ESA CCI SM products have provided remotely-sensed surface soil moisture (SSM) content with the best spatial and temporal coverage thus far, although its output spatial resolution of 25 km is too coarse for many regional and local applications. The downscaling methodology presented in this paper improves ESA CCI SM spatial resolution to 1 km using two-step approach. The first step is used as a data engineering tool and its output is used as an input for the Random forest model in the second step. In addition to improvements in terms of spatial resolution, the approach also considers the problem of data gaps. The filling of these gaps is the initial step of the procedure, which in the end produces a continuous product in both temporal and spatial domains. The methodology uses combined active and passive ESA CCI SM products in addition to in situ soil moisture observations and the set of auxiliary downscaling predictors. The research tested several variants of Random forest models to determine the best combination of ESA CCI SM products. The conclusion is that synergic use of all ESA CCI SM products together with the auxiliary datasets in the downscaling procedure provides better results than using just one type of ESA CCI SM product alone. The methodology was applied for obtaining SSM maps for the area of California, USA during 2016. The accuracy of tested models was validated using five-fold cross-validation against in situ data and the best variation of model achieved RMSE, R2 and MAE of 0.0518 m3/m3, 0.7312 and 0.0374 m3/m3, respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary.

中文翻译：

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使用ESA CCI SM产品的新型降尺度方法以获得高分辨率表层土壤水分

迄今为止，ESA CCI SM产品为遥感地表土壤水分（SSM）含量提供了最佳的时空覆盖范围，尽管其25 km的输出空间分辨率对于许多区域和本地应用而言过于粗糙。本文提出的降级方法使用两步法将ESA CCI SM空间分辨率提高到1 km。第一步用作数据工程工具，第二步将其输出用作随机森林模型的输入。除了改善空间分辨率外，该方法还考虑了数据缺口的问题。填补这些空白是程序的第一步，最后在时域和空域都产生连续的产物。该方法除了现场土壤湿度观测和辅助降尺度预测器集外，还使用主动和被动ESA CCI SM组合产品。该研究测试了随机森林模型的几种变体，以确定ESA CCI SM产品的最佳组合。结论是，与仅使用一种ESA CCI SM产品相比，在缩减规模过程中协同使用所有ESA CCI SM产品和辅助数据集可提供更好的结果。该方法已用于获取2016年美国加利福尼亚州地区的SSM地图。使用针对原位数据的五次交叉验证对测试模型的准确性进行了验证，并且模型的最佳变异实现了RMSE，R2和MAE为0.0518 m3 / m3、0.7312和0.0374 m3 / m3。