Direct-Estimation Algorithm for Mapping Daily Land-Surface Broadband Albedo From MODIS Data

TL;DR: In this article, a direct estimation algorithm for mapping daily land-surface broadband albedo from MODIS data is presented. And the robustness of this method for different angular bins, aerosol conditions, and land cover types is analyzed.

Abstract: Land surface albedo is a critical parameter in surface-energy budget studies Over the past several decades, many albedo products are generated from remote-sensing data sets The Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/Albedo algorithm is used to routinely produce eight day (16-day composite), 1-km resolution MODIS albedo products When some natural processes or human activities occur, the land-surface broadband albedo can change rapidly, so it is necessary to enhance the temporal resolution of albedo product We present a direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data The polarization and directionality of the Earth's reflectance-3/polarization and anisotropy of reflectances for atmospheric sciences coupled with observations from a Lidar BRDF database is employed as a training data set, and the 6S atmospheric radiative transfer code is used to simulate the top-of-atmosphere (TOA) reflectances Then a relationship between TOA reflectances and land-surface broadband albedos is developed using an angular bin regression method The robustness of this method for different angular bins, aerosol conditions, and land-cover types is analyzed Simulation results show that the absolute error of this algorithm is ~ 0009 for vegetation, 0012 for soil, and 0030 for snow/ice Validation of the direct-estimation algorithm against in situ measurement data shows that the proposed method is capable of characterizing the temporal variation of albedo, especially when the land-surface BRDF changes rapidly