【摘要】 土壤水分是气候、水文学研究中的重要变量,微波遥感是获取区域地表土壤水分的重要手段,而L波段更是微波土壤水分反演的最优波段。依托HiWATER黑河中游绿洲试验区的地面观测及机载PLMR微波辐射计亮温数据,利用微波辐射传输模型L-MEB,并将MODIS地表温度产品(MOD11A1)和叶面积指数产品(MYD15A2)作为模型及反演中的先验辅助信息,借助LM优化算法,通过PLMR双极化多角度的亮温观测,针对土壤水分、植被含水量(VWC)和地表粗糙度这3个主要参数,分别进行土壤水分单参数反演、土壤水分与VWC或粗糙度的双参数反演以及这3个参数的同时反演。通过对不同反演方法的比较可以得出结论,多源辅助数据及PLMR双极化、多角度信息的应用可以显著降低反演的不确定性,提高土壤水分反演精度。证明在合理的模型参数和反演策略下,SMOS的L-MEB模型和产品算法可以达到0.04 cm3/cm3的反演精度,另外无线传感器网络可以在遥感产品真实性检验中起到重要作用。更多还原

【Abstract】 Soil moisture is one of important variables in the climate and hydrology research. Remote sensing can map the soil moisture distribution at regional or global scale. Microwave remote sensing now has been the main method to retrieve soil moisture information,especially using satellite-based passive microwave radiometer. L-band is most suitable for the microwave remote sensing of soil moisture due to its longer wavelength. ESA’s Soil Moisture and Ocean Salinity( SMOS) satellite uses MIRAS( Microwave Imaging Radiometer by Aperture Synthesis) to get multi-angle and dual-polarized brightness temperatures of land surface at L-band. SMOS aims to get global surface soil moisture through radiative transfer model( L-MEB) and multi-parameter retrieval method. SMOS Level 2 surface soil moisture algorithm uses an iterative method to minimize a cost function formulated by difference between modeled and measured brightness temperature. PLMR( Polarimetric L-band Multibeam Radiometer) is an airborne simulator of SMOS MIRAS that can measure passive microwave radiation of land surface at L-band( 1. 4 GHz) dual polarization and in three different angles( 7°,21. 5° and 38. 5°). This paper uses airborne PLMR radiometer data combined with MODIS LST( MOD11A1) and LAI( MOD15A2) products to retrieve surface soil moisture in the artificial oasis experimental area of HiWATER by L-MEB radiative transfer model and LM( Levenberg-Marquardt) optimization algorithm. The three retrieving strategies are tested,including single,two and three parameters selected from soil moisture,vegetation water content and surface roughness. The comparison analysis shows the multi-angle and dual-polarized PLMR brightness temperatures combined with prior information from operational remote sensing products can obviously reduce the uncertainty of retrieval process and improve the retrieval accuracy. This paper proves that with reasonable model parameters and retrieval method,the L-MEB model can achieve 0. 04 cm3/ cm3 accuracy requirement for soil moisture retrieval. This paper also reveals the importance of using wireless sensor network in the verification of remote sensing products.更多还原