Suchergebnisse

Participation of Dr. Petropoulos has been funded by the ENViSIoN-EO Marie Skłodowska-Curie grant (grant No 752094), part of the European Union's Horizon 2020 research and innovation programme. Part of the present collaborative work was also materialised in the framework of a short Term Scientific Mission (STSM) of the HARMONIOUS Cost Action which financially supported Dr Petropoulos' visit between 4 to 15 February 2020 to the Department of Engineering of the University of Palermo, Italy. ; Η συμμετοχή του Δρ. Πετρόπουλου χρηματοδοτήθηκε από το πρόγραμμα της Ευρωπαϊκής Ένωσης για Έρευνα και Καινοτομία «Oρίζοντας 2020», δράση Marie Sklodowska - Curie , έργο ENViSIoN-EO (αριθ. 752094). Επίσης μέρος της παρούσας συλλογικής εργασίας υλοποιήθηκε στο πλαίσιο μιας βραχυπρόθεσμης επιστημονικής αποστολής της HARMONIOUS Cost Action, η οποία υποστήριξε οικονομικά την επίσκεψη του Δρ. Πετρόπουλου από τις 4 έως τις 15 Φεβρουαρίου 2020 στο Τμήμα Μηχανικών του Πανεπιστημίου του Παλέρμο, Ιταλία. ; Summarization: Information acquired from Unmanned Aerial Vehicles (UAVs) is frequently used nowadays in a variety of disciplines and research fields. The present study explores for the first time the combined use of UAVs with a newly proposed technique for estimating evaporative fraction (EF) and surface soil moisture (SSM). The investigation is performed in a typical Mediterranean setting, a citrus field with flat topography divided in two plots with different irrigation schemes, in Sicily, Italy, at which ground data acquired during an extensive field campaign in July 2019. Reasonable estimates of both EF and surface wetness were produced, with patterns in agreement to vegetation cover fragmentation, topography, and other site-specific characteristics. Validation shows average error of 0.053 for EF and of 0.040 cm3 cm−3 for SSM. The results are comparable or better to those reported in analogous studies performed in similar areas. This implies that the investigated approach performs well under the semi-arid conditions characterizing ...