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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).

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.

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.

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.

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.

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.

It is known that the soil type and its characteristics are very important to the agriculture and to maintain the health of a forest. Thus, the objective of this work was to verify, through multivariate statistical analysis techniques, the variations of evapotranspiration values as a function of the soil type in three different vegetation cover areas: sugarcane, planted forest and native forest. For this, an area of 2231.926 km2 was chosen, using monthly Landsat 8 satellite imagery with Orbit/Path 220 and Point/Row 75, between latitudes 20°43′55.2″S and 22°36′28.8″S and longitudes 46°37′48,0″W and 48°50′16,8″W, in the region of Corumbatai, northeast of the State of Sao Paulo, Brazil, collected over 24 months between April 2013 and March 2015. The evapotranspiration evaluation was obtained by satellite imagery using a hybrid model of the Two Source Energy Balance (TSEB), with a proposed adjustment methodology to convert hourly ET to monthly and annually data. The multivariate statistical analysis of monthly ET patterns across soil type classified three evapotranspiration groups in the three studied land cover areas. In the areas with sugarcane, evapotranspiration separated statistical groups in the areas of the Cerrado biome with Ferralsol (LVA) and Lithic Leptosol (RL), another group in the areas with Ferralsol (LV) and Ferralic Arenosol (RQ), and a third group of Acrisol (PVA) in an Atlantic Forest biome area. In the areas with planted forest and native forest, evapotranspiration separated a first group with soils LVA, RQ, PVA, Dystric Gleysol (GX) and RL in areas of Cerrado, a second group with LV in Atlantic Forest area and a third group just with Fibric Histosol (OY).

Publisher's version (útgefin grein), This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come., We would like to thank the members of the IAHS, EGU, AGU and IAH for supporting this initiative. The LinkedIn group and overall secretariat was hosted by the IAHS, the Splinter meeting by EGU and the Vienna Catchment Science Symposium by the Vienna Doctoral Programme on Water Resource Systems (DK W1219-N28) funded by the Austrian Science Funds (FWF)., "Peer Reviewed"

A number of studies have estimated turbulent heat fluxes by assimilating sequences of land surface temperature (LST) observations into the strong constraint-variational data assimilation (SC-VDA) approaches. The SC-VDA approaches do not account for the structural model errors and uncertainties in the micrometeorological variables. In contrast to the SC-VDA approaches, the WC-VDA approach (the so-called weak constraint-VDA) accounts for the effects of structural and model errors by adding a model error term. In this study, the WC-VDA approach is tested at six study sites with different climatic and vegetative conditions. Its performance is also compared with that of SC-VDA at the six study sites. The results show that the WC-VDA produces 10.16% and 10.15% lower root mean square errors (RMSEs) for sensible and latent heat flux estimates compared with the SC-VDA approach. The model error term can capture errors in the turbulent heat flux estimates due to errors in LST and micrometeorological measurements, as well as structural model errors, and does not allow those errors to adversely affect the turbulent heat flux estimates. The findings also indicate that the estimated model error term varies reasonably well, so as to capture the misfit between predicted and observed net radiation in different hydrological and vegetative conditions. Finally, synthetically generated positive (negative) noises are added to the hydrological input variables (e.g., LST, air temperature, air humidity, incoming solar radiation, and wind speed) to examine whether the WC-VDA approach can capture those errors. It was found that the WC-VDA approach accounts for the errors in the input data and reduces their effect on the turbulent heat flux estimates.

This paper describes the image acquisition and processing methodology, including surface emissivity and atmospheric corrections, for generating surface temperatures of two active hydrothermal systems in Yellowstone National Park. Airborne thermal infrared (8–12 μm) images were obtained annually from 2007 to 2012 using a FLIR SC640 thermal infrared camera system. Thermal infrared image acquisitions occurred under clear-sky conditions after sunset to meet the objective of providing high-spatial resolution, georectified imagery for hydrothermal monitoring. Comparisons of corrected radiative temperature maps with measured ground and water kinetic temperatures at flight times provided an assessment of temperature accuracy. A repeatable, time-sequence of images for Hot Spring Basin (2007–2012) and Norris Geyser Basin (2008–2012) documented fracture-related changes in temperature and fluid flow for both hydrothermal systems, highlighting the utility of methods for synoptic monitoring of Yellowstone National Park's hydrothermal systems.

