Understanding the role of landscape heterogeneity and its influence on the scaling behavior of surface fluxes as observed by satellite sensors with different spatial resolutions is a critical need to investigate. In this study, the effects of pixel scales on ETc estimation and other parameters that are used to calculate ETc were investigated over different vegetation surfaces in south central Nebraska, USA. Surface Energy Balance System (SEBS) was used to estimate spatially distributed ETc by combining ground‐based meteorological data for Landsat and MODIS imagery. The estimated surface energy fluxes were compared and validated to the measured Bowen Ratio Energy Balance System (BREBS) ETc fluxes. Validation results showed that Landsat has more preferable spatial resolution (30 m) to map and analyze ETc; regression models explained 91% of the variability in the observed data (RMSD = 0.064 mm/h; MBE = 0.04 mm/h). However, for MODIS‐based ETc, the regression model explained only 59% of the variability in observed ETc with a larger error (RMSD = 0.17 mm/h; MBE = 0.15 mm/h). MODIS‐based ETc was about 31% higher than the measured ETc. Imperfect assessment in MODIS‐based retrievals is due to the underlying assumption of spatial heterogeneity and coarser sensor pixel scale (500 m), which was summarized by up‐scaling the Landsat images to MODIS images using output flux aggregation and input up‐scaling procedure using simple average and nearest neighbor aggregation techniques and comparisons were made on both image and pixel scales. Aggregation results illustrate that simple average with output flux aggregation provides close interpretation in aggregating fluxes to coarser resolution than other aggregation approaches. Pixel‐by‐pixel comparison using output aggregation with simple average resulted in close agreement (error range 5%–35%) between measured and up‐scaled fluxes, compared to input up‐scaling using simple average (error range 25%–60%). Larger error in input up‐scaling is due to the changes in the surface roughness parameters due to aggregation in SEBS model. In addition, the magnitude of errors in ETc estimation was observed to be a function of the heterogeneity of the land surface and evaporative elements over the study region. Comparison between up‐scaled ETc at 480 m spatial resolution with original MODIS image at 500 m showed that the output aggregation using simple average aggregation method provided closer representation of ETc at 500 m MODIS pixel resolution than the nearest neighbor resampling method.

The regional-scale estimation of crop evapotranspiration (ET_c) over a heterogeneous surface is an important tool for the decision-makers in managing and allocating water resources. This is especially critical in the arid to semi-arid regions that require supplemental water due to insufficient precipitation, soil moisture, or groundwater. Over the years, various remote sensing-based surface energy balance (SEB) models have been developed to accurately estimate ET_c over a regional scale. However, it is important to carry out the SEB model assessment for a particular geographical setting to ensure the suitability of a model. Thus, in this study, four commonly used and contrasting remote sensing models viz. METRIC (mapping evapotranspiration at high resolution with internalized calibration), SEBAL (surface energy balance algorithm for land), S-SEBI (simplified surface energy balance index), and SEBS (surface energy balance system) were compared and used to quantify and map the spatio-temporal variation of ET_c in the semi-arid to arid inter-mountain region of Big Horn Basin, Wyoming (Landsat Path/Row: 37/29). Model estimates from 19 cloud-free Landsat 7 and 8 images were compared with the Bowen ratio energy balance system (BREBS) flux stationed in a center pivot irrigated field during 2017 (sugar beet), 2018 (dry bean), and 2019 (barley) growing seasons. The results indicated that all SEB models are effective in capturing the variation of ET_c with R² ranging in between 0.06 to 0.95 and RMSD between 0.07 to 0.15 mm h⁻¹. Pooled data over three vegetative surfaces for three years under irrigated conditions revealed that METRIC (NSE = 0.9) performed better across all land cover types, followed by SEBS (NSE = 0.76), S-SEBI (NSE = 0.73), and SEBAL (NSE = 0.65). In general, all SEB models substantially overestimated ET_c and underestimated sensible heat (H) fluxes under dry conditions when only crop residue was available at the surface. A mid-season density plot and absolute difference maps at image scale between the models showed that models involving METRIC, SEBAL, and S-SEBI are close in their estimates of daily crop evapotranspiration (ET₂₄) with pixel-wise RMSD ranged from 0.54 to 0.76 mm d⁻¹ and an average absolute difference across the study area ranged from 0.47 to 0.56 mm d⁻¹. Likewise, all the SEB models underestimated the seasonal ET_c, except SEBS.

Irrigated agriculture is a major economic contributor of the High Plains Region and it primarily relies on the High Plains Aquifer as a source of water. Over time, areas of the High Plains Aquifer have experienced drawdowns limiting its ability to supply sufficient water to sustain fully irrigated crop production. This among other reasons, including variable climatic factors and differences in state water policy, has resulted in some areas adopting and practicing deficit irrigation management. Considerable research has been conducted across the High Plains Aquifer region to identify locally appropriate deficit irrigation strategies. This review summarizes and discusses research conducted in Nebraska, Colorado, Kansas, and Texas, as well as highlights areas for future research. Editor's note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.

Highlights Artificial neural network modeling was used to predict crop water stress index lower reference canopy temperature. Root mean square error of predicted lower reference temperatures was <1.1°C for sugarbeet and Pinot noir wine grape. Energy balance model was used to dynamically predict crop water stress index upper reference canopy temperature. Crop water stress index for sugarbeet was well correlated with irrigation and soil water status. Crop water stress idex was well correlated with midday leaf water potential of wine grape. Abstract. Normalized crop canopy temperature, termed crop water stress index (CWSI), was proposed over 40 years ago as an irrigation management tool but has experienced limited adoption in production agriculture. Development of generalized crop-specific upper and lower reference temperatures is critical for implementation of CWSI-based irrigation scheduling. The objective of this study was to develop and evaluate data-driven models for predicting the reference canopy temperatures needed to compute CWSI for sugarbeet and wine grape. Reference canopy temperatures for sugarbeet and wine grape were predicted using machine learning and regression models developed from measured canopy temperatures of sugarbeet, grown in Idaho and Wyoming, and wine grape, grown in Idaho and Oregon, over five years under full and severe deficit irrigation. Lower reference temperatures (TLL) were estimated using neural network models with Nash-Sutcliffe model efficiencies exceeding 0.88 and root mean square error less than 1.1°C. The relationship between TLL minus ambient air temperature and vapor pressure deficit was represented with a linear model that maximized the regression coefficient rather than minimized the sum of squared error. The linear models were used to estimate upper reference temperatures that were nearly double the values reported in previous studies. A daily CWSI, calculated as the average of 15 min CWSI values between 13:00 and 16:00 MDT for sugarbeet and between 13:00 and 15:00 local time for wine grape, were well correlated with irrigation events and amounts. There was a significant (p < 0.001) linear relationship between the daily CWSI and midday leaf water potential of Malbec and Syrah wine grapes, with an R2 of 0.53. The data-driven models developed in this study to estimate reference temperatures enable automated calculation of the CWSI for effective assessment of crop water stress. However, measurements taken under conditions of wet canopy or low solar radiation should be disregarded as they can result in irrational values of the CWSI. Keywords: Canopy temperature, Crop water stress index, Irrigation scheduling, Leaf water potential, Sugarbeet, Wine grape.