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HighlightsMultispectral sensors mounted on the center pivot lateral were able to capture differences between rainfed and irrigated crop.Canopy temperature was strongly associated among stationary and pivot-mounted sensors with coefficient of determination ranging between 0.88 and 0.99.A cooling effect of about 2°C was observed in canopy temperature data collected from pivot mounted sensors for irrigated soybean crop.Abstract. Accurate knowledge of plant and field characteristics is crucial for irrigation management. Irrigation can potentially be better managed by utilizing data collected from various sensors installed on different platforms. The accuracy and repeatability of each data source are important considerations when selecting a sensing system suitable for irrigation management. The objective of this study was to compare data from multispectral (red and near-infrared bands) and thermal (long wave thermal infrared band) sensors mounted on different platforms to investigate their comparative usability and accuracy. The different sensor platforms included stationary posts fixed on the ground, the lateral of a center pivot irrigation system, unmanned aircraft systems (UAS), and Planet (PlanetScope multispectral imager, Planet Labs, Inc., San Francisco, Calif.) satellites. The surface reflectance data from multispectral (MS) sensors were used to compute the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). The experimental plots were managed with rainfed and irrigated treatments. Irrigation was applied according to a spatial evapotranspiration model informed with Planet satellite imagery. The NDVI and SAVI curves computed from the different sensing systems exhibited similar patterns and were able to capture differences between the rainfed and irrigated treatments when the crops were approaching senescence. Strong correlations were observed for canopy temperature measurements between the stationary and pivot-mounted infrared thermometer (IRT) sensors (p-value of less than 0.01 for the correlations) when canopy were scanned with no irrigation application (dry scans). The best correlation was obtained for the irrigated maize, which yielded r2 of 0.99, RMSE of 0.4°C, and MAE of 0.3°C. The correlation for the canopy temperature data collected during dry scan between UAS and pivot-mounted thermal sensors was weak with r2 = 0.26 to 0.28, larger RMSE values of 3.7°C and MAE values of 3.4°C. Secondary analysis between thermal data from stationary and pivot-mounted IRTs collected during wet scans (during an irrigation event) demonstrated reduced canopy temperature from pivot-mounted IRTs by approximately 2°C for irrigated soybean due to wetting of the canopy by the irrigation. Understanding the performance of these sensor systems is valuable in configuring practical design and operational considerations when using sensor feedback for irrigation management. Keywords: Center pivots, Irrigation, Multispectral, Remote sensing, Thermal, Unmanned aircraft systems.

Highlights Unmanned aerial systems (UAS) are able to provide data for precision irrigation management. Improvements are needed regarding UAS platforms, sensors, processing software, and regulations. Integration of multi-scale imagery into scientific irrigation scheduling tools are needed for technology adoption. Abstract . Several research institutes, laboratories, academic programs, and service companies around the United States have been developing programs to utilize small unmanned aerial systems (sUAS) as an instrument to improve the efficiency of in-field water and agronomical management. This article describes a decade of efforts on research and development efforts focused on UAS technologies and methodologies developed for irrigation management, including the evolution of aircraft and sensors in contrast to data from satellites. Federal Aviation Administration (FAA) regulations for UAS operation in agriculture have been synthesized along with proposed modifications to enhance UAS contributions to irrigated agriculture. Although it is feasible to use sUAS technology to produce maps of actual crop coefficients, actual crop evapotranspiration, and soil water deficits, for irrigation management, the technology and regulations need to evolve further to facilitate a successful wide adoption and application. Improvements and standards are needed in terms of cameras’ spectral (bands) ranges, radiometric resolutions and associated calibrations, fuel/power technology for longer missions, better imagery processing software, and easier FAA approval of higher altitudes flight missions among other issues. Furthermore, the sUAS technology would play a larger role in irrigated agriculture when integrating multi-scale data (sUAS, ground-based or proximal, satellite) and soil water sensors is addressed, including the need for advances on processing large amounts of data from multiple and different sources, and integration into scientific irrigation scheduling (SIS) systems for convenience of decision making. Desirable technological innovations, and features of the next generation of UAS platforms, sensors, software, and methods for irrigated agriculture, are discussed. Keywords: Agricultural water management, Irrigation prescription mapping, Irrigation scheduling, Precision irrigation, Remote sensing, Sensors, Spatial crop evaOotranspiration, Unmanned aerial systems.

Unmanned aerial systems (UAS) for collecting multispectral imagery of agricultural fields are becoming more affordable and accessible. However, there is need to validate calibration of sensors on these systems when using them for quantitative analyses such as evapotranspiration, and other modeling for agricultural applications. The results of laboratory testing of a MicaSense (Seattle, WA, USA) RedEdge™ 3 multispectral camera and MicaSense Downwelling Light Sensor (irradiance sensor) system using a calibrated integrating sphere were presented. Responses of the camera and irradiance sensor were linear over many light levels and became non-linear at light levels below expected real-world, field conditions. Simple linear corrections should suffice for most light conditions encountered during the growing season. Using an irradiance sensor or similar system may not properly account for light variability in cloudy or partly cloudy conditions as also identified by others. A simple stand for aiding in reference panel imaging was also described, which may facilitate repetitive, consistent reference panel imaging.

