Combination of satellite imagery with meteorological data for estimating reference evapotranspiration

Abstract

Abstract:The Food and Agriculture Organization of the United Nations (FAO) in its publication No. 56 of the Irrigation and Drainage Series presents the FAO Penman-Monteith procedure for the estimation of reference evapotranspiration from meteorological data, however, its calculation may be complicated in areas where there are no weather stations. This paper presents an evaluation of the potential of the Land Surface Temperature and Digital Elevation Models products derived from the MODIS and ASTER sensors, both on board the Terra EOS AM-1 satellite, for the estimation of reference evapotranspiration using the Penman-Monteith FAO-56, Hargreaves, Thornthwaite and Blaney-Criddle models. The four models were compared with the method proposed by FAO calculated with the observed data of a ground based meteorological station, finding a significant relation with the models Penman-Monteith FAO-56 and Hargreaves.Key words: Land Surface Temperature, MODIS, ASTER, reference evapotranspiration, FAO. Combinación de imágenes satelitales con datos meteorológicos para la estimación de la evapotranspiración de referenciaResumen: La Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO) en su publicación No 56 de la Serie de Riego y Drenaje presenta el procedimiento FAO Penman-Monteith para la estimación de la evapotranspiración de referencia a partir de datos meteorológicos, no obstante, su cálculo puede complicarse en zonas donde no se cuenta con estaciones meteorológicas. El presente artículo exhibe una evaluación del potencial de productos de Temperatura Superficial Terrestre y Modelos Digitales de Elevación derivados de imágenes adquiridas por los sensores MODIS y ASTER, ambos a bordo del satélite Terra EOS AM-1, para la estimación de la evapotranspiración de referencia utilizando los modelos de Penman-Monteith FAO-56, Hargreaves, Thornthwaite y Blaney-Criddle. Los cuatro modelos fueron comparados con el método propuesto por la FAO calculado con datos observados de una estación meteorológica en tierra, encontrando una relación significativa con los modelos Penman-Monteith FAO-56 y Hargreaves.Palabras clave: Temperatura Superficial Terrestre, MODIS, ASTER, evapotranspiración de referencia, FAO.

Reference evapotranspiration estimation by different methods for the sucroenergy sector of Colombia

“…These results confirmed the findings reported by Montero et al (2018), who carried out an analysis to compare ET 0 data from a satellite image and data estimated from CENICAÑA's weather data. They determined a high correlation between satellite data and that from the FAO Penman-Monteith standard method (r = 0.915).…”

“…In this case, the variables that have the highest coefficients of determination are the SR with R 2 = 0.709 (Figure 4B), similar to the findings of Montero et al (2018), and the minimum temperature with R 2 = 0.708. The latter result is contrary to those of Amorim et al (2007), who aimed to determine the direct and indirect effects of meteorological elements on ET 0 by the Penman-Monteith (FAO) and ECA methods in Mossoró, RN, Brazil.…”

Impacts of climate change and rising atmospheric CO2 on future projected reference evapotranspiration in Emilia-Romagna (Italy)

“…The main issue with the Penman-Monteith equation is data availability and the difficulty to access to these data, especially in Italy where weather monitoring networks are managed by regional and national services, without a common data sharing policy and data distribution platform (Pavan et al 2013;Pelosi et al 2020). This prompted researchers to look for alternative ways for obtaining the input data: estimation (de Carvalho et al 2013;Córdova et al 2015), weather forecast (Cai et al 2007;Silva et al 2010;Lorite et al 2015), satellite imagery (Montero et al 2018), remote sensing (Teixeira 2010), a combination of remote sensing and machine learning (Zhang et al 2018), artificial neural networks (ANNs) (Kumar et al 2002), fuzzy and neuro-fuzzy systems, and genetic algorithms (Shiri et al 2012;Kisi and Cengiz 2013). Another alternative choice is using reanalysis data.…”