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Regionalizing root-zone soil moisture estimates from ESA CCI Soil Water Index using machine learning and information on soil, vegetation, and climate

Gryllakis Emmanouil, Koutroulis Aristeidis, Alexakis Dimitrios D., Polykretis Christos, Daliakopoulos Ioannis

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/61BC4798-8F38-4042-86D8-5EFEF7A6E563-
Αναγνωριστικόhttps://doi.org/10.1029/2020WR029249-
Αναγνωριστικόhttps://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020WR029249-
Γλώσσαen-
Μέγεθος22 pagesen
ΤίτλοςRegionalizing root-zone soil moisture estimates from ESA CCI Soil Water Index using machine learning and information on soil, vegetation, and climateen
ΔημιουργόςGryllakis Emmanouilen
ΔημιουργόςΓρυλλακης Εμμανουηλel
ΔημιουργόςKoutroulis Aristeidisen
ΔημιουργόςΚουτρουλης Αριστειδηςel
ΔημιουργόςAlexakis Dimitrios D.en
ΔημιουργόςPolykretis Christosen
ΔημιουργόςDaliakopoulos Ioannisen
ΔημιουργόςΔαλιακοπουλος Ιωαννηςel
ΕκδότηςAmerican Geophysical Unionen
ΠερίληψηThe European Space Agency (ESA), through the Climate Change Initiative (CCI), is currently providing nearly 4 decades of global satellite-observed, fully homogenized soil moisture data for the uppermost 2–5 cm of the soil layer. These data are valuable as they comprise one of the most complete remotely sensed soil moisture data sets available in time and space. One main limitation of the ESA CCI soil moisture data set is the limited soil depth at which the moisture content is represented. In order to address this critical gap, we (a) estimate and calibrate the Soil Water Index using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then (b) leverage machine learning techniques and physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration. We use this calibration to assess the root-zone soil moisture for the period 2001–2018. The results are compared against the European Centre for Medium-Range Weather Forecasts, ERA5 Land, and the Famine Early Warning Systems Network Land Data Assimilation System reanalyses soil moisture data sets, showing a good agreement, mainly over mid latitudes. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large-scale soil moisture-related studies.en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2022-11-29-
Ημερομηνία Δημοσίευσης2021-
Θεματική ΚατηγορίαMachine learningen
Θεματική ΚατηγορίαCalibration of the Soil Water Indexen
Θεματική ΚατηγορίαSoil, climate, and vegetation descriptorsen
Θεματική ΚατηγορίαESA CCI Soil Water Indexen
Θεματική ΚατηγορίαEuropean Space Agency (ESA)en
Θεματική ΚατηγορίαClimate Change Initiative (CCI)en
Βιβλιογραφική ΑναφοράM. G. Grillakis, A. G. Koutroulis, D. D. Alexakis, C. Polykretis, and I. N. Daliakopoulos, “Regionalizing root-zone soil moisture estimates from ESA CCI Soil Water Index using machine learning and information on soil, vegetation, and climate,” Water Resour. Res., vol. 57, no. 5, May 2021, doi: 10.1029/2020WR029249.en

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