This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E L2A.The monthly parameters of the cross-calibration were determined and evaluated using rob...This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E L2A.The monthly parameters of the cross-calibration were determined and evaluated using robust linear regression.The snow depth in case of seasonal ice was calculated by using parameters of the crosscalibration of data from the MWRI Tb.The correlation coefficients of the H/V polarization among all channels Tb of the two sensors were higher than 0.97.The parameters of the monthly cross-calibration were useful for the snow depth retrieval using the MWRI.Data from the MWRI Tb were cross-calibrated to the AMSR-E baseline.Biases in the data of the two sensors were optimized to approximately 0 K through the cross-calibration,the standard deviations decreased significantly in the range of 1.32 K to 2.57 K,and the correlation coefficients were as high as 99%.An analysis of the statistical distributions of the histograms before and after cross-calibration indicated that the FY-3B/MWRI Tb data had been well calibrated.Furthermore,the results of the cross-calibration were evaluated by data on the daily average Tb at 18.7 GHz,23.8 GHz,and 36.5 GHz(V polarization),and at 89 GHz(H/V polarization),and were applied to the snow depths retrieval in the Arctic.The parameters of monthly cross-calibration were found to be effective in terms of correcting the daily average Tb.The results of the snow depths were compared with those of the calibrated MWRI and AMSR-E products.Biases of 0.18 cm to 0.38 cm were observed in the monthly snow depths,with the standard deviations ranging from 4.19 cm to 4.80 cm.展开更多
利用AOML(Atlantic Oceanographical and Meteorological Laboratory)SVP漂流浮标的海表面温度数据,针对30°S以南的南大洋海域,对目前主要使用的微波遥感产品(AMSR-E,Ad-vanced Microwave Scanning Radiometer for the Earth Obser...利用AOML(Atlantic Oceanographical and Meteorological Laboratory)SVP漂流浮标的海表面温度数据,针对30°S以南的南大洋海域,对目前主要使用的微波遥感产品(AMSR-E,Ad-vanced Microwave Scanning Radiometer for the Earth Observing System)反演的SST进行了较为系统的评估。结果表明,AMSR-E SST比浮标数据偏冷,偏差为-0.01℃,标准差为0.70℃。夏季的偏差为0.004℃,标准差为0.64℃;冬季的偏差为-0.06℃,标准差为0.75℃,冬季的偏差和标准差较大。温差ΔT受流速影响,随着流速的增大而减小,且这种趋势在夏季更为显著。具备托伞结构的浮标与总体情况基本一致,而无托伞结构的浮标受流速的影响要大一些。同时,温差ΔT受水汽的影响,随着水汽的增加而减小,且这种影响在冬季更大一些。进一步对4个穿极和绕极浮标的追踪分析表明,温差ΔT受大洋海流系统的影响显著。在海流大的大西洋边界流和南极绕极流中,温差ΔT的不确定性要明显大于总体情况。展开更多
利用AMSR-E观测的土壤表层亮温资料,采用简化修正的单通道算法模型(Single Channel Algorithm,SCA),反演青藏高原地区夏季2011年6-8月的表层土壤湿度。为对比验证反演结果,利用高原东部和中部的玛曲观测网和那曲观测网CTP-SMTMN(Soil Mo...利用AMSR-E观测的土壤表层亮温资料,采用简化修正的单通道算法模型(Single Channel Algorithm,SCA),反演青藏高原地区夏季2011年6-8月的表层土壤湿度。为对比验证反演结果,利用高原东部和中部的玛曲观测网和那曲观测网CTP-SMTMN(Soil Moisture and Temperature Monitoring Netw ork on the central Tibetan Plateau)的土壤湿度观测数据,以及NASA和VUA-NASA两种均基于AM SR-E的反演土壤湿度产品进行验证。结果表明:(1)与VUA-NASA产品和修改后的SCA模型反演结果相比,NASA产品在像元和区域尺度上相关系数较低,MAE(Mean Absolute Error)和RMSE(Root M ean Square Error)较高,明显低估了两个地区的土壤湿度。(2)VUA-NASA产品在玛曲地区表现良好,在那曲地区虽然相关系数较高,但MAE和RMSE同样较高,导致精度较差。(3)对比其他两种产品,修改后的SCA模型反演结果在两个地区表现出较高的相关系数(接近0.800)、较低的MAE(接近0.050m^3·m^(-3))和RMSE(接近0.060 m^3·m^(-3)),有着较高的精度。因此,可以认为修改后的SCA模型可以应用于青藏高原地区土壤湿度动态监测,为研究青藏高原地区的天气和气候变化影响及水循环过程提供了参考和借鉴。展开更多