Estimation of turbulent heat fluxes via variational data assimilation (VDA) approaches has been the subject of several studies. The VDA approaches need an adjoint model that is difficult to derive. In this study, remotely sensed land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are assimilated into the heat diffusion equation within an ensemble Kalman smoother (EnKS) approach to estimate turbulent heat fluxes. The EnKS approach is tested in the Heihe River Basin (HRB) in northwest China. The results show that the EnKS approach can estimate turbulent heat fluxes by assimilating low temporal resolution LST data from MODIS. The findings indicate that the EnKS approach performs fairly well in various hydrological and vegetative conditions. The estimated sensible (H) and latent (LE) heat fluxes are compared with the corresponding observations from large aperture scintillometer systems at three sites (namely, Arou, Daman, and Sidaoqiao) in the HRB. The turbulent heat flux estimates from EnKS agree reasonably well with the observations, and are comparable to those of the VDA approach. The EnKS approach also provides statistical information on the H and LE estimates. It is found that the uncertainties of H and LE estimates are higher over wet and/or densely vegetated areas (grassland and forest) compared to the dry and/or slightly vegetated areas (cropland, shrubland, and barren land).

Estimating actual crop evapotranspiration (ETc) is a critical component in tracking soil water availability and managing near real-time irrigation scheduling. Energy and water balance models are two common approaches for estimating daily crop ETc. The Spatial EvapoTranspiration Modeling Interface (SETMI) hybrid model combines these two approaches and has been used to increase the accuracy of modeled ETc and soil water content by assimilating actual ET values to update the soil water balance. In this study, modeled daily ETc from the two-source energy balance (TSEB), root zone water balance, and the hybrid modeling approach were compared to measured ETc from eddy covariance flux tower systems to quantify model accuracy. The TSEB model used the Priestly-Taylor approximation for estimating ETc and the water balance model was updated with reflectance-based crop coefficients. The models were informed with UAS acquired multispectral reflectance and thermal infrared imagery collected over irrigated and rainfed maize and soybean fields during the 2018-2020 growing seasons.

Study region The Nebraska Sand Hills (NSH) lies in the western part of Nebraska, United States. We chose the north-eastern, central, and western parts of NSH with distinct climate, topography, and hydrology. Study focus The study assesses the response of hundreds of shallow groundwater-fed lakes to drought. Total lake area (TLA), determined by classifying Landsat satellite images from 1984 to 2018, was juxtaposed with published Palmer Drought Severity Index (PDSI) and detrended cumulative PDSI (DeCumPDSI) at monthly and annual timescales. The PDSI and DeCumPDSI were time lagged to incorporate the preceding climatic effect (groundwater time lag) and evaluated against TLA using Bayesian regression analysis. New hydrologic insight for the region TLA in the NSH respond to the seasonal as well as long-term climatic effects moderated by topography, surface, and subsurface hydrology. A higher determination coefficient R2 and lower mean square error of TLA at annual PDSI and DeCumPDSI illustrate the effect of long-term climatic fluctuations and groundwater influence: the evaporative losses from lakes are modulated by the lake-groundwater exchange, but the groundwater recharge has a longer response time to the drought. The study provides a simple method of assessment of the climate impact that results from the satellite data, gridded climate observation, and statistics for sensitive landscape of the NSH.

This article also appears in: A tribute to Edward P. Glenn (1947-2017): a legacy of scientific environmental assessment and applications in hydrological processes., Evapotranspiration (ET) is an important parameter in hydrologic processes and modelling. In agricultural watersheds with competing uses of fresh water including irrigated agriculture, estimating crop evapotranspiration (ETc) accurately is critical for improving irrigation system and basin water management. The use of remote sensing-based basal crop coefficients is becoming a common method for estimating crop evapotranspiration for multiple crops over large areas. The Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI), based on reflectance in the red and near-infrared bands, are commonly used for this purpose. In this paper, we examine the effects of row crop orientation and soil background darkening due to shading and soil surface wetness on these two vegetation indices through modelling, coupled with a field experiment where canopy reflectance of a cotton crop at different solar zenith angles, was measured with a portable radiometer. The results show that the NDVI is significantly more affected than the SAVI by background shading and soil surface wetness, especially in north–south oriented rows at higher latitudes and could lead to a potential overestimation of crop evapotranspiration and irrigation water demand if used for basal crop coefficient estimation. Relationships between the analysed vegetation indices and canopy biophysical parameters such as crop height, fraction of cover and leaf area index also were developed for both indices., Daugherty Water for Food Global Institute; Department of Biological and irrigation Engineering, Utah State University; Institute for Sustainable Agriculture-CSIC, Cordoba, Spain; Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Grant/Award Number: RTA2005-047; Programa Nacional de Ayudas Para La Movilidad de Profesores de Universidad e Investigadores Españoles y Extranjeros of The Ministerio de Educacion Y Ciencia, Grant/Award Number: SAB2004-0200.

