Reconstruction of MODIS Spectral Reflectance under Cloudy-Sky Condition
AbstractClouds usually cause invalid observations for sensors aboard satellites, which corrupts the spatio-temporal continuity of land surface parameters retrieved from remote sensing data (e.g., MODerate Resolution Imaging Spectroradiometer (MODIS) data) and prevents the fusing of multi-source remote sensing data in the field of quantitative remote sensing. Based on the requirements of spatio-temporal continuity and the necessity of methods to restore bad pixels, primarily resulting from image processing, this study developed a novel method to derive the spectral reflectance for MODIS band of cloudy pixels in the visual–near infrared (VIS–NIR) spectral channel based on the Bidirectional Reflectance Distribution Function (BRDF) and multi-spatio-temporal observations. The proposed method first constructs the spatial distribution of land surface reflectance based on the corresponding BRDF and the solar-viewing geometry; then, a geographically weighted regression (GWR) is introduced to individually derive the spectral surface reflectance for MODIS band of cloudy pixels. A validation of the proposed method shows that a total root-mean-square error (RMSE) of less than 6% and a total R2 of more than 90% are detected, which indicates considerably better precision than those exhibited by other existing methods. Further validation of the retrieved white-sky albedo based on the spectral reflectance for MODIS band of cloudy pixels confirms an RMSE of 3.6% and a bias of 2.2%, demonstrating very high accuracy of the proposed method. View Full-Text
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Gao, B.; Gong, H.; Wang, T.; Jia, L. Reconstruction of MODIS Spectral Reflectance under Cloudy-Sky Condition. Remote Sens. 2016, 8, 727.
Gao B, Gong H, Wang T, Jia L. Reconstruction of MODIS Spectral Reflectance under Cloudy-Sky Condition. Remote Sensing. 2016; 8(9):727.Chicago/Turabian Style
Gao, Bo; Gong, Huili; Wang, Tianxing; Jia, Li. 2016. "Reconstruction of MODIS Spectral Reflectance under Cloudy-Sky Condition." Remote Sens. 8, no. 9: 727.
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