance of the canopy (R ) and soil (R ) also affect the x s H, but a systematic assessment of different methods for deﬁning these resistances within the TSEB context has not been conducted to date. Three different resistance models for canopy and soil were tested in this study for both TSEB-PT and TSEB-2T: Norman and Kustas (called NK resistance model in this paper, expressed by Equations (2) and (3)), McNaughton and Van (MV model, by Equations (4) and (5)), and Choudhury and Monteith (CM model, by Equations (6) and (7)), respectively. Because the separated temperature images illustrated in Section 2.3.1 are used as input for the TSEB-2T model, the TSEB-2T model coupled with QTS in this study is named as TSEB-2T . T T T T C AC S AC H = H + H = r C + r C (1) C S air p air p R R x s R = p (2) c T T + bu s A s C l R = (3) LAI U d +z 0 0M R = (4) C 0.36 R = l u + (5) x w F u a z d z 0_soil 0 0M h e a a k k h h c c R = e e (6) a k k h R = q (7) CM u a c F 2 1 e a l k = k u (h d ) (8) h 0 In the above equations, R is the aerodynamic resistance of the soil; R is the aero- s x dynamic resistance of the canopy; c and b are the coefﬁcients depending on the turbulent length scale in the canopy, soil-surface roughness, and turbulence intensity in the canopy; T is the soil-surface temperature (K); T is the air temperature (K); u is the wind speed S A s 1 1 0 near the soil surface (ms ); u is the friction velocity (ms ); C is derived from weight- ing a coefﬁcient in the equation for leaf boundary layer resistance over the height of the 1/2 1 2 2 canopy [48] and it is assumed to be 90 s m ; LAI is the leaf area index (m m ); l is the average leaf width (m); U is the wind speed at the heat source-sink (ms ); d +z 0 0M F is the local leaf area index; h is the canopy height; a is the heat diffusion coefﬁcient; c k k is the von Karman’s constant (0.41); z is the roughness length of the soil layer; d 0_soil 0 is the zero-plane displacement height (m); z is the aerodynamic roughness length for 0M momentum transport (m); CM is the leaf drag coefﬁcient [49]; a is the wind extinction coefﬁcient; and u is the wind speed at the canopy interface (ms ). 2.3.3. Validation Data from the Eddy Covariance Tower Energy Components Energy closure of the EC ﬂux monitored data is a concern [19,24] for validating the TSEB modeling results. Nieto et al., 2022 [24], for example, used the arithmetic-mean value for the sensible heat ﬂux and the latent heat ﬂux based on three calculated possible closure corrections to evaluate TSEB modeling results: (1) assigning all the residual error to H; (2) assigning all the residual to LE; and (3) assigning the residual proportionally Remote Sens. 2023, 15, 756 10 of 20 to H and LE by preserving the Bowen Ratio. In this research, the geometric-mean value (Equation (9)) of the sensible heat ﬂux and the geometric-mean value of the latent heat ﬂux are calculated to validate the corresponding TSEB modeling results [50], considering that the geometric-mean value is less inﬂuenced by skewed distributions compared with the arithmetic-mean value. ! 1 x = x x x (9) i 1 2 n i=1 where n is the number of values, and x are the values included in the average. Transpiration Zahn et al., 2022 [37] proposed the Conditional Eddy Covariance (CEC) method using the high frequency water vapor and CO measurements from eddy covariance measurements to estimate soil evaporation from plant transpiration, and compared results with the modiﬁed Relaxed Eddy Accumulation (MREA) method and the Flux Variance Similarity (FVS) method. They found that the CEC and MREA framework can be used as a qualitative measure to identify stomatal and non-stomatal components. Methods to evaluate the transpiration modeled by the TSEB models using these measurements are explained in Section 3.2.1. 3. Results and Discussion 3.1. TSEB Modeling Results 3.1.1. TSEB Component Comparison Considering Different Resistance Models Figure 5 shows the comparison of modeled versus measured energy components (Rn, G, H, and LE), considering different TSEB models (TSEB-PT, TSEB-2T, and TSEB-2T ) Remote Sens. 2022, 14, x FOR PEER REVIEW 11 of 21 coupled with different resistance models (NK, CM, and MV). In Figure 5, observed H and LE have been adjusted for closure using the technique discussed in Section 2.3.3. Figure 5. Scatter Figure 5. plots Scatte showing r plots showing the compariso the comparison between n between ener energy balance com gy balance components ponents measured from measured from the EC flux tower (y-axis) and the modeled energy balance components from TSEB-PT, TSEB-2T, the EC ﬂux tower (y-axis) and the modeled energy balance components from TSEB-PT, TSEB-2T, and and TSEB-2TQ (rows 1–3) using the NK, CM and MV (columns 1–3) resistance formulations (x-axis). TSEB-2T (rows 1–3) using the NK, CM and MV (columns 1–3) resistance formulations (x-axis). Statistical metrics of evaluation for each flux, model, and resistance formulation are provided in Table 1. Statistics show that the modeled Rn from different TSEB models, in general, has a good agreement with the Rn from the EC flux tower. However, the modeled G calculated via the ratio of the modeled soil net radiation has a lower agreement with the G from the EC flux tower, which may result from the constant value (0.33) adopted for the time period from 10 am to 4 pm. This suggests that a time-varying ratio needs to be used for the G estimation, based on sUAS information for different times during the day, as suggested by Nieto et al., 2019 [27]. Sensible heat estimates from TSEB models coupled with the NK and/or the MV resistance models have better agreement with tower meas- urements as quantified by the index of agreement, d (Table 1). Based on the RMSE and d values, the H and LE estimated from the TSEB-2TQ shows better agreement with meas- urement fluxes. This shows that the QTS method considering shadow and extreme pixel- value effects, characteristics of the high-resolution pixel within the smallest TSEB model- ing domain, in general improved the flux estimation. Remote Sens. 2023, 15, 756 11 of 20 Statistical metrics of evaluation for each ﬂux, model, and resistance formulation are provided in Table 1. Statistics show that the modeled Rn from different TSEB models, in general, has a good agreement with the Rn from the EC ﬂux tower. However, the modeled G calculated via the ratio of the modeled soil net radiation has a lower agreement with the G from the EC ﬂux tower, which may result from the constant value (0.33) adopted for the time period from 10 am to 4 pm. This suggests that a time-varying ratio needs to be used for the G estimation, based on sUAS information for different times during the day, as suggested by Nieto et al., 2019 [27]. Sensible heat estimates from TSEB models coupled with the NK and/or the MV resistance models have better agreement with tower measurements as quantiﬁed by the index of agreement, d (Table 1). Based on the RMSE and d values, the H and LE estimated from the TSEB-2T shows better agreement with measurement ﬂuxes. This shows that the QTS method considering shadow and extreme pixel-value effects, characteristics of the high-resolution pixel within the smallest TSEB modeling domain, in general improved the ﬂux estimation. Table 1. Statistics of the goodness of ﬁt showing the performance of each TSEB modeling result within the footprint area. N is the number of cases used for validation, RMSE is the root mean square 2 2 error (Wm ), Bias is the mean bias computed as the measured minus the modeled (Wm ), r is the Pearson correlation coefﬁcient between the measured and modeled, and d is the Willmott’s index of agreement [51]. When N is different in different groups, d is still calculated but not a representative metric to compare the model performance. TSEB-PT TSEB-PT TSEB-PT TSEB-2T TSEB-2T TSEB-2T TSEB-2T TSEB-2T TSEB-2T Q Q Q (NK) (CM) (MV) (NK) (CM) (MV) (NK) (CM) (MV) N 60 60 60 60 60 60 60 60 60 RMSE 22 22 22 21 21 21 23 23 23 Net Bias 4 5 4 5 5 5 10 10 10 radiation r 0.96 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 d 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 N 60 60 60 60 60 60 60 60 60 RMSE 41 40 41 41 41 41 39 39 39 Ground Bias 27 26 27 26 26 26 24 24 24 heat ﬂux r 0.25 0.24 0.25 0.26 0.26 0.26 0.27 0.27 0.27 d 0.52 0.52 0.52 0.54 0.54 0.54 0.55 0.55 0.55 N 60 60 60 60 60 60 60 60 60 RMSE 78 85 84 71 71 69 65 71 65 Sensible Bias 21 45 17 16 14 19 3 26 3 heat ﬂux r 0.63 0.62 0.61 0.62 0.60 0.64 0.63 0.61 0.63 d 0.78 0.74 0.76 0.78 0.77 0.79 0.77 0.75 0.77 N 60 60 60 60 60 60 60 60 60 RMSE 82 84 90 80 73 81 69 71 70 Latent heat Bias 7 32 3 34 3 36 16 13 16 ﬂux r 0.53 0.55 0.51 0.55 0.57 0.58 0.58 0.59 0.58 d 0.73 0.73 0.71 0.71 0.76 0.72 0.75 0.78 0.75 Considering that previous research adopted the NK model and that the difference between H and LE based on the NK and the MV model coupled with the TSEB-2T is small, the TSEB-2T coupled with the NK model was adopted in this research for energy component estimation. 3.1.2. Time-Based Performance of the TSEB-2T NK Model The sUAS ﬂight times were between 10 am and 4 pm local time (Table A2), which is a fairly wide time frame. Considering the change in the solar altitude and azimuth for the different overpass times, the sUAS overpasses were grouped into three different time periods. The ﬁrst time period, between 10:00 am and 11:59 am, was called the “Landsat” (LS) time period since the Landsat passes over between 10:30 am and 11:00 am (Paciﬁc Standard Time—PST). The second time period, between 12:00 pm and 1:59 pm, is called the “solar noon” (SN) time period since the sun reaches its highest point for the day at around 1:00 pm (PST). The third time period, after 2:00 pm (between 2:00 pm and 5:00 pm, PST), is called the “afternoon” (AF) time period. Remote Sens. 2022, 14, x FOR PEER REVIEW 13 of 21 Remote Sens. 2023, 15, 756 12 of 20 [26] illustrated the challenge for spatial emissivity estimation, and they proved that spatial emissivity (not a constant value) can improve TSEB model performance. Gao et al., 2021 [52] pointed out that the solar spectrum reflectance and transmittance changes along with the le Figur af wa ete 6 r conten shows the t. performance From these aof spTSEB-2T ects, the sp coupled atial and with tem the po NK ral v model ariabil in itestimating y in these parame energy ter components s (e.g., 𝜀 and at each 𝜏 ) need to time period. be furtT her ablestA3 udie contains d if the Rn the es corr tim esponding ation is to metrics be im- proved, particularly associated with the comparisons in the afternoon displayed period. in Figure 6. Figure 6. Scatter plots illustrating the performance of the TSEB-2TQ model coupled with the Norman Figure 6. Scatter plots illustrating the performance of the TSEB-2T model coupled with the Norman and Kustas (NK) resistance model at different time periods. and Kustas (NK) resistance model at different time periods. Metrics for the performance of G estimation suggest that the G ratio value (0.33) used Net radiation shows highest correlation with observations during the AF period, al- in the TSEB-2TQ model is more appropriate at the AF time period than for the LS and SN though the relationship has higher bias and yields higher RMSE than the LS and SN periods. time periods. For example, the labeled points in Figure 6 (a) and (b), “RIP760 20180806 This may result from using spatially and temporally constant values of emissivity (#) and 10:41” and “RIP760 20180805 12:33”, indicate that G was overestimated, indicating that the solar transmittance through the canopy (t ) in the TSEB. Torres-Rua et al., 2020 [26] the G ratio should be smaller than 0.33. This behavior was also noted by Nieto et al., 2019 illustrated the challenge for spatial emissivity estimation, and they proved that spatial emis- [27], who found that a double asymmetric sigmoid function gave better results than using sivity (not a constant value) can improve TSEB model performance. Gao et al., 2021 [52] a constant value, and better fits the observations than the sinusoidal function proposed pointed out that the solar spectrum reﬂectance and transmittance changes along with by Santanello and Friedl., 2003 [53]. the leaf water content. From these aspects, the spatial and temporal variability in these RMSE in sensible and latent heat flux from the TSEB-2TQ is minimized in the AF pe- parameters (e.g., # and t ) need to be further studied if the Rn estimation is to be improved, riod. Examining scenes where outliers in H and LE are observed in Figure 6c showed no particularly in the afternoon period. significant issues from the QTS model based on the separated average soil and canopy Metrics for the performance of G estimation suggest that the G ratio value (0.33) used temperatures within the corresponding footprint area, in comparison with the remaining in the TSEB-2T model is more appropriate at the AF time period than for the LS and SN im time age periods. dates (T For able A4 example, ), so the theclabeled ause of p points oor perform in Figur ae nce is un 6a,b, “RIP760 known. 20180806 10:41” and “RIP760 20180805 12:33”, indicate that G was overestimated, indicating that the G ratio 3. should 2. Trans be pismaller ration than 0.33. This behavior was also noted by Nieto et al., 2019 [27], who found that a double asymmetric sigmoid function gave better results than using a constant 3.2.1. Transpiration Estimation via CEC, MREA, and FVS value, and better ﬁts the observations than the sinusoidal function proposed by Santanello Based on the sUAS flight time, both CEC and MREA methods provided 50 transpi- and Friedl., 2003 [53]. ration estimations, while the FVS method provided 19. The CEC and MREA methods pro- RMSE in sensible and latent heat ﬂux from the TSEB-2T is minimized in the AF vided consistent estimates over the daytime period, while the FVS method often produced period. Examining scenes where outliers in H and LE are observed in Figure 6c showed no solution. Figure 7 shows that the transpiration estimated via the FVS method has a no signiﬁcant issues from the QTS model based on the separated average soil and canopy significant difference from the transpiration estimated via the CEC and MREA method. temperatures within the corresponding footprint area, in comparison with the remaining image dates (Table A4), so the cause of poor performance is unknown. 