Surface temperature (LST) is one of the main variables that involve the physical processes present on the surface of a planet and is one of the variables of greater relevance in the process of energy and water balance. Due to the importance of LST in the energy balance equation and in irrigated agriculture management, it is important to develop an estimation of methods for orbital sensors, such as Sentinel 2A and 2B, that do not have the capacity to capture the electromagnetic radiation emitted in the thermal infrared region. Thus, the aim of this work was to estimate the surface temperature from the MSI/Sentinel 2A sensor using the LST obtained from the TIRS (Thermal Infrared Sensor) present on the Landsat-8 platform as a reference. In this context, we used different methodologies of pattern recognition algorithms to establish the best model: SVMlinear (support vector machine linear), SVMradial (support vector machine radial), LM (linear regression), RIDGE, RF (random forest), Cubist, PLS (partial least squares), PCR (principal components regression), GBM (generalized boosted regression), and BRNN (Bayesian regularized neural network). After the LST was estimated by the different techniques, a set of statistical metrics was used to select the best model, which in this case was the RF. With the best model defined, we calculated the actual and reference evapotranspiration ratio (r) using the LST of this model and compared it with the LST that was obtained by Landsat-8, considered a standard, to verify the applicability of our methodology.

The lack of measurement of precipitation in large areas using fine-resolution data is a limitation in water management, particularly in developing countries. However, Version 6 of the Integrated Multi-satellitE Retrievals for GPM (IMERG) has provided a new source of precipitation information with high spatial and temporal resolution. In this study, the performance of the GPM products (Final run) in the state of Paraná, located in the southern region of Brazil, from June 2000 to December 2018 was evaluated. The daily and monthly products of IMERG were compared to the gauge data spatially distributed across the study area. Quantitative and qualitative metrics were used to analyze the performance of IMERG products to detect precipitation events and anomalies. In general, the products performed positively in the estimation of monthly rainfall events, both in volume and spatial distribution, and demonstrated limited performance for daily events and anomalies, mainly in mountainous regions (coast and southwest). This may be related to the orographic rainfall in these regions, associating the intensity of the rain, and the topography. IMERG products can be considered as a source of precipitation data, especially on a monthly scale. Product calibrations are suggested for use on a daily scale and for time-series analysis.

Data were compiled from an experiment evaluating the effects of month of calving, wintering system, and calf management on cow and calf performance to model the amount of water (green and blue) required to produce beef in Nebraska production systems. The referenced study was conducted over four years utilizing 217 cow/calf pairs per year. Cows were wintered on either native range or corn residue and month of calving was March, June, or August. Calves were managed as calf-feds or yearlings and only steer calves were included in the model. A 365-d period was utilized to estimate the cow’s contribution of water inputs based on dry matter intake throughout the year varying by production system. Diet characteristics, dry-matter intake, days on feed, average daily gain, and hot carcass weight were measurements used to estimate water utilization by the calves. The total water footprint was calculated by dividing the total amount of water used for each system (L) by the amount of boneless beef produced (kg). Production systems where cows were wintered on corn residue utilized 18% less water than systems utilizing native range as a wintering source, because of water allocations using the value fraction method. Increasing the dietary inclusion of distillers grains from 0% to 40% decreased the water footprint in the finishing phase by 29%. Utilizing by-products in replacement of primary crops decreases the overall water footprint of production. Across all scenarios modeled, more than 50% of the water footprint was allocated to the cow grazing rainfed pasture (green water) and over 98% of the water footprint was allocated to growing feed. Improvements in crop water use efficiency will benefit livestock production. The water footprint of the beef systems analyzed was 80% green water as rain, minimizing the environmental impact of beef production on freshwater use and ecological water balance.

The event continues to be the leading international conference for researchers, engineers and scientists involved in the latest developments in all aspects of earth observation, next generation satellites, atmospheric propagation, and imaging analytics.

Estimating actual crop evapotranspiration (ET) is a critical component in tracking crop water availability and managing irrigation. Various methods presently exist for modeling crop ET utilizing remotely sensed data and imagery as inputs. While more traditional remote sensing platforms like satellite and manned aircraft provide useful information, limitations exist due to lack of spatiotemporal resolution and high cost. Unmanned aerial systems (UAS) now offer greater opportunities for collecting remotely sensed data with enhanced spatiotemporal resolution that may lead to increased accuracy in estimating crop (ET), making near real-time irrigation management more feasible. The two-source energy balance (TSEB) model is one of the methods of estimating crop ET from surface energy balance fluxes. In this research, multispectral and thermal infrared imagery collected with a UAS over maize and soybean fields throughout the growing season at different vegetative stages were used in the TSEB model to estimate spatially distributed surface energy balance fluxes and daily crop ET. The estimated spatially distributed surface fluxes and ET were weighted and aggregated using a two-dimensional flux footprint and validated against measured fluxes from Eddy Covariance systems located within the fields.

