The Hi-GLASS all-wave daily net radiation product: Algorithm and product validation

The surface net radiation (R_n) represents the balance of the radiative budget on the land surface and drives many physical and biological processes. An accurate and long-term product for global daily coverage of R_n at a high spatial resolution is needed for a variety of applications at regional and local scales. This study proposes two algorithms, called the downward shortwave radiation (DSR)-based algorithm and the top-of-atmosphere (TOA)-based algorithm, to estimate R_n by using Landsat data. The DSR-based algorithm consists of three conditional models, and was developed based on the analysis of the relationship between R_n and shortwave radiation as well as ancillary information from ground measurements and various datasets. The TOA-based algorithm was developed by linking R_n to TOA observations from Landsat sensors and ancillary information. The two algorithms were developed by using the random forest method. The results of their validation against ground measurements showed that the DSR-based algorithm outperformed the TOA-based algorithm in terms of accuracy, with a determination coefficient (R²) of 0.93, root-mean-squared error (RMSE) of 17.58 Wm⁻², and bias of −4.27 Wm⁻². It was stable under various conditions. We then applied the DSR-based algorithm to generate a product of the global daily R_n, called the High-resolution (Hi)- Global LAnd Surface Satellite (GLASS), from 2013 to 2018 at a spatial resolution of 30 m under a clear sky based on remotely sensed products, including the DSR from GLASS, the normalized difference vegetation index (NDVI) obtained from Landsat, surface broadband albedo from Hi-GLASS, and meteorological factors based on reanalysis data from MERRA2. Following its validation using in-situ observations from 2013 to 2018, the overall accuracy of the daily R_n acquired by Hi-GLASS under clear sky was found to be satisfactory, with a value of R² of 0.90 and an RMSE of 25.03 Wm⁻². Moreover, compared with the daily R_n obtained from the GLASS product at a spatial resolution of 5 km, that obtained by Hi-GLASS can better characterize the surface by providing more details and capturing the variations in the measurements, especially large and small values. However, due to limitations of the available datasets and the algorithm, the data on R_n for most regions lacked information on cloudy skies and areas at high latitudes. This information thus cannot be provided by Hi-GLASS yet. Moreover, the influence of the topography on values of R_n has not been thoroughly considered. Nonetheless, values of R_n under clear sky obtained from Hi-GLASS offer promise for use in a wide range of areas, and efforts are underway to improve this product.