Evaluation of Multi-Source Soil Moisture Datasets over Central and Eastern Agricultural Area of China Using In Situ Monitoring Network

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Multi-source soil moisture (SM) products provide a vigorous tool for the estimation of soil moisture on a large scale, but it is crucial to carry out the evaluation of those products before further application. In the present work, an evaluation framework on multi-source SM datasets over central and eastern agricultural areas of China was firstly proposed, based on a dense in situ SM monitoring network of 838 stations from 11 July 2012 to 31 December 2017. Each station adopted the most accurate gravimetric method for measuring the actual soil moisture. The effects of land use types and wet–dry conditions on the performances of multi-source SM products were further analyzed. Most satellite/reanalysis SM products could capture the spatial–temporal changes in soil moisture, especially for ERA5 products that matched the closest to the station-measured SM; by contrast, those satellite products showed poor spatial–temporal performances. Such phenomenon was also quantitatively demonstrated by the four statistical metrics correlation coefficient (CC), p-value, bias and root mean squared error (RMSE) between the satellite/reanalysis SM products and the ground-observed SM series. Further, most satellite/reanalysis SM products had poor performances in Forestland and Grassland areas, with a lower CC and a larger positive bias and RMSE. Such overestimation on soil moisture is possibly influenced by the inestimable parameter vegetation geometry and the vegetation water content in the radiative transfer models. The arid areas showed the worst CC between the station-observed SM data and different satellite/reanalysis SM products; meanwhile, the humid and semi-arid areas presented larger SM estimation errors than the other areas, especially for the satellite products. The fairly dry surface soil (arid area) and open water surface contamination (humid area) are suggested to hinder the reading of microwave-based retrieval systems. Additionally, the reanalysis SM products outperformed the satellite SM products in the evaluated areas, with better spatial–temporal performances, seasonality reflection and higher accuracy on SM estimation (higher CC, and lower bias and RMSE). This is because the reanalysis datasets assimilated various sources of datasets, especially the ground-observed data, with high quality. The evaluated results could provide guidance for fusing different satellite/reanalysis products, as a new feasible alternative to monitoring SM information in the future.