An improved surface soil moisture downscaling approach over cloudy areas based on geographically weighted regression
An improved surface soil moisture downscaling approach over cloudy areas based on geographically weighted regression
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DOI：
10.1016/j.agrformet.2019.05.022
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发表时间：
2019-09
影响因子：
6.2
通讯作者：
P. Song;Jingfeng Huang;Lamin R. Mansaray
中科院分区：
文献类型：
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作者：
P. Song;Jingfeng Huang;Lamin R. Mansaray
本文提出了一种利用MODIS LST/NDVI数据对AMSR-2产品在多云地区进行降尺度的方法框架。试验在中国长江和淮河中下游43万km 2的相对较大的区域内进行，该区域具有潮湿的气候和频繁的多云天气条件。针对该地区云干扰导致的MODIS LST像元损失严重的问题，初步采用有效的LST插值方法获得了准全覆盖的逐日LST数据集。当内插的LST数据集与参考LST数据集进行验证时，得到了在1.5 K-3.5 K范围内的相当小的RMSE，该参考LST数据集是根据晴天LST与地面近地表气温之间的观测关系建立的。然后，AMSR-2 SSM和空间重采样的MODIS数据集之间建立回归方程，使用“地理加权回归（GWR）”来实现SSM降尺度过程。SSM估计缩小规模的GWR为基础的方法表现出更好的性能比那些缩小规模的传统的“通用三角形特征（UTF）”为基础的方法，鉴于他们的“非偏置RMSE（ubRMSE）"，相关系数和平均偏差相对于地面土壤水分验证数据。SSM估计从MODIS的LST输入和那些从插值LST输入进行了比较，他们表明，SSM估计缩小插值LST输入的表现只是略差（与一个ubRMSE差异不大于0.02 cm 3/cm 3）比那些由MODIS数据。时间序列分析进一步表明，基于GWR的缩小尺度SSM估计与重建的LST数据输入与地面土壤水分的变化相一致，除了极高的植被覆盖或低温地区。因此，在这项研究中提出的框架被证明是可行的可靠的缩小规模的高空间分辨率SSM估计，在多云天气条件下，减少像素丢失的一个重要应用程序的推导。
This study proposed a methodological framework for downscaling AMSR-2 surface soil moisture (SSM) products over cloudy areas using MODIS LST/NDVI datasets. The experiment was conducted in a relatively large area of 430,000 km2in the middle and lower reaches of the Yangtze and Huaihe rivers in China, which is characterized by humid climate and frequent cloudy weather conditions. As MODIS LSTs suffer from serious pixel loss due to cloud interference in this area, an effective LST interpolation method was preliminarily applied to achieve daily LST datasets with quasi-full covers. And rather small RMSEs in the range 1.5 K–3.5 K were obtained when the interpolated LST datasets were validated against a reference LST dataset built from observed relationships between LST and ground-based near-surface air temperatures on clear sky days. A regression equation was then established between AMSR-2 SSM and spatially resampled MODIS datasets using “Geographically Weighted Regression (GWR)” to implement the SSM downscaling process. SSM estimates downscaled by the GWR-based method showed a better performance over those downscaled by the traditional “universal triangle feature (UTF)” based method in view of their “non-biasedRMSEs(ubRMSEs)”, correlation coefficients, and mean biases with respect to ground-based soil moisture validation data. Comparisons between SSM estimates from MODIS LST inputs and those from interpolated LST inputs were conducted, and they showed that the SSM estimates downscaled by interpolated LST inputs performed only slightly poorer (with anubRMSEdifference no larger than 0.02 cm3/cm3) than those by MODIS data. Time series analysis further showed that the GWR-based downscaled SSM estimates with reconstructed LST data inputs are in phase with the variation in ground-based soil moisture with the exception of areas of extremely high vegetation cover or low temperatures. The framework proposed in this study thus proved feasible for the derivation of reliable downscaled high spatial resolution SSM estimates, an essential application in mitigating pixel loss under cloudy weather conditions.