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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.

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.

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.

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.

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.

This research presents an algorithm developed for calibrating the soil water balance model (SWB) in terms of total available water in the soil root zone (TAW) assimilating actual evapotranspiration (ET) data. After calibration, the TAW value is used to estimate the actual ET and water stress processes at canopy scales. This methodology also allows for the estimation of the minimum TAW value that explains the ET rate in the absence of water stress. The model was applied in three vineyards grown under rain-fed, full irrigation and deficit irrigation conditions in La Mancha (southeast Spain) and the Alentejo (southern Portugal). The values of TAW obtained for the analyzed vineyards illustrate the variability of this parameter. TAW values varied from 180 to 390 mm depending on the water availability for growing conditions. The use of these TAW values allowed for a precise estimation of the ET values, water content and water stress process for the validation campaigns. The RMSE values obtained when comparing measured and modelled ET were lower than 0.65 mm/day for each analyzed seasonal campaign. The application of this methodology in operational scenarios will allow for the estimation of TAW for each site in order to optimize the irrigation applied, even in deficit irrigation schemes.

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.

The increasing pressure on water resources in Nebraska-US and other agricultural areas requires the implementation of innovative tools and solutions for the governance of water resources and the analysis of water use efficiency. In this vein, this paper presents the application of a remote sensing based soil water balance for the study of water use in agricultural areas. The specific objectives were the identification of the temporal and spatial behavior of the irrigation water use based on the quantification of the water use deviation (irrigation water applied minus irrigation water requirements), as the main indicator and the comparative analysis of the irrigation productivity (crop yield under irrigated field minus crop yield under rainfed condition per volume of water applied by irrigation), WPi, water productivity (harvestable grain per total volume of water applied considering precipitation plus irrigation), WP, and finally water productivity based on evapotranspiration (harvested grain per total volume of water evapotranspired), WPET, in the various management zones analyzed. Additionally, we examined the impact of soil types, local weather and irrigation system (center pivot and furrow irrigation) on these indicators. The study was carried out in three Natural Resources District (Tri-Basin, Central Platte and Lower Niobrara) across Central Nebraska for the period 2004–2012 and comprised over 2000 irrigated corn fields per year. Crop water requirements were estimated using the reflectance-based crop coefficient approach developed in previous research (see Campos et al., 2017) and the field data were reported for each field monitored through cropland data layer by National Agricultural Statistics Service of USDA. The difference between modeled irrigation water requirements and field level irrigation application was significant (p

The spatial and temporal variations in the partitioning of energy into latent (LE) and sensible (H) heat flux for tropical ecosystems are not yet fully understood. In the state of Mato Grosso State, Brazil, there are three different ecosystems (Cerrado, Pantanal and the Amazon Rainforest) with distributions that vary across rainfall and humidity gradients. Our goal was to analyze the seasonal variation in microclimate, spectral reflectance, LE and H for these ecosystems and quantify how energy partitioning varies across the regional climate gradient. We used the Bowen ratio energy balance method to estimate the LE and H of a dense, evergreen ombrophylous forest near Alta Floresta (AFL), a semi-deciduous forest in the Amazon-Cerrado transition zone near Sinop (SIN), a savanna grassland at Fazenda Miranda (FMI), a managed savanna pasture at Fazenda Experimental (FEX), a seasonally flooded woodland at Baia das Pedras in the Pantanal (BPE), and a riparian forest dominated by Vochysia divergens Pohl (CAM) in the Pantanal. Annual rainfall decreased from north to south, and 83% of the annual rainfall occurred during the wet season. However, the seasonal amplitude of volumetric soil water content (VSWC) increased from north to south, because of the increased potential for seasonal flooding. The vapor pressure deficit (VPD), air temperature, solar radiation (Rg) and net radiation (Rn) also increased from north to south, which directly affected the seasonal amplitude in the enhanced vegetation index (EVI). Our data suggest that energy partitioning in the wettest sites (AFL and CAM) were driven by solar radiation instead of soil water availability, while seasonal variation in rainfall was more important for the Amazon-Cerrado transitional forest (SIN), Cerrado (FMI and FEX) and Pantanal scrublands (BPE). These patterns are discernable using appropriate satellite vegetation indices, such as the EVI, allowing spatial and temporal variations in energy partitioning to be quantified across diverse landscapes like the Amazon Basin.

