Estimating Surface BRDF/Albedo Over Rugged Terrain Using an Extended Multisensor Combined BRDF Inversion (EMCBI) Model

Abstract

Land surface albedo is a crucial variable of earth energy budget and global climate change. Rugged terrain significantly impacts surface bidirectional reflectance distribution function (BRDF) and the subsequent albedo retrieval using satellite remote sensing. Existing studies of estimating surface BRDF/albedo from satellite observations are limited to neglecting topographic impacts, resulting in large uncertainty in satellite albedo product, especially for low spatial resolution satellite sensors that are primarily regulated by subpixel-scale topographic effects. To fill this knowledge gap, we proposed an extended multisensor combined BRDF inversion (EMCBI) model to characterize subpixel-scale topographic effects, and applied this model to estimate BRDF/albedo from the Himawari-8 Advanced Himawari Imager (AHI) and Terra/Aqua moderate resolution imaging spectroradiometer (MODIS) data and finally validated the satellite-derived albedo with ground measurements of two stations located in Tibet plateau. Our results show that: 1) EMCBI can generate a daily BRDF/albedo dataset with more than 90% spatial coverage and 2) EMCBI-derived albedo agrees well with the referenced albedo corrected from ground measurement, with a root-mean-square-error (RMSE) of 0.0537 and 0.0608 for black-sky albedo (BSA) and white-sky albedo (WSA), and a mean absolute percentage error (MAPE) of 21.93% and 25.13% for BSA and WSA, respectively. These results demonstrate EMCBI has great potential for mapping large-scale high temporal resolution BRDF/albedo product over rugged terrain.