Development of four GIS models of empirical methods computing daily mean reference evapotranspiration (ETo) with MODIS LST inputs

Stavroula Dimitriadou; Konstantinos G. Nikolakopoulos

doi:10.1117/12.2636199

28 October 2022 Development of four GIS models of empirical methods computing daily mean reference evapotranspiration (ETo) with MODIS LST inputs

Stavroula Dimitriadou, Konstantinos G. Nikolakopoulos

Author Affiliations +

Proceedings Volume 12262, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV; 1226218 (2022) https://doi.org/10.1117/12.2636199
Event: SPIE Remote Sensing, 2022, Berlin, Germany

Conference Poster

Abstract

GIS models of the empirical methods Blaney-Criddle, Hammon, De Bruin and Makkink have been developed in ArcMap model-builder environment to estimate daily mean reference evapotranspiration (ETo). MODIS LST Day and Night products along with meteorological data were used as inputs. The application was on Peloponnese, Greece, for months December and August of years 2016-2019. For the validation of the models FAO PenmanMonteith (FAO-PM) was used as a reference. For the evaluation of the models Root Mean Square Error (RMSE), Mean Bias (MB) and Normalized mean Bias (NMB) were computed. Hargreaves-Samani’s and Hansen’s previous estimates were also employed. Hammon's estimates are closer to FAO-PM for December, with Hansen’s following closely. Hargreaves-Samani model, followed by De Bruin model, produced the closest estimates to FAO-PM for August. It is noteworthy that the estimates for August were significantly better than those for December for all empirical models. This is probably because of negligible wind speed values in August. FAO-PM is sensitive in the wind speed values of December. The presented empirical models are quick and accurate and can employ LST inputs of any spatial resolution.

Citation Download Citation

Stavroula Dimitriadou and Konstantinos G. Nikolakopoulos "Development of four GIS models of empirical methods computing daily mean reference evapotranspiration (ETo) with MODIS LST inputs", Proc. SPIE 12262, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIV, 1226218 (28 October 2022); https://doi.org/10.1117/12.2636199

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