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dc.creatorKovačević, Jovan
dc.creatorCvijetinović, Željko
dc.creatorStančić, Nikola
dc.creatorBrodić, Nenad
dc.creatorMihajlović, Dragan
dc.date.accessioned2020-04-14T15:48:37Z
dc.date.available2020-04-14T15:48:37Z
dc.date.issued2020
dc.identifier.issn2072-4292
dc.identifier.urihttp://grafar.grf.bg.ac.rs/handle/123456789/1908
dc.description.abstractESA 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.en
dc.language.isoensr
dc.publisherMDPIsr
dc.relationMinistry of Education, Science and Technological Development of Serbia, project TR 36020sr
dc.rightsopenAccesssr
dc.sourceRemote Sensingsr
dc.subjectsoil moisturesr
dc.subjectdownscalingsr
dc.subjectrandom forestsr
dc.subjectESA CCI SMsr
dc.titleNew Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisturesr
dc.typearticlesr
dc.rights.licenseARRsr
dc.citation.issue20
dc.citation.rankM21
dc.citation.spage1119
dc.citation.volume12
dc.identifier.doi10.3390/rs12071119
dc.identifier.fulltexthttp://grafar.grf.bg.ac.rs/bitstream/id/7326/remotesensing-12-01119-v2.pdf
dc.identifier.scopus2-s2.0-85084250268
dc.identifier.wos000537709600069
dc.type.versionpublishedVersionsr


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