Summary Diverse topography and climate in the American West have stymied efforts to accurately quantify the flux of water on the land surface at a high spatial resolution. Observations of the processes governing the earth’s water budget and energy balance are generally from disparate point measurements on the ground and have lower frequency, distinction, or confidence when remotely sensed. Combined, these terrestrial and aerial sources can offset the other’s inherent weaknesses. At a local scale, methodologies have been developed to estimate evapotranspiration. A systematic approach to calculating reference evapotranspiration at a regional scale over the western United States was explored by comparing the drivers of the North American Land Data Assimilation System weather forcing model to 704 agriculturally representative, electronic weather stations at an hourly time step. Parameters of solar radiation, air temperature, humidity, and wind speed were analyzed to identify any uncertainties and biases. Initial inspection of the weather parameter comparisons revealed unsatisfactory performance of one or more of the NLDAS parameters in several regions, but this was mollified in the calculation of reference evapotranspiration for all but the southerly portions of the study area.

Summary Satellite-based thermal infrared remote sensing has greatly contributed to the development and improvement of remote sensing-based evapotranspiration (RS-ET) mapping algorithms. Radiometric temperature (Ts) derived from thermal sensors is inherently different from the aerodynamic temperature (To) required for solving the bulk formulation of sensible heat (H). The scalar roughness length (zoh) representing heat transport mechanism and described by the dimensionless parameter kB−1 was used to account for the discrepancy between Ts and To. Surface Energy Balance Algorithm for Land (SEBAL), with its indigenous approach of linearly relating dT (near-surface temperature gradient) with Ts across the imagery, maintained that this approach would absorb the impacts of differences between Ts and To. Therefore, it utilized a constant kB−1 value of 2.3 in its initial version, and later switched to a constant zoh (z1) value of 0.1. In this study, we investigated the influence of these changes in SEBAL by testing four different approaches: (i) zoh derived from a constant kB−1 of 2.3, (ii) constant zoh (z1) = 0.1 m, (iii) constant zoh (z1) = 0.01 m, and (iv) spatially variable zoh from kB−1 parameterization. SEBAL was applied on 10 high-resolution airborne images acquired during BEAREX07-08 (Bushland Evapotranspiration and Agricultural Remote Sensing Experiment) and validated against measurements from four large weighing lysimeters installed on two irrigated and two dryland fields. The spatially variable kB−1 produced statistically different and improved ET estimates compared to that with constant kB−1 and constant z1 (zoh) approaches. SEBAL performance for irrigated fields representing high ET and complete ground cover surfaces was markedly different from that for dryland fields representing greater soil water deficits with sparser vegetation cover. A variable kB−1 value derived from a physical model generated good overall estimates while delivering improved performance for dryland agricultural systems. Overall, this study focused on the classical problem of estimating heat transfer from two contrasting hydrological regimes i.e. irrigated and dryland agriculture and illustrated the existing need for a realistic consideration of excess resistance to heat transfer in single-source resistance modeling frameworks.

Global demand for livestock products is rising, resulting in a growing demand for feed and potentially burdening freshwater resources to produce this feed. To offset this increased pressure on water resources, the environmental performance of livestock sector should continue to improve. Over the last few decades, product output per animal and feedstuff yields in the US have improved, but before now it was unclear to what extent these improvements influenced the water productivity (WP) of the livestock products. In this research, we estimate changes in WP of animal products from 1960 to 2016. We consider feed conversion ratios (dry matter intake per head divided by product output per head), feed composition per animal category, and estimated the water footprint of livestock production following the Water Footprint Network's Water Footprint Assessment methodology. The current WP of all livestock products appears to be much better than in 1960. The observed improvements in WPs are due to a number of factors, including increases in livestock productivity, feed conversion ratios and feed crop yields, the latter one reducing the water footprint of feed inputs. Monogastric animals (poultry and swine) have a high feed-use efficiency compared to ruminants (cattle), but ruminants consume relatively large portion of feed that is non-edible for humans. Per unit of energy content, milk has the largest WP followed by chicken and pork. Per gram of protein, poultry products (chicken meat, egg and turkey meat) have the largest WP, followed by cattle milk and pork. Beef has the smallest WP. These data provide important information that may aid the development of strategies to improve WP of the livestock sector. Keywords: Animal feed, Sustainability, Feed conversion efficiency, Water use, Water footprint

