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University of Delaware (2020)

Estimating crop evapotranspiration using a satellite remote sensing based energy balance model

Scarborough, Charles

Titre : Estimating crop evapotranspiration using a satellite remote sensing based energy balance model

Auteur : Scarborough, Charles

Université de soutenance  : University of Delaware

Grade : Master of Science in Geography 2020

Résumé
Evapotranspiration (ET) is the combination of transpiration from plants and evaporation from land surface sources of water. Accurate accounting of actual crop ET is critical in agricultural water management, especially in areas with intensive irrigation. Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) is a useful tool to spatially estimate actual evapotranspiration (ETa) using satellite imagery. Although METRIC has been a documented success in estimating ETa in regions around the world, especially the semi-arid regions of the western United States, it has not been applied in the Mid-Atlantic region of the United States or examined under different irrigation regimes like subsurface drip (SDI) and central pivot irrigation (CPI). This study compares cumulative ETa from corn and soybean crops under CPI and SDI crop fields and provides cumulative ETa images of the fields in southern Delaware. METRIC modeled land surface energy balance data and daily ETa (ETMETRIC) were compared to in-situ observations of incoming solar radiation (RS↓), surface temperature (Ts), net radiation (R¬n), soil heat flux (G), sensible heat flux (H), latent heat flux (LE), Eddy Covariance ETa (ETEC), and atmometer ETa (ETatm). Turbulent fluxes (H and LE) were adjusted to force energy balance closure using the Bowen ratio method. In-situ data measurements were fixed to a Delaware Environmental Observing System (DEOS) station bordering the SDI field in Warrington Farm, a University of Delaware owned irrigation research farm. METRIC analysis and validation were performed over various dates within the 2015, 2016, and 2017 growing seasons. Modeled values of RS↓, T¬s, G, and LE agreed reasonably well with observations, while Rn and H yielded large biases. Biases in H are in part due to the internal design of METRIC of internal calibration, which absorb all other energy balance biases to yield accurate values of ETa. Daily time series of ETMETRIC from the 2016 study period agreed well with ETEC, indicated by moderate correlation (R2 = 0.63) and low RMSE (0.75 mm day-1). Results from the 2017 study period were unfavorable (R2 = 0.33, RMSE of 1.13 mm day-1). Less than optimal Landsat coverage over the study area contributed to deviations from observations. Cumulative ETMETRIC was 1.5% and 2.9% greater than ETEC for the 2016 and 2017 study periods, respectively. METRIC analysis was able to detect differences in cumulative ETa¬ of corn and soybean under the CPI system and SDI system. Results indicate that METRIC is a useful tool to create spatial estimates of ETa in an agriculturally intensive area of the mid-Atlantic regio

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