Soil moisture (SM) governs the exchange of energy and water between the atmosphere and land surface. In situ measurements of SM are uneven in Iran. This knowledge gap can be filled using satellite- and model-based products. This study assessed the performance of SM products, including Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer (AMSR2), and Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (CLSM) against in situ observations considering the influence of soil texture, climate, and land cover over Lake Urmia Basin, which is the largest salt lake in Iran and the Middle East. In situ SM was measured over Lake Urmia Basin in the morning and afternoon using the time domain reflectometry (TDR) and oven drying and weighing techniques. Five statistical indicators, including correlation (R), absolute correlation (R(abs)), bias, root mean square error (RMSE), and unbiased root mean square error (ubRMSE), were applied. C-band AMSR2 products showed the best performance in grassland and croplands with the highest absolute correlation (0.63) and lowest average bias (−0.01). Among soil textures, SM products performed better in clay soils with the highest absolute correlation between C-band AMSR2 products and in situ observations (0.64) and low average bias and RMSE. Analyzing data based on climate, AMSR2 C1, and GLDAS products with the lowest average RMSE (0.08 m³m⁻³) and bias (0.01) and AMSR2 C2 with the absolute correlation of 0.6 showed the best performance in both temperate (Csa) and cold (Dsa) climate classes. For all classifications (land cover, soil texture, climate divisions), SMAP products reported the lowest average value of ubRMSE (0.03 m³m⁻³). The major contribution of the paper is finding the best SM products that can fill the gap in SM measurements data in Lake Urmia. In this analysis, the impacts of land cover, climate, and soil texture on the performance of products were considered.

土壤水分 (SM) 控制着大气和地表之间的能量和水的交换。在伊朗，SM 的原位测量是不均匀的。可以使用基于卫星和模型的产品来填补这一知识空白。本研究评估了 SM 产品的性能，包括土壤水分主动被动 (SMAP)、高级微波扫描辐射计 (AMSR2) 和全球陆地数据同化系统 (GLDAS) 集水区地表模型 (CLSM) 与原位观测的对比，考虑到乌尔米亚湖盆地的土壤质地、气候和土地覆盖，这是伊朗和中东最大的盐湖。使用时域反射计 (TDR) 和烘箱干燥和称重技术，在上午和下午对乌尔米亚湖盆地的原位 SM 进行了测量。五个统计指标，包括相关性（R），应用了绝对相关性 (R(abs))、偏差、均方根误差 (RMSE) 和无偏均方根误差 (ubRMSE)。C波段AMSR2产品在草地和农田中表现最好，绝对相关性最高（0.63），平均偏差最低（-0.01）。在土壤质地中，SM 产品在粘土中表现更好，C 波段 AMSR2 产品与原位观测值之间的绝对相关性最高 (0.64)，平均偏差和 RMSE 较低。基于气候、AMSR2 C1 和 GLDAS 产品的数据分析，平均 RMSE 最低（0.08 m 在土壤质地中，SM 产品在粘土中表现更好，C 波段 AMSR2 产品与原位观测值之间的绝对相关性最高 (0.64)，平均偏差和 RMSE 较低。基于气候、AMSR2 C1 和 GLDAS 产品的数据分析，平均 RMSE 最低（0.08 m 在土壤质地中，SM 产品在粘土中表现更好，C 波段 AMSR2 产品与原位观测值之间的绝对相关性最高 (0.64)，平均偏差和 RMSE 较低。基于气候、AMSR2 C1 和 GLDAS 产品的数据分析，平均 RMSE 最低（0.08 m³ m ^-3 ) 和偏差 (0.01) 以及绝对相关性为 0.6 的 AMSR2 C2 在温带 (Csa) 和寒冷 (Dsa) 气候类别中均表现出最佳性能。对于所有分类（土地覆盖、土壤质地、气候划分），SMAP 产品报告的 ubRMSE 平均值最低（0.03 m ³ m ^-3）。该论文的主要贡献是找到了可以填补乌尔米亚湖 SM 测量数据空白的最佳 SM 产品。在此分析中，考虑了土地覆盖、气候和土壤质地对产品性能的影响。