3.2. Transpiration 3.2.1. Transpiration Estimation via CEC, MREA, and FVS Based on the sUAS ﬂight time, both CEC and MREA methods provided 50 transpi- ration estimations, while the FVS method provided 19. The CEC and MREA methods provided consistent estimates over the daytime period, while the FVS method often pro- duced no solution. Figure 7 shows that the transpiration estimated via the FVS method has a signiﬁcant difference from the transpiration estimated via the CEC and MREA method. Remote Sens. 2023, 15, 756 13 of 20 Remote Sens. 2022, 14, x FOR PEER REVIEW 14 of 21 Figure 7. Scatter plots showing the difference between the transpiration estimated based on differ- Figure 7. Scatter plots showing the difference between the transpiration estimated based on different ent methods (CEC, MREA, and FVS). The red dashline is a reference 1:1 line. methods (CEC, MREA, and FVS). The red dashline is a reference 1:1 line. An analysis of variance (ANOVA) and a Tukey test (Table 2) was then processed to An analysis of variance (ANOVA) and a Tukey test (Table 2) was then processed to not only show the transpiration difference between different groups, but also to show if not only show the transpiration difference between different groups, but also to show if the null hypothesis (i.e., the mean transpiration between different groups is the same) was the null hypothesis (i.e., the mean transpiration between different groups is the same) was acceptable [54]. Table 2 suggests that the mean transpiration estimated via the FVS acceptable [54]. Table 2 suggests that the mean transpiration estimated via the FVS method method yielded a significant difference from estimates from the CEC and MREA methods, yielded a signiﬁcant difference from estimates from the CEC and MREA methods, and and the mean transpiration via CEC is statistically the same as the mean transpiration via the mean transpiration via CEC is statistically the same as the mean transpiration via the the MREA method. This is consistent with the findings of Zahn et al., 2022 [37]. Since the MREA method. This is consistent with the ﬁndings of Zahn et al., 2022 [37]. Since the CEC and MREA methods yielded essentially the same values, CEC values were used in CEC and MREA methods yielded essentially the same values, CEC values were used in subsequent analyses. subsequent analyses. Table 2. ANOVA and Tukey test results showing the difference between the transpiration estimated Table 2. ANOVA and Tukey test results showing the difference between the transpiration estimated based on different methods (CEC, MREA, and FVS). The null hypothesis is that the mean transpira- based on different methods (CEC, MREA, and FVS). The null hypothesis is that the mean transpiration tion between different groups is the same (shown in the last column). “Mean difference” is the mean between different groups is the same (shown in the last column). “Mean difference” is the mean difference between “Group 1” and “Group 2.” “Lower boundary” and “Upper boundary” are the difference between “Group 1” and “Group 2.” “Lower boundary” and “Upper boundary” are the lower and upper 95% confidence interval boundaries, respectively. The unit for “Mean difference,” −2 lower and upper 95% conﬁdence interval boundaries, respectively. The unit for “Mean difference,” “Lower boundary,” and “Upper boundary” is Wm . “Lower boundary,” and “Upper boundary” is Wm . The Mean Transpiration Is the Group 1 Group 2 Mean Difference p-Adj Lower Boundary Upper Boundary Same The Mean Mean Lower Upper Group 1 Group 2 p-Adj Transpiration CEC FVS −84 0.004 −152 −15 NO Difference Boundary Boundary Is the Same CEC MREA 0 0.900 −69 68 YES CEC FVS 84 0.004 152 15 NO MREA FVS −84 0.004 −152 −15 NO CEC MREA 0 0.900 69 68 YES MREA FVS 84 0.004 152 15 NO 3.2.2. Transpiration Comparison Figure 8 contains three scatter plots showing all comparisons between the transpira- 3.2.2. Transpiration Comparison tion based on CEC method and the transpiration modeled by different TSEB models and the sU Figur ASe in 8 fcontains ormation. thr T ee abscatter le A5 con plots taishowing ns the res all ulcomparisons ts from the A between NOVA and the transpiration Tukey test, based showion ng t CEC he stmethod atistical dif and ferences of the transpiration the meanmodeled values. The null h by differ y ent pothesis TSEBfor models the ANOVA and the sUAS test isinformation. that the mean v Table alu A5 e frcontains om two di the ffe rrent gro esults from ups i the s st ANOV atistica A lly and the sam Tukey e, test, and showing the last the colum statistical n suggest difs fer thences at all m ofethe an v mean alues f values. rom “GThe roup null 1” an hypothesis d “Group 2” for are thest ANOV atistical Altest y the is that samthe e. Im mean portant value ly, two from fa two ctors dif shown ferent in gr T oups able is A5 statistically explain tha the t trans same, pira and tion the esti last mate column d via suggests the CEC that and all MRE mean A me values thods h fr aom s a stron “Group ger r 1” ela and tionsh “Gr ip w oup ith 2” trar an esp statistically iration mod the eled same. via Importantly TSEB-2TQ. Th , two e first factors fact shown or is the in corre Table sponding A5 explain “that p-adtranspiration j” values, whestimated ich are 0.9via 00 (highe the CEC r and than m MREA ost o methods ther “p-a has dj” v a str alu onger es, and relationship higher than with α =transpiratio 0.05). The se n modeled cond is th via at tTSEB-2T he corre- . −2 The sponding ﬁrst factor “Mean di is the corr fferenc esponding e” is sm“alp-adj” ler than values, 10 Wm which , whi arec 0.900 h is g (higher enerally than sma most ller th other an “p other expe -adj” values, riments. and higher than = 0.05). The second is that the corresponding “Mean difference” is smaller than 10 Wm , which is generally smaller than other experiments. Remote Remote Sens Sens.. 2023 2022,, 14 15,, x FOR 756 PEER REVIEW 15 of 14 of 21 20 Remote Sens. 2022, 14, x FOR PEER REVIEW 15 of 21 Figure 8. The comparison between the transpiration based on the CEC method and the transpiration Figure 8. The comparison between the transpiration based on the CEC method and the transpiration Figure 8. The comparison between the transpiration based on the CEC method and the transpiration modeled via the TSEB models (different TSEB models with different resistance models). modeled via the TSEB models (different TSEB models with different resistance models). modeled via the TSEB models (different TSEB models with different resistance models). Table A6 is another supplement, showing the model performance displayed and il- Table A6 is another supplement, showing the model performance displayed and il- Table A6 is another supplement, showing the model performance displayed and lustrated by Figure 8 and Table A5, respectively. The “Bias” (Table A6) explains the same lustrated by Figure 8 and Table A5, respectively. The “Bias” (Table A6) explains the same illustrated by Figure 8 and Table A5, respectively. The “Bias” (Table A6) explains the same information as shown by “Mean difference” in Table A5 regarding the transpiration from information as shown by “Mean difference” in Table A5 regarding the transpiration from information as shown by “Mean difference” in Table A5 regarding the transpiration from the CEC method. Except for r and d, since the value in each column performs at a similar the CEC method. Except for r and d, since the value in each column performs at a similar the CEC method. Except for r and d, since the value in each column performs at a similar level, RMSE shows that the transpiration modeled via TSEB-2TQ, in general, is closer to level, level,RMSE RMSE shows shows th thatathe t thtranspiration e transpiratio modeled n modeled vi via TSEB-2T a TSEB-2 ,T in Q, i general, n genera is l, is closer to closer to the the transpiration estimated via the CEC method. transpiration the transpirat estimated ion estima via tedthe viaCEC the C method. EC method. However, one must consider the fact that most of the vineyard sites used in this study However Howeve,r, on onee m muu st stconsider consider the the fact factthat thatmost mostof ofthe the v vineyar ineyard d sites sitesused usedin in this thisstudy study contain a cover crop used to remove excess moisture in the early spring for controlling contain contain aa cov cover er crop used crop used to to remove remove excess excess mo moistur isture e in in the the e early arly spring spring for for contr controllin ollingg vine growth and the timing of initiating irrigation (Figure 9a). This complicates both the vine vine gro growth wthand and the thetiming timingof ofinitiating initiatingirrigation irrigation (Figur (Figure e 9 9 a). a).This Thiscomplicates complicatesboth both the the modelin modeling g of of vineya vineyar rd ET and EC-ba d ET and EC-based sedpartitioning, partitioning, since since ther there is a e is a period period of otime f time when when T modeling of vineyard ET and EC-based partitioning, since there is a period of time when T sources come from both vine and cover crop. sources come from both vine and cover crop. T sources come from both vine and cover crop. Figure 9. Two examples, (a,b), show the different interrow under the vine canopy. Figure 9. Two examples, (a,b), show the different interrow under the vine canopy. Figure 9. Two examples, (a,b), show the different interrow under the vine canopy. An EC ﬂux tower measuring water and carbon ﬂuxes for the site with a bare soil An EC flux tower measuring water and carbon fluxes for the site with a bare soil An EC flux tower measuring water and carbon fluxes for the site with a bare soil interrow as shown in Figure 9b will have T coming only from the grapevine. For the site interrow as shown in Figure 9b will have T coming only from the grapevine. For the site interrow as shown in Figure 9b will have T coming only from the grapevine. For the site shown in Figure 9a, the EC ﬂux tower cannot separate T from the grapevine and cover shown in Figure 9a, the EC flux tower cannot separate T from the grapevine and cover shown in Figure 9a, the EC flux tower cannot separate T from the grapevine and cover crop. For this situation, measurements below the vine canopy in the interrow are necessary crop. For this situation, measurements below the vine canopy in the interrow are neces- crop. For this situation, measurements below the vine canopy in the interrow are neces- for estimating the ET contribution from the cover crop using, for example, micro-Bowen sary for estimating the ET contribution from the cover crop using, for example, micro- sary for estimating the ET contribution from the cover crop using, for example, micro- ratio systems which were deployed in the SLM vineyard site for several IOPs [16]. High- Bowen ratio systems which were deployed in the SLM vineyard site for several IOPs [16]. Bowen ratio systems which were deployed in the SLM vineyard site for several IOPs [16]. resolution imagery separating interrow from the vine canopy, especially for the situation High-resolution imagery separating interrow from the vine canopy, especially for the sit- High-resolution imagery separating interrow from the vine canopy, especially for the sit- shown in Figure 9a, is necessary because ET from the interrow needs to be considered. uation shown in Figure 9a, is necessary because ET from the interrow needs to be consid- uation shown in Figure 9a, is necessary because ET from the interrow needs to be consid- Data that have been collected in the GRAPEX program will eventually shed light on this. ered. Data that have been collected in the GRAPEX program will eventually shed light on ered. Data that have been collected in the GRAPEX program will eventually shed light on From a modeling perspective, for example, this is being addressed using a three-source this. From a modeling perspective, for example, this is being addressed using a three- this. From a modeling perspective, for example, this is being addressed using a three- model (3SEB), which is a modiﬁcation of TSEB and has been initially tested in the RIP720 source model (3SEB), which is a modification of TSEB and has been initially tested in the source model (3SEB), which is a modification of TSEB and has been initially tested in the Remote Sens. 2023, 15, 756 15 of 20 vineyard using tower-based land surface temperature and found to provide a more reliable ET partitioning account for the interrow cover crop [55]. 4. Conclusions In this study, we assessed the performance of the TSEB model in energy component estimation and evapotranspiration partitioning. Three different versions of TSEB coupled with three different resistance models were used to model the energy components (Rn, G, H, and LE). Modeled estimates were compared with monitored data from the EC ﬂux tower within the corresponding footprint area. Results show that the QTS method adopted in this research can improve the estimation of H, and TSEB-2T (TSEB-2T model coupled with the QTS method for temperature separation) coupled with the NK (Norman and Kustas) resistance model can appropriately provide energy-component estimations. The ET partitioning comparison regarding transpiration illustrated that all TSEB models are statistically acceptable for ET partitioning, but the TSEB-2T showed better agreement with the CEC method. Further work, focused on augmenting the EC ﬂux tower system with measurements of ET for the interrow, upgrading the sUAS image processing system for creating near-real time products, and implementing a 3SEB formulation to explicitly account for the interrow cover crop, is necessary to accurately estimate vine transpiration [55]. These advancements will improve management practices that promote great water use efﬁciency in vineyards and will improve growers’ and researchers’ understanding of the role of cover crop and vine water use at the canopy and sub-block scale. Author Contributions: Conceptualization, R.G., A.F.T.-R., W.P.K. and H.N., methodology, R.G., A.F.T.-R., H.N., E.Z., L.H., W.P.K. and M.A., software, R.G., A.F.T.-R., H.N. and E.Z., validation, R.G., A.F.T.-R., H.N., W.P.K. and M.A., formal analysis, R.G., A.F.T.-R., W.P.K. and M.A., investigation, R.G. and E.Z., resources, E.Z., M.M.A., N.B., S.J.C., J.H.P., J.A., L.G.M., W.A.W., C.C., I.G., L.S. and N.D., data curation, A.F.T.-R., M.M.A., E.Z., N.B., S.J.C., C.C., I.G., L.S. and N.D., writing—original draft preparation, R.G. and A.F.T.-R., writing—review and editing, R.G., A.F.T.-R., E.Z., W.P.K., F.G., A.J.M., M.A., K.K., N.A. and L.S., visualization, R.G., A.F.T.-R., W.P.K., A.J.M. and M.A., supervision, R.G., A.F.T.-R., W.P.K. and M.A., project administration, A.F.T.-R., funding acquisition, A.F.T.-R. and W.P.K. All authors have read and agreed to the published version of the manuscript. Funding: This study was made possible with ﬁnancial support from the USDA-Agricultural Research Service, E&J Gallo Winery, NASA Applied Sciences Water Resources Grant NNX17AF51G and the Utah Water Research Laboratory Student Fellowship. And the APC was funded by Utah Water Research Laboratory. Data Availability Statement: The QTS method, along with demo data, is available in the CUAHSI HydroShare platform [56]. Similarly, a python program to generate fractional cover, canopy height, and canopy width over canopy height for the TSEB model based on the AggieAir images for California vineyards is also available in the CUAHSI HydroShare platform [45]. Since the authors only have partial ownership of the data and due to the large data size, only demo data are available for testing the QTS method. Acknowledgments: The authors are grateful for the extraordinary support from the Utah State Univer- sity AggieAir sUAS program staff and E&J Gallo scientific teams for data collection and analysis, and the cooperation of the vineyard management staff for logistical support and coordinating field operations with the GRAPEX team. The authors would like to thank Ayman Nassar for his preliminary work in TSEB model and footprint-area calculation, and also thanks to Carri Richards and Micah Safsten for editing the manuscript. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer. Conﬂicts of Interest: The authors declare no conﬂict of interest. Remote Sens. 2023, 15, 756 16 of 20 Appendix A Table A1. Study-site geographic information. Study Sites Latitude Longitude Elevation above the Sea Level (m) 0 00 0 00 SLM 38 16 49.76 121 7 3.35 40 0 00 0 00 BAR 38 45 4.91 122 58 28.77 120 0 00 0 00 RIP760 36 50 20.52 120 12 36.60 62 0 00 0 00 RIP720 36 50 57.27 120 10 26.50 62 Table A2. The ﬂight date and time of the sUAS platform over vineyards. Azimuth and elevation of the sun corresponding to the time are also shown. Sites Year Month Day Time Flight Azimuth Elevation 2018 6 19 11:20 144.1 74.0 2018 6 19 13:17 236.1 68.8 2018 6 19 15:38 269.8 41.8 2018 7 12 12:29 201.0 74.2 RIP 720-1 2018 7 12 15:32 266.5 43.1 RIP 720-2 2018 7 13 10:40 123.3 66.3 RIP 720-3 2018 7 13 15:22 264.6 45.1 RIP 720-4 2018 8 5 10:44 132.4 63.3 2018 8 5 12:33 198.9 69.2 2018 8 6 10:41 131.2 62.8 2019 5 4 10:25 130.1 60.9 2018 6 19 11:20 144.1 74.0 2018 6 19 13:17 236.1 68.8 2018 6 19 15:38 269.8 41.8 2018 7 12 12:29 201.0 74.2 2018 7 12 15:32 266.5 43.1 RIP 760 2018 7 13 10:40 123.3 66.3 2018 8 5 10:44 132.4 63.3 2018 8 5 12:33 198.9 69.2 2018 8 6 10:41 131.2 62.8 2017 8 8 10:52 144.9 63.6 2017 8 9 10:43 141.1 62.3 2019 6 27 10:41 131.9 68.9 2019 6 27 12:07 193.6 74.2 2019 6 27 14:21 255.2 54.7 BAR012 2019 7 29 10:51 140.8 65.8 2019 7 29 13:09 224.2 64.4 2019 7 30 10:28 130.9 62.5 2019 7 30 13:09 223.9 64.2 2019 7 30 15:40 264.2 37.5 2014 8 9 10:41 136.3 61.5 2015 6 2 10:43 131.9 67.9 2015 6 2 14:07 250.2 57.2 SLM001 2015 7 11 10:35 125.1 65.5 2015 7 11 14:14 250.1 57.3 2019 5 3 10:38 139.1 62.0 2014 8 9 10:41 136.3 61.5 2015 6 2 10:43 131.9 67.9 2015 6 2 14:07 250.2 57.2 SLM002 2015 7 11 10:35 125.1 65.5 2015 7 11 14:14 250.1 57.3 Remote Sens. 2023, 15, 756 17 of 20 Table A3. The performance of the TSEB-2T model coupled with the Norman and Kustas (NK) resistance model at different research sites with different times shown by different evaluation metrics. “LS” stands for the results that occurred in Landsat time; “SN” near solar noon; and “AF” afternoon. The unit of RMSE and Bias is Wm . Net Radiation Ground Heat Flux Sensible Heat Flux Latent Heat Flux Time Periods N RMSE Bias r N RMSE Bias r N RMSE Bias r N RMSE Bias r LS 29 21 9 0.91 29 45 28 0.43 29 66 2 0.33 29 68 16 0.62 SN 17 21 3 0.79 17 40 28 0.38 17 69 23 0.63 17 81 49 0.64 AF 14 29 23 0.96 14 20 12 0.64 14 58 8 0.63 14 56 22 0.46 Table A4. Separated average soil and canopy temperatures within the corresponding footprint area via the QTS model (the temperature unit is C). Soil–Canopy Sonic Air Soil Canopy Site Date Time Temperature Temperature Temperature Temperature Difference SLM001 20150711 14:14 28.1 32.9 28.7 4.2 SLM002 20150711 14:14 30.7 32.9 28.7 4.2 BAR012 20190627 14:21 25.7 31.0 26.6 4.4 BAR012 20190730 15:40 30.9 34.2 29.4 4.8 RIP760 20180619 15:38 32.1 36.2 31.6 4.6 RIP720-1 20180619 15:38 34.0 35.5 32.1 3.4 RIP720-1 20180712 15:32 38.3 36.8 33.1 3.7 RIP720-1 20180713 15:22 38.1 36.7 33.3 3.4 RIP720-2 20180619 15:38 34.5 37.3 32.5 4.8 RIP720-2 20180712 15:32 38.8 37.8 33.0 4.8 RIP720-2 20180713 15:22 38.5 38.6 34.4 4.2 RIP720-3 20180713 15:22 38.5 35.1 31.1 4.0 RIP720-4 20180619 15:38 35.9 35.6 31.8 3.8 RIP720-4 20180713 15:22 40.5 37.1 32.9 4.2 Table A5. ANOVA and Tukey test results showing the difference between the transpiration calculated via the CEC method and the transpiration modeled via the TSEB models. The null hypothesis is that the mean transpiration between different groups is the same. “Mean difference” is the mean difference between “Group 1” and “Group 2.” “Lower boundary” and “Upper boundary” are the lower and upper 95% conﬁdence interval boundaries, respectively. “CEC” represents the transpiration calculated via the CEC method. The unit for “Mean difference,” “Lower boundary,” and “Upper boundary” is Wm . The Mean Mean Lower Upper Group 1 Group 2 p-Adj Transpiration Difference Boundary Boundary Is the Same CEC TSEB-PT (NK) 25 0.674 69 18 YES CEC TSEB-PT (CM) 16 0.900 60 27 YES CEC TSEB-PT (MV) 32 0.372 75 12 YES CEC TSEB-2T (NK) 36 0.194 80 7 YES CEC TSEB-2T (CM) 30 0.456 74 13 YES CEC TSEB-2T (MV) 39 0.132 82 5 YES CEC TSEB-2T (NK) 10 0.900 53 34 YES CEC TSEB-2T (CM) 7 0.900 51 36 YES CEC TSEB-2T (MV) 9 0.900 53 34 YES Q Remote Sens. 2023, 15, 756 18 of 20 Table A6. Metrics for model evaluation shown in Figure 8. N is the number of scatters in Figure 8; RMSE is the root mean square error; Bias is the mean bias computed as the observed minus the predicted; r is the Pearson correlation coefﬁcient between the observed and the predicted; and d is Willmott’s index of agreement. TSEB-PT TSEB-2T TSEB-2T NK CM MV NK CM MV NK CM MV N 50 50 50 50 50 50 50 50 50 RMSE 71 68 77 84 77 83 72 70 71 Bias 25 16 32 36 30 39 10 7 9 r 0.58 0.58 0.56 0.54 0.55 0.56 0.54 0.54 0.54 d 0.73 0.73 0.72 0.71 0.72 0.72 0.73 0.72 0.73 References 1. Virnodkar, S.S.; Pachghare, V.K.; Patil, V.C.; Jha, S.K. Remote Sensing and Machine Learning for Crop Water Stress Determination in Various Crops: A Critical Review. Precis. Agric. 