Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations

Abstract

California growers face challenges with water shortages and there is a strong need to use the least amount of water while optimizing yield. Timely information on evapotranspiration (ET), a dominant component of crop consumptive water use, is critical for growers to tailor irrigation management based on in-field spatial variability and in-season variations. We evaluated the performance of a remote sensing-based approach, Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), in mapping ET over an almond orchard in California, driven by Landsat satellite observations. Reference ET from a network of weather stations over well-watered grass (ET o ) was used for the internal calibration and for deriving ET at daily and extended time period, instead of alfalfa based reference evapotranspiration (ET r ). Our study showed that METRIC daily ET estimates during Landsat overpass dates agreed well with the field measurements. During 2009-2012, a root mean square error (RMSE) of 0.53 mm/day and a coefficient of determination (R 2 ) of 0.87 were found between METRIC versus observed daily ET. Monthly ET estimates had a higher accuracy, with a RMSE of 12.08 mm/month, a R 2 of 0.90, and a relatively small relative mean difference (RMD) of 9.68% during 2009-2012 growing seasons. Net radiation and Normalized Difference Vegetation Index (NDVI) from remote sensing observations were highly correlated with spatial and temporal ET estimates. An empirical model was developed to estimate daily ET using NDVI, net radiation (R n ), and vapor pressure deficit (VPD). The validation showed that the accuracy of this easy-to-use empirical method was slightly lower than that of METRIC but still reasonable, with a RMSE of 0.71 mm/day when compared to ground measurements. The remote sensing based ET estimate will support a variety of State and local interests in water use and irrigation management, for both planning and regulatory/compliance purposes, and it provides the farmers observation-based guidance for site-specific and time-sensitive irrigation management.

Optimization of Water Consumption Distribution Based on Crop Suitability in the Middle Reaches of Heihe River

“…The simulated values of ET a during the whole growing season (the growth period days of wheat and maize are 133 and 144, respectively) directly obtained by the distributed water consumption model (Equation 20and (21)) are shown in Figure 4, and are in agreement with the spatial interpolated values. The determination coefficient (R 2 ), mean absolute error (MAE), and root mean squared error (RMSE) for wheat are 0.58, 72.87 mm, and 52.55 mm.…”

“…For this reason, some studies began to focus on the spatiotemporal variability in water consumption. The net radiation and Normalized Difference Vegetation Index (NDVI) were found to be the driving factors of the temporal and spatial variations in crop water use, and a number of empirical models were established accordingly in reported areas [21]. However, due to the inconsistency in driving factors within a region, there were few studies on estimating regional crop water consumption through the driving factors.…”

Influence of Vineyard Topography on Actual Evapotranspiration Using Uav in Southern Brazil

“…O método utilizado para o cálculo da Evapotranspiração atual (Eta) foi o algoritmo METRIC (Mapeamento de Evapotranspiração com Calibração Interna), que considera a transpiração da vegetação e a evapotranspiração do solo como um todo (HE et al, 2017). Este algoritmo foi processado por meio do software Quantum Gis (QGIS).…”

Estimating Growing Season Evapotranspiration and Transpiration of Major Crops over a Large Irrigation District from HJ-1A/1B Data Using a Remote Sensing-Based Dual Source Evapotranspiration Model

“…Fortunately, the application of remote sensing (RS) technology in earth observation provides an alternative approach for mapping ET at regional and even global scales [5,8,23,24]. Accordingly, various RS-based ET models based on surface energy balance equation have been developed to adapt to different underlying surface characteristics [25], such as the Surface Energy Balance Algorithm for Land (SEBAL) [26], the Two-Source Energy Balance Model (TSEB) [27], and the Hybrid dual-source scheme and Trapezoid framework based ET Model (HTEM) [28].…”