International Journal of Remote Sensing

Shen, Yan; Xiong, Anyuan; Hong, Yang; Yu, Jingjing; Pan, Yang; Chen, Zhuoqi; Saharia, Manabendra

doi: 10.1080/01431161.2014.960612pmid: N/A

This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005–2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods – arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic mean – show insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.

Dabboor, M.; Howell, S.; Shokr, M.; Yackel, J.

doi: 10.1080/01431161.2014.960614pmid: N/A

In multidimensional observations, many classification algorithms (supervised or unsupervised) require the selection of optimum bands in which the classes are most distinct. The Jeffries–Matusita (JM) distance is widely used as a separability criterion for optimal band selection and evaluation of classification results. Its original form is based on the assumption of normal distribution of the data. However, in the case of the covariance/coherency matrix of synthetic aperture radar (SAR) polarimetry, the data follow the complex Wishart distribution. In this article, we calculate the JM separability criterion for the case of the complex Wishart distribution. The updated formulation is used for: (1) the estimation of the separability between classes in fully polarimetric SAR data and to evaluate two standard polarimetric SAR classification algorithms, the Wishart and the expectation maximization algorithms, and (2) the classification of fully polarimetric SAR images based on the derived JM separability for the case of complex Wishart distribution. Fully polarimetric RADARSAT-2 images over sea ice in the Canadian Arctic are used to classify different ice surfaces and open water.

Lang, Wenhui; Wu, Jie; Zhang, Xi; Yang, Xuezhi; Meng, Junmin

doi: 10.1080/01431161.2014.960615pmid: N/A

This article discusses the use of spaceborne polarimetric L-band and C-band synthetic aperture radar (SAR) data for sea-ice detection and classification. The benefits of combining L-band with C-band polarimetric quantities for supervised sea-ice classification in the Eastern Weddell Sea, Antarctica, are investigated. In the experiments, we compared the performance of a maximum likelihood (ML) classifier when used with the combined preferred polarimetric parameters and the individual ones, respectively. The relation between the classification accuracy and the preferred number of polarimetric parameters for classification was examined as well as whether principal component analysis (PCA) and locally linear embedding (LLE) can be used to reduce the dimensionality of the parameter sets. Combining dual-frequency polarimetric quantities improves classification accuracy compared to using individual single-frequency polarimetric quantities. By increasing the dimensionality of the preferred polarimetric parameter sets, the classification using high dimensionality can either result in improvements over the smaller subsets or result in no significant differences. Therefore, using all available polarimetric quantities over the study region is recommended. Further, data fusion with a PCA-based approach is found to be beneficial for sea-ice detection and classification, and poor results have been produced with an LLE-based approach.

Wang, Zhijie; Coburn, Craig A.; Ren, Xiaomeng; Teillet, Philippe M.

doi: 10.1080/01431161.2014.960616pmid: N/A

Compared to non-imaging instruments, imaging spectrometers (ISs) can provide detailed information to investigate the influence of scene components on the bidirectional reflectance distribution function (BRDF) of a mixed target. The research reported in this article investigated soil surface reflectance changes as a function of scene components (i.e. illuminated pixels and shaded pixels), illumination and viewing zenith angles, and wavelength. Image-based BRDF data of both rough and smooth soil surfaces were acquired in a laboratory setting at three different illumination zenith angles and at four different viewing zenith angles over the full 360° azimuth range, at an interval of 20°, using a Specim V10E IS (Specim, Spectral Imaging Ltd., Oulu, Finland) mounted on the University of Lethbridge Goniometer System version 2.5 (ULGS-2.5). The BRDF of the smooth soil surface was dominated by illuminated pixels, whereas the shaded pixels were a larger component of the BRDF of the rough soil surface. As the illumination zenith angle was changed from 60° to 45° and then to 30°, the shadowing effect decreased, regardless of the soil surface. Soil surface reflectance was generally higher at the backscattering view zenith angles and decreased continuously to forward scattering view zenith angles in the light principal plane, regardless of the wavelength, due to the Specim V10E IS seeing more illuminated pixels in the backscattering angles than in the forward scattering angles. Higher soil surface reflectance was observed at higher illumination and viewing zenith angle combinations. For both soil surface roughness categories, the BRDF exhibited a greater range of values in the near-infrared than at the visible wavelengths. This research enhances our understanding of soil BRDF for various soil roughness and illumination conditions.