SAFER (Simple Algorithm for Evapotranspiration Retrieving) is a relatively new algorithm applied successfully to estimate actual crop evapotranspiration (ET) at different spatial scales of different crops in Brazil. However, its use for monitoring irrigated crops is scarce and needs further investigation. This study assessed the performance of SAFER to estimate ET of irrigated corn in a Brazilian semiarid region. The study was conducted in São Desidério, Bahia State, Brazil, in corn-cropped areas in no-tillage systems and irrigated by central pivots. SAFER algorithm with original regression coefficients (a = 1.8 and b = –0.008) was initially tested during the growing seasons of 2014, 2015, and 2016. SAFER performed very poorly for estimating corn ET, with RMSD values greater than 1.18 mm d –1 for 12 fields analyzed and NSE values < 0 in most fields. To improve estimates, SAFER regression coefficients were calibrated (using 2014 and 2015 data) and validated with 2016 data, with the resulting coefficients a and b equal to 0.32 and –0.0013, respectively. SAFER performed well for ET estimation after calibration, with r 2 and NSE values equal to 0.91 and RMSD = 0.469 mm d –1 . SAFER also showed good performance (r 2 = 0.86) after validation, with the lowest RMSD (0.58 mm d –1 ) values for the set of 14 center pivots in this growing season. The results support the use of calibrated SAFER algorithm as a tool for estimating water consumption in irrigated corn fields in semiarid conditions.

Water productivity benchmarks for irrigated and rainfed agriculture will provide relevant information to manage scarce water resources and control groundwater level decline. We analyse the temporal and spatial variation of the water productivity (WP) of maize and soybean in Nebraska, with WP defined as harvested crop weight per total evapotraspiration. The results show that WP of both maize and soybean increase from west to east within Nebraska and have increased over the last 25 years, mainly due to the increase in crop yields (land productivity). We derive WP benchmarks for each crop per climate zone. Increasing actual WPs in the state to benchmark levels will increase yields by 21 % for maize and by 19 % for soybean. The WP benchmark levels for the two crops presented here will help formulating targets for closing water productivity gaps and improving the sustainability of water use in the state.

Evapotranspiration ( E T ) provides a strong connection between surface energy and hydrological cycles. Advancements in remote sensing techniques have increased our understanding of energy and terrestrial water balances as well as the interaction between surface and atmosphere over large areas. In this study, we computed surface energy fluxes using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm and a simplified adaptation of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for automated endmember selection. Our main purpose was to assess and compare the accuracy of the automated calibration of the SEBAL algorithm using two different sources of meteorological input data (ground measurements from an eddy covariance flux tower and reanalysis data from Modern-Era Reanalysis for Research and Applications version 2 (MERRA-2)) to estimate the dry season partitioning of surface energy and water fluxes in a transitional area between tropical rainforest and savanna. The area is located in Brazil and is subject to deforestation and cropland expansion. The SEBAL estimates were validated using eddy covariance measurements (2004 to 2006) from the Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA) at the Bananal Javaés (JAV) site. Results indicated a high accuracy for daily ET, using both ground measurements and MERRA-2 reanalysis, suggesting a low sensitivity to meteorological inputs. For daily ET estimates, we found a root mean square error (RMSE) of 0.35 mm day−1 for both observed and reanalysis meteorology using accurate quantiles for endmembers selection, yielding an error lower than 9% (RMSE compared to the average daily ET). Overall, the ET rates in forest areas were 4.2 mm day−1, while in grassland/pasture and agricultural areas we found average rates between 2.0 and 3.2 mm day−1, with significant changes in energy partitioning according to land cover. Thus, results are promising for the use of reanalysis data to estimate regional scale patterns of sensible heat (H) and latent heat (LE) fluxes, especially in areas subject to deforestation.

In Table 2, the unit for nitrogen, phosphate, and potash should have been Mg instead of g (Figure Presented).

