Estimation of land surface albedo from a single GCOM-C/SGLI surface reflectance by using multi-regression model

Abstract

In this paper, we present an algorithm for estimating terrestrial albedo for the product of Global Change Observation Mission-Climate (GCOM-C)/Second generation Global Imager (SGLI), that was launched in Dec. 2017 by Japan Aerospace Exploration Agency (JAXA), Japan. The algorithm is composed of spectral albedo estimation, narrowband-to-broadband albedo conversion and multi-regression model estimation so that only a single-day reflectance observation is available. In estimating spectral albedo, we derives coefficients of kernel-driven bidirectional reflectance distribution function (BRDF) model. The experiments by using in-situ data of bare soil and deciduous broadleaf forests show that the proposed method have potential to estimating albedos with acceptable accuracy of the root mean square of errors (RMSE) of 2.2×10^-2 and 4.3×10^-2.