Chen, Jie; Deng, Min; Mei, Xiaoming; Chen, Tieqiao; Shao, Quanbin; Hong, Liang

doi: 10.1080/01431161.2014.960617pmid: N/A

Image segmentation is the premise of object-based image analysis (OBIA), and obtaining an optimal segmentation result has been a desire for many researchers. This article proposes an optimal segmentation method for a high-resolution remote-sensing image that is guided by spatial features of area and boundary. This method achieves an optimal result through stepwise refinement on multi-scale segmentations. First, boundary strength is integrated into the choice for the optimal scale based on an improved unsupervised evaluation. Then, under-segmented objects (USOs) and over-segmented objects (OSOs) at the selected optimal scale are identified using a heterogeneity histogram and a slider-like threshold with the guidance of area and boundary. Finally, the corresponding objects, in a specific finer segmentation, are taken to replace the USOs at the optimal scale, and then the USOs and OSOs are refined by an effective merging mechanism. A heterogeneity-change-based merging criterion considering boundary, shape, spectral, and texture features is constructed for the merging of neighbouring objects. The proposed method is more effective than the unsupervised image segmentation evaluation and refinement (UISER) method as it uses spatial features to guide optimal choice of scale, and USO and OSO identification and refinement. Comparative experiments show that the spatial features used in the proposed method are effective for achieving an enhanced segmentation result.

Sati, Ankur Prabhat; Mohan, Manju

doi: 10.1080/01431161.2014.960618pmid: N/A

The hazardous combination of smoke and pollutant gases, smog, is harmful for health. The harmful smog episodes over London, the Meuse Valley, and Donora are some of the well-known pollution episodes formed due to the mixture of smoky fumes and adverse meteorological conditions. A severe smog episode was observed over Delhi, India, during November 2012, resulting in very low visibility and various respiratory problems. Very high values of pollutants (particulate matter, PM10 as high as 989 µg m−3, PM2.5 as high as 585 µg m−3, and nitrogen dioxide as high as 540 µg m−3) were measured all over Delhi during the smog episode. In the study done, episodes of different nature and intensity are analysed based on remote-sensing data for 3 years (2010–2012): one of regional origin (the Delhi smog episode of 2012) and another of local origin (Diwali). Remote-sensing and in situ data have revealed an insight into the genesis and temporal and spatial variance during these episodes. Extensive use of satellite-derived parameters such as fire maps, the ultra violet aerosol index from the Aura satellite, and aerosol optical depth is made in the present study along with the output trajectories from the Hybrid Single-Particle Lagrangian-Integrated Trajectory model and in situ data. It is observed that during the smog episode all the aerosol optical depth, ultra violet aerosol index, PM2.5, and PM10 values surpassed those of the Diwali period (which in itself is a major dreaded annual air pollution event in the city) by a considerable amount at all stations across Delhi. The parameters used from the remote-sensing data and the ground-based observations at various stations across Delhi are very well in agreement with the intensity of smog episodes. The analysis clearly shows that regional pollution can have a greater contribution towards deteriorating air quality than local pollution under adverse meteorological conditions and is in agreement with other similar studies over Delhi.

Sánchez-Lopera, José; Lerma, José Luis

doi: 10.1080/01431161.2014.960619pmid: N/A

In recent years, light detection and ranging (lidar) systems have been intensively used in different urban applications such as map updating, communication analysis, virtual city modelling, risk assessment, and monitoring. A prerequisite to enhance lidar data content is to differentiate ground (bare earth) points that yield digital terrain models and off-terrain points in order to classify urban objects and vegetation. The increasing demand for a fast and efficient algorithm to extract three-dimensional urban features was the motive behind this work. A new combined approach to extract bare-earth points is proposed, and a novel methodology to automatically classify airborne laser data into different objects in an urban area is presented. In addition, a new concept of angular classification is introduced to differentiate buildings from vegetation and other small objects. The new angular classifier analyses the distribution of bare-earth points around unclassified point clusters to determine whether a cluster can be classified either as building or as vegetation. The experimental results confirm the high accuracy achieved to automatically classify urban objects in flat complex areas.