Thermal and multispectral remote sensing data from low-altitude aircraft can provide high spatial resolution necessary for sub-field (≤ 10 m) and plant canopy (≤ 1 m) scale evapotranspiration (ET) monitoring. In this study, high-resolution (sub-meter-scale) thermal infrared and multispectral shortwave data from aircraft are used to map ET over vineyards in central California with the two-source energy balance (TSEB) model and with a simple model having operational immediate capabilities called DATTUTDUT (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature). The latter uses contextual information within the image to scale between radiometric land surface temperature (TR) values representing hydrologic limits of potential ET and a non-evaporative surface. Imagery from 5 days throughout the growing season is used for mapping ET at the sub-field scale. The performance of the two models is evaluated using tower-based measurements of sensible (H) and latent heat (LE) flux or ET. The comparison indicates that TSEB was able to derive reasonable ET estimates under varying conditions, likely due to the physically based treatment of the energy and the surface temperature partitioning between the soil/cover crop inter-row and vine canopy elements. On the other hand, DATTUTDUT performance was somewhat degraded presumably because the simple scaling scheme does not consider differences in the two sources (vine and inter-row) of heat and temperature contributions or the effect of surface roughness on the efficiency of heat exchange. Maps of the evaporative fraction (EF = LE/(H + LE)) from the two models had similar spatial patterns but different magnitudes in some areas within the fields on certain days. Large EF discrepancies between the models were found on 2 of the 5 days (DOY 162 and 219) when there were significant differences with the tower-based ET measurements, particularly using the DATTUTDUT model. These differences in EF between the models translate to significant variations in daily water use estimates for these 2 days for the vineyards. Model sensitivity analysis demonstrated the high degree of sensitivity of the TSEB model to the accuracy of the TR data, while the DATTUTDUT model was insensitive to systematic errors in TR as is the case with contextual-based models. However, it is shown that the study domain and spatial resolution will significantly influence the ET estimation from the DATTUTDUT model. Future work is planned for developing a hybrid approach that leverages the strengths of both modeling schemes and is simple enough to be used operationally with high-resolution imagery.

Nitrogen management in crops is a key activity for agricultural production. Methods that can determine the levels of this element in plants in a quick and non-invasive way are extremely important for improving production systems. Within several fronts of study on this subject, proximal and remote sensing methods are promising techniques. In this regard, this research sought to demonstrate the relationships between variations in leaf nitrogen content (LNC) and sugarcane spectral behaviour. The work was carried out in three experimental areas in São Paulo State, Brazil, with different soils, varieties and nitrogen rates during the 2012/13 and 2013/14 seasons. A significant correlation was observed between the LNC and variations in the sugarcane spectra. The green and red-edge spectral bands were the most consistent and stable predictors of LNC among the evaluated harvests. Stepwise multiple linear regression analysis (MSLR) generated better models for LNC estimation when calibrated with experimental area, independent of the variety. The present research demonstrates that specific wavelengths are associated with the variation in LNC in sugarcane, and these are reported in the green region (near 550 nm) and in the red-edge wavelengths (680 to 720 nm). These results may help in future research on the direct in situ application of nitrogen fertilizers. Nitrogen management in crops is a key activity for agricultural production. Methods that can determine the levels of this element in plants in a quick and non-invasive way are extremely important for improving production systems. Within several fronts of study on this subject, proximal and remote sensing methods are promising techniques. In this regard, this research sought to demonstrate the relationships between variations in leaf nitrogen content (LNC) and sugarcane spectral behaviour. The work was carried out in three experimental areas in São Paulo State, Brazil, with different soils, varieties and nitrogen rates during the 2012/13 and 2013/14 seasons. A significant correlation was observed between the LNC and variations in the sugarcane spectra. The green and red-edge spectral bands were the most consistent and stable predictors of LNC among the evaluated harvests. Stepwise multiple linear regression analysis (MSLR) generated better models for LNC estimation when calibrated with experimental area, independent of the variety. The present research demonstrates that specific wavelengths are associated with the variation in LNC in sugarcane, and these are reported in the green region (near 550 nm) and in the red-edge wavelengths (680 to 720 nm). These results may help in future research on the direct in situ application of nitrogen fertilizers.

