Simulations of Microwave Land Surface Emissivity Using FengYun-3D Microwave Radiation Imager Data: A Case in the Tibetan Plateau

Abstract

Accurate information on microwave land surface emissivity (MLSE) is important for satellite data assimilation. In this article, a new random forest (RF) algorithm is developed for retrieving MLSE under all-sky conditions. Using Level-1 brightness temperature data from the FengYun-3D (FY-3D) microwave radiation imager in 2022, two global MLSE daily product datasets, clear-sky (FY-3D1) and clear/cloudy (FY-3D2), were obtained by using one-dimensional variational method and microwave radiative transfer method, respectively. Based on the global spatiotemporal consistency assessment, a high-quality daily MLSE training dataset for the Tibetan Plateau was selected from the two datasets. Then, ten land surface parameters from routine observation were chosen as input features to the RF model to simulate the MLSE under all-sky conditions in the Tibetan Plateau. The results show that both FY-3D1 and FY-3D2 MLSE datasets are comparable to the international mainstream MLSE products in quality, while the clear sky FY-3D1 is likely to be better than the clear/cloudy FY-3D2 MLSE. Land surface roughness, vegetation optical thickness, normalized vegetation index, and land cover type are identified as the most important factors affecting MLSE in the Tibetan Plateau. The RF model can effectively simulate the MLSE in the frequency range of 10.65–89.0 GHz under all-sky conditions. The coefficients of determination ( R ² ) for horizontal polarization and vertical polarization range from 0.86 (10.65 GHz) to 0.91 (18.7 GHz) and from 0.60 (10.65 GHz) to 0.74 (89.0 GHz), respectively. The root mean square errors for horizontal polarization and vertical polarization range from 0.017 (23.8 GHz) to 0.023 (10.65 GHz) and from 0.016 (10.65 GHz) to 0.019 (89.0 GHz), respectively. These results indicate that machine learning is likely to be an effective method for future all-sky simulation of MLSE.