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首页> 外文期刊>Journal of the American Water Resources Association >Soil Moisture Assessment Based on Multi-Source Remotely Sensed Data in the Huaihe River Basin, China
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Soil Moisture Assessment Based on Multi-Source Remotely Sensed Data in the Huaihe River Basin, China

机译:基于多源远程感测数据的土壤水分评估淮河盆地淮河流域数据

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The reliable estimation of soil moisture on spatial and temporal scales is of fundamental importance in improving the impact assessment of drought and flood on plant productivity and enhancing our understanding of the link between the water and biogeochemical cycles. Currently, remotely sensed data can offer a chance to improve spatial variability, especially in environments with scarce ground-based data. To obtain soil moisture information with high resolution and high precision in such mountainous areas as the Huaihe River Basin, we combined the advantages of MODerate-resolution Imaging Spectroradiometer (MODIS) optical remote sensing sensor that has high spatial resolution and sensitive characteristics to surface covering type and Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) passive microwave sensor that has high temporal resolution and the characteristics of small interference by clouds. Five years of AMSR-E and MODIS data (2006-2010) were fused based on the wavelet transform (WT) method. The soil moisture in the Huaihe River Basin was extracted using these fused remotely sensed data. The results showed that the soil moisture from AMSR-E and MODIS fusion data could capture soil moisture dynamics well. The accuracies of soil moisture from AMSR-E and MODIS fusion data based on WT were better than those from single remote sensing data. The soil moisture from MODIS and AMSR-E fusion data was more sensitive to the season, especially in spring, summer, and autumn. The accuracy of soil moisture from MODIS and AMSR-E fusion data in time and space varied in the Huaihe Basin. From the time series, high accuracies of soil moisture were observed in the spring of 2009 and in the spring and winter of 2010. From the spatial series, high accuracies of soil moisture were found in Zhumadian and Bozhou stations, and the worst accuracies were observed in Huaiyin and Shangqiu stations, which were primarily related to some factors such as local topography, vegetation coverage, and precipitation. These findings will provide interesting insights that can be useful for developing measures to prevent drought and flood disasters on a regional scale in China.
机译:空间和时间尺度对土壤水分的可靠估计是对改善干旱和洪水对植物生产力的影响评估以及加强我们对水与生物地球化学循环之间联系的影响的基本重要性。目前,远程感测数据可以提供有机会提高空间可变性,尤其是在具有稀缺基于地面数据的环境中。在淮河流域等山区以高分辨率和高精度获得土壤湿度信息,我们将中频分辨率成像光谱探测器(MODIS)光学遥感传感器的优点组合在具有高空间分辨率和敏感特性的表面覆盖型在地球观测系统(AMSR-E)被动微波传感器上的先进微波扫描辐射计,具有高时间分辨率和云间干扰的特点。五年的AMSR-E和MODIS数据(2006-2010)基于小波变换(WT)方法融合。利用这些融合的远程感测数据提取了淮河盆地的土壤水分。结果表明,来自AMSR-E和MODIS融合数据的土壤水分可能井井用水分动力学。基于WT的AMSR-E和MODIS融合数据的土壤水分的准确性优于来自单一遥感数据的水分。来自MODIS和AMSR-E融合数据的土壤水分对本赛季更敏感,特别是在春季,夏季和秋季。淮河盆地时间和空间中土壤水分和AMSR-E融合数据的准确性。从时间序列,在2009年春天和2010年春季和冬季观察到高精度的土壤水分。从空间系列,在驻众一和博州站发现了高精度的土壤水分,观察到最差的准确性在淮阴和商丘站,主要与局部地形,植被覆盖和降水等一些因素相关。这些调查结果将提供有趣的见解,可以有助于制定防止在中国区域规模上的干旱和洪水灾害的措施。

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