The Microwave Radiation Imager(MWRI),boarded on the FY-3 series satellites:FY-3B,FY-3C,and FY-3D,is the first satellite-based microwave radiometer in China,commencing passive microwave brightness temperature data acqu...The Microwave Radiation Imager(MWRI),boarded on the FY-3 series satellites:FY-3B,FY-3C,and FY-3D,is the first satellite-based microwave radiometer in China,commencing passive microwave brightness temperature data acquisition since 2010.The Advanced Microwave Scanning Radiometer 2(AMSR2) boarded on the Global Change Observation Mission 1st-Water(GCOM-W1),has been operational since 2012.Despite the FY-3 series satellites are equipped with the same MWRI and all MWRIs sharing comparable parameters and configurations as AMSR2,disparities in observation times and satellite platforms result in inconsistencies in the data obtained by different satellites,which further impacting the consistency of retrieved geophysical parameters.To improve the consistency of brightness temperatures from FY-3B,FY-3C,FY-3D/MWRI,and GCOM-W1/AMSR2,cross-calibrations were conducted among brightness temperatures at ten-channel from above four platforms.The consistency of derived snow depth from MWRIs and AMSR2 in China before and after the calibration were also analyzed.The results show that the correlation coefficients of brightness temperatures at all channels between sensors exceed0.98.After cross-calibration,the RMSEs and biases of brightness temperatures at all frequencies and snow depth in China derived from them reduce to varying degrees.The consistencies in both brightness temperatures and snow depth of FY-3B/MWRI,FY-3D/MWRI,and AMSR2 are higher than those of FY-3C and others.These findings advocate for the utilization of cross-calibrated brightness temperatures from FY-3B/MWRI,FY-3D/MWRI,and AMSR2,which share similar satellite overpass time,to derived a long-term snow depth dataset.展开更多
基金supported by the National Natural Science Foun-dation of China(42125604,42171143)Innovative Development Project of China Meteorological Administration(CXFZ 2022J039).
文摘The Microwave Radiation Imager(MWRI),boarded on the FY-3 series satellites:FY-3B,FY-3C,and FY-3D,is the first satellite-based microwave radiometer in China,commencing passive microwave brightness temperature data acquisition since 2010.The Advanced Microwave Scanning Radiometer 2(AMSR2) boarded on the Global Change Observation Mission 1st-Water(GCOM-W1),has been operational since 2012.Despite the FY-3 series satellites are equipped with the same MWRI and all MWRIs sharing comparable parameters and configurations as AMSR2,disparities in observation times and satellite platforms result in inconsistencies in the data obtained by different satellites,which further impacting the consistency of retrieved geophysical parameters.To improve the consistency of brightness temperatures from FY-3B,FY-3C,FY-3D/MWRI,and GCOM-W1/AMSR2,cross-calibrations were conducted among brightness temperatures at ten-channel from above four platforms.The consistency of derived snow depth from MWRIs and AMSR2 in China before and after the calibration were also analyzed.The results show that the correlation coefficients of brightness temperatures at all channels between sensors exceed0.98.After cross-calibration,the RMSEs and biases of brightness temperatures at all frequencies and snow depth in China derived from them reduce to varying degrees.The consistencies in both brightness temperatures and snow depth of FY-3B/MWRI,FY-3D/MWRI,and AMSR2 are higher than those of FY-3C and others.These findings advocate for the utilization of cross-calibrated brightness temperatures from FY-3B/MWRI,FY-3D/MWRI,and AMSR2,which share similar satellite overpass time,to derived a long-term snow depth dataset.
文摘微波传感器获得的土壤水分产品空间分辨率一般都很粗,而流域尺度上的研究需要中高分辨率的土壤水分数据。用MODIS逐日地表温度产品MOD11A1和逐日地表反射率产品MOD09GA构建温度—植被指数特征空间,并计算得到TVDI(Temperature Vegetation Dryness Index)指数,它与土壤水分呈负相关关系,能够反映土壤水分的空间分布模式,但并不是真实的土壤水分值。在AMSR-E像元尺度上求得TVDI与土壤水分的负相关系数,进而对VUA AMSR-E土壤水分产品进行降尺度计算得到0.01°分辨率的真实土壤水分值。经NAFE06(The National Airborne Field Experiment 2006)试验地面采样数据验证,降尺度后的土壤水分均方根误差平均值为6.1%。