2020, 21, 1121–1155. [CrossRef] 2. Ahmad, U.; Alvino, A.; Marino, S. A Review of Crop Water Stress Assessment Using Remote Sensing. Remote Sens. 2021, 13, 4155. [CrossRef] 3. Kool, D.; Kustas, W.P.; Ben-Gal, A.; Agam, N. Energy Partitioning between Plant Canopy and Soil, Performance of the Two-Source Energy Balance Model in a Vineyard. Agric. For. Meteorol. 2021, 300, 108328. [CrossRef] 4. Zhang, X.Y.; Jin, J.; Zeng, X.; Hawkins, C.P.; Neto, A.A.M.; Niu, G.Y. The Compensatory CO2 Fertilization and Stomatal Closure Effects on Runoff Projection From 2016–2099 in the Western United States. Water Resour. Res. 2022, 58, e2021WR030046. [CrossRef] 5. Wei, Z.; Lee, X.; Wen, X.; Xiao, W. Evapotranspiration Partitioning for Three Agro-Ecosystems with Contrasting Moisture Conditions: A Comparison of an Isotope Method and a Two-Source Model Calculation. Agric. For. Meteorol. 2018, 252, 296–310. [CrossRef] 6. Xue, J.; Anderson, M.C.; Gao, F.; Hain, C.; Yang, Y.; Knipper, K.R.; Kustas, W.P.; Yang, Y. Mapping Daily Evapotranspiration at Field Scale Using the Harmonized Landsat and Sentinel-2 Dataset, with Sharpened VIIRS as a Sentinel-2 Thermal Proxy. Remote Sens. 2021, 13, 3420. [CrossRef] 7. Safre, A.L.S.; Nassar, A.; Torres-Rua, A.F.; Aboutalebi, M.; Saad, C.C.J.; Manzione, R.L.; Teixeira, A.H.D.C.; Prueger, J.H.; McKee, L.G.; Alﬁeri, J.G.; et al. Performance of Sentinel-2 SAFER ET Model for Daily and Seasonal Estimation of Grapevine Water Consumption. Irrig. Sci. 2022, 1, 1–20. [CrossRef] 8. Nassar, A.; Torres-Rua, A.F.; Hipps, L.E.; Kustas, W.P.; McKee, M.; Stevens, D.; Nieto, H.; Keller, D.; Gowing, I.; Coopmans, C. Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem. Remote Sens. 2022, 14, 372. [CrossRef] 9. Bellvert, J.; Jofre-Cekalovic, ´ C.; Pelechá, A.; Mata, M.; Nieto, H. Feasibility of Using the Two-Source Energy Balance Model (TSEB) with Sentinel-2 and Sentinel-3 Images to Analyze the Spatio-Temporal Variability of Vine Water Status in a Vineyard. Remote Sens. 2020, 12, 2299. [CrossRef] 10. Gao, F.; Kustas, W.P.; Anderson, M.C. A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land. Remote Sens. 2012, 4, 3287–3319. [CrossRef] 11. Xue, J.; Anderson, M.C.; Gao, F.; Hain, C.; Sun, L.; Yang, Y.; Knipper, K.R.; Kustas, W.P.; Torres-Rua, A.F.; Schull, M. Sharpening ECOSTRESS and VIIRS Land Surface Temperature Using Harmonized Landsat-Sentinel Surface Reﬂectances. Remote Sens. Environ. 2020, 251, 112055. [CrossRef] [PubMed] 12. Yang, Y.; Anderson, M.C.; Gao, F.; Xue, J.; Knipper, K.; Hain, C. Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System. Remote Sens. 2022, 14, 1772. [CrossRef] 13. De Castro, A.I.; Shi, Y.; Maja, J.M.; Peña, J.M. Uavs for Vegetation Monitoring: Overview and Recent Scientiﬁc Contributions. Remote Sens. 2021, 13, 2139. [CrossRef] 14. Tunca, E.; Köksal, E.S.; Torres-Rua, A.F.; Kustas, W.P.; Nieto, H.S. Estimation of Bell Pepper Evapotranspiration Using Two-Source Energy Balance Model Based on High-Resolution Thermal and Visible Imagery from Unmanned Aerial Vehicles. Appl. Remote Sens. 2022, 16, 022204. [CrossRef] 15. Long, D.S.; Engel, R.E.; Siemens, M.C. Measuring Grain Protein Concentration with In-Line Near Infrared Reﬂectance Spec- troscopy. Agron. J. 2008, 100, 247–252. [CrossRef] 16. Kustas, W.P.; Anderson, M.C.; Alﬁeri, J.G.; Knipper, K.; Torres-Rua, A.F.; Parry, C.K.; Nieto, H.; Agam, N.; White, W.A.; Gao, F.; et al. The Grape Remote Sensing Atmospheric Proﬁle and Evapotranspiration Experiment. Bull. Am. Meteorol. Soc. 2018, 99, 1791–1812. [CrossRef] 17. Norman, J.M.; Kustas, W.P.; Humes, K.S. Source Approach for Estimating Soil and Vegetation Energy Fluxes in Observations of Directional Radiometric Surface Temperature. Agric. For. Meteorol. 1995, 77, 263–293. [CrossRef] 18. Kustas, W.P.; Norman, J.M. Use of Remote Sensing for Evapotranspiration Monitoring over Land Surfaces. Hydrol. Sci. J. 1996, 41, 495–516. [CrossRef] Remote Sens. 2023, 15, 756 19 of 20 19. Kustas, W.P.; Nieto, H.; Garcia-Tejera, O.; Bambach, N.; McElrone, A.J.; Gao, F.; Alﬁeri, J.G.; Hipps, L.E.; Prueger, J.H.; Torres-Rua, A.F.; et al. Impact of Advection on Two-Source Energy Balance (TSEB) Canopy Transpiration Parameterization for Vineyards in the California Central Valley. Irrig. Sci. 2022, 40, 575–591. [CrossRef] 20. Alﬁeri, J.G.; Kustas, W.P.; Nieto, H.; Prueger, J.H.; Hipps, L.E.; McKee, L.G.; Gao, F.; Los, S. Inﬂuence of Wind Direction on the Surface Roughness of Vineyards. Irrig. Sci. 2019, 37, 359–373. [CrossRef] 21. Nassar, A.; Torres-Rua, A.F.; Kustas, W.P.; Nieto, H.; McKee, M.; Hipps, L.E.; Stevens, D.; Alﬁeri, J.G.; Prueger, J.H.; Alsina, M.M.; et al. Inﬂuence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and SUAS Imagery in Vineyards. Remote Sens. 2020, 12, 342. [CrossRef] [PubMed] 22. Nassar, A.; Torres-Rua, A.F.; Kustas, W.P.; Nieto, H.; McKee, M.; Hipps, L.E.; Alﬁeri, J.G.; Prueger, J.H.; Alsina, M.M.; McKee, L.G.; et al. To What Extend Does the Eddy Covariance Footprint Cutoff Inﬂuence the Estimation of Surface Energy Fluxes Using Two Source Energy Balance Model and High-Resolution Imagery in Commercial Vineyards? In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; Thomasson, J.A., Torres-Rua, A.F., Eds.; SPIE: Bellingham, WA, USA, 2020; Volume 11414, p. 16. 23. Nassar, A.; Torres-rua, A.F.; Kustas, W.P.; Alﬁeri, J.G.; Hipps, L.E.; Prueger, J.H.; Nieto, H.; Alsina, M.M.; White, W.A.; McKee, L.; et al. Assessing Daily Evapotranspiration Methodologies from One-time-of-day Suas and Ec Information in the Grapex Project. Remote Sens. 2021, 13, 2887. [CrossRef] [PubMed] 24. Nieto, H.; Alsina, M.M.; Kustas, W.P.; García-Tejera, O.; Chen, F.; Bambach, N.; Gao, F.; Alﬁeri, J.G.; Hipps, L.E.; Prueger, J.H.; et al. Evaluating Different Metrics from the Thermal-Based Two-Source Energy Balance Model for Monitoring Grapevine Water Stress. Irrig. Sci. 2022, 40, 697–713. [CrossRef] 25. Nieto, H.; Bellvert, J.; Kustas, W.P.; Alﬁeri, J.G.; Gao, F.; Prueger, J.H.; Torres-Rua, A.F.; Hipps, L.E.; Elarab, M.; Song, L. Unmanned Airborne Thermal and Mutilspectral Imagery for Estimating Evapotranspiration in Irrigated Vineyards. In Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Forth Worth, TX, USA, 23–27 July 2017; pp. 5510–5513. 26. Torres-Rua, A.F.; Ticlavilca, A.M.; Aboutalebi, M.; Nieto, H.; Alsina, M.M.; White, A.; Prueger, J.H.; Alﬁeri, J.G.; Hipps, L.E.; McKee, L.G.; et al. Estimation of Evapotranspiration and Energy Fluxes Using a Deep-Learning-Based High-Resolution Emissivity Model and the Two-Source Energy Balance Model with SUAS Information. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; Thomasson, J.A., Torres-Rua, A.F., Eds.; SPIE: Bellingham, WA, USA, 2020; Volume 11414, p. 10. 27. Nieto, H.; Kustas, W.P.; Torres-Rúa, A.F.; Alﬁeri, J.G.; Gao, F.; Anderson, M.C.; White, W.A.; Song, L.; Alsina, M.M.; Prueger, J.H.; et al. Evaluation of TSEB Turbulent Fluxes Using Different Methods for the Retrieval of Soil and Canopy Component Temperatures from UAV Thermal and Multispectral Imagery. Irrig. Sci. 2019, 37, 389–406. [CrossRef] [PubMed] 28. Kang, Y.; Gao, F.; Anderson, M.C.; Kustas, W.P.; Nieto, H.; Knipper, K.; Yang, Y.; White, W.A.; Alfieri, J.G.; Torres-Rua, A.F.; et al. Evaluation of Satellite Leaf Area Index in California Vineyards for Improving Water Use Estimation. Irrig. Sci. 2022, 40, 531–551. [CrossRef] 29. Aboutalebi, M.; Torres-Rua, A.F.; McKee, M.; Kustas, W.P.; Nieto, H.; Alsina, M.M.; White, A.; Prueger, J.H.; McKee, L.; Alﬁeri, J.G.; et al. Downscaling UAV Land Surface Temperature Using a Coupled Wavelet-Machine Learning-Optimization Algorithm and Its Impact on Evapotranspiration. Irrig. Sci. 2022, 40, 553–574. [CrossRef] 30. Gao, R.; Torres-Rua, A.F.; Aboutalebi, M.; White, W.A.; Anderson, M.C.; Kustas, W.P.; Agam, N.; Alsina, M.M.; Alﬁeri, J.G.; Hipps, L.E.; et al. LAI Estimation across California Vineyards Using SUAS Multi-Seasonal Multi-Spectral, Thermal, and Elevation Information and Machine Learning. Irrig. Sci. 2022, 1, 1–29. [CrossRef] 31. Knipper, K.; Anderson, M.C.; Bambach, N.; Kustas, W.P.; Gao, F.; Zahn, E.; Hain, C.; Mcelrone, A.; Belﬁore, O.R.; Castro, S.; et al. Evaluation of Partitioned Evaporation and Transpiration Estimates within the DisALEXI Modeling Framework over Irrigated Crops in California. Remote Sens. 2023, 15, 68. [CrossRef] 32. Aboutalebi, M.; Torres-Rua, A.F.; McKee, M.; Nieto, H.; Kustas, W.P.; Coopmans, C. The Impact of Shadows on Partitioning of Radiometric Temperature to Canopy and Soil Temperature Based on the Contextual Two-Source Energy Balance Model (TSEB-2T). In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV; Thomasson, J.A., McKee, M., Moorhead, R.J., Eds.; SPIE: Bellingham, WA, USA, 2019; Volume 11008, p. 3. 33. Aboutalebi, M.; Torres-Rua, A.F.; Kustas, W.P.; Nieto, H.; Coopmans, C.; McKee, M. Assessment of Different Methods for Shadow Detection in High-Resolution Optical Imagery and Evaluation of Shadow Impact on Calculation of NDVI, and Evapotranspiration. Irrig. Sci. 2019, 37, 407–429. [CrossRef] 34. Aboutalebi, M.; Torres-Rua, A.F.; McKee, M.; Kustas, W.P.; Nieto, H.; Alsina, M.M.; White, A.; Prueger, J.H.; McKee, L.; Alﬁeri, J.G.; et al. Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspira- tion Models. Remote Sens. 2019, 12, 50. [CrossRef] 35. Gao, R.; Torres-Rua, A.F.; Nassar, A.; Hipps, L.; Nieto, H.; Aboutalebi, M.; White, W.A.; Anderson, M.; Kustas, W.P.; Alsina, M.M.; et al. TSEB Modeling and the Comparison between the Model Results and the Eddy-Covariance Monitored Data within the Footprint Area. CUAHSI HydroShare 2021. [CrossRef] 36. Gao, R.; Torres-Rua, A.F.; Nassar, A.; Alﬁeri, J.G.; Aboutalebi, M.; Hipps, L.E.; Ortiz, N.B.; Mcelrone, A.J.; Coopmans, C.; Kustas, W.P.; et al. Evapotranspiration Partitioning Assessment Using a Machine-Learning-Based Leaf Area Index and the Two-Source Energy Balance Model with SUAV Information; Thomasson, J.A., Torres-Rua, A.F., Eds.; SPIE: Bellingham, WA, USA, 2021; Volume 11747, p. 21. Remote Sens. 2023, 15, 756 20 of 20 37. Zahn, E.; Bou-Zeid, E.; Good, S.P.; Katul, G.G.; Thomas, C.K.; Ghannam, K.; Smith, J.A.; Chamecki, M.; Dias, N.L.; Fuentes, J.D.; et al. Direct Partitioning of Eddy-Covariance Water and Carbon Dioxide Fluxes into Ground and Plant Components. Agric. For. Meteorol. 2022, 315. [CrossRef] 38. Kljun, N.; Calanca, P.; Rotach, M.W.; Schmid, H.P. A Simple Two-Dimensional Parameterisation for Flux Footprint Prediction (FFP). Geosci. Model Dev. 2015, 8, 3695–3713. [CrossRef] 39. Gao, R.; Nassar, A.; Torres-Rua, A.F.; Hipps, L.; Aboutalebi, M.; White, W.A.; Anderson, M.; Kustas, W.P.; Alsina, M.M.; Alﬁeri, J.; et al. Footprint Area Generating Based on Eddy Covariance Records. CUAHSI HydroShare 2021. [CrossRef] 40. Gao, R.; Torres-Rua, A.F. Features Extraction from the LAI2200C Plant Canopy Analyzer. CUAHSI HydroShare 2021. [CrossRef] 41. Kustas, W.P.; Agam, N.; Alﬁeri, J.G.; McKee, L.G.; Prueger, J.H.; Hipps, L.E.; Howard, A.M.; Heitman, J.L. Below Canopy Radiation Divergence in a Vineyard: Implications on Interrow Surface Energy Balance. Irrig. Sci. 2019, 37, 227–237. [CrossRef] 42. Gao, R.; Alsina, M.M.; Torres-Rua, A.F.; Hipps, L.E.; Kustas, W.P.; White, W.A.; Anderson, M.C.; Alﬁeri, J.G.; Dokoozlian, N.; Nieto, H.; et al. Exploratory Analysis of Vineyard Leaf Water Potential against UAS Multispectral and Temperature Information; Thomasson, J.A., Torres-Rua, A.F., Eds.; SPIE: Bellingham, WA, USA, 2022; Volume 12114, pp. 160–185. 43. Torres-Rua, A.F. Vicarious Calibration of SUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature. Sensors 2017, 17, 1499. [CrossRef] 44. Bambach, N.; Kustas, W.P.; Alﬁeri, J.G.; Prueger, J.H.; Hipps, L.E.; McKee, L.; Castro, S.J.; Volk, J.; Alsina, M.M.; McElrone, A.J. Evapotranspiration Uncertainty at Micrometeorological Scales: The Impact of the Eddy Covariance Energy Imbalance and Correction Methods. Irrig. Sci. 2022, 40, 445–461. [CrossRef] 45. Gao, R.; Torres-Rua, A.F. A Python-Based Program Generating a Part of Information Based on AggieAir Images for the TSEB Model: Taking California Vineyards as an Example. CUAHSI HydroShare 2022. [CrossRef] 46. Gao, R.; Torres-Rua, A.F.; Aboutalebi, M.; White, W.A.; Anderson, M.; Kustas, W.P.; Agam, N.; Alsina, M.M.; Alﬁeri, J.; Hipps, L.; et al. Feature Extraction Approaches for Leaf Area Index Estimation in California Vineyards via Machine Learning Algorithms. CUAHSI HydroShare 2021. [CrossRef] 47. Kustas, W.P.; Norman, J.M. A Two-Source Approach for Estimating Turbulent Fluxes Using Multiple Angle Thermal Infrared Observations. Water Resour. Res. 1997, 33, 1495–1508. [CrossRef] 48. McNaughton, K.G.; Hurk, B.J.J.M.V.D. A “Lagrangian” Revision of the Resistors in the Two-Layer Model for Calculating the Energy Budget of a Plant Canopy. Boundary-Layer Meteorol. 1995, 74, 261–288. [CrossRef] 49. Choudhury, B.J.; Monteith, J.L. A Four-layer Model for the Heat Budget of Homogeneous Land Surfaces. Q. J. R. Meteorol. Soc. 1988, 114, 373–398. [CrossRef] 50. Gao, R.; Torres-Rua, A.F.; Hipps, L.E.; Kustas, W.P.; Anderson, M.C.; White, W.A.; Alﬁeri, J.G.; Alsina, M.M.; Dokoozlian, N.; Nieto, H.; et al. Assessment of TSEB-PT and -2T in ET Partitioning Estimation over California Commercial Vineyards Based on SUAS Information; SPIE: Bellingham, WA, USA, 2022; Volume 12114, p. 121140I. 51. Willmott, C.J. Some Comments on the Evaluation of Model Performance. Bull.- Am. Meteorol. Soc. 1982, 63, 1309–1313. [CrossRef] 52. Gao, Y.; Tang, B.; Lu, B.; Ji, G.; Ye, H. Investigation on the Effects of Water Loss on the Solar Spectrum Reﬂectance and Transmittance of Osmanthus Fragrans Leaves Based on Optical Experiment and PROSPECT Model. RSC Adv. 2021, 11, 37268–37275. [CrossRef] 53. Santanello, J.A.; Friedl, M.A. Diurnal Covariation in Soil Heat Flux and Net Radiation. J. Appl. Meteorol. 2003, 42, 851–862. [CrossRef] 54. Montgomery, D.C.; Runger, G.C. Applied Statistics and Probability for Engineers; John Wiley&Sons: Hoboken, NJ, USA, 2010. 55. Burchard-Levine, V.; Nieto, H.; Kustas, W.P.; Gao, F.; Alﬁeri, J.G.; Prueger, J.H.; Hipps, L.E.; Bambach-Ortiz, N.; McElrone, A.J.; Castro, S.J.; et al. Application of a Remote-Sensing Three-Source Energy Balance Model to Improve Evapotranspiration Partition- ing in Vineyards. Irrig. Sci. 2022, 40, 593–608. [CrossRef] 56. Temperature Separation via Eliminating Shadow-Pixel Inﬂuence Based on High-Resolution SUAS Image in California Vineyards. CUAHSI HydroShare 4. 2023. Available online: https://doi.org/10.4211/hs.c0876501581f46c698727dc956cc2d73 (accessed on 18 January 2023). Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Remote Sensing Multidisciplinary Digital Publishing Institute http://www.deepdyve.com/lp/multidisciplinary-digital-publishing-institute/et-partitioning-assessment-using-the-tseb-model-and-suas-information-IHTVu0nvKy

