SMAP, RS-DTVGM, and in-situ monitoring: Which performs best in presenting the soil moisture in the middle-high latitude frozen area in the Sanjiang Plain, China?
SMAP, RS-DTVGM, and in-situ monitoring: Which performs best in presenting the soil moisture in the middle-high latitude frozen area in the Sanjiang Plain, China?
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SMAP、RS-DTVGM和原位监测：哪个最能呈现中国三江平原中高纬度冻区土壤湿度？
DOI：
10.1016/j.jhydrol.2018.12.023
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发表时间：
2019-04
影响因子：
6.4
通讯作者：
王志伟
中科院分区：
文献类型：
--
作者：
娄和震;杨胜天;郝芳华;蒋玲梅;赵长森;任霄玉;王玥;王志伟
土壤水分是占全球陆地面积约30%的中高纬冻土区的核心生态水文要素。三江平原是我国中高纬度冰冻区最大的沼泽低平原，是重要的商品粮产区。然而，没有发表的研究表明，该地区的土壤水分数据的准确性，从现场测量;生态水文模型的性能也是未知的。采用三重配置法，比较了SMAP产品、RS-DTVGM模型和两个自建土壤水分监测站对三江平原土壤水分信息的获取能力。连续采集642 d的土壤水分数据，数据期分为3个冻结期（FT）和2个非冻结期（NFT）。计算并比较了三种方法的有效数据提供率。从三重配置方法，均方根误差（RMSE）的SMAP，模型和现场监测数据集分别为0.03，0.08和0.05。SMAP产品在FT和NFT期间的有效数据提供率分别为27.1%和57.5%。SMAP产品和原位监测数据集之间的Pearson相关系数为0.301（FT期间）和0.557（NFT期间）。RS-DTVGM模型的数据提供率为100%。该模型的Pearson相关系数在FT期间为0.101，但在NFT期间达到0.726。在中高纬度地区，三种土壤水分数据集的精度排序为SMAP >现场监测>模式模拟。SMAP和RS-DTVGM模型虽然不能很好地反映FT期土壤水分的真实的信息，但都能较好地模拟NFT期的土壤水分。相对于SMAP方法，RS-DTVGM模型的结果与土壤水分实测数据的关系更密切。原位法能提供准确的土壤水分数据，但成本高限制了其应用。未来，这三种方法的数据应该被同化在一起，以测量土壤水分数据的高精度和高时空分辨率在这个重要的地区。
Soil moisture is a core ecohydrological element in the middle–high latitude frozen area, which occupies approximately 30% of the global land area. The Sanjiang Plain, an important commercial grain production area, is the largest swampy low plain in a middle-high latitude frozen area in China. However, no published studies on this region have clarified the accuracy of soil moisture data from in-situ measurements; the performance of ecohydrological models are also unknown. In this study, we compared the ability of the SMAP product, a RS-DTVGM model, and two self-erected soil moisture monitoring stations to capture soil moisture information in the Sanjiang Plain by using the Triple Collocation method. Soil moisture data were collected for 642 continuous days, and the data period was divided into three frozen periods (FT) and two non-frozen (NFT) periods. Valid data supply rates of the three methods were also calculated and compared. From the Triple Collocation method, the root mean square error (RMSE) of the SMAP, model and in-situ monitoring data sets were 0.03, 0.08, and 0.05. The valid data supply rates of the SMAP product in the FT and NFT periods were 27.1% and 57.5%, respectively. The Pearson correlation coefficient between the SMAP product and the in-situ monitoring dataset was 0.301 for the FT period and 0.557 for the NFT period. The RS-DTVGM model had a data supply rate of 100%. The Pearson correlation coefficient for this model was 0.101 for the FT period, but reached 0.726 for the NFT period. For the three soil moisture data sets, the accuracy ranking in this middle-high latitude area was SMAP > in-situ monitoring > model simulation. Although neither SMAP nor the RS-DTVGM model could capture the real soil moisture information for the FT period, both could accurately simulate the soil moisture in the NFT period. The results from the RS-DTVGM model demonstrated a closer relationship with in-situ soil moisture measurement data relative to the SMAP method. The in-situ method provided accurate soil moisture data, but its application is limited by high cost. In the future, the data from the three methods should be assimilated together to measure soil moisture data with high accuracy and high spatial and temporal resolution in this important area.
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