Assessing reference evapotranspiration at regional scale based on remote sensing, weather forecast and GIS tools

Ramírez-Cuesta, Juan Miguel; Cruz-Blanco, M.; Santos, C.; Lorite, Ignacio J.

doi:10.1016/j.jag.2016.10.004

Cited by 18 publications

(5 citation statements)

References 41 publications

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“…De Bruin et al (2010Bruin et al ( , 2016 proposed the LSA-SAF methodology to estimate ET o from daily values of downward solar radiation obtained with radiometers onboard the geostationary satellite Meteosat Second Generation (MSG), and from air temperature at 2 m provided by operational weather forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF). Other applications were reported by Cammalleri andCiraolo (2013), Cruz-Blanco et al (2015) and Ramírez-Cuesta et al (2017). The advantage of using the LSA-SAF methodology to compute ET o was recently discussed by Trigo et al (2018) andTrigo (2019), namely to overcome problems referring to non-reference weather sites, including the effects of aridity and advection.…”

Section: Grass Reference Evapotranspiration and Its Computation With Reduced Datasetsmentioning

confidence: 99%

Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables

Paredes

Pereira

Almorox

et al. 2020

Agricultural Water Management

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The computation of the grass reference evapotranspiration with the FAO56 Penman-Monteith equation (PM-ET o ) requires data on maximum and minimum air temperatures (T max , T min ), actual vapour pressure (e a ), shortwave solar radiation (R s ), and wind speed at 2 m height (u 2 ). Nonetheless, related datasets are often not available, are incomplete, or have uncertain quality. To overcome these limitations, several alternatives were considered in FAO56, while many other procedures were tested and proposed in very numerous papers. The present study reviews the computational procedures relative to predicting the missing variables from temperature, i.e., the PM temperature approach (PMT), and estimating ET o with the Hargreaves-Samani (HS) equation. For the PMT approach, procedures refer to predicting: (a) the dew point temperature (T dew ) from the minimum or the mean air temperature; (b) shortwave solar radiation (R s ) from the air temperature difference (TD = T max -T min ) combined with a calibrated radiation adjustment coefficient (k Rs ); and (c) wind speed (u 2 ) using a default value or a regional or local average. The adequateness of computing T dew from air temperature was reassessed and the preference for using an average u 2 has been defined. To ease the estimation of R s, for the PMT approach and the coefficient of the HS equation, multiple linear regression equations for predicting k Rs were developed using local averages of the temperature difference (TD), relative humidity (RH) and wind speed as independent variables. All variables were obtained from the Mediterranean set of CLIMWAT climatic data. Two types of k Rs equations were developed: climate-focused equations specific to four climate types -humid, sub-humid, semi-arid, and hyper-arid and arid -, and a global one, applicable to any type of climate. The usability of the k Rs equations for the PMT and HS methods was assessed with independent data sets from Bolivia, Inner Mongolia, Iran, Portugal and Spain, covering a variety of climates, from hyper-arid to humid. With this purpose, ET o estimated with PMT and HS (ET o PMT and ET o HS ) were compared with PM-ET o computed with full data sets to evaluate the usability of the k Rs equations. Adopting the climate-focused k Rs equations with ET o PMT , the RMSE averaged 0.59, 0.64, 0.65 and 0.72 mm d −1 for humid, sub-humid, semi-arid, and arid and hyper-arid climates, respectively, while the RMSE values relative to ET o HS when using the respective climate-focused k Rs equations averaged 0.58, 0.60, 0.60 and 0.69 mm d −1 for the same climates. These results are similar to those obtained with the k Rs global equation. The accuracy of the PMT approach when using the k Rs equations was also evaluated when one, two, or all three T dew , R s and u 2 variables are missing and the resulting goodness-of-fit indicators demonstrated the advantage of the combined use of observed and estimated weather variables. The usability of the k Rs equations for an efficient parameterization of both the PMT approach and ...

