Estimating Land Surface All-Wave Daily Net Radiation From VIIRS Top-of-Atmosphere Data

Be aware of the significance of land surface net radiation (R-n), there is a need for accurate long-term and high spatial resolution global R-n estimates based on satellite data. Herein, we propose a novel globally applicable, highly effective algorithm for estimating daily R-n directly from Visible Infrared Imaging Radiometer Suite (VIIRS) top-of-atmosphere (TOA) observations ranging from 2011 to present, using the eXtreme Gradient Boosting (XGBoost) method. This algorithm, named the constraint conditional model (CCM), consists of five conditional models (namely, cases 1-5 model) divided by the combination of the length of daytime (dt), the instantaneous sky condition, and the surface broadband albedo, and the daily downward shortwave radiation (DSR) from ERA5-Land was introduced as a physical constraint when dt>9 , in which case R-n is dominated by R-si (incoming solar radiation). The validation accuracy of CCM was satisfactory against the ground measurements, yielding a root-mean-square error (RMSE) of 18.95 Wm(-2), a bias of 0.056 Wm(-2), and an R2 of 0.89. The algorithm exhibited superior accuracy and robustness compared to GLASS-MODIS and ERA5-Land under spatiotemporally independent validation samples. This indicates the potential of VIIRS to extent MODIS R-n products for generating long-term global daily R-n data.