Crop yield forecasting at the field level is essential for decision-making and the prediction of agricultural economic returns for farmers. Thus, this study evaluated the performance of a methodology for corn yield prediction in irrigated fields in the western region of the state of Bahia, Brazil. This methodology integrates a time series of the basal crop coefficient (Kcb) estimated from the soil-adjusted vegetation index (SAVI) into a simple model based on the water productivity as presented in the FAO-66 manual. In this context, an extensive field-level dataset of 52 center pivot fields of cultivated with corn was used for four consecutive growing seasons (2013 to 2016). Surface reflectance images from the Landsat series were used to calculate the SAVI. The methodology performance was assessed through RMSE, RRMSE, MBE, MAE, and r². The results revealed that the difference between the predicted and actual yield values ranged between −12.2% and 18.8% but that the majority of the estimates remained between −10% and 10%, considering that a single harvest index (HI) was used for the hybrids cultivated in the growing seasons of 2014, 2015 and 2016. After a new reanalysis (by grouping the similar hybrids and using specific HIs), the performance of the predictions increased, especially for the Pioneer hybrids; the majority of the differences between the predicted yield values and the measured yield values remained between -5% and 5%. The results of this research showed that it is essential to work with different HIs when considering different hybrids and years under different weather conditions.

In this study, Surface Energy Balance Algorithm for Land (SEBAL) was evaluated for its ability to derive aerodynamic components and surface energy fluxes from very high resolution airborne remote sensing data acquired during the Bushland Evapotranspiration and Agricultural Remote Sensing Experiment 2008 (BEAREX08) in Texas, USA. Issues related to hot and cold pixel selection and the underlying assumptions of difference between air and surface temperature (dT) being linearly related to the surface temperature were also addressed. Estimated instantaneous evapotranspiration (ET) and other components of the surface energy balance were compared with measured data from four large precision weighing lysimeter fields, two each managed under irrigation and dryland conditions. Instantaneous ET was estimated with overall mean bias error and root mean square error (RMSE) of 0.13 and 0.15 mm h � 1 (23.8 and 28.2%) respectively, where relatively large RMSE was contributed by dryland field. Sensitivity analysis of the hot and cold pixel selection indicated that up to 20% of the variability in ET estimates could be attributed to differences in the surface energy balance and roughness properties of the anchor pixels. Adoption of an excess resistance to heat transfer parameter model into SEBAL significantly improved the instantaneous ET estimates.

Remote sensing of evapotranspiration (ET) has evolved over the last 20 years with the development of more robust energy balance approaches and the availability of timely remotely sensed imagery from satellite sensors. This has allowed the use of remote sensing for near-real time water management in irrigated systems in the western United States. In this paper a hybrid ET approach is applied to irrigated and non-irrigated cotton fields at the BEAREX08 experimental site using airborne remote sensing inputs under highly advective conditions, taking advantage of the available root zone soil water content measurements for verification of model output. The modeling approach is based on coupling the Two-Source-Energy Balance (TSEB) and the reflectance-based crop coefficient models. The TSEB model provides estimates of real crop ET while the reflectance-based crop coefficient approach allows for updating the basal crop coefficient and the interpolation and extrapolation of ET between the dates of remote sensing inputs facilitating the maintenance of a soil water balance in the root zone of the crop. Actual ET estimates using the TSEB model were compared with measured ET using eddy covariance systems deployed in four cotton fields during the BEAREX08 experiment. Estimates of soil water content in the soil profile of both irrigated and rain fed cotton fields were compared with measurements at different depths using neutron probe observations. Data assimilation techniques were applied to update soil water content values using estimates based on actual ET from the TSEB model. Results indicate that the hybrid ET modeling approach using data assimilation produced reliable daily ET interpolated between remote sensing observations and significantly improved soil water content estimates throughout the root zone profile compared to applying the crop-coefficient technique in a water balance model without the actual ET inputs.