Accurate estimates of sensible (H) and latent (LE) heat fluxes and actual evapotranspiration (ET) are required for monitoring vegetation growth and improved agricultural water management. A large aperture scintillometer (LAS) was used to provide these estimates with the objective of quantifying the effects of surface heterogeneity due to soil moisture and vegetation growth variability. The study was conducted over drip-irrigated vineyards located in a semi-arid region in Albacete, Spain during summer 2007. Surface heterogeneity was characterized by integrating eddy covariance (EC) observations of H, LE and ET, land surface temperature (LST) and normalized difference vegetation index (NDVI) data from Landsat and MODIS sensors, LST from an infrared thermometer (IRT), a data fusion model, and a two-source surface energy balance model. The EC observations showed 16% lack of closure during unstable atmospheric conditions and was corrected using the residual method. The comparison between the LAS and EC measurements of H, LE, and ET showed root mean square difference (RMSD) of 25 W m&minus, 2, 19 W m&minus, 2, and 0.41 mm day&minus, 1, respectively. LAS overestimated H and underestimated both LE and ET by 24 W m&minus, 2, 34 W m&minus, 2, and 0.36 mm day&minus, 1, respectively. The effects of soil moisture on LAS measurement of H was evaluated using the Bowen ratio, &beta, Discrepancies between HLAS and HEC were higher at &beta, &le, 0.5 but improved at 1 &ge, &beta, &gt, 0.5 and &beta, 1.0 with R2 of 0.76, 0.78, and 0.82, respectively. Variable vineyard growth affected LAS performance as its footprints saw lower NDVILAS compared to that of the EC (NDVIEC) by ~0.022. Surface heterogeneity increased during wetter periods, as characterized by the LST&ndash, NDVI space and temperature vegetation dryness index (TVDI). As TVDI increased (decreased) during drier (wetter) conditions, the discrepancies between HLAS and HEC, as well as LELAS and LEEC Re decreased (increased). Thresholds of TVDI of 0.3, 0.25, and 0.5 were identified, above which better agreements between LAS and EC estimates of H, LE, and ET, respectively, were obtained. These findings highlight the effectiveness and ability of LAS in monitoring vegetation growth over heterogonous areas with variable soil moisture, its potential use in supporting irrigation scheduling and agricultural water management over large regions.

Improvements in soil water balance modeling can be beneficial for optimizing irrigation management to account for spatial variability in soil properties and evapotranspiration (ET). A remote-sensing-based ET and water balance model was tested for irrigation management in an experiment at two University of Nebraska-Lincoln research sites located near Mead and Brule, Nebraska. Both fields included a center pivot equipped with variable rate irrigation (VRI). The study included maize in 2015 and 2016 and soybean in 2016 at Mead, and maize in 2016 at Brule, for a total of 210 plot-years. Four irrigation treatments were applied at Mead, including: VRI based on a remote sensing model (VRI-RS); VRI based on neutron probe soil water content measurement (VRI-NP); uniform irrigation based on neutron probe measurement; and rainfed. Only the VRI-RS and uniform treatments were applied at Brule. Landsat 7 and 8 imagery were used for model input. In 2015, the remote sensing model included reflectance-based crop coefficients for ET estimation in the water balance. In 2016, a hybrid component of the model was activated, which included energy-balance-modeled ET as an input. Both 2015 and 2016 had above-average precipitation at Mead; subsequently, irrigation amounts were relatively low. Seasonal irrigation was greatest for the VRI-RS treatment in all cases because of drift in the water balance model. This was likely caused by excessive soil evaporation estimates. Irrigation application for the VRI-NP at Mead was about 0 mm, 6 mm, and –12 mm less in separate analyses than for the uniform treatment. Irrigation for the VRI-RS was about 40 mm, 50 mm, and –98 mm greater in separate analyses than the uniform at Mead and about 18 mm greater at Brule. For maize at Mead, treatment effects were primarily limited to hydrologic responses (e.g., ET), with differences in yield generally attributed to random error. Rainfed soybean yields were greater than VRI-RS yields, which may have been related to yield loss from lodging, perhaps due to over-irrigation. Regarding the magnitude of spatial variability in the fields, soil available water capacity generally ranked above ET, precipitation, and yield. Future research should include increased cloud-free imagery frequency, incorporation of soil water content measurements into the model, and improved wet soil evaporation and drainage estimates.

This paper develops and proposes a simplified operational remote sensing approach to assist crop growth models in reproducing actual processes in the field by relating satellite based remote sensing data and key canopy biophysical parameters. While relationships between spectral vegetation indices (VI) and biomass production have been conducted in the past, we specifically pursue the relationship between crop transpiration and biomass production as described in the FAO-66 Aquacrop manual. The authors point to a possible general relationship between a transpiration coefficient (herein we propose the basal crop coefficient, Kcb, as a proxy) and biomass production. In parallel, many studies have demonstrated the well-established relationship between Kcb and remote sensing based VI. Thus, the relationship between both parameters has a strong basis but must be demonstrated. We analyze the relationship between biomass production and the reflectance based Kcb using field data obtained during 11 years in irrigated and rainfed soybeans and maize in eastern Nebraska. The analysis confirms that the relationship is strong and paves the way for the use of remote sensing data for a quantitative analysis of crop biomass production and yield.

