ANN-based mapping of monthly reference crop evapotranspiration by using altitude, latitude and longitude data in Fars province, Iran

Manesh, Saeedeh Shirin; Ahani, Hossein; Rezaeianzadeh, Mehdi

doi:10.1007/s10668-013-9465-x

Cited by 9 publications

(6 citation statements)

References 47 publications

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“…Usually, ET o is obtained by lysimeters, although it can also be obtained from data measured by meteorological stations, using models such as the Penman-Monteith equation FAO-56, Thorntwaite, Linacre, Hargreaves, Kharrufa, Turc, Papdakis, Priestly-Taylor, FAO-Blaney-Criddle or the Radiation Method (Vicente-Serrano et al, 2014), and then it's interpolated or extrapolated according to the case (Manesh et al, 2014), however, extrapolation of ET field data to larger scales is costly, time-consuming and complex because of the heterogeneity of a large-area terrain (Byun et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Combination of satellite imagery with meteorological data for estimating reference evapotranspiration

2018

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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.

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Section: Introductionmentioning

confidence: 99%

Combination of satellite imagery with meteorological data for estimating reference evapotranspiration

2018

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“…Furthermore, many recent studies have found that selecting an appropriate training function is vitally essential for assuring the predictive ability and reliability of ANN in other fields [59][60][61]. Despite the fact that ANN has been recognized as the most popular method to simulate the ET as well as the carbon fluxes at ecosystem level based on the eddy covariance-measured data [62][63][64], the influences of various training algorithms on the ANN performance have never been examined to date.…”

Section: Discussionmentioning

confidence: 99%

Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems

Dou

Yang

2018

Advances in Meteorology

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Remarkable progress has been made over the last decade toward characterizing the mechanisms that dominate the exchange of water vapor between the biosphere and the atmosphere. This is attributed partly to the considerable development of machine learning techniques that allow the scientific community to use these advanced tools for approximating the nonlinear processes affecting the variation of water vapor in terrestrial ecosystems. Three novel machine learning approaches, namely, group method of data handling, extreme learning machine (ELM), and adaptive neurofuzzy inference system (ANFIS), were developed to simulate and forecast the daily evapotranspiration (ET) at four different grassland sites based on the flux tower data using the eddy covariance method. These models were compared with the extensively utilized data-driven models, including artificial neural network, generalized regression neural network, and support vector machine (SVM). Moreover, the influences of internal functions on their corresponding models (SVM, ELM, and ANFIS) were investigated together. It was demonstrated that most developed models did good job of simulating and forecasting daily ET at the four sites. In addition to strengths of robustness and simplicity, the newly proposed methods achieved the estimates comparable to those of the conventional approaches and accordingly can be used as promising alternatives to traditional methods. It was further discovered that the generalization performance of the ELM, ANFIS, and SVM models strongly depended on their respective internal functions, especially for SVM.

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“…NINYEROLA et al 2000, 2007aMARTÍNEZ-COB 1996;BROWN and COMRIE 2002;VICENTE-SERRANO et al 2003;WAGNER et al 2012;DI PIAZZA et al 2011), evapotranspiration (e.g. MARTÍNEZ-COB 1996;VICENTE-SERRANO et al 2007;SHIRIN MANESH et al 2013) and snow cover (e.g. LÓ NOGUÉS-BRAVO 2005, 2006; LÓ PEZ BLANCHET and LEHNING 2010).…”

Section: Introductionmentioning

confidence: 99%

Geospatial Predictive Modelling for Climate Mapping of Selected Severe Weather Phenomena Over Poland: A Methodological Approach

Walawender

Ustrnul

2016

Pure Appl. Geophys.

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The main purpose of the study is to introduce methods for mapping the spatial distribution of the occurrence of selected atmospheric phenomena (thunderstorms, fog, glaze and rime) over Poland from 1966 to 2010 (45 years). Limited in situ observations as well the discontinuous and location-dependent nature of these phenomena make traditional interpolation inappropriate. Spatially continuous maps were created with the use of geospatial predictive modelling techniques. For each given phenomenon, an algorithm identifying its favourable meteorological and environmental conditions was created on the basis of observations recorded at 61 weather stations in Poland. Annual frequency maps presenting the probability of a day with a thunderstorm, fog, glaze or rime were created with the use of a modelled, gridded dataset by implementing predefined algorithms. Relevant explanatory variables were derived from NCEP/NCAR reanalysis and downscaled with the use of a Regional Climate Model. The resulting maps of favourable meteorological conditions were found to be valuable and representative on the country scale but at different correlation (r) strength against in situ data (from r = 0.84 for thunderstorms to r = 0.15 for fog). A weak correlation between gridded estimates of fog occurrence and observations data indicated the very local nature of this phenomenon. For this reason, additional environmental predictors of fog occurrence were also examined. Topographic parameters derived from the SRTM elevation model and reclassified CORINE Land Cover data were used as the external, explanatory variables for the multiple linear regression kriging used to obtain the final map. The regression model explained 89 % of annual frequency of fog variability in the study area. Regression residuals were interpolated via simple kriging.

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ANN-based mapping of monthly reference crop evapotranspiration by using altitude, latitude and longitude data in Fars province, Iran

Cited by 9 publications

References 47 publications

Combination of satellite imagery with meteorological data for estimating reference evapotranspiration

Combination of satellite imagery with meteorological data for estimating reference evapotranspiration

Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems

Geospatial Predictive Modelling for Climate Mapping of Selected Severe Weather Phenomena Over Poland: A Methodological Approach

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