Snow parameter retrieval (SPR) algorithm for the GCOM-C/SGLI sensor: validation over the Greenland ice sheet

This paper presents a new snow parameter retrieval (SPR) algorithm for the Global Change Observation Mission-Climate/Second Generation Global Imager (GCOM-C/SGLI) instrument (2018-present). This algorithm combines accurate radiative transfer model (RTM) simulations and Scientific Machine Learning (SciML) methods, Multi-Layer Neural-Network (MLNN) techniques in particular. It provides pixel-by-pixel optically equivalent snow grain size in two layers (i.e., a thin surface snow layer and a deep snow layer), snow impurity concentration and broadband blue- and black-sky albedo which constitute standard SGLI products. In addition, this RTM-SciML algorithm retrieves aerosol optical depth and provides an important retrieval error quality flag. This retrieval error flag, established by comparing reflectances obtained from RTM simulations using the retrieved snow and aerosol parameters as input with the measured reflectances, provides a pixel-by-pixel quality check of the retrieval parameters. Application of the RTM-SciML algorithm to SGLI images obtained over the Greenland Ice Sheet revealed a significant change in snow parameters from a cold July 2018 to a warm July 2019. The inferred blue-sky albedo was in general agreement with field measurements with RMSE = 0.0517 and MAPE = 4.64% for shortwave albedo at the SIGMA-A site, and the black-sky albedo, inferred from retrieved snow parameters, was found to be similar (within 5% relative difference) to the blue-sky values. Although developed specifically for application to data obtained by the SGLI imager, the SPR algorithm can easily be adapted for application to other similar multi-spectral sensors, such as MODIS (already done), VIIRS, and OLCI.