Accurate generation of spatial soil water maps is useful for many types of irrigation management. A hybrid remote sensing evapotranspiration (ET) model combining reflectance-based basal crop coefficients (Kcbrf) and a two-source energy balance (TSEB) model was modified and validated for use in real-time irrigation management. We modeled spatial ET for maize and soybean fields in eastern Nebraska for the 2011-2013 growing seasons. We used Landsat 5, 7, and 8 imagery as remote sensing inputs. In the TSEB, we used the Priestly-Taylor (PT) approximation for canopy latent heat flux, as in the original model formulations. We also used the Penman-Monteith (PM) approximation for comparison. We compared energy balance fluxes and computed ET with measurements from three eddy covariance systems within the study area. Net radiation was underestimated by the model when data from a local weather station were used as input, with mean bias error (MBE) of -33.8 to -40.9 W m-2. The measured incident solar radiation appeared to be biased low. The net radiation model performed more satisfactorily when data from the eddy covariance flux towers were input into the model, with MBE of 5.3 to 11.2 W m-2. We removed bias in the daily energy balance ET using a dimensionless multiplier that ranged from 0.89 to 0.99. The bias-corrected TSEB ET, using weather data from a local weather station and with local ground data in thermal infrared imagery corrections, had MBE = 0.09 mm d-1 (RMSE = 1.49 mm d-1) for PM and MBE = 0.04 mm d-1 (RMSE = 1.18 mm d-1) for PT. The hybrid model used statistical interpolation to combine the two ET estimates. We computed weighting factors for statistical interpolation to be 0.37 to 0.50 for the PM method and 0.56 to 0.64 for the PT method. Provisions were added to the model, including a real-time crop coefficient methodology, which allowed seasonal crop coefficients to be computed with relatively few remote sensing images. This methodology performed well when compared to basal crop coefficients computed using a full season of input imagery. Water balance ET compared favorably with the eddy covariance data after incorporating the TSEB ET. For a validation dataset, the magnitude of MBE decreased from -0.86 mm d-1 (RMSE = 1.37 mm d-1) for the Kcbrf alone to -0.45 mm d-1 (RMSE = 0.98 mm d-1) and -0.39 mm d-1 (RMSE = 0.95 mm d-1) with incorporation of the TSEB ET using the PM and PT methods, respectively. However, the magnitudes of MBE and RMSE were increased for a running average of daily computations in the full May-October periods. The hybrid model did not necessarily result in improved model performance. However, the water balance model is adaptable for real-time irrigation scheduling and may be combined with forecasted reference ET, although the low temporal frequency of satellite imagery is expected to be a challenge in real-time irrigation management. Keywords: Center-pivot irrigation, ET estimation methods, Evapotranspiration, Irrigation scheduling, Irrigation water balance, Model validation, Variable-rate irrigation.

The combination of Unmanned/Unoccupied Aerial Vehicle (UAV) data and deep learning, especially convolutional neural networks (CNNs), offers robust new tools for precision land cover mapping. However, its successful application is highly dependent on local experiences that are rarely documented, resulting in practical limitations during implementation. Cost-effective deep learning frameworks for fast deployment are required. This study presents a deep learning adaptation framework, named Auto-UNet++, trying to streamline wetland mapping tasks (including training data labeling and organizing). The framework treats mapping tasks as an intact semantic segmentation pipeline and then integrates automatic strategies into each step to reduce human intervention. These automatic strategies are achieved by standard computer vision techniques, including multi-view (MV) imaging—highly overlapped UAV images over an area (for labeling/voting), unsupervised clustering (for labeling), multi-scale CNN (for feature extraction), and attention mechanism—a CNN design used to select informative features from input (for feature exploration/selection). The framework was tested on playa wetland mapping in the Rainwater Basin, Nebraska, USA, with multispectral UAV datasets. Generally, the multi-scale CNN mapping task achieved a high of 87% overall accuracy and over 90% accuracy in water delineation. The results indicate that the multi-view and attention strategies have the potential to improve segmentation performance, and together with unsupervised learning, save considerable labor/expertise. Interestingly, evidence shows that the band/scale attention (weight) is adaptively associated with the land cover percentages per input image, indicating spatial contexts captured. This finding highlights the potential usages of the attention rule in automatic feature exploration, selection, and model interpretation. The framework illustrating a highly automated deep learning deployment on small MV datasets facilitates cost-effective wetland cover mapping. Although limitations exist, the study demonstrated the possibility of where/how conventional segmentation pipelines can be improved in typical UAV wetland mapping tasks. The framework and findings are useful for similar applications (including non-UAV studies) that only have limited time, labor, and expertise to implement sophisticated semantic segmentation models.