Loading next page...

References (47)

Zhongwang Wei, X. Lee, X. Wen, W. Xiao (2017)
Evapotranspiration partitioning for three agro-ecosystems with contrasting moisture conditions: a comparison of an isotope method and a two-source model calculation
Agricultural and Forest Meteorology, 252
M. Aboutalebi, Alfonso Torres-Rua, M. McKee, H. Nieto, W. Kustas, C. Coopmans (2019)
The impact of shadows on partitioning of radiometric temperature to canopy and soil temperature based on the contextual two-source energy balance model (TSEB-2T)
, 11008
W. Kustas, N. Agam, J. Alfieri, L. McKee, J. Prueger, L. Hipps, A. Howard, J. Heitman (2018)
Below canopy radiation divergence in a vineyard: implications on interrow surface energy balance
Irrigation Science, 37
H. Nieto, M. Alsina, W. Kustas, O. García-Tejera, Fan Chen, N. Bambach, F. Gao, J. Alfieri, L. Hipps, J. Prueger, L. McKee, E. Zahn, E. Bou‐Zeid, A. McElrone, S. Castro, N. Dokoozlian (2022)
Evaluating different metrics from the thermal-based two-source energy balance model for monitoring grapevine water stress
Irrigation Science, 40
W. Kustas, Martha Anderson, J. Alfieri, K. Knipper, Alfonso Torres-Rua, C. Parry, H. Nieto, N. Agam, W. White, F. Gao, L. McKee, J. Prueger, L. Hipps, Sebastian Los, M. Alsina, L. Sanchez, B. Sams, N. Dokoozlian, M. McKee, S. Jones, Yun Yang, T. Wilson, Fangni Lei, A. McElrone, J. Heitman, A. Howard, Kirk Post, F. Melton, C. Hain (2020)
THE GRAPE REMOTE SENSING ATMOSPHERIC PROFILE AND EVAPOTRANSPIRATION EXPERIMENT
Ying Gao, Bo Tang, Beibei Lu, Guojian Ji, Hongwei Ye (2021)
Investigation on the effects of water loss on the solar spectrum reflectance and transmittance of Osmanthus fragrans leaves based on optical experiment and PROSPECT model
RSC Advances, 11
M. Aboutalebi, Alfonso Torres-Rua, W. Kustas, H. Nieto, C. Coopmans, M. McKee (2018)
Assessment of different methods for shadow detection in high-resolution optical imagery and evaluation of shadow impact on calculation of NDVI, and evapotranspiration
Irrigation Science, 37
K. Knipper, Martha Anderson, N. Bambach, W. Kustas, F. Gao, E. Zahn, C. Hain, A. McElrone, O. Belfiore, S. Castro, M. Alsina, S. Saa (2022)
Evaluation of Partitioned Evaporation and Transpiration Estimates within the DisALEXI Modeling Framework over Irrigated Crops in California
Remote. Sens., 15
A. Nassar, Alfonso Torres-Rua, W. Kustas, J. Alfieri, L. Hipps, J. Prueger, H. Nieto, M. Alsina, W. White, L. McKee, C. Coopmans, L. Sanchez, N. Dokoozlian (2021)
Assessing Daily Evapotranspiration Methodologies from One-Time-of-Day sUAS and EC Information in the GRAPEX Project
Remote sensing, 13
A. Nassar, Alfonso Torres-Rua, W. Kustas, H. Nieto, M. McKee, L. Hipps, J. Alfieri, J. Prueger, M. Alsina, L. McKee, C. Coopmans, L. Sanchez, N. Dokoozlian (2020)
To what extent does the Eddy Covariance footprint cutoff influence the estimation of surface energy fluxes using two source energy balance model and high-resolution imagery in commercial vineyards?
, 11414
K. McNaughton, B. Hurk (1995)
A ‘Lagrangian’ revision of the resistors in the two-layer model for calculating the energy budget of a plant canopy
Boundary-Layer Meteorology, 74
J. Santanello, M. Friedl (2003)
Diurnal Covariation in Soil Heat Flux and Net Radiation
Journal of Applied Meteorology, 42
Joaquim Bellvert, Christian Jofre-Cekalovic, A. Pelechá, M. Mata, H. Nieto (2020)
Feasibility of Using the Two-Source Energy Balance Model (TSEB) with Sentinel-2 and Sentinel-3 Images to Analyze the Spatio-Temporal Variability of Vine Water Status in a Vineyard
Remote. Sens., 12
E. Tunca, E. Köksal, Alfonso Torres-Rua, W. Kustas, H. Nieto (2022)
Estimation of bell pepper evapotranspiration using two-source energy balance model based on high-resolution thermal and visible imagery from unmanned aerial vehicles
Journal of Applied Remote Sensing, 16
Ana Castro, Yeyin Shi, J. Maja, J. Peña (2021)
UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions
Remote. Sens., 13
J. Alfieri, W. Kustas, H. Nieto, J. Prueger, L. Hipps, L. McKee, F. Gao, Sebastian Los (2018)
Influence of wind direction on the surface roughness of vineyards
Irrigation Science, 37
N. Bambach, W. Kustas, J. Alfieri, J. Prueger, L. Hipps, L. McKee, S. Castro, J. Volk, M. Alsina, A. McElrone (2022)
Evapotranspiration uncertainty at micrometeorological scales: the impact of the eddy covariance energy imbalance and correction methods
Irrigation Science, 40
Anderson Safre, A. Nassar, Alfonso Torres-Rua, Mayhar Aboutalebi, J. Saad, R. Manzione, Antônio Teixeira, J. Prueger, L. McKee, J. Alfieri, L. Hipps, H. Nieto, W. White, María Alsina, L. Sanchez, W. Kustas, N. Dokoozlian, F. Gao, Martha Anderson (2022)
Performance of Sentinel-2 SAFER ET model for daily and seasonal estimation of grapevine water consumption
Irrigation Science, 40
Xue‐Yan Zhang, Jiming Jin, X. Zeng, C. Hawkins, A. Neto, G. Niu (2022)
The Compensatory CO2 Fertilization and Stomatal Closure Effects on Runoff Projection From 2016–2099 in the Western United States
Water Resources Research, 58
Jie Xue, Martha Anderson, F. Gao, C. Hain, Yun Yang, K. Knipper, W. Kustas, Yang Yang (2021)
Mapping Daily Evapotranspiration at Field Scale Using the Harmonized Landsat and Sentinel-2 Dataset, with Sharpened VIIRS as a Sentinel-2 Thermal Proxy
Remote. Sens., 13
E. Zahn, E. Bou‐Zeid, S. Good, G. Katul, Christoph Thomas, K. Ghannam, J. Smith, M. Chamecki, N. Dias, J. Fuentes, J. Alfieri, H. Kwon, Kelly Caylor, Zhiqiu Gao, K. Soderberg, N. Bambach, L. Hipps, J. Prueger, W. Kustas (2022)
Direct partitioning of eddy-covariance water and carbon dioxide fluxes into ground and plant components
Agricultural and Forest Meteorology
J. Norman, W. Kustas, K. Humes (1995)
Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature
Agricultural and Forest Meteorology, 77
Rui Gao, Alfonso Torres-Rua, M. Aboutalebi, W. White, Martha Anderson, W. Kustas, N. Agam, M. Alsina, J. Alfieri, L. Hipps, N. Dokoozlian, H. Nieto, F. Gao, L. McKee, J. Prueger, L. Sanchez, A. McElrone, N. Bambach-Ortiz, C. Coopmans, Ian Gowing (2022)
LAI estimation across California vineyards using sUAS multi-seasonal multi-spectral, thermal, and elevation information and machine learning
Irrigation Science, 40
N. Kljun, P. Calanca, M. Rotach, H. Schmid (2015)
A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP)
Geoscientific Model Development, 8
Jie Xue, Martha Anderson, F. Gao, C. Hain, Liang Sun, Yun Yang, K. Knipper, W. Kustas, Alfonso Torres-Rua, M. Schull (2020)
Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized Landsat-Sentinel surface reflectances
Remote sensing of environment, 251
W. Kustas, J. Norman (1997)
A two‐source approach for estimating turbulent fluxes using multiple angle thermal infrared observations
Water Resources Research, 33
Alfonso Torres-Rua (2017)
Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature
Sensors (Basel, Switzerland), 17
Yanghui Kang, F. Gao, Martha Anderson, W. Kustas, H. Nieto, K. Knipper, Yun Yang, W. White, J. Alfieri, Alfonso Torres-Rua, M. Alsina, A. Karnieli (2022)
Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation
Irrigation Science, 40
C. Willmott (1982)
Some Comments on the Evaluation of Model Performance
Bulletin of the American Meteorological Society, 63
B. Choudhury, J. Monteith (1988)
A four-layer model for the heat budget of homogeneous land surfaces
Quarterly Journal of the Royal Meteorological Society, 114
D. Long, R. Engel, M. Siemens (2008)
Measuring Grain Protein Concentration with In-line Near Infrared Reflectance Spectroscopy
Agronomy Journal, 100
M. Aboutalebi, Alfonso Torres-Rua, M. McKee, W. Kustas, H. Nieto, M. Alsina, Alex White, J. Prueger, L. McKee, J. Alfieri, L. Hipps, C. Coopmans, L. Sanchez, N. Dokoozlian (2022)
Downscaling UAV land surface temperature using a coupled wavelet-machine learning-optimization algorithm and its impact on evapotranspiration
Irrigation Science, 40
Alfonso Torres-Rua, A. Ticlavilca, M. Aboutalebi, H. Nieto, M. Alsina, Alex White, J. Prueger, J. Alfieri, L. Hipps, L. McKee, W. Kustas, C. Coopmans, N. Dokoozlian (2020)
Estimation of evapotranspiration and energy fluxes using a deep-learning-based high-resolution emissivity model and the two-source energy balance model with sUAS information
, 11414
A. Nassar, Alfonso Torres-Rua, W. Kustas, H. Nieto, M. McKee, L. Hipps, D. Stevens, J. Alfieri, J. Prueger, M. Alsina, L. McKee, C. Coopmans, L. Sanchez, N. Dokoozlian (2020)
Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards
Remote sensing, 12
W. Kustas, J. Norman (1996)
Use of remote sensing for evapotranspiration monitoring over land surfaces
Hydrological Sciences Journal-journal Des Sciences Hydrologiques, 41
M. Aboutalebi, Alfonso Torres-Rua, M. McKee, W. Kustas, H. Nieto, M. Alsina, Alex White, J. Prueger, L. McKee, J. Alfieri, L. Hipps, C. Coopmans, N. Dokoozlian (2019)
Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models
Remote sensing, 12
Vicente Burchard-Levine, H. Nieto, W. Kustas, F. Gao, J. Alfieri, J. Prueger, L. Hipps, N. Bambach-Ortiz, A. McElrone, S. Castro, M. Alsina, L. McKee, E. Zahn, E. Bou‐Zeid, N. Dokoozlian (2022)
Application of a remote-sensing three-source energy balance model to improve evapotranspiration partitioning in vineyards
Irrigation Science, 40
Yun Yang, Martha Anderson, F. Gao, Jie Xue, K. Knipper, C. Hain (2022)
Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System
Remote. Sens., 14
W. Kustas, H. Nieto, O. García-Tejera, N. Bambach, A. McElrone, F. Gao, J. Alfieri, L. Hipps, J. Prueger, Alfonso Torres-Rua, Martha Anderson, K. Knipper, M. Alsina, L. McKee, E. Zahn, E. Bou‐Zeid, N. Dokoozlian (2022)
Impact of advection on two-source energy balance (TSEB) canopy transpiration parameterization for vineyards in the California Central Valley
Irrigation Science, 40
Uzair Ahmad, A. Alvino, S. Marino (2021)
A Review of Crop Water Stress Assessment Using Remote Sensing
Remote. Sens., 13
Shyamal Virnodkar, V. Pachghare, V. Patil, S. Jha (2020)
Remote sensing and machine learning for crop water stress determination in various crops: a critical review
Precision Agriculture, 21
H. Nieto, W. Kustas, Alfonso Torres-Rua, J. Alfieri, F. Gao, Martha Anderson, W. White, Lisheng Song, M. Alsina, J. Prueger, M. McKee, Manal Elarab, L. McKee (2018)
Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery
Irrigation Science, 37
F. Gao, W. Kustas, Martha Anderson (2012)
A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land
Remote. Sens., 4
Unmanned airborne thermal and mutilspectral imagery for estimating evapotranspiration in irrigated vineyards
A. Nassar, Alfonso Torres-Rua, L. Hipps, W. Kustas, M. McKee, D. Stevens, H. Nieto, Daniela Keller, Ian Gowing, C. Coopmans (2022)
Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem
Remote. Sens., 14
D. Kool, W. Kustas, A. Ben-Gal, N. Agam (2021)
Energy partitioning between plant canopy and soil, performance of the two-source energy balance model in a vineyard
Agricultural and Forest Meteorology, 300

Publisher: Multidisciplinary Digital Publishing Institute
Copyright: © 1996-2024 MDPI (Basel, Switzerland) unless otherwise stated Disclaimer Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. Terms and Conditions Privacy Policy
ISSN: 2072-4292
DOI: 10.3390/rs15030756
Publisher site: See Article on Publisher Site

