联合同化MODIS地表温度与机载L波段微波亮度温度估计土壤水分

发布时间：2018-06-29 13:10

本文选题：数据同化 + 土壤水分　；参考：《遥感技术与应用》2017年04期

【摘要】：构建了基于通用陆面模型(CoLM,Common Land Model)、微波辐射传输模型L-MEB(Lband Microwave Emission of the Biosphere)和集合平滑算法(EnKS,Ensemble Kalman Smoother)的土壤水分数据同化框架,用于联合同化MODIS地表温度和机载L波段被动微波亮温数据。以2012年HiWATER试验期间中游大满超级站为实验站点,分析了3种LAI数据产品对土壤温度模拟结果的影响,进而分析了联合同化地表温度和微波亮度温度对土壤水分估计结果的影响。研究结果表明:3种LAI数据对土壤温度模拟结果的影响显著,MODIS LAI产品在该研究区显著低估,导致土壤温度模拟结果高估4~6K;同化亮度温度、同化地表温度以及联合同化两者均可以改进土壤水分的估计精度,联合同化地表温度和亮度温度对于土壤水分的改进最为显著,土壤水分同化结果的RMSE减少31%~53%。
[Abstract]:Based on the Common Land Model, L-MEB (Lband Microwave permission of the Biosphere) and EnKSSemble Kalman Smoother, a soil moisture data assimilation framework is constructed to assimilate both MODIS surface temperature and airborne L-band passive microwave bright temperature data. The effects of three Lai data products on soil temperature simulation results were analyzed by using the Daiman super station in the middle reaches of the HiWATER experiment in 2012, and the effects of combined assimilation surface temperature and microwave brightness temperature on the soil moisture estimation results were analyzed. The results showed that the effect of three Lai data on soil temperature simulation results was significant. The MODIS Lai products were significantly underestimated in the study area, which led to the overestimation of soil temperature simulation results by 4 kg and assimilation brightness temperature. Both assimilation surface temperature and combined assimilation can improve the estimation accuracy of soil moisture. The improvement of soil moisture by combined assimilation surface temperature and brightness temperature is the most significant, and the RMSE of soil water assimilation results is reduced by 31% and 53%.
【作者单位】：中国科学院西北生态环境资源研究院黑河遥感试验研究站;中国科学院西北生态环境资源研究院甘肃省遥感重点实验室;中国科学院大学;武汉大学资源与环境科学学院;
【基金】：国家自然科学基金项目(41271358、91325106)资助
【分类号】：S152.7;TP79

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