数字农科院2.0

Estimate of Cloudy-Sky Surface Emissivity From Passive Microwave Satellite Data Using Machine Learning

文献类型： 外文期刊

作者： Zhu X.-M.; Song X.-N.; Leng P.; Li Z.-L.; Li X.-T.; Gao L.; Guo D.

作者机构：

关键词： Atmospheric temperature; Decision trees; Error analysis; Learning systems; Microwave measurement; Microwave sensors; Microwaves; Radiometers; Remote sensing; Surface measurement; All-weather; AMSR-E product; Atmospheric modeling; Cloud-computing; Land surface; Maximum-likelihood estimation; Microwave imaging; Microwave land surface emissivity; Microwave radiometry; Random forests; Surface emissivity; Maximum likelihood estimation; AMSR-E; clear sky; climate conditions; emissivity; error analysis; estimation method; hydrometeorology; machine learning; MODIS; radiometer; satellite data

期刊名称： IEEE Transactions on Geoscience and Remote Sensing

ISSN： 0196-2892

年卷期： 2022 年 60 卷

页码：

收录情况： SCIE（2022版） ; EI（2022版）

摘要： The derivation of microwave land surface emissivity (MLSE) under various weather conditions from the microwave radiometer plays a crucial role in acquiring land surface and atmospheric parameters. Nevertheless, currently, most existing studies mainly focus on the clear-sky scenarios due to a lack of cloudy-sky land surface temperature (LST) and uncertainties in simulating the scattering and emission properties of atmospheric hydrometeors. Under this background, with satellite observations and the random forest (RF) model, this study proposes a method to estimate the MLSE under cloudy skies. First, clear-sky MLSEs with satisfactory accuracy are retrieved by using the brightness temperatures (BTs) from the Advanced Microwave Scanning Radiometer-Earth sensor, LSTs from the Moderate Resolution Imaging Spectroradiometer (MODIS), and atmospheric profiles from the ERA5 reanalysis. Then, the relation among the clear-sky MLSE and related impact factors is built with the RF and extended to the cloudy-sky environment for generating all-weather MLSEs with a 0.25°. The results show that the input datasets present a considerable impact on the calculation of instantaneous MLSE, and a 5.73 K bias of ERA5 LST may generate a 0.014-0.021 error in the MLSE from 6.9-to 89-GHz horizontal polarization, while the impacts of BT and profile uncertainties on the MLSE are smaller. The retrieved clear-sky MLSE is coincident with the existing MLSE for the spatiotemporal variations, and there is an average difference range from-0.035 to 0.035 in January 2008. Meanwhile, the constructed RF model can successfully apply to cloudy-sky status and recover the MLSE image gaps affected by cloud contamination. © 1980-2012 IEEE.

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