A Comprehensive Assessment of Modis-Derived Instantaneous Net Surface Shortwave Radiation using the in-Situ Fluxnet Database

Net Surface Shortwave Radiation (NSSR) is a key component of the surface radiation budget, which controls the energy, water exchanges, and many physical processes. The primary purpose of this study is to build a concise and feasible model of estimating NSSR with data of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Random Forest (RF) machine learning method was applied to building the model with the FULXNET in situ observations because of its powerful ability in nonlinear fitting. Total 17 variables are considered in RF model for retrieving NSSR, and thousands of parameters combinations were carried to obtained optimal parameters of the proposed model. The Bias, RMSE, and R2 of estimated instantaneous NSSR in 95 selected sites during 2014 whole year are -0.085 W m-2, 28.274 W m-2 and 0.989, respectively. Consequently, retrieval of instantaneous NSSR with RF method would be believed to be an efficient method in the future by considering its concise process and great accuracy.