Abstract

remote sensing Article ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards 1, 1 2 3 4 5 Rui Gao * , Alfonso F. Torres-Rua , Hector Nieto , Einara Zahn , Lawrence Hipps , William P. Kustas , 6 7 7 8 5 5 Maria Mar Alsina , Nicolas Bambach , Sebastian J. Castro , John H. Prueger , Joseph Alfieri , Lynn G. McKee , 5 5 7,9 5 10 11 William A. White , Feng Gao , Andrew J. McElrone , Martha Anderson , Kyle Knipper , Calvin Coopmans , 1 12 6 6 Ian Gowing , Nurit Agam , Luis Sanchez and Nick Dokoozlian Civil and Environmental Engineering Department, Utah State University, Old Main Hill, Logan, UT 84322, USA Institute of Agricultural Sciences—CSIC, 28006 Madrid, Spain Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA Plants, Soils, and Climate Department, Utah State University, Old Main Hill, Logan, UT 84322, USA U.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA E & J Gallo Winegrowing Research, Modesto, CA 95354, USA Department of Land, Air, and Water Resources, University of California, Davis, CA 95616, USA U.S. Department of Agriculture, Agricultural Research Service, National Laboratory for Agriculture and the Environment, Ames, IA 50011, USA USDA-Agricultural Research Service, Davis, CA 95616, USA Sustainable Agriculture Water Systems Research Unit, USDA ARS, Davis, CA 95616, USA Electrical and Computer Engineering Department, Utah State University, Old Main Hill, Logan, UT 84322, USA Blaustein Institutes for Desert Research, Sede-Boqer Campus, Ben-Gurion University of the Negev, Be’er Sheva 84990, Israel * Correspondence: [email protected] or [email protected] Abstract: Evapotranspiration (ET) is a crucial part of commercial grapevine production in California, and the partitioning of this quantity allows the separate assessment of soil and vine water and energy Citation: Gao, R.; Torres-Rua, A.F.; ﬂuxes. This partitioning has an important role in agriculture since it is related to grapevine stress, Nieto, H.; Zahn, E.; Hipps, L.; Kustas, yield quality, irrigation efﬁciency, and growth. Satellite remote sensing-based methods provide an W.P.; Alsina, M.M.; Bambach, N.; opportunity for ET partitioning at a subﬁeld scale. However, medium-resolution satellite imagery Castro, S.J.; Prueger, J.H.; et al. ET Partitioning Assessment Using the from platforms such as Landsat is often insufﬁcient for precision agricultural management at the TSEB Model and sUAS Information plant scale. Small, unmanned aerial systems (sUAS) such as the AggieAir platform from Utah across California Central Valley State University enable ET estimation and its partitioning over vineyards via the two-source energy Vineyards. Remote Sens. 2023, 15, 756. balance (TSEB) model. This study explores the assessment of ET and ET partitioning (i.e., soil water https://doi.org/10.3390/rs15030756 evaporation and plant transpiration), considering three different resistance models using ground- based information and aerial high-resolution imagery from the Grape Remote sensing Atmospheric Academic Editor: Gabriel Senay Proﬁle and Evapotranspiration eXperiment (GRAPEX). We developed a new method for temperature Received: 4 January 2023 partitioning that incorporated a quantile technique separation (QTS) and high-resolution sUAS Revised: 20 January 2023 information. This new method, coupled with the TSEB model (called TSEB-2T ), improved sensible Accepted: 24 January 2023 heat ﬂux (H) estimation, regarding the bias, with around 61% and 35% compared with the H from Published: 28 January 2023 the TSEB-PT and TSEB-2T, respectively. Comparisons among ET partitioning estimates from three different methods (Modiﬁed Relaxed Eddy Accumulation—MREA; Flux Variance Similarity—FVS; and Conditional Eddy Covariance—CEC) based on EC ﬂux tower data show that the transpiration Copyright: © 2023 by the authors. estimates obtained from the FVS method are statistically different from the estimates from the MREA Licensee MDPI, Basel, Switzerland. and the CEC methods, but the transpiration from the MREA and CEC methods are statistically This article is an open access article the same. By using the transpiration from the CEC method to compare with the transpiration distributed under the terms and modeled by different TSEB models, the TSEB-2T shows better agreement with the transpiration conditions of the Creative Commons obtained via the CEC method. Additionally, the transpiration estimation from TSEB-2T coupled Attribution (CC BY) license (https:// with different resistance models resulted in insigniﬁcant differences. This comparison is one of the creativecommons.org/licenses/by/ 4.0/). Remote Sens. 2023, 15, 756. https://doi.org/10.3390/rs15030756 https://www.mdpi.com/journal/remotesensing Remote Sens. 2023, 15, 756 2 of 20 ﬁrst for evaluating ET partitioning estimation from sUAS imagery based on eddy covariance-based partitioning methods. Keywords: temperature separation; ET partitioning; transpiration; transpiration ratio; TSEB-PT; TSEB-2T; energy closure; sUAS; California vineyards 1. Introduction Climate change and water scarcity are elevating the importance of sustainable irri- gation management, as agriculture accounts for approximately 70% of the worldwide freshwater demands [1]. Precision irrigation management can improve crop growing status and yield production [2] and prevent soil erosion, while also balancing the rela- tionship between urban and agricultural water distribution. The accurate estimation of evapotranspiration (ET) and its component ﬂuxes transpiration (T) and soil evaporation (E), along with detailed corresponding spatial information at the sub-ﬁeld (plant) scale, is of particular interest for supporting site-speciﬁc, precision irrigation management [3,4]. Quantiﬁcation of the percentage of ET arising from T aids the understanding of changes in carbon assimilation and water cycling in a changing environment [5]; however, obtaining estimates of ET partitioning at spatiotemporal scales pertinent to management activities remains challenging. Remote sensing techniques provide a path for ET mapping and monitoring at the ﬁeld scale using satellite and airborne imagery [6–10]. Although satellites can generate useful timeseries imagery for research and ﬁeld-scale management over broad areas, the relatively coarse spatial resolution of these images, especially the satellite thermal infrared (TIR) resolution used in surface energy balance models, limits its application to ﬁeld and sub-ﬁeld scales with their utility for precision applications [11,12]. Conversely, sUAS, a type of platform equipped with high-resolution sensors, can potentially provide high-resolution data to meet precision agricultural requirements [9]. UAVs are not only a cost-effective tool for obtaining high-resolution data, but also a ﬂexible platform that users can equip with sensors and schedule ﬂight times based on their requirements [13–15]. High-resolution aerial images and ground measurements collected by the Grape Remote sensing Atmospheric Proﬁling and Evapotranspiration eXperiment (GRAPEX) program [16] provide a unique opportunity for ET monitoring and mapping over California vineyards at the plant scale. A two-source energy balance (TSEB) model [17,18] has been used to connect those two types of data, upscaling the spatial scale from a single vine scale to the vineyard scale. With the corresponding eddy-covariance ﬂux tower monitoring ET on the ground, these sites provide an excellent comparison for ET modeling [19–26] and transpiration partitioning via TSEB models at the plant scale. Two versions of the TSEB model have been designed to accommodate the resolution of input surface temperature data. For coarser resolution imagery that does not allow for the direct separation of soil and canopy temperatures, Norman et al., 1995 [17] developed a method to retrieve soil and canopy temperatures by using a single observation of the bulk directional radiometric temperature. This method iteratively adjusts a Priestley–Taylor coefﬁcient controlling the transpiration ﬂux to ﬁnd the realistic solution, and is referred to here as the TSEB-PT model. A second method has been developed to use higher resolution (sub-meter) land-surface temperature (LST) imagery supporting the separation of soil and canopy temperatures, known as TSEB-2T [27]. Leaf area index (LAI) and LST are two key inputs used by both TSEB versions to partition evaporative ﬂuxes between the soil and the canopy—or, in vineyards, between the grape vine and interrow soil or cover crop [28,29]. Although Gao et al., 2022 [30] used machine learning techniques to generate a robust approach to estimate LAI at the plant scale for vineyards across California, challenges related to modeling and evaluating TSEB partitioning remain. Remote Sens. 2023, 15, 756 3 of 20 One such challenge arises because separated canopy and soil temperatures signiﬁ- cantly affect available energy partitioning and the sensible heat ﬂuxes from the soil and canopy components and, thus, ultimately the soil evaporation and plant transpiration [31]. Errors in the soil and canopy temperatures can result in overestimation or underestimation of soil evaporation and plant transpiration [9]. Previous research has used the relation- ship between the normalized difference vegetation index (NDVI) and the corresponding temperature value to obtain separated temperatures [21,26,32]. However, errors resulting from shadows [33,34], image quality, etc., can affect the relationship between NDVI and temperature, which in turn can affect TSEB modeling results. Another challenge is determining the optimal TSEB model framework for ET partition- ing. Several previous studies have shown that TSEB-2T can estimate ET more accurately than TSEB-PT [27]. However, another study for a vineyard in Israel using ground-based LST observations with ground-based measurements of soil E, and eddy covariance (EC) based ET to derive T (T = ET E), suggested TSEB-2T does poorly in partitioning ET compared to TSEB-PT [3]. In that study, they found improvements were needed in soil heat ﬂux estimation, a better algorithm for radiation partitioning, and accounting for vine canopy structure to improve the partitioning using TSEB-PT. The ﬁnal challenge is how to verify the TSEB estimated E and T. While high-frequency EC ﬂux monitoring data are useful for the model validation of total ET [35,36], E and T are not directly measured by the EC ﬂux tower. Fortunately, several techniques have been developed to partition EC water and carbon dioxide ﬂuxes into ground and plant components [37], including Modiﬁed Relaxed Eddy Accumulation, MREA; Flux-Variance Similarity, FVS; and Conditional Eddy-Covariance, CEC. This potentially provides a method for comparison with remote sensing-based estimates aggregated over the EC tower footprint [38], and Nassar et al., 2020 [22] and Gao et al., 2021 [39,40] discussed the footprint calculation for the EC tower in California vineyards. The objectives of this research are (1) to improve the method for temperature separation based on high-resolution LST imagery; (2) to evaluate the performance of different TSEB models coupled with different aerodynamic resistance models in comparison with energy components measured by the EC flux tower; and (3) to quantify the performance of ET partitioning via TSEB models. The modeling and measurement approaches are first described in the Materials and Methods section, and then they are intercompared toward identifying an optimal configuration in assessing ET and ET partitioning in vineyard systems. 2. Materials and Methods 2.1. Study Area This study is part of the ongoing GRAPEX project started in 2013, which seeks to im- prove water-use efﬁciency through the modeling of ET and plant stress in vineyards [41,42]. Vineyard blocks included in this study were located in three different climatic regions in Cal- ifornia. Vineyard blocks equipped with EC ﬂux towers BAR012 and BAR007 were furthest north, in Sonoma County, approximately 6 km south of Cloverdale, CA; EC ﬂux towers SLM001 and SLM002 were located in Sacramento County, approximately 20 km northeast of Lodi, CA; and block RIP 720 equipped with four different EC ﬂux towers (RIP 720-1, RIP 720-2, RIP 720-3, and RIP 720-4) in the same vineyard block and EC ﬂux tower RIP 760 were located in Madera County, about 30 km west of Fresno, CA. The four EC ﬂux towers in block RIP 720 were intended to monitor the ﬂux from the corresponding sub-blocks with different amounts of irrigation applied to cause variations in vine stress, as it was a variable rate deﬁcit irrigation (VRDI) study site. Figure 1 shows the geographical location of each set of vineyard blocks. The position and name of the EC ﬂux towers are labeled with a red cross symbol and white font, respectively, in Figure 1, and the study-site geographic information is presented in Table A1. Remote Sens. 2022, 14, x FOR PEER REVIEW 4 of 21 2. Materials and Methods 2.1. Study Area This study is part of the ongoing GRAPEX project started in 2013, which seeks to improve water-use efficiency through the modeling of ET and plant stress in vineyards [41,42]. Vineyard blocks included in this study were located in three different climatic regions in California. Vineyard blocks equipped with EC flux towers BAR012 and BAR007 were furthest north, in Sonoma County, approximately 6 km south of Cloverdale, CA; EC flux towers SLM001 and SLM002 were located in Sacramento County, approximately 20 km northeast of Lodi, CA; and block RIP 720 equipped with four different EC flux towers (RIP 720-1, RIP 720-2, RIP 720-3, and RIP 720-4) in the same vineyard block and EC flux tower RIP 760 were located in Madera County, about 30 km west of Fresno, CA. The four EC flux towers in block RIP 720 were intended to monitor the flux from the corresponding sub-blocks with different amounts of irrigation applied to cause variations in vine stress, as it was a variable rate deficit irrigation (VRDI) study site. Figure 1 shows the geograph- ical location of each set of vineyard blocks. The position and name of the EC flux towers Remote Sens. 2023, 15, 756 4 of 20 are labeled with a red cross symbol and white font, respectively, in Figure 1, and the study-site geographic information is presented in Table A1. Figure 1. Study areas in California and the position of EC flux towers at each research site. The Figure 1. Study areas in California and the position of EC ﬂux towers at each research site. The position of each EC flux tower within the respective research sites is marked by a red cross and the position of each EC ﬂux tower within the respective research sites is marked by a red cross and the corresponding tower name in white font. corresponding tower name in white font. 2.2. Data 2.2. Data 2.2.1. sUAS Platform Collection 2.2.1. sUAS Platform Collection Remote sensing data gathered via the AggieAir sUAS platform Remote sensing data gathered via the AggieAir sUAS platform (https://uwrl.usu. (https://uwrl.usu.edu/aggieair/, accessed on 10 January 2020) between 2014 and 2019 were edu/aggieair/, accessed on 10 January 2020) between 2014 and 2019 were used in this study. used in this study. Details of the data are presented in Nassar et al., 2021 [23] and in Table Details of the data are presented in Nassar et al., 2021 [23] and in Table A2. These data A2. These data include 4-band spectral images (B, G, R, and NIR) at 10 × 10 cm resolution, include 4-band spectral images (B, G, R, and NIR) at 10 10 cm resolution, digital surface 2 2 2 digital surface model (DSM) data at 10 × 10 cm resolution, and thermal imagery (Tr) at 60 model (DSM) data at 10 10 cm resolution, and thermal imagery (Tr) at 60 60 cm × 60 cm resolution [43]. Images of 6 bands collected via the AggieAir sUAS platform are resolution [43]. Images of 6 bands collected via the AggieAir sUAS platform are included included as an example, and can be seen in Gao et al. 2022 [30]. as an example, and can be seen in Gao et al. 2022 [30]. 2.2.2. Eddy-Covariance Flux Tower Data High-frequency eddy covariance (EC) ﬂux data were also collected in conjunction with intensive observation periods (IOPs) at the tower sites identiﬁed in Figure 1. Tower measurements of net radiation (Rn, Wm ), latent heat ﬂux (or evapotranspiration rate, 2 2 2 LE, Wm ), sensible heat ﬂux (H, Wm ), and soil surface heat ﬂux (G, Wm ) are used in this study to assess the TSEB-PT and TSEB-2T output. More information about the EC ﬂux tower can be found in Kustas et al., 2018 [16] and Bambach et al., 2022 [44], while details about energy closure and ET partitioning for the EC tower data are provided in Section 2.3.3. 2.3. Methodology Figure 2 shows a ﬂowchart of the process for comparing ET rate (LE converted to mass units of mm d ) and ET partitioning between the EC ﬂux tower monitored data and the TSEB modeling results within the corresponding footprint area. The top 5 boxes, along with surface temperature in the second row, are the inputs for the TSEB models. Canopy height, the ratio of canopy width and height, and fractional cover are obtained with a python program [45]; LAI is obtained from the products of recent studies [30,46], using Remote Sens. 2022, 14, x FOR PEER REVIEW 5 of 21 2.2.2. Eddy-Covariance Flux Tower Data High-frequency eddy covariance (EC) flux data were also collected in conjunction with intensive observation periods (IOPs) at the tower sites identified in Figure 1. Tower −2 measurements of net radiation (Rn, Wm ), latent heat flux (or evapotranspiration rate, −2 −2 −2 LE, Wm ), sensible heat flux (H, Wm ), and soil surface heat flux (G, Wm ) are used in this study to assess the TSEB-PT and TSEB-2T output. More information about the EC flux tower can be found in Kustas et al., 2018 [16] and Bambach et al., 2022 [44], while details about energy closure and ET partitioning for the EC tower data are provided in Section 2.3.3. 2.3. Methodology Figure 2 shows a flowchart of the process for comparing ET rate (LE converted to −1 mass units of mm d ) and ET partitioning between the EC flux tower monitored data and the TSEB modeling results within the corresponding footprint area. The top 5 boxes, along Remote Sens. 2023, 15, 756 with surface temperature in the second row, are the inputs for the TSEB models. Canopy 5 of 20 height, the ratio of canopy width and height, and fractional cover are obtained with a python program [45] ; LAI is obtained from the products of recent studies [30,46], using sUAS information and ground-based LAI measurements via machine learning approach. sUAS information and ground-based LAI measurements via machine learning approach. In this study, the weather data are obtained from the flux tower instrumentation. The In this study, the weather data are obtained from the ﬂux tower instrumentation. The TSEB-2T model requires partitioned temperature input (canopy and soil temperature), but TSEB-2T model requires partitioned temperature input (canopy and soil temperature), but other inputs to the two model formulations are the same. other inputs to the two model formulations are the same. Figure 2. Flowchart showing the process of comparing ET rate and ET partitioning from TSEB models Figure 2. Flowchart showing the process of comparing ET rate and ET partitioning from TSEB mod- within the footprint area. The top 5 boxes, along with surface temperature in the second row, are the els within the footprint area. The top 5 boxes, along with surface temperature in the second row, are the inputs for the TSEB models. The ET rate and T/ET were extracted within the corresponding inputs for the TSEB models. The ET rate and T/ET were extracted within the corresponding footprint footprint area and then compared with the EC flux tower monitored data. area and then compared with the EC ﬂux tower monitored data. A A p python-pr ython-pro ogram gram tool tool de developed veloped b by y G Gao ao eet t al. al., , 20 2021 21 [3 [35 5] ] was was used used to ex to extract tract T TSEB SEB modeling modeling re results sultson on L LEEand and ET p ET partitioning artitioning w within ithin the thfootprint e footprin ar t a earea around around each eatower ch tower for comparison with EC ﬂux tower measurements using the approach by Kljun et al., 2015 [38]. for comparison with EC flux tower measurements using the approach by Kljun et al., 2015 [38]. 