Application and validation of many thermal remote sensing-based energy balance models involve the use of local meteorological inputs of incoming solar radiation, wind speed and air temperature as well as accurate land surface temperature (LST), vegetation cover and surface flux measurements. For operational applications at large scales, such local information is not routinely available. In addition, the uncertainty in LST estimates can be several degrees due to sensor calibration issues, atmospheric effects and spatial variations in surface emissivity. Time differencing techniques using multi-temporal thermal remote sensing observations have been developed to reduce errors associated with deriving the surface-air temperature gradient, particularly in complex landscapes. The Dual-Temperature-Difference (DTD) method addresses these issues by utilizing the Two-Source Energy Balance (TSEB) model of Norman et al. (1995) [1] , and is a relatively simple scheme requiring meteorological input from standard synoptic weather station networks or mesoscale modeling. A comparison of the TSEB and DTD schemes is performed using LST and flux observations from eddy covariance (EC) flux towers and large weighing lysimeters (LYs) in irrigated cotton fields collected during BEAREX08, a large-scale field experiment conducted in the semi-arid climate of the Texas High Plains as described by Evett et al. (2012) [2] . Model output of the energy fluxes (i.e., net radiation, soil heat flux, sensible and latent heat flux) generated with DTD and TSEB using local and remote meteorological observations are compared with EC and LY observations. The DTD method is found to be significantly more robust in flux estimation compared to the TSEB using the remote meteorological observations. However, discrepancies between model and measured fluxes are also found to be significantly affected by the local inputs of LST and vegetation cover and the representativeness of the remote sensing observations with the local flux measurement footprint.

Quantifying turbulent fluxes of heat and water vapor over heterogeneous surfaces presents unique challenges. For example, in many arid and semi-arid regions, parcels of irrigated cropland are juxtaposed with hot, dry surfaces. Contrasting surface conditions can result in the advection of warm dry air over an irrigated crop surface where it increases the water vapor deficit and, thereby, atmospheric demand. If sufficient water is available, this can significantly enhance evaporative water loss from the irrigated field. The scale and frequency of turbulent eddies over an irrigated surface during periods of strong advection is not fully understood. High frequency (20 Hz) data were acquired over irrigated cotton, wheat stubble, and rangeland fields during the 2008 growing season as part of the Bushland Evapotranspiration and Agricultural Remote Sensing Experiment (BEAREX08). Spectral analysis of momentum and scalar quantities including heat and water vapor revealed low frequency features in the turbulence structure due to the penetration of the surface boundary layer by large-scale eddies during periods of unusually strong advection. Wavelet analysis was applied to assess specific events contributing to the spatial and temporal structure of turbulent flux eddies. The analysis showed that low frequency contributions were linked to both local and regional scale advective processes. These results clearly point to a need to better understand surface energy balance exchange for heterogeneous surfaces in arid and semi-arid regions under conditions of strong local and regional advection.

Discrepancies can arise among surface flux measurements collected using disparate techniques due to differences in both the instrumentation and theoretical underpinnings of the different measurement methods. Using data collected primarily within a pair of irrigated cotton fields as a part of the 2008 Bushland Evapotranspiration and Remote Sensing Experiment (BEAREX08), flux measurements collected with two commonly-used methods, eddy covariance (EC) and lysimetry (LY), were compared and substantial differences were found. Daytime mean differences in the flux measurements from the two techniques could be in excess of 200 W m−2 under strongly advective conditions. Three causes for this disparity were found: (i) the failure of the eddy covariance systems to fully balance the surface energy budget, (ii) flux divergence due to the local advection of warm, dry air over the irrigated cotton fields, and (iii) the failure of lysimeters to accurately represent the surface properties of the cotton fields as a whole. Regardless of the underlying cause, the discrepancy among the flux measurements underscores the difficulty in collecting these measurements under strongly advective conditions. It also raises awareness of the uncertainty associated with in situ micrometeorological measurements and the need for caution when using such data for model validation or as observational evidence to definitively support or refute scientific hypotheses.