Groundwater recharge rate estimation is crucial to sustainable development of aquifers in intensely pumped regions, such as the Urucuia Aquifer System (UAS). A sedimentary aquifer in Western Bahia, Brazil, that underlies one of the major agricultural areas of the country where there has been major growth of irrigated areas. This study seeks to evaluate the recharge component of the water budget in the UAS area, based on three complementary techniques. The double-ring infiltrometer test was used to evaluate surface infiltration capacity, an important control on recharge. Water level data from wells (2011–2019 period, 19 wells) in the Brazilian Geological Survey's Integrated Groundwater Monitoring Network (RIMAS-CPRM) was used to estimate the aquifer recharge using the water table fluctuation (WTF) method. Additionally, this study used the Soil Water Assessment Tool (SWAT) model in two selected sub-basins to estimate deep recharge from the surface hydrological data. The results of the infiltrometer tests show a notable difference in the infiltration rates between the natural vegetation zones and cropped areas. The WTF and SWAT simulations results suggest similar ranges of recharge rate (an average of 24% of precipitation, in both methods). Results of the study indicate equivalence of these methods to estimate the recharge in sedimentary unconfined aquifers as UAS.

Irrigation has a great impact on global food security as it contributes to the majority of the world’s agricultural food supply. It is essential to judiciously utilize water resources through efficient irrigation management since the majority of U.S. groundwater aquifers are rapidly depleting. Thus, quantification of the relationships between water depletion and environmental factors is important for understanding crop response to varying levels of water stresses that depletion can cause. The objectives of this research were to: 1) investigate the relationship between root zone water depletion (Drw) and canopy temperature differential (ΔT) at different ranges of Drw; and 2) develop upper (water stressed) and lower (non-water stressed) baselines for quantification of crop water stress index (CWSI) in a sub-humid climate. The research was conducted over maize and soybean during 2018, 2019, and 2020 growing seasons. Sensor node stations comprising of an infrared thermometer and three soil water sensors were installed at various sites over maize and soybean fields. ΔT tends to increase with the increase in Drw when the range of Drw includes values greater than 170 mm for maize and values greater than 160 mm for soybean. The results indicate that ΔT and Drw are unrelated until a soil-water depletion threshold is attained, and these Drw threshold values could be considered as indicators to trigger irrigation for efficient agricultural water management. To the best of the authors’ knowledge, the research is the first to develop upper and lower CWSI baselines for east-central Nebraska. The baselines developed in this study could facilitate the quantification of CWSI for irrigation scheduling of maize and soybean in east-Central Nebraska. Future work should aim to investigate the potential in using Drw and/or ΔT to determine efficient water allocation and if a threshold CWSI could be used for timing of irrigation to prevent yield loss.

With the increasing attention to site-specific or variable rate irrigation management, it is helpful to reconsider the quantity and placement of soil water monitoring locations in this context. Volumetric soil water content (θv) was monitored using a neutron probe (NP) at 72 locations in a center pivot irrigated field in eastern Nebraska. Variance reduction and temporal stability analyses were performed on θv from shallow (∼top 46 cm) and full profile (∼122 cm) readings for four monitoring cycles in the 2015 growing season and 2016 preseason. Eleven additional cycles were included for a subset of the data for the temporal stability analysis. The spatial correlation scale for θv was found to be less than the closest spacing of monitoring locations in the study (i.e.

Planting density can influence the competitiveness among plants mainly by water, nutrients and solar radiation, directly affecting yield. The purpose of this study was to evaluate the productivity of different soybean cultivars at different planting populations under irrigated and rainfed conditions. The experiment was conducted during the 2014/15 growing season, under center pivot irrigation in Chapadão do Sul – MS, Brazil. The experimental design was randomized complete block design with split-plot, as follows: Irrigated and Rainfed (plots) x Six cultivars (subplots) x Three Plant Populations (sub-subplots) with 4 replications. The irrigation management was realized by Penman-Monteith-FAO method. The cultivars used were: NA 5909 RR, DM 5958 IPRO, Anta 82 RR, M 7110 IPRO, Desafio RR and M 7739 IPRO. Populations indicated by the holders of the seeds were tested, and also population 20% above and 20% below with the hypothesis that in different humidity conditions, different cultivars may have different answers depending on their populations. In rainfed condition productivity and mass of one hundred grains were influenced by water deficit. Cultivar Desafio RR presented the best performance under irrigation, with productivity of 6174 kg ha−1.