Crop biomass (Bio) is one of the most important parameters of a crop, and knowledge of it before harvest is essential to help farmers in their decision making. Both green and dry Bio can be estimated from vegetation spectral indices (VIs) because they have a close relationship with accumulated absorbed photosynthetically active radiation (APAR), which is proportional to total Bio. The aims of this study were to analyze the potential capacity of spectral vegetation indices in estimating corn green biomass based on their relationship with the photosynthetic vegetation sub-pixel fraction derived from spectral mixture analysis and to analyze the best interval of VI accumulation (days) for corn grain yield estimation. Field data of center pivots cultivated with corn during the irrigation seasons of 2015 and 2018 and Landsat 8 and Sentinel 2 images were used. The EVI produced the best results; Pearson's correlation coefficient, RMSE and Willmott’s index reached 0.99, 6.5%, and 0.948, respectively. Among the nine potential VIs analyzed, the EVI, SAVI and OSAVI were considered the first, second and third best performing for corn green Bio estimation, respectively, based on their comparison to the photosynthetic vegetation sub-pixel fraction (fPV), and the time intervals that extended until 120 days after sowing showed the best results for corn grain yield estimation.

Variable Rate Irrigation (VRI) considers spatial variability in soil and plant characteristics to optimize irrigation management in agricultural fields. The advent of unmanned aircraft systems (UAS) creates an opportunity to utilize high-resolution (spatial and temporal) imagery into irrigation management due to decreasing costs, ease of operation, and reduction of regulatory constraints. This research aimed to evaluate the use of UAS data for VRI, and to quantify the potential of VRI in terms of relative crop and water response. Irrigation treatments were: (1) VRI using Landsat imagery (VRI-L), (2) VRI using UAS imagery (VRI-U), (3) uniform (U), and (4) rainfed (R). An updated remote-sensing-based evapotranspiration and water balance model, incorporating soil water measurements, was used to make prescriptions for the VRI treatments at a field site in eastern Nebraska. In 2017, the mean prescribed seasonal irrigation depth (Ip) for VRI-L was significantly greater (α = 0.05) than the Ip for U for soybean. In 2018, Ip for soybean was greatest for VRI-U treatment followed by the U and VRI-L treatments, with all being significantly different from each other. No significant differences in Ip for maize were observed in 2017 or 2018. In all crop-year combinations, the VRI and U treatments had significantly greater evapotranspiration (ET) than the R treatment. Yield differences among treatments were not significant (except for rainfed maize compared to VRI-L in 2017). For maize in 2017, IWUE for VRI-L was comparable to the U treatment. The UAS imagery was a better match for the scale of crop management than Landsat imagery, particularly for thermal data. The multispectral UAS data was successfully used in the crop coefficient ET model for real-time irrigation, but using UAS to determine accurate canopy temperatures for surface energy balance modeling remains a challenge.