2.3.1. Temperature Separation This study uses the normalized difference vegetation index (NDVI) as an indicator to separate the total surface radiometric temperature, gridded at 3.6 m resolution, into representative canopy and soil temperature grids at 3.6 m resolution. This method is based on work from prior studies [21,22,26,27,30,34]. In this study, we also included a framework to remove shadow effects in the temperature partitioning process (Figure 3). The removal process is divided into 4 steps. (1) Shadow pixels are identiﬁed geometrically at the time of satellite overpass based on DSM data at 0.15 m pixel level. (2) Shadow pixels are aggregated from 0.15 m to 0.60 m pixel scale, with any 0.60 m pixel containing at least one 0.15 m shadow pixel recognized as a shadow pixel. The reason for choosing 0.60 m is because the coarse resolution for the thermal images is 0.6 m. (3) NDVI is generated based on the 0.15 m optical image and then aggregated to the 0.60 m pixel level. (4) Within each 3.6 m pixel in the ﬁnal modeling domain, the 0.6 m temperature, NDVI, and shadow data are aligned. Any 0.6 m temperature and NDVI pixels that are collocated with a shadow pixel are ignored in building the temperature-NDVI relationship used in the temperature partitioning, as described below. Remote Sens. 2022, 14, x FOR PEER REVIEW 6 of 21 2.3.1. Temperature Separation This study uses the normalized difference vegetation index (NDVI) as an indicator to separate the total surface radiometric temperature, gridded at 3.6 m resolution, into representative canopy and soil temperature grids at 3.6 m resolution. This method is based on work from prior studies [21,22,26,27,30,34]. In this study, we also included a frame- work to remove shadow effects in the temperature partitioning process (Figure 3). The removal process is divided into 4 steps. (1) Shadow pixels are identified geometrically at the time of satellite overpass based on DSM data at 0.15 m pixel level. (2) Shadow pixels are aggregated from 0.15 m to 0.60 m pixel scale, with any 0.60 m pixel containing at least one 0.15 m shadow pixel recognized as a shadow pixel. The reason for choosing 0.60 m is because the coarse resolution for the thermal images is 0.6 m. (3) NDVI is generated based on the 0.15 m optical image and then aggregated to the 0.60 m pixel level. (4) Within each 3.6 m pixel in the final modeling domain, the 0.6 m temperature, NDVI, and shadow data are aligned. Any 0.6 m temperature and NDVI pixels that are collocated with a shadow pixel are ignored in building the temperature-NDVI relationship used in the temperature Remote Sens. 2023, 15, 756 6 of 20 partitioning, as described below. Figure 3. Flowchart showing the ideal temperature separation process for a single TSEB model pixel Figure 3. Flowchart showing the ideal temperature separation process for a single TSEB model pixel (3.6 m resolution). (3.6 m resolution). In previous research, NDVI thresholds were created to identify the category of each In previous research, NDVI thresholds were created to identify the category of each pixel in the model pixeldomain. in the mo For del example, domain. For in ex the am 1:1 plplot e, in th shown e 1:1 p in lot Figur shown e 3 i , n NDVI Figur= e 3, 0.3 Nis DVI = 0.3 is recognized as recogni the threshold zed as the thre to identify shold whether to identi or fy not whethe the pixel r or not (point) the pix isesenescent l (point) is senescent cover cover crop stubble (interrow); the pixel is identified as an interrow pixel when the NDVI value crop stubble (interrow); the pixel is identiﬁed as an interrow pixel when the NDVI value is lower than 0.30. Likewise, the pixel is identified as a vegetation pixel when the NDVI is lower than 0.30. Likewise, the pixel is identiﬁed as a vegetation pixel when the NDVI value is higher than 0.65. The corresponding soil and vegetation temperature are normally value is higher than 0.65. The corresponding soil and vegetation temperature are normally averaged based on the temperature values within the corresponding zone, with NDVI < averaged based on the temperature values within the corresponding zone, with NDVI < 0.3 0.3 for soil zone and NDVI > 0.65 for vegetation zone, respectively. for soil zone and NDVI > 0.65 for vegetation zone, respectively. In some cases, the plot of temperature vs. NDVI shows low correlation, with signifi- In some cases, the plot of temperature vs. NDVI shows low correlation, with signiﬁcant cant scatter. Figure 4 is one such example, showing the temperature separation process scatter. Figure 4 is one such example, showing the temperature separation process for for one 3.6 m modeling pixel at SLM (9 August 2014, 10:41 am, the air temperature is one 3.6 m modeling pixel at SLM (9 August 2014, 10:41 am, the air temperature is around 27.7 C). Figure 4a–c displays an 0.15 m resolution spectral image of the modeling pixel, the corresponding 0.6 m resolution temperature image, and the 0.6 m resolution NDVI image, respectively. The three pixels highlighted by black dashed boxes in Figure 4b,c contain shadows, and the 0.15 m resolution shadows are represented by the red squares in Figure 4c. The solid trend line in Figure 4d is generated based on all (36) points, and the corresponding slope and intercept are shown on the ﬁgure. According to previous research experience, the separated soil temperature is calculated based on the trend line at the soil NDVI threshold (e.g., NDVI = 0.4), and the separated vegetation temperature is averaged based on the pixel temperature within the pure vegetation zone. In this case, the separated soil temperature is potentially underestimated, and the separated vegetation temperature is overestimated due to the large spread in values in the pure vegetation zone. The maximum spread vegetation temperatures is around 5 C; however, relatively small changes in the assumed canopy temperature will impact TSEB-2T [3], so it is important to better constrain temperature samples considered in determining the endpoint pure soil and vegetation temperatures. Remote Sens. 2022, 14, x FOR PEER REVIEW 7 of 21 around 27.7 °C). Figure 4a–c displays an 0.15 m resolution spectral image of the modeling pixel, the corresponding 0.6 m resolution temperature image, and the 0.6 m resolution NDVI image, respectively. The three pixels highlighted by black dashed boxes in Figure 4b,c contain shadows, and the 0.15 m resolution shadows are represented by the red squares in Figure 4c. The solid trend line in Figure 4d is generated based on all (36) points, and the corresponding slope and intercept are shown on the figure. According to previous research experience, the separated soil temperature is calculated based on the trend line at the soil NDVI threshold (e.g., NDVI = 0.4), and the separated vegetation temperature is averaged based on the pixel temperature within the pure vegetation zone. In this case, the separated soil temperature is potentially underestimated, and the separated vegetation temperature is overestimated due to the large spread in values in the pure vegetation zone. The maximum spread vegetation temperatures is around 5 °C; however, relatively small changes in the assumed canopy temperature will impact TSEB-2T [3], so it is im- Remote Sens. 2023, 15, 756 7 of 20 portant to better constrain temperature samples considered in determining the endpoint pure soil and vegetation temperatures. Figure 4. One example showing the performance of the method in one TSEB modeling pixel (3.6 m Figure 4. One example showing the performance of the method in one TSEB modeling pixel (3.6 m resolution grid) to separate the temperature as canopy and soil temperature. (a) Spectral image at resolution grid) to separate the temperature as canopy and soil temperature. (a) Spectral image at 0.15 m resolution, along with (b) co-collected temperature image and (c) generated NDVI image at 0.15 m resolution, along with (b) co-collected temperature image and (c) generated NDVI image at 0.6 m resolution. Pixels highlighted with the dashed line in (b) and (c) represent the locations of 0.6 m resolution. Pixels highlighted with the dashed line in (b,c) represent the locations of shadow at shadow at 0.6 m pixel scale, and the 0.15 m red pixels in (b) represent shadow locations at 0.15 m 0.6 m pixel scale, and the 0.15 m red pixels in (b) represent shadow locations at 0.15 m pixel scale; pixel scale; (d) linear relationship between temperature and NDVI considering 36 pairs of pixels (d) linear relationship between temperature and NDVI considering 36 pairs of pixels within the 3.6 m within the 3.6 m grid. The red points highlighted by dashed lines represent the temperatures from grid. The red points highlighted by dashed lines represent the temperatures from the shadow pixels. the shadow pixels. The pure vegetation zone whose x-axis value is higher than 0.70 and the pure soil zone whose x-axis value is lower than 0.40 are displayed at each side of the x-axis; (e) Within The pure vegetation zone whose x-axis value is higher than 0.70 and the pure soil zone whose x-axis the pure vegetation zone, pixels with temperatures higher than its 75th percentile temperature are value is lower than 0.40 are displayed at each side of the x-axis; (e) Within the pure vegetation zone, pixels with temperatures higher than its 75th percentile temperature are highlighted by dash-lined boxes; (f) pixel locations where the temperature is higher than its 75th percentile temperature are highlighted on the temperature image; (g) box plots for soil region, NDVI 2 [0, 0.40], vegetation region, NDVI 2 [0.70, 1], and the middle part region, NDVI 2 (0.4, 0.7). The 50th and 75th percentile temperatures within the pure vegetation zone are shown on the right side; (h) linear relationship between temperature and NDVI obtained by eliminating vegetation-temperature pixels above the 75th percentile temperature, highlighted by the red dashed-line box. The reason for the high variation of vegetation and soil temperature within a TSEB modeling pixel is potentially coming from the data collection and data processing. The imagery collection process is ﬁnished based on multiple spectral sensors, and the pixels of each sensor do not perfectly align with each other. During the imagery processing, the image–pixel alignment issue is still difﬁcult to address. Therefore, it potentially results in a high variation of temperature in a TSEB modeling pixel. However, this issue can be addressed by upgrading the sensor in the future work, or ﬂying the sensor at a lower Remote Sens. 2023, 15, 756 8 of 20 elevation. Another reason for the high variation of vegetation temperature is because of the vegetation type within the TSEB modeling pixel. The interrow pixel is a mixture of bare soil and senescent cover crop stubble, and the senescent cover crop stubble is short and not well irrigated. When upscaling NDVI from 0.15 m to 0.6 m pixels, most interrow pixels are recognized as healthy vegetation pixels. Therefore, the temperature of the interrow vegetation pixel is higher than the temperature of the vine vegetation, which is well irrigated compared with the senescent cover crop stubble. Quartile tests were performed to optimize the removal of contaminated pure vegeta- tion pixel temperatures. For example, the averaged vegetation temperature, considering all vegetation pixels, is around 32.4 C. If the pixel with a corresponding temperature higher than the 50th (75th) percentile of all vegetation–pixel temperatures is eliminated, the corresponding vegetation temperature is around 31.3 C (32.9 C) (Figure 4e–g). Based on extensive testing, the 75th percentile of the vegetation temperature was identiﬁed as the threshold to eliminate the high-vegetation temperature effect on the vegetation temperature estimation, based on further data analysis. This temperature-separation method is named quantile temperature separation (QTS). Another modiﬁcation in this QTS method relates to the linear relationship between the NDVI and temperature. Typically, pixel temperature decreases with increasing NDVI within a TSEB modeling pixel (e.g., 3.6 m resolution pixel). After the elimination of high vegetation temperatures in the pure vegetation zone, some high points in the middle region (NDVI 2 [0.40, 0.70]) still remain (Figure 4h). These anomalous pixels can affect the linear relationship between the temperature and NDVI [27]. Therefore, a tool called RANSACRegressor (Scikit-learn developers) from the “sklearn.linear_model” is used in this study. This tool is an iterative method for the robust estimation of parameters from a subset of inliers from the complete dataset. The three points highlighted by the red dash-line box (Figure 4h), for example, were eliminated by the tool and then the linear relationship was obtained based on the remaining red points. At the end, a soil temperature was estimated based on the linear relationship at 0.40 (NDVI value). If there was at least one soil pixel within the TSEB modeling pixel, the soil temperature was calculated as an average value based on the temperature value on the soil pixels. The canopy temperature was calculated as the average temperature of pixels above 0.70 NDVI and within the lower 75th percentile of temperature in that vegetation zone. If there were no vegetation pixels found in that TSEB modeling pixel, an “NAN” value was used to represent the canopy temperature. 2.3.2. TSEB Model The two-source energy balance (TSEB) model has been widely used for ET estimation over agricultural lands (e.g., corn, soybeans, cotton, grapevines, almonds, pastures and grazing lands) based on ground, aerial and satellite remote sensing data. A schematic diagram from Kustas et al., 2018 [16] shows the TSEB model resistance network for the sensible heat ﬂux, and lists the set of equations used to obtain the iterative solution. The soil and canopy temperatures constrain the sensible heat ﬂuxes, net radiation, and soil heat ﬂux with the added initial estimate of canopy latent heat ﬂux based on the Priestley–Taylor (PT). This version of the TSEB model is called TSEB-PT [17]. For applications using higher resolution (e.g., sUAS), thermal imagery of the soil and canopy temperatures are derived using the methods described in Section 2.3.1. This version of the model is referred to as TSEB-2T [27]. It is also noted that an earlier study by Kustas and Norman., 1997 [47], using radiometric temperatures at signiﬁcantly different viewing angles, could estimate soil and canopy temperatures. In the TSEB, net radiation, including soil and canopy net radiation, is estimated based on a set of land surface parameters (e.g., longwave emissions from soil, canopy, and sky, solar transmittance through the canopy; canopy and soil albedo). The ground heat flux, G, is estimated as a fraction of the soil net radiation (R ). Nieto et al., 2019 [27] show the empirical nS Remote Sens. 2023, 15, 756 9 of 20 G/R curve fit as a function of time of the day. Considering that all sUAS images were nS collected between 10 am to 4 pm, a constant G-ratio value (0.33) is used in this research. Equation (1) shows the sensible heat ﬂux calculation—the difference between TSEB-PT and TSEB-2T lies in the approach to obtaining T and T . In addition to these component C S temperatures, the aerodynamic resistance of the canopy (R ) and soil (R ) also affect the x s H, but a systematic assessment of different methods for deﬁning these resistances within the TSEB context has not been conducted to date. Three different resistance models for canopy and soil were tested in this study for both TSEB-PT and TSEB-2T: Norman and Kustas (called NK resistance model in this paper, expressed by Equations (2) and (3)), McNaughton and Van (MV model, by Equations (4) and (5)), and Choudhury and Monteith (CM model, by Equations (6) and (7)), respectively. Because the separated temperature images illustrated in Section 2.3.1 are used as input for the TSEB-2T model, the TSEB-2T model coupled with QTS in this study is named as TSEB-2T . T T T T C AC S AC H = H + H = r C + r C (1) C S air p air p R R x s R = p (2) c T T + bu s A s C l R = (3) LAI U d +z 0 0M R = (4) C 0.36 R = l u + (5) x w F u a z d z 0_soil 0 0M h e a a k k h h c c R = e e (6) a k k h R = q (7) CM u a c F 2 1 e a l k = k u (h d ) (8) h 0 In the above equations, R is the aerodynamic resistance of the soil; R is the aero- s x dynamic resistance of the canopy; c and b are the coefﬁcients depending on the turbulent length scale in the canopy, soil-surface roughness, and turbulence intensity in the canopy; T is the soil-surface temperature (K); T is the air temperature (K); u is the wind speed S A s 1 1 0 near the soil surface (ms ); u is the friction velocity (ms ); C is derived from weight- ing a coefﬁcient in the equation for leaf boundary layer resistance over the height of the 1/2 1 2 2 canopy [48] and it is assumed to be 90 s m ; LAI is the leaf area index (m m ); l is the average leaf width (m); U is the wind speed at the heat source-sink (ms ); d +z 0 0M F is the local leaf area index; h is the canopy height; a is the heat diffusion coefﬁcient; c k k is the von Karman’s constant (0.41); z is the roughness length of the soil layer; d 0_soil 0 is the zero-plane displacement height (m); z is the aerodynamic roughness length for 0M momentum transport (m); CM is the leaf drag coefﬁcient [49]; a is the wind extinction coefﬁcient; and u is the wind speed at the canopy interface (ms ). 2.3.3. Validation Data from the Eddy Covariance Tower Energy Components Energy closure of the EC ﬂux monitored data is a concern [19,24] for validating the TSEB modeling results. Nieto et al., 2022 [24], for example, used the arithmetic-mean value for the sensible heat ﬂux and the latent heat ﬂux based on three calculated possible closure corrections to evaluate TSEB modeling results: (1) assigning all the residual error to H; (2) assigning all the residual to LE; and (3) assigning the residual proportionally Remote Sens. 2023, 15, 756 10 of 20 to H and LE by preserving the Bowen Ratio. In this research, the geometric-mean value (Equation (9)) of the sensible heat ﬂux and the geometric-mean value of the latent heat ﬂux are calculated to validate the corresponding TSEB modeling results [50], considering that the geometric-mean value is less inﬂuenced by skewed distributions compared with the arithmetic-mean value. ! 1 x = x x x (9) i 1 2 n i=1 where n is the number of values, and x are the values included in the average. Transpiration Zahn et al., 2022 [37] proposed the Conditional Eddy Covariance (CEC) method using the high frequency water vapor and CO measurements from eddy covariance measurements to estimate soil evaporation from plant transpiration, and compared results with the modiﬁed Relaxed Eddy Accumulation (MREA) method and the Flux Variance Similarity (FVS) method. They found that the CEC and MREA framework can be used as a qualitative measure to identify stomatal and non-stomatal components. Methods to evaluate the transpiration modeled by the TSEB models using these measurements are explained in Section 3.2.1. 3. Results and Discussion 3.1. TSEB Modeling Results 3.1.1. TSEB Component Comparison Considering Different Resistance Models Figure 5 shows the comparison of modeled versus measured energy components (Rn, G, H, and LE), considering different TSEB models (TSEB-PT, TSEB-2T, and TSEB-2T ) Remote Sens. 2022, 14, x FOR PEER REVIEW 11 of 21 coupled with different resistance models (NK, CM, and MV). In Figure 5, observed H and LE have been adjusted for closure using the technique discussed in Section 2.3.3. Figure 5. Scatter Figure 5. plots Scatte showing r plots showing the compariso the comparison between n between ener energy balance com gy balance components ponents measured from measured from the EC flux tower (y-axis) and the modeled energy balance components from TSEB-PT, TSEB-2T, the EC ﬂux tower (y-axis) and the modeled energy balance components from TSEB-PT, TSEB-2T, and and TSEB-2TQ (rows 1–3) using the NK, CM and MV (columns 1–3) resistance formulations (x-axis). TSEB-2T (rows 1–3) using the NK, CM and MV (columns 1–3) resistance formulations (x-axis). Statistical metrics of evaluation for each flux, model, and resistance formulation are provided in Table 1. Statistics show that the modeled Rn from different TSEB models, in general, has a good agreement with the Rn from the EC flux tower. However, the modeled G calculated via the ratio of the modeled soil net radiation has a lower agreement with the G from the EC flux tower, which may result from the constant value (0.33) adopted for the time period from 10 am to 4 pm. This suggests that a time-varying ratio needs to be used for the G estimation, based on sUAS information for different times during the day, as suggested by Nieto et al., 2019 [27]. Sensible heat estimates from TSEB models coupled with the NK and/or the MV resistance models have better agreement with tower meas- urements as quantified by the index of agreement, d (Table 1). Based on the RMSE and d values, the H and LE estimated from the TSEB-2TQ shows better agreement with meas- urement fluxes. This shows that the QTS method considering shadow and extreme pixel- value effects, characteristics of the high-resolution pixel within the smallest TSEB model- ing domain, in general improved the flux estimation. Remote Sens. 2023, 15, 756 11 of 20 Statistical metrics of evaluation for each ﬂux, model, and resistance formulation are provided in Table 1. Statistics show that the modeled Rn from different TSEB models, in general, has a good agreement with the Rn from the EC ﬂux tower. However, the modeled G calculated via the ratio of the modeled soil net radiation has a lower agreement with the G from the EC ﬂux tower, which may result from the constant value (0.33) adopted for the time period from 10 am to 4 pm. This suggests that a time-varying ratio needs to be used for the G estimation, based on sUAS information for different times during the day, as suggested by Nieto et al., 2019 [27]. Sensible heat estimates from TSEB models coupled with the NK and/or the MV resistance models have better agreement with tower measurements as quantiﬁed by the index of agreement, d (Table 1). Based on the RMSE and d values, the H and LE estimated from the TSEB-2T shows better agreement with measurement ﬂuxes. This shows that the QTS method considering shadow and extreme pixel-value effects, characteristics of the high-resolution pixel within the smallest TSEB modeling domain, in general improved the ﬂux estimation. Table 1. Statistics of the goodness of ﬁt showing the performance of each TSEB modeling result within the footprint area. N is the number of cases used for validation, RMSE is the root mean square 2 2 error (Wm ), Bias is the mean bias computed as the measured minus the modeled (Wm ), r is the Pearson correlation coefﬁcient between the measured and modeled, and d is the Willmott’s index of agreement [51]. When N is different in different groups, d is still calculated but not a representative metric to compare the model performance. TSEB-PT TSEB-PT TSEB-PT TSEB-2T TSEB-2T TSEB-2T TSEB-2T TSEB-2T TSEB-2T Q Q Q (NK) (CM) (MV) (NK) (CM) (MV) (NK) (CM) (MV) N 60 60 60 60 60 60 60 60 60 RMSE 22 22 22 21 21 21 23 23 23 Net Bias 4 5 4 5 5 5 10 10 10 radiation r 0.96 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 d 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 N 60 60 60 60 60 60 60 60 60 RMSE 41 40 41 41 41 41 39 39 39 Ground Bias 27 26 27 26 26 26 24 24 24 heat ﬂux r 0.25 0.24 0.25 0.26 0.26 0.26 0.27 0.27 0.27 d 0.52 0.52 0.52 0.54 0.54 0.54 0.55 0.55 0.55 N 60 60 60 60 60 60 60 60 60 RMSE 78 85 84 71 71 69 65 71 65 Sensible Bias 21 45 17 16 14 19 3 26 3 heat ﬂux r 0.63 0.62 0.61 0.62 0.60 0.64 0.63 0.61 0.63 d 0.78 0.74 0.76 0.78 0.77 0.79 0.77 0.75 0.77 N 60 60 60 60 60 60 60 60 60 RMSE 82 84 90 80 73 81 69 71 70 Latent heat Bias 7 32 3 34 3 36 16 13 16 ﬂux r 0.53 0.55 0.51 0.55 0.57 0.58 0.58 0.59 0.58 d 0.73 0.73 0.71 0.71 0.76 0.72 0.75 0.78 0.75 Considering that previous research adopted the NK model and that the difference between H and LE based on the NK and the MV model coupled with the TSEB-2T is small, the TSEB-2T coupled with the NK model was adopted in this research for energy component estimation. 