New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture
Апстракт
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 dete...rmine 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.
Кључне речи:
soil moisture / downscaling / random forest / ESA CCI SMИзвор:
Remote Sensing, 2020, 12, 20, 1119-Издавач:
- MDPI
Финансирање / пројекти:
- Ministry of Education, Science and Technological Development of Serbia, project TR 36020
DOI: 10.3390/rs12071119
ISSN: 2072-4292
WoS: 000537709600069
Scopus: 2-s2.0-85084250268
Институција/група
GraFarTY - JOUR AU - Kovačević, Jovan AU - Cvijetinović, Željko AU - Stančić, Nikola AU - Brodić, Nenad AU - Mihajlović, Dragan PY - 2020 UR - https://grafar.grf.bg.ac.rs/handle/123456789/1908 AB - 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. PB - MDPI T2 - Remote Sensing T1 - New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture IS - 20 SP - 1119 VL - 12 DO - 10.3390/rs12071119 ER -
@article{ author = "Kovačević, Jovan and Cvijetinović, Željko and Stančić, Nikola and Brodić, Nenad and Mihajlović, Dragan", year = "2020", 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, 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.", publisher = "MDPI", journal = "Remote Sensing", title = "New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture", number = "20", pages = "1119", volume = "12", doi = "10.3390/rs12071119" }
Kovačević, J., Cvijetinović, Ž., Stančić, N., Brodić, N.,& Mihajlović, D.. (2020). New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture. in Remote Sensing MDPI., 12(20), 1119. https://doi.org/10.3390/rs12071119
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. in Remote Sensing. 2020;12(20):1119. doi:10.3390/rs12071119 .
Kovačević, Jovan, Cvijetinović, Željko, Stančić, Nikola, Brodić, Nenad, Mihajlović, Dragan, "New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture" in Remote Sensing, 12, no. 20 (2020):1119, https://doi.org/10.3390/rs12071119 . .