Abstract As an important parameter in Land surface system research, surface soil moisture (SSM) links the surface water and groundwater that plays a key role in water resources, agricultural management and global warming studies. Remote sensing techniques provide a direct and convenient means to estimate SSM on a regional scale. In this study, the performance of the normalized land surface temperature-vegetation index (LST-VI) model was evaluated using the in situ soil moisture measurements at Hetao irrigation region of Inner Mongolia that is a representative semi-arid area with relatively uniform underlying surface. The model was used to estimate soil moisture from HJ-1B and Landsat 8 images on clear days in 2014–2017. The overall SSM estimation accuracy was high, and the average RMSE was approximately 0.04 m3/m3. Moreover, a systematic sensitivity analysis was conducted for the input parameters and other impact factors. The results demonstrated that the model could credibly monitor the regional surface soil water content.

中文翻译：

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内蒙古河套灌区遥感与Noah水文模型结合土壤水分估算的性能评价

摘要 作为地表系统研究的重要参数，地表土壤水分（SSM）将地表水和地下水联系起来，在水资源、农业管理和全球变暖研究中起着关键作用。遥感技术为在区域尺度上估计 SSM 提供了一种直接而方便的方法。在这项研究中，使用内蒙古河套灌区的原位土壤湿度测量来评估归一化地表温度 - 植被指数 (LST-VI) 模型的性能，河套灌区是具有相对均匀下垫面的代表性半干旱地区。 . 该模型用于估计 2014-2017 年晴天的 HJ-1B 和 Landsat 8 图像的土壤水分。整体 SSM 估计精度较高，平均 RMSE 约为 0.04 m3/m3。而且，对输入参数和其他影响因素进行了系统的敏感性分析。结果表明，该模型能够可靠地监测区域表层土壤含水量。