Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy

Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation... Methods for chlorosis detection and physiological condition monitoring in Vitis vinifera L. through accurate chlorophyll a and b content ( C ab ) estimation at leaf and canopy levels are presented in this manuscript. A total of 24 vineyards were identified for field and airborne data collection with the Compact Airborne Spectrographic Imager (CASI), the Reflective Optics System Imaging Spectrometer (ROSIS) and the Digital Airborne Imaging Spectrometer (DAIS-7915) hyperspectral sensors in 2002 and 2003 in northern Spain, comprising 103 study areas of 10 × 10 m in size, with a total of 1467 leaves collected for determination of pigment concentration. A subsample of 605 leaves was used for measuring the optical properties of reflectance and transmittance with a Li-Cor 1800-12 Integrating Sphere coupled by a 200 μm diameter single mode fiber to an Ocean Optics model USB2000 spectrometer. Several narrow-band vegetation indices were calculated from leaf reflectance spectra, and the PROSPECT leaf optical model was used for inversion using the extensive database of leaf optical properties. Results showed that the best indicators for chlorophyll content estimation in V. vinifera L. leaves were narrow-band hyperspectral indices calculated in the 700–750 nm spectral region ( r 2 ranging between 0.8 and 0.9), with poor performance of traditional indices such as the Normalized Difference Vegetation Index (NDVI). Results for other biochemicals indicated that the Structure Insensitive Pigment Index (SIPI) and the Photochemical Reflectance Index (PRI) were more sensitive to carotenoids C x + c and chlorophyll–carotenoid ratios C ab / C x + c than to chlorophyll content C ab . Chlorophyll a and b estimation by inversion of the PROSPECT leaf model on V. vinifera L. spectra was successful, yielding a determination coefficient of r 2 = 0.95, with an RMSE = 5.3 μg/cm 2 . The validity of leaf-level indices for chlorophyll content estimation at the canopy level in V. vinifera L. was studied using the scaling-up approach that links PROSPECT and rowMCRM canopy reflectance simulation to account for the effects of vineyard structure, vine dimensions, row orientation and soil and shadow effects on the canopy reflectance. The index calculated as a combination of the Transformed Chlorophyll Absorption in Reflectance Index (TCARI), and the Optimized Soil-Adjusted Vegetation Index (OSAVI) in the form TCARI/OSAVI was the most consistent index for estimating C ab on aggregated and pure vine pixels extracted from 1 m CASI and ROSIS hyperspectral imagery. Predictive relationships were developed with PROSPECT–rowMCRM model between C ab and TCARI/OSAVI as function of LAI, using field-measured vine dimensions and image-extracted soil background, row-orientation and viewing geometry values. Prediction relationships for C ab content with TCARI/OSAVI were successfully applied to the 103 study sites imaged on 24 fields by ROSIS and CASI airborne sensors, yielding r 2 = 0.67 and RMSE = 11.5 μg/cm 2 . http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Remote Sensing of Environment Elsevier

Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy

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Publisher
Elsevier
Copyright
Copyright © 2005 Elsevier Inc.
ISSN
0034-4257
DOI
10.1016/j.rse.2005.09.002
Publisher site
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Abstract

Methods for chlorosis detection and physiological condition monitoring in Vitis vinifera L. through accurate chlorophyll a and b content ( C ab ) estimation at leaf and canopy levels are presented in this manuscript. A total of 24 vineyards were identified for field and airborne data collection with the Compact Airborne Spectrographic Imager (CASI), the Reflective Optics System Imaging Spectrometer (ROSIS) and the Digital Airborne Imaging Spectrometer (DAIS-7915) hyperspectral sensors in 2002 and 2003 in northern Spain, comprising 103 study areas of 10 × 10 m in size, with a total of 1467 leaves collected for determination of pigment concentration. A subsample of 605 leaves was used for measuring the optical properties of reflectance and transmittance with a Li-Cor 1800-12 Integrating Sphere coupled by a 200 μm diameter single mode fiber to an Ocean Optics model USB2000 spectrometer. Several narrow-band vegetation indices were calculated from leaf reflectance spectra, and the PROSPECT leaf optical model was used for inversion using the extensive database of leaf optical properties. Results showed that the best indicators for chlorophyll content estimation in V. vinifera L. leaves were narrow-band hyperspectral indices calculated in the 700–750 nm spectral region ( r 2 ranging between 0.8 and 0.9), with poor performance of traditional indices such as the Normalized Difference Vegetation Index (NDVI). Results for other biochemicals indicated that the Structure Insensitive Pigment Index (SIPI) and the Photochemical Reflectance Index (PRI) were more sensitive to carotenoids C x + c and chlorophyll–carotenoid ratios C ab / C x + c than to chlorophyll content C ab . Chlorophyll a and b estimation by inversion of the PROSPECT leaf model on V. vinifera L. spectra was successful, yielding a determination coefficient of r 2 = 0.95, with an RMSE = 5.3 μg/cm 2 . The validity of leaf-level indices for chlorophyll content estimation at the canopy level in V. vinifera L. was studied using the scaling-up approach that links PROSPECT and rowMCRM canopy reflectance simulation to account for the effects of vineyard structure, vine dimensions, row orientation and soil and shadow effects on the canopy reflectance. The index calculated as a combination of the Transformed Chlorophyll Absorption in Reflectance Index (TCARI), and the Optimized Soil-Adjusted Vegetation Index (OSAVI) in the form TCARI/OSAVI was the most consistent index for estimating C ab on aggregated and pure vine pixels extracted from 1 m CASI and ROSIS hyperspectral imagery. Predictive relationships were developed with PROSPECT–rowMCRM model between C ab and TCARI/OSAVI as function of LAI, using field-measured vine dimensions and image-extracted soil background, row-orientation and viewing geometry values. Prediction relationships for C ab content with TCARI/OSAVI were successfully applied to the 103 study sites imaged on 24 fields by ROSIS and CASI airborne sensors, yielding r 2 = 0.67 and RMSE = 11.5 μg/cm 2 .

Journal

Remote Sensing of EnvironmentElsevier

Published: Nov 30, 2005

References

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