为了解不同土壤湿度产品在云南省的适用性和可靠性，基于云南省的94个站点数据以及TC(triple-collocation)方法评估5种不同土壤湿度数据在云南省的适用性及不同干湿条件下的表现，包括ERA5-Land（the fifth global atmospheric analysis dataset for the land component of the European Centre for Medium-Range Weather Forecasts（ECMWF））、GLDAS（Global Land Data Assimilation System）、SMAP（Soil Moisture Active and Passive）、MERRA-2（Modern-Era Retrospective Analysis for Research and Applications, Version 2）和ESA CCI（European Space Agency Climate Change Initiative）。结果表明：与站点数据相比，5个产品的偏差均为正偏差（0.090~0.122），明显高估了云南省的土壤湿度，但变化趋势与幅度一致，都能捕获到土壤湿度的时间变化。基于站点数据的评估结果显示：在年尺度上，ERA5-Land和SMAP与站点数据吻合程度最高，相关系数（R）分别为0.456和0.454，其次是ESA CCI（0.439）；干湿季的评估结果显示，所有产品相关性均低于年尺度，且湿季高于干季，但湿季表现出更大的正偏差，其中SMAP在干季（0.323）和湿季(0.418)均表现最优。基于TC方法的评估结果显示：ERA5-Land（0.925）和ESA CCI（0.931）相关性最高；其次是GLDAS（0.890）和MERRA-2（0.864）；干湿季的评估结果与站点数据的评估一致，相较于年尺度大部分产品的相关性也呈下降趋势，且干季降幅更大；SMAP干湿季R分别为0.828和0.770，表现最差；MERRA-2湿季的R（0.912）和ESD（error standard deviation）（0.020）优于其年尺度评估结果。综合来看，ESA CCI相关性较高且精度最好，更适用于云南省表层土壤湿度的监测。

Soil moisture is an important variable for vegetation growth, drought monitoring and agricultural water management, controlling water, energy and carbon exchange processes. Although traditionally observed soil moisture is highly accurate, it is difficult to obtain and spatially poorly representative; fortunately, with the development of satellites and reanalysis data, the difficulty of insufficient observed soil moisture data has been alleviated. As an indirect estimate of soil moisture, satellite and reanalysis data inevitably produce spatio-temporal systematic biases and random errors, especially in a region with complex climate and topography such as Yunnan Province, the applicability of these soil moisture products is still unclear, so it is necessary to assess the quality of these products.This study aimed to evaluate the applicability of five different soil moisture data in Yunnan Province and their performance under different wet and dry conditions, including ERA5-Land, GLDAS, SMAP, MERRA-2, and ESA CCI, were evaluated based on in-situ data and the triple-collocation (TC) in Yunnan Province. The results show that: (1) compared with the site data, the deviations of the five products were all positive (0.090-0.122), which significantly overestimated soil moisture in Yunnan Province, but the trends and magnitudes of the changes were consistent, and all of them were able to capture the temporal changes of soil moisture; (2) the assessment results based on the in-situ data showed that, on the yearly scale, ERA5-Land (0.456) and SMAP (0.454) matched the site data the highest, followed by ESA CCI (0.439); the assessment results of dry and wet seasons showed that all product correlations were lower than the annual scale, and the wet season was higher than the dry season, but the wet season showed a greater positive bias, with SMAP performing optimally in both the dry season (0.323) and the wet season (0.418); and (3) the results of the assessment based on the TC method showed that the correlations of ERA5-Land (0.925) and ESA CCI (0.931) had the highest correlation, followed by GLDAS (0.890) and MERRA-2 (0.864), the assessment results of the dry and wet seasons were consistent with the assessment of the site data, and the correlation of most of the products also showed a decreasing trend in comparison with the annual scale with a larger decrease in the dry season; and the R for SMAP dry and wet seasons were 0.828 and 0.770, respectively, which were the worst performers in both cases; The R (0.912) and ESD (0.020) of MERRA-2 in the wet season were better than its annual scale assessment. Taken together, ESA CCI has higher correlation and best accuracy, and is more suitable for monitoring surface soil moisture in Yunnan Province.The assessment results based on in-situ data and the TC method exhibited similarities but also differences. Both methods indicated that ERA5-Land and ESA CCI performed well across both assessments. Additionally, the performance of different products varied between the dry and wet seasons. However, there was a discrepancy in the performance of SMAP and MERRA-2 between the two assessment methods. In the site-based assessment, SMAP demonstrated high accuracy while MERRA-2 showed lower accuracy. Conversely, in the TC-based assessment, SMAP performed the poorest while MERRA-2 exhibited improved performance. Integrating both assessment methods enhanced our understanding of the accuracy and applicability of the different soil moisture products.