The definitive version is available at: http://www.elsevier.com/locate/agwat, The purpose of this work is to contribute to the development of a combined approach to evaluate irrigated areas based on: (1) irrigation performance analysis intended to assess the productive impacts of irrigation practices and infrastructures, and (2) water accounting focused on the hydrological impacts of water use. Ador-Simulation, a combined model that simulates irrigation, water delivery, and crop growth and production was applied in a surface irrigated area (1213 ha) located in the Bear River Irrigation Project, Utah, U.S.A.. A soil survey, a campaign of on-farm irrigation evaluations and an analysis of the database from the Bear River Canal Company and other resources were performed in order to obtain the data required to simulate the water flows of the study area in 2008. Net land productivity (581 US$ ha−1) was 20% lower than the potential value, whereas on-farm irrigation efficiency (IE) averaged only 60%. According to the water accounting, water use amounted to 14.24 Mm3, 86% of which was consumed through evapotranspiration or otherwise non-recoverable. Gross water productivity over depleted water reached 0.132 US$ m−3. In addition, two strategies for increasing farm productivity were analyzed. These strategies intended to improve water management and infrastructures raised on-farm IE to 90% reducing the gap between current and potential productivities by about 50%. Water diverted to the project was reduced by 2.64 Mm3. An analysis based on IE could lead to think that this volume would be saved. However, the water accounting showed that actually only 0.91 Mm3 would be available for alternative uses. These results provide insights to support the decision-making processes of farmers, water user associations, river basin authorities and policy makers. Water accounting overcomes the limitations and hydrological misunderstandings of traditional analysis based on irrigation efficiency to assess irrigated areas in the context of water scarcity and competitive agricultural markets., The postdoctoral contract of S. Lecina was funded by the Spanish Ministry of Science and Innovation (MICINN) and the Spanish Foundation for Science and Technology (FECYT). C.A.C. dos Santos was funded by the Brazilian National Council for Scientific and Technological Development (CNPq). The research was partially supported by the Utah Agricultural Experiment Station, UTA00793, and the Remote Sensing Services Laboratory (RSSL), Dept. of Civil and Environmental Engineering, Utah State University.

A combined methodology of basal crop coefficient ( K cb ) derived from vegetation indices (VI) obtained from satellite images and a daily soil water balance in the root zone of the crop was proposed to accurately estimate the daily grape crop coefficient and actual evapotranspiration. The modeled values were compared with field measurements of crop evapotranspiration (ET) using an energy balance eddy-covariance flux tower and adjusted for closure using the measured Bowen ratio. A linear relation between K cb and VI for vineyard was obtained, K cb = 1.44 × NDVI-0.10 and K cb = 1.79 × SAVI-0.08. The correlation of the measured crop coefficient ( K c ) and modeled ( K crf ) exhibits a linear tendency, K c = 0.96 K crf , r 2 = 0.67. Other derived parameters such as weekly K c and daily and weekly ET show good consistency with measurements and higher coefficients of determination. The study of the soil water balance suggests the importance of soil water storage in grapes within the La Mancha region. These results validate the use of remote sensing as a tool for the estimation of evapotranspiration of irrigated wine grapes planted on trellis systems.

