New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

Abstract

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, R 2 and MAE of 0.0518 m 3 /m 3 , 0.7312 and 0.0374 m 3 /m 3 , respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary.

Sensitive Feature Evaluation for Soil Moisture Retrieval Based on Multi-Source Remote Sensing Data with Few In-Situ Measurements: A Case Study of the Continental U.S.

“…Soil moisture (SM) is usually defined as a volume of water stored within the unsaturated zone [1,2], and surface (0-5 cm) SM is an important variable associated with global terrestrial water, energy, and carbon cycles [3,4]. Therefore, it is necessary to obtain accurate and timely SM data.…”

A Semi-Physical Approach for Downscaling Satellite Soil Moisture Data in a Typical Cold Alpine Area, Northwest China

“…However, in large areas with complex underlying surfaces where SM is jointly controlled by many factors, the statistical relationship between SM and any single environmental variable is subject to great spatiotemporal heterogeneity, limiting their applicability in those areas. In recent years, with advances in computer techniques, machine learning has been more often employed in SM downscaling studies [31][32][33]. The performance of using machine learning appears to be satisfactory when ample training samples are available.…”

Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

“…The spatial resolution of the ESA CCI SM data is too coarse for some applications and downscaling techniques have been explored to obtain a finer spatial resolution (e.g., [42]). Several validation/prediction exercises of the ESA CCI SM data [26,[41][42][43][44][45] have been done since the product was made available, for example, in drought studies [36,46], land-atmosphere interactions studies [47], climate trend analyses [40,48], detection of irrigated areas [49] and crop growth modelling [50]. Enlarging the geographical distribution of studies by applying the product certainly helps to widen the validation of SM estimates [51], especially in regions where the existing network of reference ground stations is sparse, as is the case in the western Iberian Peninsula [43].…”