Sismanidis, Panagiotis; Karathanassi, Vassilia; Kolokoussis, Polychronis

doi: 10.1080/01431161.2014.960620pmid: N/A

Cast shadows caused by sparse clouds usually degrade spaceborne and airborne imagery. They result from the decrease of the direct solar beam due to the presence of a non-transparent cloud. The reduction of the downwelling solar flux density can be quantified during an air campaign, if the aircraft flies beneath the cloud and is equipped with an add-on instrument that measures the total downwelling solar irradiance. The objective of this work is to exploit such data for the de-shadowing of airborne hyperspectral imagery. Initially, the specific illumination and viewing conditions during the image acquisition, which allow the use of at-sensor downwelling irradiance data for the de-shadowing of airborne hyperspectral imagery, are outlined. Then a methodology is proposed that estimates the radiometric enhancement coefficients from the at-sensor irradiance data and correlates them with the image data using a shadow map. Improvements of the quality of the shadow maps are suggested. Performance assessment showed that at-sensor irradiance data could be satisfactorily utilized for compensating the cast shadow effects on remotely sensed imagery. It also highlighted the importance of generating and using an accurate shadow map and the particular difficulties for the air campaign planning raised by the requirement of exploitable at-sensor irradiance data.

Kabashnikov, V.; Milinevsky, G.; Chaikovsky, A.; Miatselskaya, N.; Danylevsky, V.; Aculinin, A.; Kalinskaya, D.; Korchemkina, E.; Bovchaliuk, A.; Pietruczuk, A.; Sobolewsky, P.; Bovchaliuk, V.

doi: 10.1080/01431161.2014.960621

Parinussa, R.M.; Wang, G.; Holmes, T.R.H.; Liu, Y.Y.; Dolman, A.J.; de Jeu, R.A.M.; Jiang, T.; Zhang, P.; Shi, J.

doi: 10.1080/01431161.2014.960622pmid: N/A

Soil moisture retrievals from China’s recently launched meteorological Fengyun-3B satellite are presented. An established retrieval algorithm – the Land Parameter Retrieval Model (LPRM) – was applied to observations of the Microwave Radiation Imager (MWRI) onboard this satellite. The newly developed soil moisture retrievals from this satellite mission may be incorporated in an existing global microwave-based soil moisture database. To reach consistency with an existing data set of multi-satellite soil moisture retrievals, an intercalibration step was applied to correct brightness temperatures for sensor differences between MWRI and the radiometer of the Tropical Rainfall Measuring Mission’s (TRMM’s) Microwave Imager (TMI), resulting from their individual calibration procedures. The newly derived soil moisture and vegetation optical depth product showed a high degree of consistency with parallel retrievals from both TMI and WindSat, the two satellites that are observing during the same time period and are already part of the LPRM database. High correlation (R > 0.60 at night-time) between the LPRM and official MWRI soil moisture products was shown over the validation networks experiencing semiarid climate conditions. The skills drop below 0.50 over forested regions, with the performance of the LPRM product slightly better than the official MWRI product. To demonstrate the promising use of the MWRI soil moisture in drought monitoring, a case study for a recent and unusually dry East Asian summer Monsoon was conducted. The MWRI soil moisture products are able to effectively delineate the regions that are experiencing a considerable drought, highly in agreement with spatial patterns of precipitation and temperature anomalies. The results in this study give  confidence in the soil moisture retrievals from the MWRI onboard Fengyun-3B. The integration of the newly derived products into the existing database will allow a better understanding the diurnal, seasonal and interannual variations, and long-term (35 year) changes of soil moisture at the global scale, consequently enhancing hydrological, meteorological, and climate studies.