The Caatinga biome, located in the northeastern region of Brazil, is the most populated dryland region on the planet and extremely vulnerable to land degradation due to climatological and anthropogenic factors. Energy partitioning substantially influences the local climate and affects the water cycle, which is of utmost importance for the economy and livelihood of the region. Recently, eddy covariance (EC) towers were installed in the area; thus, the scientific community can thoroughly assess the water and energy fluxes over this unique biome. While EC towers have a high degree of accuracy, they only measure energy fluxes over a small land footprint. Given the biome spatial heterogeneity, the use of EC-based techniques has the limitation of not comprehensively representing water and energy fluxes profiles over the entire region. Incorporating remote sensing (RS) data into the landscape analysis is a feasible solution to overcome this issue, given that satellite data can capture the phenomena represented by the EC measurements across large spatial scales. Our research studied the capability of the Surface Energy Balance Algorithm for Land (SEBAL) and MOD16-ET products to represent the EC measurements regarding energy and mass exchange, with an ultimate objective of applying the best approach to assess these fluxes regionally. We applied the SEBAL model using only remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The MOD16-ET model uses a different approach but is also based on MODIS data. Our analysis was based on three years (2014–2016) of data, which was limited to the availability of the EC tower data. We found that for the EC-based measurements, energy balance closure (EBC) achieved an average of 0.84, which is considerably high for the region. This is possibly due to the EC tower being installed on a preserved Caatinga plot, with reduced heterogeneity and higher plant density. When analyzing RS-based products to represent ET profiles in the region, we found that the SEBAL model accurately represented water fluxes during the wet season but not the dry season, whereas the MOD16-ET showed a better agreement with EC-based water fluxes throughout all the seasons. SEBAL inaccuracy in drylands is partially due to the narrow range between the cold and hot pixels in an image, as the algorithm relies on this range for input parameters, especially in the dry season. Therefore, we concluded that MOD16-ET is capable of better-representing water fluxes in the Caatinga region. We analyzed the fluxes regionally and quantified annual ET for the three years. These results are especially relevant for local policymakers on dealing with water and landscape issues in a region where the livelihood and well-being of the population is inextricably bound to water availability.

Understanding how irrigation is used across agricultural landscapes is essential to support efforts to grow more food while reducing pressures on limited freshwater resources. However, to date, few studies have analyzed the underlying spatial and temporal variability in farmers' individual water use decisions at a landscape scale. We compare estimates of irrigation water requirements derived using state-of-the-art remote sensing models with metered abstraction records for 1,400 fields over a 13-year period in the U.S. state of Nebraska, one of the world's most intensively irrigated agricultural regions. We show that farmers' observed water use decisions often diverge significantly from biophysical estimates of crop irrigation requirements. In particular, our findings are consistent with widespread use of water conservation practices by farmers in drought years as an adaptive response to rising irrigation costs and regulatory water supply constraints in these years. We also demonstrate that, in any individual year, farmers' observed water use exhibits large field-to-field variability, which cannot be explained fully by differences in weather, soil type, crop choice, or technology. Our results highlight the value of using both in-situ monitoring and remote sensing to evaluate farmers' individual water use behavior and understand likely responses to future changes in climate or water policy. Moreover, our findings also demonstrate potential challenges for current efforts in developed and developing countries to apply model-based approaches for field-level water use accounting and enforcement of irrigation water rights.

How a predator uses its landscape to move through its territory and acquire prey is a fundamental question for scientific research. The influence of abiotic and biotic factors on space use of large carnivores has profound implications for their future management and conservation. In the Pantanal, Brazil, jaguars (Panthera onca) are the apex predator, but conflicts with cattle depredations pose a risk to their future conservation. We examined whether behavioral state, sex, and season influenced how jaguars used the landscape in the Pantanal. To accomplish this, we radio‐collared four females and six males; radio‐collared jaguars were monitored for 76 radio‐months with 11,787 GPS locations acquired. We developed resource selection functions (RSFs) examining how female and male jaguars used their landscape during three behavioral states (moving, killing native prey, killing cattle) during two seasons (dry, wet). From the RSF models, we found similar variables and relationships of landscape characteristics that jaguars selected for when moving and when depredating native prey and cattle. While moving, jaguars selected for locations that were either in dense cover or very near dense cover, with higher plant diversity and closer to water than available across the landscape. While null models suggested jaguars opportunistically depredated native prey in the dry season and cattle in the wet season, there was some indication they selected for specific landscape characteristics, mainly dense cover when killing cattle in the dry season and native prey in the wet season. Both sexes killed native prey and cattle within dense cover or close to dense cover as expected of an ambush predator. Particular habitat types were not important as long as there was dense cover for concealment. Additionally, jaguars killed prey closer to water than was available on the landscape. The similar variables across the models showed the importance of dense cover and distance to dense cover during all three behavioral states indicating jaguars in the Pantanal were “always on the prowl.” Understanding the spatial requirements for jaguars during the acquisition of native prey and cattle may lead to improved management strategies to allow for continued coexistence of jaguars in an area of traditional cattle production.

This paper presents the potential of remotely sensed data in addressing spatially distributed irrigation equity, adequacy, and sustainability. The surface energy balance algorithm for land ...