3.1.2. Time-Based Performance of the TSEB-2T NK Model The sUAS ﬂight times were between 10 am and 4 pm local time (Table A2), which is a fairly wide time frame. Considering the change in the solar altitude and azimuth for the different overpass times, the sUAS overpasses were grouped into three different time periods. The ﬁrst time period, between 10:00 am and 11:59 am, was called the “Landsat” (LS) time period since the Landsat passes over between 10:30 am and 11:00 am (Paciﬁc Standard Time—PST). The second time period, between 12:00 pm and 1:59 pm, is called the “solar noon” (SN) time period since the sun reaches its highest point for the day at around 1:00 pm (PST). The third time period, after 2:00 pm (between 2:00 pm and 5:00 pm, PST), is called the “afternoon” (AF) time period. Remote Sens. 2022, 14, x FOR PEER REVIEW 13 of 21 Remote Sens. 2023, 15, 756 12 of 20 [26] illustrated the challenge for spatial emissivity estimation, and they proved that spatial emissivity (not a constant value) can improve TSEB model performance. Gao et al., 2021 [52] pointed out that the solar spectrum reflectance and transmittance changes along with the le Figur af wa ete 6 r conten shows the t. performance From these aof spTSEB-2T ects, the sp coupled atial and with tem the po NK ral v model ariabil in itestimating y in these parame energy ter components s (e.g., 𝜀 and at each 𝜏 ) need to time period. be furtT her ablestA3 udie contains d if the Rn the es corr tim esponding ation is to metrics be im- proved, particularly associated with the comparisons in the afternoon displayed period. in Figure 6. Figure 6. Scatter plots illustrating the performance of the TSEB-2TQ model coupled with the Norman Figure 6. Scatter plots illustrating the performance of the TSEB-2T model coupled with the Norman and Kustas (NK) resistance model at different time periods. and Kustas (NK) resistance model at different time periods. Metrics for the performance of G estimation suggest that the G ratio value (0.33) used Net radiation shows highest correlation with observations during the AF period, al- in the TSEB-2TQ model is more appropriate at the AF time period than for the LS and SN though the relationship has higher bias and yields higher RMSE than the LS and SN periods. time periods. For example, the labeled points in Figure 6 (a) and (b), “RIP760 20180806 This may result from using spatially and temporally constant values of emissivity (#) and 10:41” and “RIP760 20180805 12:33”, indicate that G was overestimated, indicating that the solar transmittance through the canopy (t ) in the TSEB. Torres-Rua et al., 2020 [26] the G ratio should be smaller than 0.33. This behavior was also noted by Nieto et al., 2019 illustrated the challenge for spatial emissivity estimation, and they proved that spatial emis- [27], who found that a double asymmetric sigmoid function gave better results than using sivity (not a constant value) can improve TSEB model performance. Gao et al., 2021 [52] a constant value, and better fits the observations than the sinusoidal function proposed pointed out that the solar spectrum reﬂectance and transmittance changes along with by Santanello and Friedl., 2003 [53]. the leaf water content. From these aspects, the spatial and temporal variability in these RMSE in sensible and latent heat flux from the TSEB-2TQ is minimized in the AF pe- parameters (e.g., # and t ) need to be further studied if the Rn estimation is to be improved, riod. Examining scenes where outliers in H and LE are observed in Figure 6c showed no particularly in the afternoon period. significant issues from the QTS model based on the separated average soil and canopy Metrics for the performance of G estimation suggest that the G ratio value (0.33) used temperatures within the corresponding footprint area, in comparison with the remaining in the TSEB-2T model is more appropriate at the AF time period than for the LS and SN im time age periods. dates (T For able A4 example, ), so the theclabeled ause of p points oor perform in Figur ae nce is un 6a,b, “RIP760 known. 20180806 10:41” and “RIP760 20180805 12:33”, indicate that G was overestimated, indicating that the G ratio 3. should 2. Trans be pismaller ration than 0.33. This behavior was also noted by Nieto et al., 2019 [27], who found that a double asymmetric sigmoid function gave better results than using a constant 3.2.1. Transpiration Estimation via CEC, MREA, and FVS value, and better ﬁts the observations than the sinusoidal function proposed by Santanello Based on the sUAS flight time, both CEC and MREA methods provided 50 transpi- and Friedl., 2003 [53]. ration estimations, while the FVS method provided 19. The CEC and MREA methods pro- RMSE in sensible and latent heat ﬂux from the TSEB-2T is minimized in the AF vided consistent estimates over the daytime period, while the FVS method often produced period. Examining scenes where outliers in H and LE are observed in Figure 6c showed no solution. Figure 7 shows that the transpiration estimated via the FVS method has a no signiﬁcant issues from the QTS model based on the separated average soil and canopy significant difference from the transpiration estimated via the CEC and MREA method. temperatures within the corresponding footprint area, in comparison with the remaining image dates (Table A4), so the cause of poor performance is unknown. 3.2. Transpiration 3.2.1. Transpiration Estimation via CEC, MREA, and FVS Based on the sUAS ﬂight time, both CEC and MREA methods provided 50 transpi- ration estimations, while the FVS method provided 19. The CEC and MREA methods provided consistent estimates over the daytime period, while the FVS method often pro- duced no solution. Figure 7 shows that the transpiration estimated via the FVS method has a signiﬁcant difference from the transpiration estimated via the CEC and MREA method. Remote Sens. 2023, 15, 756 13 of 20 Remote Sens. 2022, 14, x FOR PEER REVIEW 14 of 21 Figure 7. Scatter plots showing the difference between the transpiration estimated based on differ- Figure 7. Scatter plots showing the difference between the transpiration estimated based on different ent methods (CEC, MREA, and FVS). The red dashline is a reference 1:1 line. methods (CEC, MREA, and FVS). The red dashline is a reference 1:1 line. An analysis of variance (ANOVA) and a Tukey test (Table 2) was then processed to An analysis of variance (ANOVA) and a Tukey test (Table 2) was then processed to not only show the transpiration difference between different groups, but also to show if not only show the transpiration difference between different groups, but also to show if the null hypothesis (i.e., the mean transpiration between different groups is the same) was the null hypothesis (i.e., the mean transpiration between different groups is the same) was acceptable [54]. Table 2 suggests that the mean transpiration estimated via the FVS acceptable [54]. Table 2 suggests that the mean transpiration estimated via the FVS method method yielded a significant difference from estimates from the CEC and MREA methods, yielded a signiﬁcant difference from estimates from the CEC and MREA methods, and and the mean transpiration via CEC is statistically the same as the mean transpiration via the mean transpiration via CEC is statistically the same as the mean transpiration via the the MREA method. This is consistent with the findings of Zahn et al., 2022 [37]. Since the MREA method. This is consistent with the ﬁndings of Zahn et al., 2022 [37]. Since the CEC and MREA methods yielded essentially the same values, CEC values were used in CEC and MREA methods yielded essentially the same values, CEC values were used in subsequent analyses. subsequent analyses. Table 2. ANOVA and Tukey test results showing the difference between the transpiration estimated Table 2. ANOVA and Tukey test results showing the difference between the transpiration estimated based on different methods (CEC, MREA, and FVS). The null hypothesis is that the mean transpira- based on different methods (CEC, MREA, and FVS). The null hypothesis is that the mean transpiration tion between different groups is the same (shown in the last column). “Mean difference” is the mean between different groups is the same (shown in the last column). “Mean difference” is the mean difference between “Group 1” and “Group 2.” “Lower boundary” and “Upper boundary” are the difference between “Group 1” and “Group 2.” “Lower boundary” and “Upper boundary” are the lower and upper 95% confidence interval boundaries, respectively. The unit for “Mean difference,” −2 lower and upper 95% conﬁdence interval boundaries, respectively. The unit for “Mean difference,” “Lower boundary,” and “Upper boundary” is Wm . “Lower boundary,” and “Upper boundary” is Wm . The Mean Transpiration Is the Group 1 Group 2 Mean Difference p-Adj Lower Boundary Upper Boundary Same The Mean Mean Lower Upper Group 1 Group 2 p-Adj Transpiration CEC FVS −84 0.004 −152 −15 NO Difference Boundary Boundary Is the Same CEC MREA 0 0.900 −69 68 YES CEC FVS 84 0.004 152 15 NO MREA FVS −84 0.004 −152 −15 NO CEC MREA 0 0.900 69 68 YES MREA FVS 84 0.004 152 15 NO 3.2.2. Transpiration Comparison Figure 8 contains three scatter plots showing all comparisons between the transpira- 3.2.2. Transpiration Comparison tion based on CEC method and the transpiration modeled by different TSEB models and the sU Figur ASe in 8 fcontains ormation. thr T ee abscatter le A5 con plots taishowing ns the res all ulcomparisons ts from the A between NOVA and the transpiration Tukey test, based showion ng t CEC he stmethod atistical dif and ferences of the transpiration the meanmodeled values. The null h by differ y ent pothesis TSEBfor models the ANOVA and the sUAS test isinformation. that the mean v Table alu A5 e frcontains om two di the ffe rrent gro esults from ups i the s st ANOV atistica A lly and the sam Tukey e, test, and showing the last the colum statistical n suggest difs fer thences at all m ofethe an v mean alues f values. rom “GThe roup null 1” an hypothesis d “Group 2” for are thest ANOV atistical Altest y the is that samthe e. Im mean portant value ly, two from fa two ctors dif shown ferent in gr T oups able is A5 statistically explain tha the t trans same, pira and tion the esti last mate column d via suggests the CEC that and all MRE mean A me values thods h fr aom s a stron “Group ger r 1” ela and tionsh “Gr ip w oup ith 2” trar an esp statistically iration mod the eled same. via Importantly TSEB-2TQ. Th , two e first factors fact shown or is the in corre Table sponding A5 explain “that p-adtranspiration j” values, whestimated ich are 0.9via 00 (highe the CEC r and than m MREA ost o methods ther “p-a has dj” v a str alu onger es, and relationship higher than with α =transpiratio 0.05). The se n modeled cond is th via at tTSEB-2T he corre- . −2 The sponding ﬁrst factor “Mean di is the corr fferenc esponding e” is sm“alp-adj” ler than values, 10 Wm which , whi arec 0.900 h is g (higher enerally than sma most ller th other an “p other expe -adj” values, riments. and higher than = 0.05). The second is that the corresponding “Mean difference” is smaller than 10 Wm , which is generally smaller than other experiments. Remote Remote Sens Sens.. 2023 2022,, 14 15,, x FOR 756 PEER REVIEW 15 of 14 of 21 20 Remote Sens. 2022, 14, x FOR PEER REVIEW 15 of 21 Figure 8. The comparison between the transpiration based on the CEC method and the transpiration Figure 8. The comparison between the transpiration based on the CEC method and the transpiration Figure 8. The comparison between the transpiration based on the CEC method and the transpiration modeled via the TSEB models (different TSEB models with different resistance models). modeled via the TSEB models (different TSEB models with different resistance models). modeled via the TSEB models (different TSEB models with different resistance models). Table A6 is another supplement, showing the model performance displayed and il- Table A6 is another supplement, showing the model performance displayed and il- Table A6 is another supplement, showing the model performance displayed and lustrated by Figure 8 and Table A5, respectively. The “Bias” (Table A6) explains the same lustrated by Figure 8 and Table A5, respectively. The “Bias” (Table A6) explains the same illustrated by Figure 8 and Table A5, respectively. The “Bias” (Table A6) explains the same information as shown by “Mean difference” in Table A5 regarding the transpiration from information as shown by “Mean difference” in Table A5 regarding the transpiration from information as shown by “Mean difference” in Table A5 regarding the transpiration from the CEC method. Except for r and d, since the value in each column performs at a similar the CEC method. Except for r and d, since the value in each column performs at a similar the CEC method. Except for r and d, since the value in each column performs at a similar level, RMSE shows that the transpiration modeled via TSEB-2TQ, in general, is closer to level, level,RMSE RMSE shows shows th thatathe t thtranspiration e transpiratio modeled n modeled vi via TSEB-2T a TSEB-2 ,T in Q, i general, n genera is l, is closer to closer to the the transpiration estimated via the CEC method. transpiration the transpirat estimated ion estima via tedthe viaCEC the C method. EC method. However, one must consider the fact that most of the vineyard sites used in this study However Howeve,r, on onee m muu st stconsider consider the the fact factthat thatmost mostof ofthe the v vineyar ineyard d sites sitesused usedin in this thisstudy study contain a cover crop used to remove excess moisture in the early spring for controlling contain contain aa cov cover er crop used crop used to to remove remove excess excess mo moistur isture e in in the the e early arly spring spring for for contr controllin ollingg vine growth and the timing of initiating irrigation (Figure 9a). This complicates both the vine vine gro growth wthand and the thetiming timingof ofinitiating initiatingirrigation irrigation (Figur (Figure e 9 9 a). a).This Thiscomplicates complicatesboth both the the modelin modeling g of of vineya vineyar rd ET and EC-ba d ET and EC-based sedpartitioning, partitioning, since since ther there is a e is a period period of otime f time when when T modeling of vineyard ET and EC-based partitioning, since there is a period of time when T sources come from both vine and cover crop. sources come from both vine and cover crop. T sources come from both vine and cover crop. Figure 9. Two examples, (a,b), show the different interrow under the vine canopy. Figure 9. Two examples, (a,b), show the different interrow under the vine canopy. Figure 9. Two examples, (a,b), show the different interrow under the vine canopy. An EC ﬂux tower measuring water and carbon ﬂuxes for the site with a bare soil An EC flux tower measuring water and carbon fluxes for the site with a bare soil An EC flux tower measuring water and carbon fluxes for the site with a bare soil interrow as shown in Figure 9b will have T coming only from the grapevine. For the site interrow as shown in Figure 9b will have T coming only from the grapevine. For the site interrow as shown in Figure 9b will have T coming only from the grapevine. For the site shown in Figure 9a, the EC ﬂux tower cannot separate T from the grapevine and cover shown in Figure 9a, the EC flux tower cannot separate T from the grapevine and cover shown in Figure 9a, the EC flux tower cannot separate T from the grapevine and cover crop. For this situation, measurements below the vine canopy in the interrow are necessary crop. For this situation, measurements below the vine canopy in the interrow are neces- crop. For this situation, measurements below the vine canopy in the interrow are neces- for estimating the ET contribution from the cover crop using, for example, micro-Bowen sary for estimating the ET contribution from the cover crop using, for example, micro- sary for estimating the ET contribution from the cover crop using, for example, micro- ratio systems which were deployed in the SLM vineyard site for several IOPs [16]. High- Bowen ratio systems which were deployed in the SLM vineyard site for several IOPs [16]. Bowen ratio systems which were deployed in the SLM vineyard site for several IOPs [16]. resolution imagery separating interrow from the vine canopy, especially for the situation High-resolution imagery separating interrow from the vine canopy, especially for the sit- High-resolution imagery separating interrow from the vine canopy, especially for the sit- shown in Figure 9a, is necessary because ET from the interrow needs to be considered. uation shown in Figure 9a, is necessary because ET from the interrow needs to be consid- uation shown in Figure 9a, is necessary because ET from the interrow needs to be consid- Data that have been collected in the GRAPEX program will eventually shed light on this. ered. Data that have been collected in the GRAPEX program will eventually shed light on ered. Data that have been collected in the GRAPEX program will eventually shed light on From a modeling perspective, for example, this is being addressed using a three-source this. From a modeling perspective, for example, this is being addressed using a three- this. From a modeling perspective, for example, this is being addressed using a three- model (3SEB), which is a modiﬁcation of TSEB and has been initially tested in the RIP720 source model (3SEB), which is a modification of TSEB and has been initially tested in the source model (3SEB), which is a modification of TSEB and has been initially tested in the Remote Sens. 2023, 15, 756 15 of 20 vineyard using tower-based land surface temperature and found to provide a more reliable ET partitioning account for the interrow cover crop [55]. 4. Conclusions In this study, we assessed the performance of the TSEB model in energy component estimation and evapotranspiration partitioning. Three different versions of TSEB coupled with three different resistance models were used to model the energy components (Rn, G, H, and LE). Modeled estimates were compared with monitored data from the EC ﬂux tower within the corresponding footprint area. Results show that the QTS method adopted in this research can improve the estimation of H, and TSEB-2T (TSEB-2T model coupled with the QTS method for temperature separation) coupled with the NK (Norman and Kustas) resistance model can appropriately provide energy-component estimations. The ET partitioning comparison regarding transpiration illustrated that all TSEB models are statistically acceptable for ET partitioning, but the TSEB-2T showed better agreement with the CEC method. Further work, focused on augmenting the EC ﬂux tower system with measurements of ET for the interrow, upgrading the sUAS image processing system for creating near-real time products, and implementing a 3SEB formulation to explicitly account for the interrow cover crop, is necessary to accurately estimate vine transpiration [55]. These advancements will improve management practices that promote great water use efﬁciency in vineyards and will improve growers’ and researchers’ understanding of the role of cover crop and vine water use at the canopy and sub-block scale. Author Contributions: Conceptualization, R.G., A.F.T.-R., W.P.K. and H.N., methodology, R.G., A.F.T.-R., H.N., E.Z., L.H., W.P.K. and M.A., software, R.G., A.F.T.-R., H.N. and E.Z., validation, R.G., A.F.T.-R., H.N., W.P.K. and M.A., formal analysis, R.G., A.F.T.-R., W.P.K. and M.A., investigation, R.G. and E.Z., resources, E.Z., M.M.A., N.B., S.J.C., J.H.P., J.A., L.G.M., W.A.W., C.C., I.G., L.S. and N.D., data curation, A.F.T.-R., M.M.A., E.Z., N.B., S.J.C., C.C., I.G., L.S. and N.D., writing—original draft preparation, R.G. and A.F.T.-R., writing—review and editing, R.G., A.F.T.-R., E.Z., W.P.K., F.G., A.J.M., M.A., K.K., N.A. and L.S., visualization, R.G., A.F.T.-R., W.P.K., A.J.M. and M.A., supervision, R.G., A.F.T.-R., W.P.K. and M.A., project administration, A.F.T.-R., funding acquisition, A.F.T.