Geospatial crop water use mapping is critical for field-scale site-specific irrigation management. Landsat 7/8 satellite imagery with a widely adopted METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) energy balance model (LM approach) estimates accurate evapotranspiration (ET) but limits field-scale spatiotemporal (30 m pixel⁻¹, ~16 days) mapping. A study was therefore conducted to map actual ET of commercially grown irrigated-field crops (spearmint, potato, and alfalfa) at very high-resolution (7 cm pixel⁻¹). Six small unmanned aerial system (UAS)-based multispectral and thermal infrared imagery campaigns were conducted (two for each crop) at the same time as the Landsat 7/8 overpass. Three variants of METRIC model were used to process the UAS imagery; UAS-METRIC-1, -2, and -3 (UASM-1, -2, and -3) and outputs were compared with the standard LM approach. ET root mean square differences (RMSD) between LM-UASM-1, LM-UASM-2, and LM-UASM-3 were in the ranges of 0.2–2.9, 0.5–0.9, and 0.5–2.7 mm day⁻¹, respectively. Internal calibrations and sensible heat fluxes majorly resulted in such differences. UASM-2 had the highest similarity with the LM approach (RMSD: 0.5–0.9, ET_dep,abs (daily ET departures): 2–14%, r (Pearson correlation coefficient) = 0.91). Strong ET correlations between UASM and LM approaches (0.7–0.8, 0.7–0.8, and 0.8–0.9 for spearmint, potato, and alfalfa crops) suggest equal suitability of UASM approaches as LM to map ET for a range of similar crops. UASM approaches (Coefficient of variation, CV: 6.7–24.3%) however outperformed the LM approach (CV: 2.1–11.2%) in mapping spatial ET variations due to large number of pixels. On-demand UAS imagery may thus help in deriving high resolution site-specific ET maps, for growers to aid in timely crop water management.

Site-specific irrigation management for perennial crops such as grape requires water use assessments at high spatiotemporal resolution. In this study, small unmanned-aerial-system (UAS)-based imaging was used with a modified mapping evapotranspiration at high resolution with internalized calibration (METRIC) energy balance model to map water use (UASM-ET approach) of a commercial, surface, and direct-root-zone (DRZ) drip-irrigated vineyard. Four irrigation treatments, 100%, 80%, 60%, and 40%, of commercial rate (CR) were also applied, with the CR estimated using soil moisture data and a non-stressed average crop coefficient of 0.5. Fourteen campaigns were conducted in the 2018 and 2019 seasons to collect multispectral (ground sampling distance (GSD): 7 cm/pixel) and thermal imaging (GSD: 13 cm/pixel) data. Six of those campaigns were near Landsat 7/8 satellite overpass of the field site. Weather inputs were obtained from a nearby WSU-AgWeatherNet station (1 km). First, UASM-ET estimates were compared to those derived from soil water balance (SWB) and conventional Landsat-METRIC (LM) approaches. Overall, UASM-ET (2.70 ± 1.03 mm day⁻¹ [mean ± std. dev.]) was higher than SWB-ET (1.80 ± 0.98 mm day⁻¹). However, both estimates had a significant linear correlation (r = 0.64–0.81, p < 0.01). For the days of satellite overpass, UASM-ET was statistically similar to LM-ET, with mean absolute normalized ET departures (ET_d,MAN) of 4.30% and a mean r of 0.83 (p < 0.01). The study also extracted spatial canopy transpiration (UASM-T) maps by segmenting the soil background from the UASM-ET, which had strong correlation with the estimates derived by the standard basal crop coefficient approach (T_d,MAN = 14%, r = 0.95, p < 0.01). The UASM-T maps were then used to quantify water use differences in the DRZ-irrigated grapevines. Canopy transpiration (T) was statistically significant among the irrigation treatments and was highest for grapevines irrigated at 100% or 80% of the CR, followed by 60% and 40% of the CR (p < 0.01). Reference T fraction (T_rF) curves established from the UASM-T maps showed a notable effect of irrigation treatment rates. The total water use of grapevines estimated using interpolated T_rF curves was highest for treatments of 100% (425 and 320 mm for the 2018 and 2019 seasons, respectively), followed by 80% (420 and 317 mm), 60% (391 and 318 mm), and 40% (370 and 304 mm) of the CR. Such estimates were within 5% to 11% of the SWB-based water use calculations. The UASM-T-estimated water use was not the same as the actual amount of water applied in the two seasons, probably because DRZ-irrigated vines might have developed deeper or lateral roots to fulfill water requirements outside the irrigated soil volume. Overall, results highlight the usefulness of high-resolution imagery toward site-specific water use management of grapevines.