The integration of remotely sensed data into models of evapotranspiration (ET) facilitates the estimation of water consumption across agricultural regions. To estimate regional ET, two basic types of remote sensing approaches have been successfully applied. The first approach computes a surface energy balance using the radiometric surface temperature for estimating the sensible heat flux (H), and obtaining ET as a residual of the energy balance. This paper compares the performance of three different surface energy balance algorithms: an empirical one-source energy balance model; a one-source model calibrated using inverse modeling of ET extremes (namely ET = 0 and ET at potential) which are assumed to exist within the satellite scene; and a two-source (soil + vegetation) energy balance model. The second approach uses vegetation indices derived from canopy reflectance data to estimate basal crop coefficients that can be used to convert reference ET to actual crop ET. This approach requires local meteorological and soil data to maintain a water balance in the root zone of the crop. Output from these models was compared to sensible and latent heat fluxes measured during the soil moisture–atmosphere coupling experiment (SMACEX) conducted over rain-fed corn and soybean crops in central Iowa. The root mean square differences (RMSD) of the estimation of instantaneous latent and heat fluxes were less than 50 W m−2 for the three energy balance models. The two-source energy balance model gave the lowest RMSD (30 W m−2) and highest r2 values in comparison with measured fluxes. In addition, three schemes were applied for upscaling instantaneous flux estimates from the energy balance models (at the time of satellite overpass) to daily integrated ET, including conservation of evaporative fraction and fraction of reference ET. For all energy balance models, an adjusted evaporative fraction approach produced the lowest RMSDs in daily ET of 0.4–0.6 mm d−1. The reflectance-based crop coefficient model yielded RMSD values of 0.4 mm d−1, but tended to significantly overestimate ET from corn during a prolonged drydown period. Crop stress can be directly detected using radiometric surface temperature, but ET modeling approaches-based solely on vegetation indices will not be sensitive to stress until there is actual reduction in biomass or changes in canopy geometry.

This paper describes procedures used to map riparian vegetation in the middle Rio Grande River, New Mexico. Airborne multi-spectral digital images were acquired at 0.5 m spatial resolution over the riparian corridor of the Middle Rio Grande River in July 2001. The images were corrected for lens vignetting effects, lens radial distortions, rectified to a base map, mosaicked, calibrated in terms of reflectance and classified. The classification accuracy was assessed using ground truth information obtained through comprehensive field campaigns and independent ground truth information. Surface areas of vegetation classes and in-stream features were extracted from the classified imagery. A longitudinal vegetation distribution analysis was conducted to study the changes in vegetation and water surface areas along the river. This analysis showed an increase in surface areas of the invasive type of vegetation Tamarisk ( Tamarix ramosissima ) in the downstream direction corresponding to decreases in water surface areas and flow. This indicates significant impacts on the river ecosystem due to many factors. The high resolution airborne remote sensing proved to be a powerful tool for mapping riparian vegetation which is very hard to map using satellite imagery due to its complexity, high diversity, and spatial variability occurring at finer scales.

The present paper describes the development and validation of canopy reflectance-based crop coefficients for potato using canopy reflectance (measured using hand-held radiometers and high resolution multispectral digital imagery), and extensive crop biophysical sampling in selected potato growing fields in and around Kimberly, Idaho, during 1998 and 1999 seasons. Daily crop evapotranspiration was estimated using basal and canopy reflectance-based crop coefficients, and a hydrologic water balance was conducted in the plant root zone. Independent measurements of actual soil moisture measurements were made by neutron probe and gravimetric methods, and used to validate the simulated results. The results validate the reflectance-based crop coefficient method. High resolution multispectral aerial imagery was used to highlight the spatial variability of actual crop water demand in the study fields.

The Los Alamos Raman lidar has been used to make high resolution (25 m) estimates of the evapotranspiration rate over adjacent corn and soybean canopies. The lidar makes three-dimensional measurements of the water vapor content of the atmosphere directly above the canopy that are inverted using Monin–Obukhov similarity theory. This may be used to examine the relationship between evapotranspiration and surface moisture/soil type. Lidar estimates of evapotranspiration reveal a high degree of spatial variability over corn and soybean fields that may be associated with small elevation changes in the area. The spatial structure of the variability is characterized using a structure function and correlation function approach. The power law relationship found by other investigators for soil moisture is not clear in the data for evapotranspiration, nor is the data a straight line over the measured lags. The magnitude of the structure function and the slope changes with time of day, with a probable connection to the amount of evapotranspiration and the spatial variability of the water vapor source. The data used was taken during the soil moisture–atmosphere coupling experiment (SMACEX) conducted in the Walnut Creek Watershed near Ames, Iowa in June and July 2002.

