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Article

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

Faculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia
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Remote Sens. 2020, 12(7), 1119; https://doi.org/10.3390/rs12071119
Received: 18 February 2020 / Revised: 28 March 2020 / Accepted: 30 March 2020 / Published: 1 April 2020
(This article belongs to the Special Issue Remote Sensing of Regional Soil Moisture)
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. View Full-Text
Keywords: soil moisture; downscaling; random forest; ESA CCI SM soil moisture; downscaling; random forest; ESA CCI SM
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MDPI and ACS Style

Kovačević, J.; Cvijetinović, Ž.; Stančić, N.; Brodić, N.; Mihajlović, D. New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture. Remote Sens. 2020, 12, 1119. https://doi.org/10.3390/rs12071119

AMA Style

Kovačević J, Cvijetinović Ž, Stančić N, Brodić N, Mihajlović D. New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture. Remote Sensing. 2020; 12(7):1119. https://doi.org/10.3390/rs12071119

Chicago/Turabian Style

Kovačević, Jovan, Željko Cvijetinović, Nikola Stančić, Nenad Brodić, and Dragan Mihajlović. 2020. "New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture" Remote Sensing 12, no. 7: 1119. https://doi.org/10.3390/rs12071119

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