Accurate estimation of reference evapotranspiration (ET_r) is important for irrigation planning, water resource management, and preserving agricultural and forest habitats. The widely used Penman–Monteith equation (ASCE-PM) estimates ET_r across various timescales using ground weather station data. However, discrepancies persist between estimated ET_r and measured ET_r obtained from weighing lysimeters (ET_r-lys), particularly in advective environments. This study assessed different machine learning (ML) models in comparison to ASCE-PM for ET_r estimation in highly advective conditions. Various variable combinations, representing both radiation and aerodynamic components, were organized for evaluation. Eleven datasets (DT) were created for the daily timescale, while seven were established for hourly and quarter-hourly timescales. ML models were optimized by a genetic algorithm (GA) and included support vector regression (GA-SVR), random forest (GA-RF), artificial neural networks (GA-ANN), and extreme learning machines (GA-ELM). Meteorological data and direct measurements of well-watered alfalfa grown under reference ET conditions obtained from weighing lysimeters and a nearby weather station in Bushland, Texas (1996–1998), were used for training and testing. Model performance was assessed using metrics such as root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R²). ASCE-PM consistently underestimated alfalfa ET across all timescales (above 7.5 mm/day, 0.6 mm/h, and 0.2 mm/h daily, hourly, and quarter-hourly, respectively). On hourly and quarter-hourly timescales, datasets predominantly composed of radiation components or a blend of radiation and aerodynamic components demonstrated superior performance. Conversely, datasets primarily composed of aerodynamic components exhibited enhanced performance on a daily timescale. Overall, GA-ELM outperformed the other models and was thus recommended for ET_r estimation at all timescales. The findings emphasize the significance of ML models in accurately estimating ET_r across varying temporal resolutions, crucial for effective water management, water resources, and agricultural planning.

Conventional methods for crop lodging assessments need accurate ground observations and tend to be laborious. Lodging assessment methods and accuracy can thus be improved using remote sensing data from small unmanned aerial systems (UASs) and low orbiting satellites (LOSs). With such aim, imagery to assess spearmint crop lodging was acquired using a small UAS at two ground sample distances (GSDs) of 0.01 and 0.03 m. Crop surface model (CSM) and six image color features were extracted from small UAS-based data. These features were then classified into not lodged (NL), partially lodged (PL), and lodged (L) groups. Mean and majority feature classes were obtained for 50 regions of interest (ROI) of size 1 m² each. Features were compared with visual crop lodging ratings using Pearson correlation (r) and Cohen's kappa (CK) coefficients. CSM showed higher assessment accuracy with r ≈ 0.85 and CK > 0.60. Mean percentage red (%R) was observed to have the strongest correlation with visual ratings (r= 0.75 and CK = 0.40-0.59) followed by mean percentage blue (%B), both at 0.01-m GSD. The percentage of lodging calculated from %R and %B maps was also contrasted with similar estimates from LOS-based imagery at 3.00-m GSD with no statistical differences found at 5% level.

A computer model was developed to simulate the varying depths of water applied to the ground due to the intermittent movements of a typical center pivot. The stop–go model inputs include the sprinkler application depth, the sprinkler pattern, that pattern’s wetted radius, the center pivot’s % timer setting, the move cycle time, and the end tower maximum travel speed. The model outputs were the depth of application in the pivot’s movement direction, the distribution uniformity (DU), and the coefficient of uniformity (CU). The results revealed that the pivot circular application uniformity is mostly a function of the move distance as a percent of the sprinklers’ wetted radius. This, in turn, is a function of the percent timer setting, the cycle time, and the end tower travel speed. Due to this, the high-speed center pivots had corresponding lower application uniformities compared to low-speed machines, and sprinklers with larger wetted radii resulted in better uniformity. Shortening the cycle time also resulted in higher uniformity coefficients. Based on these results, it is recommended that the run time be set based on the pivot end-tower’s travel speed and sprinkler wetted radius, such that the end tower move distance is equivalent to the sprinkler wetted radius. This will reduce wear to the tower motors due to the on–off cycling, especially for slow travel settings and for sprinklers with larger wetted radii. The ponding depths at different percent of the move distance, for the potential runoff, were estimated, which were set to be equivalent to the wetted radius.

Wind drift and evaporation loss (WDEL) of mid-elevation spray application (MESA) and low-elevation spray application (LESA) sprinklers on a center pivot and linear-move irrigation machines are measured and reported to be about 20% and 3%, respectively. It is important to estimate the fraction of WDEL that cools and humidifies the microclimate causing evapotranspiration (ET) suppression, mitigating the measured irrigation system losses. An experiment was conducted in 2018 and 2019 in a commercial spearmint field near Toppenish, Washington. The field was irrigated with an 8-span center pivot equipped with MESA but had three spans that were converted to LESA. All-in-one weather sensors (ATMOS-41) were installed just above the crop canopy in the middle of each MESA and LESA span and nearby but outside of the pivot field (control) to record meteorological parameters on 1 min intervals. The ASCE Penman–Monteith (ASCE-PM) standardized reference equations were used to calculate grass reference evapotranspiration (ET_o) from this data on a one-minute basis. A comparison was made for the three phases of before, during, and after the irrigation system passed the in-field ATMOS-41 sensors. In addition, a small unmanned aerial system (UAS) was used to capture 5-band multispectral (ground sampling distance [GSD]: 7 cm/pixel) and thermal infrared images (GSD: 13 cm/pixel) while the center pivot irrigation system was irrigating the field. This imagery data was used to estimate crop evapotranspiration (ET_c) using a UAS-METRIC energy balance model. The UAS-METRIC model showed that the estimated ET_c under MESA was suppressed by 0.16 mm/day compared to the LESA. Calculating the ET_o by the ASCE-PM method showed that the instantaneous ET_o rate under the MESA was suppressed between 8% and 18% compared to the LESA. However, as the time of the ET suppression was short, the total amount of the estimated suppressed ET of the MESA was less than 0.5% of the total applied water. Overall, the total reduction in the ET due to the microclimate modifications from wind drift and evaporation losses were small compared to the reported 17% average differences in the irrigation application efficiency between the MESA and the LESA. Therefore, the irrigation application efficiency differences between these two technologies were very large even if the ET suppression by wind drift and evaporation losses was accounted for.