Methodologies based on earth observation remote sensing allow for a precise estimation of actual water requirements for irrigated crops across large areas. In spite of the many number of experiments using or analyzing the relationship between the basal crop coefficient (Kcb) and the soil adjusted vegetation index (SAVI) for maize, the development of new maize hybrid varieties with modifications related to canopy architecture suggest a possible change of the leaf area index (LAI) for maximum Kcb and its relationship with the SAVI or other vegetation indices. In addition, we noted a lack of analysis of these relationships for cultivated soybean. The objective of this paper is to analyze the Kcb, SAVI and LAI relationships in maize and soybean based on the non-linear relationships proposed by Choudhury et al. (1994). In addition, we propose a modification of the Choudhury et al. (1994) approach based on field-based experimental evidence of a minimum Kcb greater than 0. For sites with limited field data, we also analyze the utility of a simple linear regression based on the Kcb and SAVI values for bare soil and maximum Kcb values. The resulting Kcb-SAVI relationships are assimilated into a remote sensing based soil water balance model. The results of the model are analyzed in terms of irrigation requirements and crop evapotranspiration (ETa) for 11 growing seasons in two fields cultivated with irrigated and rain-fed maize and soybean in eastern Nebraska. Comparisons of measured and modelled ETa values indicate a good agreement, with RMSE lower than 0.7 mm d−1 for weekly averaged values. The comparison of actual irrigation applied and irrigation requirements indicate the central pivot systems could not supply adequate water in some growing seasons with higher demands.

Crop simulation models are valuable tools for quantifying crop yield response to water, and for devising strategies to improve agricultural water management. However, applicability of the majority of crop models is limited greatly by a failure to provide open-access to model source code. In this study, we present an open-source version of the FAO AquaCrop model, which simulates efficiently water-limited crop production across diverse environmental and agronomic conditions. Our model, called AquaCrop-OpenSource (AquaCrop-OS), can be run in multiple programming languages and operating systems. Support for parallel execution reduces significantly simulation times when applying the model in large geospatial frameworks, for long-run policy analysis, or for uncertainty assessment. Furthermore, AquaCrop-OS is compliant with the Open Modelling Interface standard facilitating linkage to other disciplinary models, for example to guide integrated water resources planning., This research was funded in part by USDA NIFA Award number 2012-67003-23227 and by a PhD studentship from the Grantham Institute at Imperial College London. Further support was provided by the Daugherty Water for Food Global Institute at the University of Nebraska.

Variable rate irrigation (VRI) may improve center pivot irrigation management, including deficit irrigation. A remote-sensing-based evapotranspiration model was implemented with Landsat imagery to manage irrigations for a VRI equipped center pivot irrigated field located in West-Central Nebraska planted to maize in 2017 and soybean in 2018. In 2017, the study included VRI using the model and uniform irrigation using neutron attenuation for full irrigation with no intended water stress (VRI-Full and Uniform-Full treatments, respectively). In 2018, two deficit irrigation treatments were added (VRI-Deficit and Uniform-Deficit, respectively) and the model was modified in an attempt to reduce water balance drift; model performance was promising, as it was executed unaided by measurements of soil water content throughout the season. VRI prescriptions did not correlate well with available water capacity (R2 < 0.4); however, they correlated better with modeled ET in 2018 (R2 = 0. 69, VRI-Full; R2 = 0.55, VRI-Deficit). No significant differences were observed in total intended gross irrigation depth in 2017 (VRI-Full = 351 mm, Uniform Full = 344). However, in 2018, VRI resulted in lower mean prescribed gross irrigation than the corresponding uniform treatments (VRI-Full = 265 mm, Uniform Full = 282 mm, VRI-Deficit = 234 mm, and Uniform Deficit = 267 mm). Notwithstanding the differences in prescribed irrigation (in 2018), VRI did not affect dry grain yield, with no statistically significant differences being found between any treatments in either year (F = 0.03, p-value = 0.87 in 2017; F = 0.00, p-value = 0.96 for VRI/Uniform and F = 0.01, p-value = 0.93 for Full/Deficit in 2018). Likewise, any reduction in irrigation application apparently did not result in detectable reductions in deep percolation potential or actual evapotranspiration. Additional research is needed to further vet the model as a deficit irrigation management tool. Suggested model improvements include a continuous function for water stress and an optimization routine in computing the basal crop coefficient.

Knowing the variation of the water consumption of a crop or vegetation can help to avoid damages caused by the lack of water, besides allowing a better knowledge of the environment around. Thus, variations in evapotranspiration in sugarcane, planted forest, and native forest were examined as a function of relief variations and terrain exposure in the northeast sector of the State of Sao Paulo, Brazil (20°43′55.2″S–22°36′28.8″S, 46°37′48.0″W–48°50′16.8″W). These areas, covering a total of 2232 km2, were studied using monthly Landsat 8 satellite data (Path 220 and Row 75) collected over 24 months from April 2013 to March 2015. Thus, this study aimed to use a hybrid Two Source Energy Balance (TSEB), adjusted to convert hourly ET to monthly and annually data, in obtaining ET for two years to areas with sugarcane, planted and native forests and evaluate how this evapotranspiration behaves spatially for some parameters, as altitude, slope, exposure faces of the terrain and biomes, using multivariate analysis. The results identified three evapotranspiration groups: highest evapotranspiration in the highest altitudes and topographic slope; medium evapotranspiration in medium altitudes and slopes; and low evapotranspiration at low altitudes and slopes. The highest rates of evaporation occurred in summer and fall, when temperatures were highest.