-R. and W.P.K. All authors have read and agreed to the published version of the manuscript. Funding: This study was made possible with ﬁnancial support from the USDA-Agricultural Research Service, E&J Gallo Winery, NASA Applied Sciences Water Resources Grant NNX17AF51G and the Utah Water Research Laboratory Student Fellowship. And the APC was funded by Utah Water Research Laboratory. Data Availability Statement: The QTS method, along with demo data, is available in the CUAHSI HydroShare platform [56]. Similarly, a python program to generate fractional cover, canopy height, and canopy width over canopy height for the TSEB model based on the AggieAir images for California vineyards is also available in the CUAHSI HydroShare platform [45]. Since the authors only have partial ownership of the data and due to the large data size, only demo data are available for testing the QTS method. Acknowledgments: The authors are grateful for the extraordinary support from the Utah State Univer- sity AggieAir sUAS program staff and E&J Gallo scientific teams for data collection and analysis, and the cooperation of the vineyard management staff for logistical support and coordinating field operations with the GRAPEX team. The authors would like to thank Ayman Nassar for his preliminary work in TSEB model and footprint-area calculation, and also thanks to Carri Richards and Micah Safsten for editing the manuscript. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer. Conﬂicts of Interest: The authors declare no conﬂict of interest. Remote Sens. 2023, 15, 756 16 of 20 Appendix A Table A1. Study-site geographic information. Study Sites Latitude Longitude Elevation above the Sea Level (m) 0 00 0 00 SLM 38 16 49.76 121 7 3.35 40 0 00 0 00 BAR 38 45 4.91 122 58 28.77 120 0 00 0 00 RIP760 36 50 20.52 120 12 36.60 62 0 00 0 00 RIP720 36 50 57.27 120 10 26.50 62 Table A2. The ﬂight date and time of the sUAS platform over vineyards. Azimuth and elevation of the sun corresponding to the time are also shown. Sites Year Month Day Time Flight Azimuth Elevation 2018 6 19 11:20 144.1 74.0 2018 6 19 13:17 236.1 68.8 2018 6 19 15:38 269.8 41.8 2018 7 12 12:29 201.0 74.2 RIP 720-1 2018 7 12 15:32 266.5 43.1 RIP 720-2 2018 7 13 10:40 123.3 66.3 RIP 720-3 2018 7 13 15:22 264.6 45.1 RIP 720-4 2018 8 5 10:44 132.4 63.3 2018 8 5 12:33 198.9 69.2 2018 8 6 10:41 131.2 62.8 2019 5 4 10:25 130.1 60.9 2018 6 19 11:20 144.1 74.0 2018 6 19 13:17 236.1 68.8 2018 6 19 15:38 269.8 41.8 2018 7 12 12:29 201.0 74.2 2018 7 12 15:32 266.5 43.1 RIP 760 2018 7 13 10:40 123.3 66.3 2018 8 5 10:44 132.4 63.3 2018 8 5 12:33 198.9 69.2 2018 8 6 10:41 131.2 62.8 2017 8 8 10:52 144.9 63.6 2017 8 9 10:43 141.1 62.3 2019 6 27 10:41 131.9 68.9 2019 6 27 12:07 193.6 74.2 2019 6 27 14:21 255.2 54.7 BAR012 2019 7 29 10:51 140.8 65.8 2019 7 29 13:09 224.2 64.4 2019 7 30 10:28 130.9 62.5 2019 7 30 13:09 223.9 64.2 2019 7 30 15:40 264.2 37.5 2014 8 9 10:41 136.3 61.5 2015 6 2 10:43 131.9 67.9 2015 6 2 14:07 250.2 57.2 SLM001 2015 7 11 10:35 125.1 65.5 2015 7 11 14:14 250.1 57.3 2019 5 3 10:38 139.1 62.0 2014 8 9 10:41 136.3 61.5 2015 6 2 10:43 131.9 67.9 2015 6 2 14:07 250.2 57.2 SLM002 2015 7 11 10:35 125.1 65.5 2015 7 11 14:14 250.1 57.3 Remote Sens. 2023, 15, 756 17 of 20 Table A3. The performance of the TSEB-2T model coupled with the Norman and Kustas (NK) resistance model at different research sites with different times shown by different evaluation metrics. “LS” stands for the results that occurred in Landsat time; “SN” near solar noon; and “AF” afternoon. The unit of RMSE and Bias is Wm . Net Radiation Ground Heat Flux Sensible Heat Flux Latent Heat Flux Time Periods N RMSE Bias r N RMSE Bias r N RMSE Bias r N RMSE Bias r LS 29 21 9 0.91 29 45 28 0.43 29 66 2 0.33 29 68 16 0.62 SN 17 21 3 0.79 17 40 28 0.38 17 69 23 0.63 17 81 49 0.64 AF 14 29 23 0.96 14 20 12 0.64 14 58 8 0.63 14 56 22 0.46 Table A4. Separated average soil and canopy temperatures within the corresponding footprint area via the QTS model (the temperature unit is C). Soil–Canopy Sonic Air Soil Canopy Site Date Time Temperature Temperature Temperature Temperature Difference SLM001 20150711 14:14 28.1 32.9 28.7 4.2 SLM002 20150711 14:14 30.7 32.9 28.7 4.2 BAR012 20190627 14:21 25.7 31.0 26.6 4.4 BAR012 20190730 15:40 30.9 34.2 29.4 4.8 RIP760 20180619 15:38 32.1 36.2 31.6 4.6 RIP720-1 20180619 15:38 34.0 35.5 32.1 3.4 RIP720-1 20180712 15:32 38.3 36.8 33.1 3.7 RIP720-1 20180713 15:22 38.1 36.7 33.3 3.4 RIP720-2 20180619 15:38 34.5 37.3 32.5 4.8 RIP720-2 20180712 15:32 38.8 37.8 33.0 4.8 RIP720-2 20180713 15:22 38.5 38.6 34.4 4.2 RIP720-3 20180713 15:22 38.5 35.1 31.1 4.0 RIP720-4 20180619 15:38 35.9 35.6 31.8 3.8 RIP720-4 20180713 15:22 40.5 37.1 32.9 4.2 Table A5. ANOVA and Tukey test results showing the difference between the transpiration calculated via the CEC method and the transpiration modeled via the TSEB models. The null hypothesis is that the mean transpiration between different groups is the same. “Mean difference” is the mean difference between “Group 1” and “Group 2.” “Lower boundary” and “Upper boundary” are the lower and upper 95% conﬁdence interval boundaries, respectively. “CEC” represents the transpiration calculated via the CEC method. The unit for “Mean difference,” “Lower boundary,” and “Upper boundary” is Wm . The Mean Mean Lower Upper Group 1 Group 2 p-Adj Transpiration Difference Boundary Boundary Is the Same CEC TSEB-PT (NK) 25 0.674 69 18 YES CEC TSEB-PT (CM) 16 0.900 60 27 YES CEC TSEB-PT (MV) 32 0.372 75 12 YES CEC TSEB-2T (NK) 36 0.194 80 7 YES CEC TSEB-2T (CM) 30 0.456 74 13 YES CEC TSEB-2T (MV) 39 0.132 82 5 YES CEC TSEB-2T (NK) 10 0.900 53 34 YES CEC TSEB-2T (CM) 7 0.900 51 36 YES CEC TSEB-2T (MV) 9 0.900 53 34 YES Q Remote Sens. 2023, 15, 756 18 of 20 Table A6. Metrics for model evaluation shown in Figure 8. N is the number of scatters in Figure 8; RMSE is the root mean square error; Bias is the mean bias computed as the observed minus the predicted; r is the Pearson correlation coefﬁcient between the observed and the predicted; and d is Willmott’s index of agreement. TSEB-PT TSEB-2T TSEB-2T NK CM MV NK CM MV NK CM MV N 50 50 50 50 50 50 50 50 50 RMSE 71 68 77 84 77 83 72 70 71 Bias 25 16 32 36 30 39 10 7 9 r 0.58 0.58 0.56 0.54 0.55 0.56 0.54 0.54 0.54 d 0.73 0.73 0.72 0.71 0.72 0.72 0.73 0.72 0.73 References 1. Virnodkar, S.S.; Pachghare, V.K.; Patil, V.C.; Jha, S.K. Remote Sensing and Machine Learning for Crop Water Stress Determination in Various Crops: A Critical Review. Precis. Agric. 2020, 21, 1121–1155. [CrossRef] 2. Ahmad, U.; Alvino, A.; Marino, S. A Review of Crop Water Stress Assessment Using Remote Sensing. Remote Sens. 2021, 13, 4155. [CrossRef] 3. Kool, D.; Kustas, W.P.; Ben-Gal, A.; Agam, N. Energy Partitioning between Plant Canopy and Soil, Performance of the Two-Source Energy Balance Model in a Vineyard. Agric. For. Meteorol. 2021, 300, 108328. [CrossRef] 4. Zhang, X.Y.; Jin, J.; Zeng, X.; Hawkins, C.P.; Neto, A.A.M.; Niu, G.Y. The Compensatory CO2 Fertilization and Stomatal Closure Effects on Runoff Projection From 2016–2099 in the Western United States. Water Resour. Res. 2022, 58, e2021WR030046. [CrossRef] 5. Wei, Z.; Lee, X.; Wen, X.; Xiao, W. Evapotranspiration Partitioning for Three Agro-Ecosystems with Contrasting Moisture Conditions: A Comparison of an Isotope Method and a Two-Source Model Calculation. Agric. For. Meteorol. 2018, 252, 296–310. [CrossRef] 6. Xue, J.; Anderson, M.C.; Gao, F.; Hain, C.; Yang, Y.; Knipper, K.R.; Kustas, W.P.; Yang, Y. Mapping Daily Evapotranspiration at Field Scale Using the Harmonized Landsat and Sentinel-2 Dataset, with Sharpened VIIRS as a Sentinel-2 Thermal Proxy. Remote Sens. 2021, 13, 3420. [CrossRef] 7. Safre, A.L.S.; Nassar, A.; Torres-Rua, A.F.; Aboutalebi, M.; Saad, C.C.J.; Manzione, R.L.; Teixeira, A.H.D.C.; Prueger, J.H.; McKee, L.G.; Alﬁeri, J.G.; et al. Performance of Sentinel-2 SAFER ET Model for Daily and Seasonal Estimation of Grapevine Water Consumption. Irrig. Sci. 2022, 1, 1–20. [CrossRef] 8. Nassar, A.; Torres-Rua, A.F.; Hipps, L.E.; Kustas, W.P.; McKee, M.; Stevens, D.; Nieto, H.; Keller, D.; Gowing, I.; Coopmans, C. Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem. Remote Sens. 2022, 14, 372. [CrossRef] 9. Bellvert, J.; Jofre-Cekalovic, ´ C.; Pelechá, A.; Mata, M.; Nieto, H. Feasibility of Using the Two-Source Energy Balance Model (TSEB) with Sentinel-2 and Sentinel-3 Images to Analyze the Spatio-Temporal Variability of Vine Water Status in a Vineyard. Remote Sens. 2020, 12, 2299. [CrossRef] 10. Gao, F.; Kustas, W.P.; Anderson, M.C. A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land. Remote Sens. 2012, 4, 3287–3319. [CrossRef] 11. Xue, J.; Anderson, M.C.; Gao, F.; Hain, C.; Sun, L.; Yang, Y.; Knipper, K.R.; Kustas, W.P.; Torres-Rua, A.F.; Schull, M. Sharpening ECOSTRESS and VIIRS Land Surface Temperature Using Harmonized Landsat-Sentinel Surface Reﬂectances. Remote Sens. Environ. 2020, 251, 112055. [CrossRef] [PubMed] 12. Yang, Y.; Anderson, M.C.; Gao, F.; Xue, J.; Knipper, K.; Hain, C. Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System. Remote Sens. 2022, 14, 1772. [CrossRef] 13. De Castro, A.I.; Shi, Y.; Maja, J.M.; Peña, J.M. Uavs for Vegetation Monitoring: Overview and Recent Scientiﬁc Contributions. Remote Sens. 2021, 13, 2139. [CrossRef] 14. Tunca, E.; Köksal, E.S.; Torres-Rua, A.F.; Kustas, W.P.; Nieto, H.S. Estimation of Bell Pepper Evapotranspiration Using Two-Source Energy Balance Model Based on High-Resolution Thermal and Visible Imagery from Unmanned Aerial Vehicles. Appl. Remote Sens. 2022, 16, 022204. [CrossRef] 15. Long, D.S.; Engel, R.E.; Siemens, M.C. Measuring Grain Protein Concentration with In-Line Near Infrared Reﬂectance Spec- troscopy. Agron. J. 2008, 100, 247–252. [CrossRef] 16. Kustas, W.P.; Anderson, M.C.; Alﬁeri, J.G.; Knipper, K.; Torres-Rua, A.F.; Parry, C.K.; Nieto, H.; Agam, N.; White, W.A.; Gao, F.; et al. The Grape Remote Sensing Atmospheric Proﬁle and Evapotranspiration Experiment. Bull. Am. Meteorol. Soc. 2018, 99, 1791–1812. [CrossRef] 17. Norman, J.M.; Kustas, W.P.; Humes, K.S. Source Approach for Estimating Soil and Vegetation Energy Fluxes in Observations of Directional Radiometric Surface Temperature. Agric. For. Meteorol. 1995, 77, 263–293. [CrossRef] 18. Kustas, W.P.; Norman, J.M. Use of Remote Sensing for Evapotranspiration Monitoring over Land Surfaces. Hydrol. Sci. J. 1996, 41, 495–516. [CrossRef] Remote Sens. 2023, 15, 756 19 of 20 19. Kustas, W.P.; Nieto, H.; Garcia-Tejera, O.; Bambach, N.; McElrone, A.J.; Gao, F.; Alﬁeri, J.G.; Hipps, L.E.; Prueger, J.H.; Torres-Rua, A.F.; et al. Impact of Advection on Two-Source Energy Balance (TSEB) Canopy Transpiration Parameterization for Vineyards in the California Central Valley. Irrig. Sci. 2022, 40, 575–591. [CrossRef] 20. Alﬁeri, J.G.; Kustas, W.P.; Nieto, H.; Prueger, J.H.; Hipps, L.E.; McKee, L.G.; Gao, F.; Los, S. Inﬂuence of Wind Direction on the Surface Roughness of Vineyards. Irrig. Sci. 2019, 37, 359–373. [CrossRef] 21. Nassar, A.; Torres-Rua, A.F.; Kustas, W.P.; Nieto, H.; McKee, M.; Hipps, L.E.; Stevens, D.; Alﬁeri, J.G.; Prueger, J.H.; Alsina, M.M.; et al. Inﬂuence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and SUAS Imagery in Vineyards. Remote Sens. 2020, 12, 342. [CrossRef] [PubMed] 22. Nassar, A.; Torres-Rua, A.F.; Kustas, W.P.; Nieto, H.; McKee, M.; Hipps, L.E.; Alﬁeri, J.G.; Prueger, J.H.; Alsina, M.M.; McKee, L.G.; et al. To What Extend Does the Eddy Covariance Footprint Cutoff Inﬂuence the Estimation of Surface Energy Fluxes Using Two Source Energy Balance Model and High-Resolution Imagery in Commercial Vineyards? In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; Thomasson, J.A., Torres-Rua, A.F., Eds.; SPIE: Bellingham, WA, USA, 2020; Volume 11414, p. 16. 23. Nassar, A.; Torres-rua, A.F.; Kustas, W.P.; Alﬁeri, J.G.; Hipps, L.E.; Prueger, J.H.; Nieto, H.; Alsina, M.M.; White, W.A.; McKee, L.; et al. Assessing Daily Evapotranspiration Methodologies from One-time-of-day Suas and Ec Information in the Grapex Project. Remote Sens. 2021, 13, 2887. [CrossRef] [PubMed] 24. Nieto, H.; Alsina, M.M.; Kustas, W.P.; García-Tejera, O.; Chen, F.; Bambach, N.; Gao, F.; Alﬁeri, J.G.; Hipps, L.E.; Prueger, J.H.; et al. Evaluating Different Metrics from the Thermal-Based Two-Source Energy Balance Model for Monitoring Grapevine Water Stress. Irrig. Sci. 2022, 40, 697–713. [CrossRef] 25. Nieto, H.; Bellvert, J.; Kustas, W.P.; Alﬁeri, J.G.; Gao, F.; Prueger, J.H.; Torres-Rua, A.F.; Hipps, L.E.; Elarab, M.; Song, L. Unmanned Airborne Thermal and Mutilspectral Imagery for Estimating Evapotranspiration in Irrigated Vineyards. In Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Forth Worth, TX, USA, 23–27 July 2017; pp. 5510–5513. 26. Torres-Rua, A.F.; Ticlavilca, A.M.; Aboutalebi, M.; Nieto, H.; Alsina, M.M.; White, A.; Prueger, J.H.; Alﬁeri, J.G.; Hipps, L.E.; McKee, L.G.; et al. Estimation of Evapotranspiration and Energy Fluxes Using a Deep-Learning-Based High-Resolution Emissivity Model and the Two-Source Energy Balance Model with SUAS Information. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; Thomasson, J.A., Torres-Rua, A.F., Eds.; SPIE: Bellingham, WA, USA, 2020; Volume 11414, p. 10. 27. Nieto, H.; Kustas, W.P.; Torres-Rúa, A.F.; Alﬁeri, J.G.; Gao, F.; Anderson, M.C.; White, W.A.; Song, L.; Alsina, M.M.; Prueger, J.H.; et al. Evaluation of TSEB Turbulent Fluxes Using Different Methods for the Retrieval of Soil and Canopy Component Temperatures from UAV Thermal and Multispectral Imagery. Irrig. Sci. 2019, 37, 389–406. [CrossRef] [PubMed] 28. Kang, Y.; Gao, F.; Anderson, M.C.; Kustas, W.P.; Nieto, H.; Knipper, K.; Yang, Y.; White, W.A.; Alfieri, J.G.; Torres-Rua, A.F.; et al. Evaluation of Satellite Leaf Area Index in California Vineyards for Improving Water Use Estimation. Irrig. Sci. 2022, 40, 531–551. [CrossRef] 29. Aboutalebi, M.; Torres-Rua, A.F.; McKee, M.; Kustas, W.P.; Nieto, H.; Alsina, M.M.; White, A.; Prueger, J.H.; McKee, L.; Alﬁeri, J.G.; et al. Downscaling UAV Land Surface Temperature Using a Coupled Wavelet-Machine Learning-Optimization Algorithm and Its Impact on Evapotranspiration. Irrig. Sci. 2022, 40, 553–574. [CrossRef] 30. Gao, R.; Torres-Rua, A.F.; Aboutalebi, M.; White, W.A.; Anderson, M.C.; Kustas, W.P.; Agam, N.; Alsina, M.M.; Alﬁeri, J.G.; Hipps, L.E.; et al. LAI Estimation across California Vineyards Using SUAS Multi-Seasonal Multi-Spectral, Thermal, and Elevation Information and Machine Learning. Irrig. Sci. 2022, 1, 1–29. [CrossRef] 31. Knipper, K.; Anderson, M.C.; Bambach, N.; Kustas, W.P.; Gao, F.; Zahn, E.; Hain, C.; Mcelrone, A.; Belﬁore, O.R.; Castro, S.; et al. Evaluation of Partitioned Evaporation and Transpiration Estimates within the DisALEXI Modeling Framework over Irrigated Crops in California. Remote Sens. 2023, 15, 68. [CrossRef] 32. Aboutalebi, M.; Torres-Rua, A.F.; McKee, M.; Nieto, H.; Kustas, W.P.; Coopmans, C. The Impact of Shadows on Partitioning of Radiometric Temperature to Canopy and Soil Temperature Based on the Contextual Two-Source Energy Balance Model (TSEB-2T). In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV; Thomasson, J.A., McKee, M., Moorhead, R.J., Eds.; SPIE: Bellingham, WA, USA, 2019; Volume 11008, p. 3. 33. Aboutalebi, M.; Torres-Rua, A.F.; Kustas, W.P.; Nieto, H.; Coopmans, C.; McKee, M. Assessment of Different Methods for Shadow Detection in High-Resolution Optical Imagery and Evaluation of Shadow Impact on Calculation of NDVI, and Evapotranspiration. Irrig. Sci. 2019, 37, 407–429. [CrossRef] 34. Aboutalebi, M.; Torres-Rua, A.F.; McKee, M.; Kustas, W.P.; Nieto, H.; Alsina, M.M.; White, A.; Prueger, J.H.; McKee, L.; Alﬁeri, J.G.; et al. Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspira- tion Models. Remote Sens. 2019, 12, 50. [CrossRef] 35. Gao, R.; Torres-Rua, A.F.; Nassar, A.; Hipps, L.; Nieto, H.; Aboutalebi, M.; White, W.A.; Anderson, M.; Kustas, W.P.; Alsina, M.M.; et al. TSEB Modeling and the Comparison between the Model Results and the Eddy-Covariance Monitored Data within the Footprint Area. CUAHSI HydroShare 2021. [CrossRef] 36. Gao, R.; Torres-Rua, A.F.; Nassar, A.; Alﬁeri, J.G.; Aboutalebi, M.; Hipps, L.E.; Ortiz, N.B.; Mcelrone, A.J.; Coopmans, C.; Kustas, W.P.; et al. Evapotranspiration Partitioning Assessment Using a Machine-Learning-Based Leaf Area Index and the Two-Source Energy Balance Model with SUAV Information; Thomasson, J.A., Torres-Rua, A.F., Eds.; SPIE: Bellingham, WA, USA, 2021; Volume 11747, p. 21. Remote Sens. 2023, 15, 756 20 of 20 37. Zahn, E.; Bou-Zeid, E.; Good, S.P.; Katul, G.G.; Thomas, C.K.; Ghannam, K.; Smith, J.A.; Chamecki, M.; Dias, N.L.; Fuentes, J.D.; et al. Direct Partitioning of Eddy-Covariance Water and Carbon Dioxide Fluxes into Ground and Plant Components. Agric. For. Meteorol. 2022, 315. [CrossRef] 38. Kljun, N.; Calanca, P.; Rotach, M.W.; Schmid, H.P. A Simple Two-Dimensional Parameterisation for Flux Footprint Prediction (FFP). Geosci. Model Dev. 2015, 8, 3695–3713. [CrossRef] 39. Gao, R.; Nassar, A.; Torres-Rua, A.F.; Hipps, L.; Aboutalebi, M.; White, W.A.; Anderson, M.; Kustas, W.P.; Alsina, M.M.; Alﬁeri, J.; et al. Footprint Area Generating Based on Eddy Covariance Records. CUAHSI HydroShare 2021. [CrossRef] 40. Gao, R.; Torres-Rua, A.F. Features Extraction from the LAI2200C Plant Canopy Analyzer. CUAHSI HydroShare 2021. [CrossRef] 41. Kustas, W.P.; Agam, N.; Alﬁeri, J.G.; McKee, L.G.; Prueger, J.H.; Hipps, L.E.; Howard, A.M.; Heitman, J.L. Below Canopy Radiation Divergence in a Vineyard: Implications on Interrow Surface Energy Balance. Irrig. Sci. 2019, 37, 227–237. [CrossRef] 42. Gao, R.; Alsina, M.M.; Torres-Rua, A.F.; Hipps, L.E.; Kustas, W.P.; White, W.A.; Anderson, M.C.; Alﬁeri, J.G.; Dokoozlian, N.; Nieto, H.; et al. Exploratory Analysis of Vineyard Leaf Water Potential against UAS Multispectral and Temperature Information; Thomasson, J.A., Torres-Rua, A.F., Eds.; SPIE: Bellingham, WA, USA, 2022; Volume 12114, pp. 160–185. 43. Torres-Rua, A.F. Vicarious Calibration of SUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature. Sensors 2017, 17, 1499. [CrossRef] 44. Bambach, N.; Kustas, W.P.; Alﬁeri, J.G.; Prueger, J.H.; Hipps, L.E.; McKee, L.; Castro, S.J.; Volk, J.; Alsina, M.M.; McElrone, A.J. Evapotranspiration Uncertainty at Micrometeorological Scales: The Impact of the Eddy Covariance Energy Imbalance and Correction Methods. Irrig. Sci. 2022, 40, 445–461. [CrossRef] 45. Gao, R.; Torres-Rua, A.F. A Python-Based Program Generating a Part of Information Based on AggieAir Images for the TSEB Model: Taking California Vineyards as an Example. CUAHSI HydroShare 2022. [CrossRef] 46. Gao, R.; Torres-Rua, A.F.; Aboutalebi, M.; White, W.A.; Anderson, M.; Kustas, W.P.; Agam, N.; Alsina, M.M.; Alﬁeri, J.; Hipps, L.; et al. Feature Extraction Approaches for Leaf Area Index Estimation in California Vineyards via Machine Learning Algorithms. CUAHSI HydroShare 2021. [CrossRef] 47. Kustas, W.P.; Norman, J.M. A Two-Source Approach for Estimating Turbulent Fluxes Using Multiple Angle Thermal Infrared Observations. Water Resour. Res. 1997, 33, 1495–1508. [CrossRef] 48. McNaughton, K.G.; Hurk, B.J.J.M.V.D. A “Lagrangian” Revision of the Resistors in the Two-Layer Model for Calculating the Energy Budget of a Plant Canopy. Boundary-Layer Meteorol. 1995, 74, 261–288. [CrossRef] 49. Choudhury, B.J.; Monteith, J.L. A Four-layer Model for the Heat Budget of Homogeneous Land Surfaces. Q. J. R. Meteorol. Soc. 1988, 114, 373–398. [CrossRef] 50. Gao, R.; Torres-Rua, A.F.; Hipps, L.E.; Kustas, W.P.; Anderson, M.C.; White, W.A.; Alﬁeri, J.G.; Alsina, M.M.; Dokoozlian, N.; Nieto, H.; et al. Assessment of TSEB-PT and -2T in ET Partitioning Estimation over California Commercial Vineyards Based on SUAS Information; SPIE: Bellingham, WA, USA, 2022; Volume 12114, p. 121140I. 51. Willmott, C.J. Some Comments on the Evaluation of Model Performance. Bull.- Am. Meteorol. Soc. 1982, 63, 1309–1313. [CrossRef] 52. Gao, Y.; Tang, B.; Lu, B.; Ji, G.; Ye, H. Investigation on the Effects of Water Loss on the Solar Spectrum Reﬂectance and Transmittance of Osmanthus Fragrans Leaves Based on Optical Experiment and PROSPECT Model. RSC Adv. 2021, 11, 37268–37275. [CrossRef] 53. Santanello, J.A.; Friedl, M.A. Diurnal Covariation in Soil Heat Flux and Net Radiation. J. Appl. Meteorol. 2003, 42, 851–862. [CrossRef] 54. Montgomery, D.C.; Runger, G.C. Applied Statistics and Probability for Engineers; John Wiley&Sons: Hoboken, NJ, USA, 2010. 55. Burchard-Levine, V.; Nieto, H.; Kustas, W.P.; Gao, F.; Alﬁeri, J.G.; Prueger, J.H.; Hipps, L.E.; Bambach-Ortiz, N.; McElrone, A.J.; Castro, S.J.; et al. Application of a Remote-Sensing Three-Source Energy Balance Model to Improve Evapotranspiration Partition- ing in Vineyards. Irrig. Sci. 2022, 40, 593–608. [CrossRef] 56. Temperature Separation via Eliminating Shadow-Pixel Inﬂuence Based on High-Resolution SUAS Image in California Vineyards. CUAHSI HydroShare 4. 2023. Available online: https://doi.org/10.4211/hs.c0876501581f46c698727dc956cc2d73 (accessed on 18 January 2023). Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Journal

Remote Sensing – Multidisciplinary Digital Publishing Institute

Published: Jan 28, 2023

Keywords: temperature separation; ET partitioning; transpiration; transpiration ratio; TSEB-PT; TSEB-2T; energy closure; sUAS; California vineyards

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards

ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards

ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards

References (47)

Abstract

Journal

Recommended Articles

There are no references for this article.

Our policy towards the use of cookies