This study evaluates the potential of AMSR2 (Advance Microwave Scanning Radiometer2) data for the estimation of Volumetric Soil Moisture (VSM) for bare and agricultural areas. At the first step, the sensitivity of the Microwave Polarization Difference Index (MPDI) to variations in soil and vegetation characteristics were examined at different frequencies. At lower frequencies, the signal attenuation due to vegetation is minimal and thus, denser vegetation usually depolarizes the soil emission. Interestingly, the results also reveal that at higher frequencies, the sensitivity of V and H polarizations over relatively dense vegetation covers is not the same at all. Therefore, MPDI at both low and high frequencies can be a good indicator of the soil moisture and Vegetation Water Content (VWC), respectively. After evaluation of AMSR2 datasets, a model called Multi-channel/MPDI-based Land Parameters Retrieval Model (MMLPRM) is proposed. The MMLPRM optimizes optical depth of vegetation and soil dielectric constant, with simultaneous retrieval of soil moisture and surface temperature by using the AMSR2 brightness temperature data. This algorithm also includes the surface roughness parameters to increase the soil moisture retrieval efficiency. In this way, calibration and validation have been done, using in situ observations of 50 monitoring stations obtained from the International Soil Moisture Network (ISMN) over the United States. Consequently, the analysis on the MMLPRM retrieval model demonstrates its potential and usefulness for soil moisture retrieval. The outcome of this study will help in estimating the accurate soil moisture to optimize the irrigation management strategies and help in water conservation.

这项研究评估了AMSR2（高级微波扫描辐射计2）数据在估算裸露和农业地区的土壤含水量（VSM）方面的潜力。第一步，在不同频率下检查了微波极化差异指数（MPDI）对土壤和植被特征变化的敏感性。在较低的频率下，由于植被引起的信号衰减极小，因此，密度较大的植被通常会使土壤辐射消极化。有趣的是，结果还表明，在较高的频率下，相对致密的植被覆盖层上的V和H极化的敏感性根本不相同。因此，低频和高频下的MPDI分别可以很好地指示土壤湿度和植被含水量（VWC）。在评估了AMSR2数据集之后，提出了一种基于多通道/ MPDI的土地参数检索模型（MMLPRM）。MMLPRM通过使用AMSR2亮度温度数据优化植被的光学深度和土壤介电常数，同时检索土壤水分和地表温度。该算法还包括表面粗糙度参数，以提高土壤水分的回收效率。通过这种方式，使用从美国国际土壤水分网络（ISMN）获得的50个监测站​​的现场观测结果，进行了校准和验证。因此，对MMLPRM检索模型的分析证明了其在土壤水分检索中的潜力和实用性。