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土壤水分是水文循环中的关键因素,尤其对旱区的生态环境具有十分重要的意义。微波遥感是反演土壤水分的有效手段,而植被是影响土壤水分反演精度的重要因素。因此,对土壤水分的反演需要考虑植被的影响。本文以内蒙古乌审旗为研究区,利用Radarsat-2雷达数据与TM光学数据,对旱区稀疏植被覆盖地表土壤水分反演进行研究。利用TM数据,分别选取NDVI和NDWI指数对植被含水量进行反演,通过水云模型消除植被层对土壤后向散射系数的影响;在此基础上,根据研究区地表植被特性,提出一种基于AIEM模型的反演土壤水分的改进算法,反演了不同粗糙度参数、不同极化(VV极化和HH极化)条件下的研究区土壤水分。反演结果与野外实测数据的对比结果表明,本文提出的基于地表植被特性的土壤水分改进算法,具有更好的适应性;土壤水分反演模式Mv lhσvv(1)(VV极化方式下采用NDVI去除植被影响的反演模式)更适合于旱区考虑稀疏植被覆盖影响的地表土壤水分的反演。
Soil moisture is a key factor in the hydrological cycle, especially for the ecological environment in arid areas. Microwave remote sensing is an effective method to invert soil moisture, and vegetation is an important factor affecting the accuracy of soil moisture retrieval. Therefore, the inversion of soil moisture needs to consider the influence of vegetation. In this paper, Wushen Banner of Inner Mongolia as a research area, the use of Radarsat-2 radar data and TM optical data, arid area sparse vegetation cover surface soil moisture inversion study. Using TM data, the NDVI and NDWI indices were selected respectively to retrieve the vegetation water content, and the water cloud model was used to eliminate the influence of the vegetation layer on the soil backscattering coefficient. Based on the characteristics of the surface vegetation in the study area, AIEM model to retrieve soil moisture. The soil moisture of the study area under different polarization parameters (VV polarization and HH polarization) was retrieved. The comparison between the inversion results and the field data shows that the proposed soil moisture improvement algorithm based on the characteristics of the surface vegetation has a better adaptability. The soil moisture inversion model Mv lhσvv (1) (NDVI Inversion model to remove vegetation influence) is more suitable for the inversion of surface soil moisture that considers the influence of sparse vegetation cover in arid areas.