Surface shortwave net radiation (SSNR) flux is essential for the determination of the radiation energy balance between the atmosphere and the Earth's surface. The satellite-derived intermediate SSNR data are strongly needed to bridge the gap between existing coarse-resolution SSNR products and point-based measurements. In this study, four different machine learning (ML) algorithms were tested to estimate the SSNR from the Landsat Thematic Mapper (TM)/ Enhanced Thematic Mapper Plus (ETM+) top-of-atmosphere (TOA) reflectance and other ancillary information (i.e., clearness index, water vapor) at instantaneous and daily scales under all sky conditions. The four ML algorithms include the multivariate adaptive regression splines (MARS), backpropagation neural network (BPNN), support vector regression (SVR), and gradient boosting regression tree (GBRT). Collected in-situ measurements were used to train the global model (using all data) and the conditional models (in which all data were divided into subsets and the models were fitted separately). The validation results indicated that the GBRT-based global model (GGM) performs the best at both the instantaneous and daily scales. For example, the GGM based on the TM data yielded a coefficient of determination value (R 2 ) of 0.88 and 0.94, an average root mean square error (RMSE) of 73.23 W·m -2 (15.09%) and 18.76 W·m -2 (11.2%), and a bias of 0.64 W·m -2 and -1.74 W·m -2 for instantaneous and daily SSNR, respectively. Compared to the Global LAnd Surface Satellite (GLASS) daily SSNR product, the daily TM-SSNR showed a very similar spatial distribution but with more details. Further analysis also demonstrated the robustness of the GGM for various land cover types, elevation, general atmospheric conditions, and seasons as the difference between the incoming and outgoing shortwave radiation fluxes (0.3-3 µm) at the ground level, and determines the surface radiative energy balance during the daytime [2,3]. Therefore, it plays an important role in most energy-consuming ecosystem procedures, for example, it is the dominant source of melting energy on most glaciers [4], and is relevant to the processes of crop evapotranspiration [5], as well as the temporal variation of land surface temperature [6].The use of point-based shortwave radiation field measurements are believed to have higher accuracy than other sources, but limited due to the low spatial density of measurements. Therefore, numerous studies have been devoted to the development of shortwave radiation estimation algorithm and products generation [7][8][9][10][11][12][13][14]. At present, the shortwave radiation algorithms can be separated into two categories: Model-based and satellite-based. Based on climate models' simulations, a gap of more than 10 W·m -2 in global annual mean SSNR with 10 km ≥ pixel scale still existed [15]. Nowadays, satellite observations have become a key source for surface energy budget component estimation because of its unique advantages [16]. Several algorithms have been successfully developed...
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