Reconstruction of MODIS Spectral Reflectance under Cloudy-Sky Condition

Abstract

Clouds 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 R 2 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.

Prediction of High-Quality MODIS-NPP Product Data

“…Several methods for reducing noise and constructing high-quality MODIS datasets have been proposed, applied, and evaluated in recent years [10][11][12][13][14][15][16]. Fast Fourier transform (FFT) and wavelet transform (WT) approaches are often used when evaluating MODIS time-series data [17][18][19][20].…”

Satellite characterization of drought over cultivated areas in Central America

“…They however have limitations when all spectral bands are polluted by clouds; thus, complementary information is insufficient to reconstruct cloudy areas. The temporalbased methods using optimal information interpolated from consecutive observations of the same spectral band at the same location to reconstruct cloudy areas seem to produce more accurate results (Gao et al 2016;Jin and Xu 2013;Xu and Shen 2013). However, they require that the time interval between the target pixels in the cloudy image and reference pixels in consecutive images must be temporally short enough.…”