Surface Incident Shortwave Radiation (ISR) is an essential parameter for the earth’s energy budget. Although many reanalysis and satellite-derived products have been published, the insufficient accuracy and resolutions limited their utilization. Trying to meet the requirements, Ph.D. candidate Yi Zhang, Profs. Shunlin Liang, Dongdong Wang from GEOG, Prof. Tao He from Wuhan University and Dr. Yunyue Yu from NOAA presented an optimization-based algorithm for ISR estimation from MODIS observations. The research has been published on Remote Sensing of Environment on Mar. 18, 2018.

The abstract of the paper reads:

Surface incident shortwave radiation (ISR) is a crucial parameter in the land surface radiation budget. Many reanalysis, observation-based, and satellite-derived global radiation products have been developed but often have insufficient accuracy and spatial resolution for many applications. In this paper, we propose a method based on a radiative transfer model for estimating surface ISR from Moderate Resolution Imaging

Spectroradiometer (MODIS) Top of Atmosphere (TOA) observations by optimizing the surface and atmospheric variables with a cost function. This approach consisted of two steps: retrieving surface bidirectional reflectance distribution function parameters, aerosol optical depth (AOD), and cloud optical depth (COD); and subsequently calculating surface ISR. Validation against measurements at seven Surface Radiation Budget Network

(SURFRAD) sites resulted in an R^2 of 0.91, a bias of −6.47 W/m^2, and a root mean square error (RMSE) of 84.17 W/m^2 (15.12%) for the instantaneous results. Validation at eight high-latitude snow-covered Greenland Climate Network (GC-Net) sites resulted in an R^2 of 0.86, a bias of −21.40 W/m^2, and an RMSE of 84.77 W/m^2 (20.96%). These validation results show that the proposed method is much more accurate than the previous studies (usually with RMSEs of 80-150 W/m^2). We further investigated whether incorporating additional satellite products, such as the MODIS surface broadband albedo (MCD43), aerosol (MOD/MYD04), and cloud products (MOD/MYD06), as constraints in the cost function would improve the accuracy. When the AOD and COD estimates were constrained, RMSEs were reduced to 62.19 W/m^2 (12.12%) and 71.70 W/m^2 (17.74%) at the SURFRAD and GC-Net sites, respectively. This algorithm could estimate surface ISR with MODIS TOA observations over both snow-free and seasonal/permanent snow-covered surfaces. The algorithm performed well at high-latitude sites, which is very useful for radiation budget research in the Polar Regions.