The Microwave Land Surface Emissivity(MLSE)atlas and instantaneous simulation of all-sky/all-surface MLSE are important prerequisites for satellite data assimilation.A ten-day/month synthesized FengYun-3D MLSE atlas(N...The Microwave Land Surface Emissivity(MLSE)atlas and instantaneous simulation of all-sky/all-surface MLSE are important prerequisites for satellite data assimilation.A ten-day/month synthesized FengYun-3D MLSE atlas(New_FY3D)was constructed by the two global MLSE daily product datasets,clear-sky(FY-3D1)and clear/cloudy(FY-3D2),which were retrieved from the same FY-3D MicroWave Radiation Imager(MWRI)Level-1 brightness temperature(BT)data from 2021 to 2022,respectively.Then,a set of global MLSE label samples based on the New_FY3D,including 14 surface geophysical parameters,was obtained for an instantaneous global MLSE simulation at a 0.10°spatial resolution by adopting the extreme gradient boosting(XGBoost)machine learning method.Finally,the FengYun-3F(FY-3F)MWRI-II BT simulations using the Advanced Radiative Transfer Modeling System(ARMS)based on the above different MLSE products were evaluated.The results show that the New_FY3D atlas performs well,and the BT simulation at the top of atmosphere is better than that of FY-3D1,FY-3D2,and the international mainstream TELSEM2(Version 2.0 for a Tool to Estimate Land Surface Emissivities in the Microwaves)atlas.Surface roughness,vegetation coverage,land cover type,and snow cover are vital parameters for MLSE simulation.The XGBoost model can accurately simulate all-sky/all-surface MLSE instantaneously over the frequency range 10.65–89.0 GHz.The average simulation determination coefficients(R^(2))under clear-sky and cloud-sky conditions are 0.925 and 0.901,respectively,and the average root-mean-square errors(RMSEs)are 0.018 and 0.021,respectively.Large simulation errors occur in permanent wetland,ice and snow,and urban and built-up areas.With a standard deviation of 6.6 K,the BT simulation based on an XGBoost simulated MLSE is better than those based on New_FY3D and TELSEM2.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.U2242211)the Hunan Provincial Natural Science Foundation Major Project(Grant No.2021JC0009).
文摘The Microwave Land Surface Emissivity(MLSE)atlas and instantaneous simulation of all-sky/all-surface MLSE are important prerequisites for satellite data assimilation.A ten-day/month synthesized FengYun-3D MLSE atlas(New_FY3D)was constructed by the two global MLSE daily product datasets,clear-sky(FY-3D1)and clear/cloudy(FY-3D2),which were retrieved from the same FY-3D MicroWave Radiation Imager(MWRI)Level-1 brightness temperature(BT)data from 2021 to 2022,respectively.Then,a set of global MLSE label samples based on the New_FY3D,including 14 surface geophysical parameters,was obtained for an instantaneous global MLSE simulation at a 0.10°spatial resolution by adopting the extreme gradient boosting(XGBoost)machine learning method.Finally,the FengYun-3F(FY-3F)MWRI-II BT simulations using the Advanced Radiative Transfer Modeling System(ARMS)based on the above different MLSE products were evaluated.The results show that the New_FY3D atlas performs well,and the BT simulation at the top of atmosphere is better than that of FY-3D1,FY-3D2,and the international mainstream TELSEM2(Version 2.0 for a Tool to Estimate Land Surface Emissivities in the Microwaves)atlas.Surface roughness,vegetation coverage,land cover type,and snow cover are vital parameters for MLSE simulation.The XGBoost model can accurately simulate all-sky/all-surface MLSE instantaneously over the frequency range 10.65–89.0 GHz.The average simulation determination coefficients(R^(2))under clear-sky and cloud-sky conditions are 0.925 and 0.901,respectively,and the average root-mean-square errors(RMSEs)are 0.018 and 0.021,respectively.Large simulation errors occur in permanent wetland,ice and snow,and urban and built-up areas.With a standard deviation of 6.6 K,the BT simulation based on an XGBoost simulated MLSE is better than those based on New_FY3D and TELSEM2.