Microwave-based remote sensing algorithms for mapping soil moisture are sensitive to water contained in surface vegetation at moderate levels of canopy cover. Correction schemes require spatially distributed estimates of vegetation water content at scales comparable to that of the microwave sensor footprint (10 1 to 10 4 m). This study compares the relative utility of high-resolution (1.5 m) aircraft and coarser-resolution (30 m) Landsat imagery in upscaling an extensive set of ground-based measurements of canopy biophysical properties collected during the Soil Moisture Experiment of 2002 (SMEX02) within the Walnut Creek Watershed. The upscaling was accomplished using expolinear relationships developed between spectral vegetation indices and measurements of leaf area index, canopy height, and vegetation water content. Of the various indices examined, a Normalized Difference Water Index (NDWI), derived from near- and shortwave-infrared reflectances, was found to be least susceptible to saturation at high levels of leaf area index. With the aircraft data set, which did not include a short-wave infrared water absorption band, the Optimized Soil Adjusted Vegetation Index (OSAVI) yielded best correlations with observations and highest saturation levels. At the observation scale (10 m), LAI was retrieved from both NDWI and OSAVI imagery with an accuracy of 0.6, vegetation water content at 0.7 kg m −2 , and canopy height to within 0.2 m. Both indices were used to estimate field-scale mean canopy properties and variability for each of the intensive soil-moisture-sampling sites within the watershed study area. Results regarding scale invariance over the SMEX02 study area in transformations from band reflectance and vegetation indices to canopy biophysical properties are also presented.

The Los Alamos National Laboratory scanning Raman lidar was used to measure the three-dimensional moisture field over a salt cedar canopy. A critical question concerning these measurements is; what are the spatial properties of the source region that contributes to the observed three-dimensional moisture field? Traditional methods used to address footprint properties rely on point sensor time-series data and the assumption of Taylor’s hypothesis to transform temporal data into the spatial domain. In this paper, the analysis of horizontal source-area size is addressed from direct lidar-based spatial analysis of the moisture field, eddy covariance co-spectra, and a dedicated footprint model. The results of these analysis techniques converged on the microscale average source region of between 25 and 75 m under ideal conditions. This work supports the concept that the scanning lidar can be used to map small scale boundary layer processes, including riparian zone moisture fields and fluxes.

The appropriate spatial scale for a distributed energy balance model was investigated by: (a) determining the scale of variability associated with the remotely sensed and GIS-generated model input data; and (b) examining the effects of input data spatial aggregation on model response. The semi-variogram and the characteristic length calculated from the spatial autocorrelation were used to determine the scale of variability of the remotely sensed and GIS-generated model input data. The data were collected from two hillsides at Upper Sheep Creek, a sub-basin of the Reynolds Creek Experimental Watershed, in southwest Idaho. The data were analyzed in terms of the semivariance and the integral of the autocorrelation. The minimum characteristic length associated with the variability of the data used in the analysis was 15 m. Simulated and observed radiometric surface temperature fields at different spatial resolutions were compared. The correlation between agreement simulated and observed fields sharply declined after a 10×10 m 2 modeling grid size. A modeling grid size of about 10×10 m 2 was deemed to be the best compromise to achieve: (a) reduction of computation time and the size of the support data; and (b) a reproduction of the observed radiometric surface temperature.

Airborne multispectral videography is becoming a popular research and monitoring tool with the advent of charge coupled device (CCD) video technologies. However, most existing systems are not calibrated, resulting in radiometrically inaccurate imagery caused by nonuniform irradiance sensitivity of the CCD imaging arrays and lens vignetting. The latter effect results in a higher concentration of radiation in the center of the images. This article describes the Airborne Multispectral Video/Radiometer Remote Sensing System developed at Utah State University as well as the laboratory procedure developed to calibrate the spectral video imagery for radiance variations across the image plane.

Simultaneous measurements of the Bowen ratio were made at different fetch-to-height ratios over an aerodynamically smooth plot of bermudagrass which was downwind of an aerodynamically rough field of cotton. The measurements were used to evaluate effects of fetch and measurement height on Bowen ratio estimates of latent and sensible heat fluxes. The measurements indicated that significant boundary-layer adjustment occurred within the first 15 m of fetch. For the conditions encountered, the Bowen ratio and its variability increased with measurement height. However, departure from ideal fetch (measurement within the equilibrium sublayer) had only a small effect on flux estimates. The data suggest that when the Bowen ratio is small, the method can be used successfully at fetch-to-height ratios as low as 20:1, much less than the often-quoted value of 100:1.