Thermal infrared remote sensing have been extensively applied to estimate global- or regional- extent surface soil moisture. Meanwhile, potentials of the remotely sensed data for farm-scale retrieval of root zone soil moisture (RZSM) as well as estimation of soil hydraulic parameters, have been rarely investigated. Using Ensemble Kalman Filter, we propose a new methodology to assimilate land surface temperature (LST) of both MODIS and LANDSAT-8, into the soil temperature module of HYDRUS-1D model. The main objectives are to estimate soil hydraulic parameters and to retrieve RZSM with high spatiotemporal resolution independent of any in-situ measurements of soil temperature or moisture. However, we consider some modeling scenarios by which we assimilate in-situ measurements of soil moisture into the soil moisture module of the HYDRUS-1D model to provide a reference to compare with results of the LST assimilation scenarios. We apply the proposed methodology to a farm located in Moghan irrigation district, Ardabil province of Iran, which has in-situ soil moisture measurements. Even in the least accurate scenario of ours by which MODIS-LST was assimilated, RMSE varies in the range of 0.012–0.013 cm³·cm⁻³ demonstrated to be superior compared to preceding recent works in the literature of satellite soil moisture retrieval. Moreover, the scenario of assimilating LANDSAT-LST data leads to higher parameter uncertainty compared to the assimilation of solely in-situ soil moisture or MODIS-LST which is related to higher temporal resolution of both in-situ and MODIS data compared to LANDSAT data and the error stems from the algorithm of deriving LANDSAT-LST. Accordingly, our study recommend that assimilation of the satellite-based land surface temperature of both LANDSAT-8 and MODIS are appropriate alternatives for expensive in-situ measurement.

热红外遥感已广泛应用于估计全球或区域范围的地表土壤水分。同时，很少研究遥感数据在农场规模反演根区土壤水分（RZSM）以及估计土壤水力参数方面的潜力。使用集成卡尔曼滤波器，我们提出了一种将 MODIS 和 LANDSAT-8 的地表温度 (LST) 同化到 HYDRUS-1D 模型的土壤温度模块中的新方法。主要目标是估计土壤水力参数并以高时空分辨率检索 RZSM，而与土壤温度或湿度的任何原位测量无关。然而，我们考虑了一些模拟情景，通过这些模拟情景，我们将土壤水分的原位测量值同化到 HYDRUS-1D 模型的土壤水分模块中，以提供与 LST 同化情景结果进行比较的参考。我们将建议的方法应用于位于伊朗阿尔达比勒省 Moghan 灌溉区的农场，该农场具有现场土壤湿度测量。即使在我们最不准确的情况下，MODIS-LST 被同化，RMSE 在 0.012–0.013 cm 的范围内变化³ ·cm ^-3被证明比卫星土壤水分反演文献中的近期工作更优越。此外，与仅同化原位土壤水分或 MODIS-LST 相比，同化 LANDSAT-LST 数据的方案导致更高的参数不确定性，这与与 LANDSAT 数据相比，原位和 MODIS 数据具有更高的时间分辨率和错误源于导出 LANDSAT-LST 的算法。因此，我们的研究建议将 LANDSAT-8 和 MODIS 的基于卫星的地表温度同化是昂贵的原位测量的合适替代方案。