Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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| URI | http://purl.tuc.gr/dl/dias/61BC4798-8F38-4042-86D8-5EFEF7A6E563 | - |
| Αναγνωριστικό | https://doi.org/10.1029/2020WR029249 | - |
| Αναγνωριστικό | https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020WR029249 | - |
| Γλώσσα | en | - |
| Μέγεθος | 22 pages | en |
| Τίτλος | Regionalizing root-zone soil moisture estimates from ESA CCI Soil Water Index using machine learning and information on soil, vegetation, and climate | en |
| Δημιουργός | Gryllakis Emmanouil | en |
| Δημιουργός | Γρυλλακης Εμμανουηλ | el |
| Δημιουργός | Koutroulis Aristeidis | en |
| Δημιουργός | Κουτρουλης Αριστειδης | el |
| Δημιουργός | Alexakis Dimitrios D. | en |
| Δημιουργός | Polykretis Christos | en |
| Δημιουργός | Daliakopoulos Ioannis | en |
| Δημιουργός | Δαλιακοπουλος Ιωαννης | el |
| Εκδότης | American Geophysical Union | en |
| Περίληψη | The 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 |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2022-11-29 | - |
| Ημερομηνία Δημοσίευσης | 2021 | - |
| Θεματική Κατηγορία | Machine learning | en |
| Θεματική Κατηγορία | Calibration of the Soil Water Index | en |
| Θεματική Κατηγορία | Soil, climate, and vegetation descriptors | en |
| Θεματική Κατηγορία | ESA CCI Soil Water Index | en |
| Θεματική Κατηγορία | European Space Agency (ESA) | en |
| Θεματική Κατηγορία | Climate Change Initiative (CCI) | en |
| Βιβλιογραφική Αναφορά | M. 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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