基金The National Key Research and Development Program of China under contract Nos 2019YFA0607001 and2016YFC1402704the Global Change Research Program of China under contract No.2015CB9539011
文摘This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E L2A.The monthly parameters of the cross-calibration were determined and evaluated using robust linear regression.The snow depth in case of seasonal ice was calculated by using parameters of the crosscalibration of data from the MWRI Tb.The correlation coefficients of the H/V polarization among all channels Tb of the two sensors were higher than 0.97.The parameters of the monthly cross-calibration were useful for the snow depth retrieval using the MWRI.Data from the MWRI Tb were cross-calibrated to the AMSR-E baseline.Biases in the data of the two sensors were optimized to approximately 0 K through the cross-calibration,the standard deviations decreased significantly in the range of 1.32 K to 2.57 K,and the correlation coefficients were as high as 99%.An analysis of the statistical distributions of the histograms before and after cross-calibration indicated that the FY-3B/MWRI Tb data had been well calibrated.Furthermore,the results of the cross-calibration were evaluated by data on the daily average Tb at 18.7 GHz,23.8 GHz,and 36.5 GHz(V polarization),and at 89 GHz(H/V polarization),and were applied to the snow depths retrieval in the Arctic.The parameters of monthly cross-calibration were found to be effective in terms of correcting the daily average Tb.The results of the snow depths were compared with those of the calibrated MWRI and AMSR-E products.Biases of 0.18 cm to 0.38 cm were observed in the monthly snow depths,with the standard deviations ranging from 4.19 cm to 4.80 cm.
文摘利用AOML(Atlantic Oceanographical and Meteorological Laboratory)SVP漂流浮标的海表面温度数据,针对30°S以南的南大洋海域,对目前主要使用的微波遥感产品(AMSR-E,Ad-vanced Microwave Scanning Radiometer for the Earth Observing System)反演的SST进行了较为系统的评估。结果表明,AMSR-E SST比浮标数据偏冷,偏差为-0.01℃,标准差为0.70℃。夏季的偏差为0.004℃,标准差为0.64℃;冬季的偏差为-0.06℃,标准差为0.75℃,冬季的偏差和标准差较大。温差ΔT受流速影响,随着流速的增大而减小,且这种趋势在夏季更为显著。具备托伞结构的浮标与总体情况基本一致,而无托伞结构的浮标受流速的影响要大一些。同时,温差ΔT受水汽的影响,随着水汽的增加而减小,且这种影响在冬季更大一些。进一步对4个穿极和绕极浮标的追踪分析表明,温差ΔT受大洋海流系统的影响显著。在海流大的大西洋边界流和南极绕极流中,温差ΔT的不确定性要明显大于总体情况。
文摘利用AMSR-E观测的土壤表层亮温资料,采用简化修正的单通道算法模型(Single Channel Algorithm,SCA),反演青藏高原地区夏季2011年6-8月的表层土壤湿度。为对比验证反演结果,利用高原东部和中部的玛曲观测网和那曲观测网CTP-SMTMN(Soil Moisture and Temperature Monitoring Netw ork on the central Tibetan Plateau)的土壤湿度观测数据,以及NASA和VUA-NASA两种均基于AM SR-E的反演土壤湿度产品进行验证。结果表明:(1)与VUA-NASA产品和修改后的SCA模型反演结果相比,NASA产品在像元和区域尺度上相关系数较低,MAE(Mean Absolute Error)和RMSE(Root M ean Square Error)较高,明显低估了两个地区的土壤湿度。(2)VUA-NASA产品在玛曲地区表现良好,在那曲地区虽然相关系数较高,但MAE和RMSE同样较高,导致精度较差。(3)对比其他两种产品,修改后的SCA模型反演结果在两个地区表现出较高的相关系数(接近0.800)、较低的MAE(接近0.050m^3·m^(-3))和RMSE(接近0.060 m^3·m^(-3)),有着较高的精度。因此,可以认为修改后的SCA模型可以应用于青藏高原地区土壤湿度动态监测,为研究青藏高原地区的天气和气候变化影响及水循环过程提供了参考和借鉴。
