Evaluation of Multi-Source Soil Moisture Datasets over Central and Eastern Agricultural Area of China Using In Situ Monitoring Network
Evaluation of Multi-Source Soil Moisture Datasets over Central and Eastern Agricultural Area of China Using In Situ Monitoring Network
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DOI：
10.3390/rs13061175
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Yanqing Yang;Jianyun Zhang;Z. Bao;T. Ao;Guoqing Wang;Houfa Wu;Jie Wang
中科院分区：
文献类型：
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作者：
Yanqing Yang;Jianyun Zhang;Z. Bao;T. Ao;Guoqing Wang;Houfa Wu;Jie Wang
多源土壤湿度（SM）产品为大规模土壤湿度估算提供了强有力的工具，但在进一步应用之前对这些产品进行评估至关重要。本工作基于2012年7月11日至2017年12月31日838个站点的密集原位SM监测网络，首次提出了中国中东部农区多源SM数据集的评估框架。每个站点均采用最准确的重力法测量实际土壤湿度。进一步分析了土地利用类型和干湿条件对多源SM产品性能的影响。大多数卫星/再分析SM产品可以捕获土壤湿度的时空变化，尤其是与台站测量SM最匹配的ERA5产品；相比之下，这些卫星产品的时空性能较差。卫星/再分析SM产品与地面观测SM系列之间的四个统计指标相关系数（CC）、p值、偏差和均方根误差（RMSE）也定量地证明了这种现象。此外，大多数卫星/再分析SM产品在林地和草原地区的表现较差，CC较低，正偏差和RMSE较大。这种对土壤湿度的高估可能是受到辐射传输模型中不可估计的植被几何参数和植被含水量的影响。干旱地区的台站观测SM数据与不同卫星/再分析SM产品之间的CC最差；同时，湿润和半干旱地区的SM估计误差比其他地区更大，尤其是卫星产品。建议相当干燥的表层土壤（干旱地区）和开放水域表面污染（潮湿地区）阻碍基于微波的检索系统的读取。此外，在评估区域，再分析SM产品的性能优于卫星SM产品，具有更好的时空性能、季节性反映和更高的SM估计精度（更高的CC、更低的偏差和RMSE）。这是因为再分析数据集高质量地同化了各种数据集，特别是地面观测数据。评估结果可以为融合不同的卫星/再分析产品提供指导，作为未来监测SM信息的新的可行替代方案。
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.