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Section: Grass Reference Evapotranspiration and Its Computation With Reduced Datasetsmentioning

confidence: 99%

Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables

Paredes

Pereira

Almorox

et al. 2020

Agricultural Water Management

View full text Add to dashboard Cite

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“…Generally, micro-meteorological conditions gradually evolve during a day without experiencing abrupt changes, specially in Spring and Summer seasons, when the conditions are generally more steady (Ramírez-Cuesta et al, 2017b). However, there exist certain days when conditions are dynamic during the day and noteworthily vary even in short periods of time.…”

Section: Discussionmentioning

confidence: 99%

Influence of short-term surface temperature dynamics on tree orchards energy balance fluxes

et al. 2022

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Land surface temperature (LST) plays an essential role in developing and applying precision agriculture protocols, especially for calculating crop evapotranspiration (ETc) by surface energy balance (SEB) approaches; and for determining crop water status. However, LST is quite dependent on the meteorological conditions, which can rapidly vary. This variability, together with the limited meterological data acquisition frequency in most weather stations, can lead to the miscalculation of the SEB components, especially relevant when used for irrigation purposes.The present study assessed the temporal dynamic of LST in a very short period of time (20-minutes) through the acquisition of multiple thermal imagery. Additionally, a combination of SEB approach with Eddy Covariance technique was performed for quantifying the effect that LST variations have on the sensible (H) and latent (LE) heat fluxes.Even under steady meteorological conditions, temporal variations in LST of 3.5 and 4.0 K were observed for tree canopy and sunny bare soil surfaces, respectively. These LST oscillations reached values of about 7.8 and 17.9 K for tree canopies and bare soil when heterogeneous meteorological conditions were observed (i.e. cloud presence). Such LST differences translated into H and LE differences of about 26 and 19%, respectively; with variations up to 5 (for H) and 2.7 times (for LE) under fast-varying meteorological conditions.The obtained results suggest the necessity of acquiring thermal imagery when steady meteorological conditions exist or, otherwise, ensuring the collection of instantaneous meteorological data for applying post-processing corrections. This is of importance when incorporating the obtained ETc maps into precision irrigation protocols.

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“…Traditionally ET is estimated for points or patches on the land surface but spatial heterogeneity in land surface characteristics precludes robust upscaling to the regional scale [6][7][8]. However, surface energy balance models using remote sensing data (specifically terrain, soil humidity, air and land surface temperature) enables accurate ET estimates at the regional scale [6,[9][10][11]. For example, in the Haihe River Basin of China, ground verification studies suggested the root mean square error associated with estimates of ET was approximately 0.32 mm/3 h [12].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Evapotranspiration at a 3-Minute Time Interval Based on Remote Sensing Data and SEBAL Model

Armstrong

Chang

2020

Applied Sciences

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Using remote sensing to estimate evapotranspiration minute frequency is the basis for accurately calculating hourly and daily evapotranspiration from the regional scale. However, from the existing research, it is difficult to use remote sensing data to estimate evapotranspiration minute frequency. This paper uses GF-4 and moderate-resolution imaging spectroradiometer (MODIS) data in conjunction with the Surface Energy Balance Algorithm for Land (SEBAL) model to estimate ET at a 3-min time interval in part of China and South Korea, and compares those simulation results with that from field measured data. According to the spatial distribution of ET derived from GF-4 and MODIS, the texture of ET derived from GF-4 is more obvious than that of MODIS, and GF-4 is able to express the variability of the spatial distribution of ET. Meanwhile, according to the value of ET derived from both GF-4 and MODIS, results from these two satellites have significant linear correlation, and ET derived from GF-4 is higher than that from MODIS. Since the temporal resolution of GF-4 is 3 min, the land surface ET at a 3-min time interval could be obtained by utilizing all available meteorological and remote sensing data, which avoids error associated with extrapolating instantaneously from a single image.

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Assessing reference evapotranspiration at regional scale based on remote sensing, weather forecast and GIS tools

Cited by 18 publications

References 41 publications

Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables

Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables

Influence of short-term surface temperature dynamics on tree orchards energy balance fluxes

Simulation of Evapotranspiration at a 3-Minute Time Interval Based on Remote Sensing Data and SEBAL Model

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