New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface 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 impr...

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Main Authors: Jovan Kovačević, Željko Cvijetinović, Nikola Stančić, Nenad Brodić, Dragan Mihajlović
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/12/7/1119
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spelling doaj-beffe3c548924251a2221191e024a6412020-11-25T03:49:28ZengMDPI AGRemote Sensing2072-42922020-04-01121119111910.3390/rs12071119New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil MoistureJovan Kovačević0Željko Cvijetinović1Nikola Stančić2Nenad Brodić3Dragan Mihajlović4Faculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, SerbiaFaculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, SerbiaFaculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, SerbiaFaculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, SerbiaFaculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, SerbiaESA 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<sup>2</sup> and MAE of 0.0518 m<sup>3</sup>/m<sup>3</sup>, 0.7312 and 0.0374 m<sup>3</sup>/m<sup>3</sup>, respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary.https://www.mdpi.com/2072-4292/12/7/1119soil moisturedownscalingrandom forestESA CCI SM
collection DOAJ
language English
format Article
sources DOAJ
author Jovan Kovačević
Željko Cvijetinović
Nikola Stančić
Nenad Brodić
Dragan Mihajlović
spellingShingle Jovan Kovačević
Željko Cvijetinović
Nikola Stančić
Nenad Brodić
Dragan Mihajlović
New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture
Remote Sensing
soil moisture
downscaling
random forest
ESA CCI SM
author_facet Jovan Kovačević
Željko Cvijetinović
Nikola Stančić
Nenad Brodić
Dragan Mihajlović
author_sort Jovan Kovačević
title New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture
title_short New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture
title_full New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture
title_fullStr New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture
title_full_unstemmed New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture
title_sort new downscaling approach using esa cci sm products for obtaining high resolution surface soil moisture
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2020-04-01
description 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<sup>2</sup> and MAE of 0.0518 m<sup>3</sup>/m<sup>3</sup>, 0.7312 and 0.0374 m<sup>3</sup>/m<sup>3</sup>, respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary.
topic soil moisture
downscaling
random forest
ESA CCI SM
url https://www.mdpi.com/2072-4292/12/7/1119
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