Demonstration of a Daily High-Resolution (375-m) ALEXI Evapotranspiration Product for the NENA Region

While the current constellation of geostationary sensors provides near-global coverage (60N to 60S) – it requires merging data from 7 satellites [resolving time differences; view angles; atmospheric correction]. Polar orbiting sensors such as MODIS and VIIRS provide daily global coverage of LST at higher resolutions than GEO sensors but at only two times per day

Preface: Earth Observation for Integrated Water and Basin Management: Challenges for adaptation to a changing environment

Integrated river basin management involves a sound knowledge of water and land interactions, and impacts from and feedbacks to human activity. Remote sensing has been an efficient and increasingly promising means of gathering direct information of the Earth surface, as well as information on water and energy fluxes. The recent generation of high-resolution sensors offers a huge potential for monitoring, assessing, and modelling our changing environment in a context of uncertainty about how future climate conditions will affect the current water resource and basin management framework. Moreover, large amounts of data are now available posing a challenging opportunity to the scientific community for both exploring and transforming these data into readily usable information products for different end-users in our societies

Evaluation of variable rate irrigation using a remote-sensing-based model

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 (VRINP); 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 VRIRS was about 40 mm, 50 mm, and –98 mm greater in separate analyses than the uniform at Mead and about 18mm 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

Evaluation of the Water Footprint of Beef Cattle Production in Nebraska

Data were compiled on feed usage to model the amount of water needed to produce beef in typical Nebraska production systems. 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. Therefore, the water footprint (gallons of water required to produce one pound of boneless meat) was decreased by 18%. In addition, increasing the dietary inclusion of distillers grains from 0% to 40% decreased the water footprint in the finishing phase by 29%, again based on water allocation. Utilizing corn residue and distillers grains in Nebraska beef cattle systems decreases the overall water footprint of production. Additionally, the water footprint of the systems analyzed was 80% green water as rain, minimizing the environmental impact of beef production on freshwater use and ecological water balance

Variable Rate Irrigation of Maize and Soybean in West-Central Nebraska under Full and Deficit Irrigation

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 \u3c 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 = 351mm, Uniform Full = 344). However, in 2018, VRI resulted in lower mean prescribed gross irrigation than the corresponding uniform treatments (VRI-Full = 265mm, Uniform Full = 282mm, VRI-Deficit = 234mm, and Uniform Deficit = 267mm). 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 = 0.87 in 2017; F = 0.00, p = 0.96 for VRI/Uniform and F = 0.01, p = 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

Evaluation of the Water Footprint of Beef Cattle Production in Nebraska

Data were compiled on feed usage to model the amount of water needed to produce beef in typical Nebraska production systems. 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. Therefore, the water footprint (gallons of water required to produce one pound of boneless meat) was decreased by 18%. In addition, increasing the dietary inclusion of distillers grains from 0% to 40% decreased the water footprint in the finishing phase by 29%, again based on water allocation. Utilizing corn residue and distillers grains in Nebraska beef cattle systems decreases the overall water footprint of production. Additionally, the water footprint of the systems analyzed was 80% green water as rain, minimizing the environmental impact of beef production on freshwater use and ecological water balance

Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska

Thermal sensing provides rapid and accurate estimation of crop water stress through canopy temperature data. Canopy temperature is highly dependent on the transpiration rate of the leaves. It is usually assumed that any reduction in crop evapotranspiration (ET) leads to crop yield loss. As a result, an increase in canopy temperature due to a decrease in crop ET would indicate crop yield loss. This research evaluated the hypothesis that crop water stress could be detected using canopy temperature measurements (increased leaf temperature) from infrared thermometers (IRTs) before incurring crop yield loss. This would be possible in a narrow range when the photosynthesis rate (and carbon assimilation) is limited by solar radiation (energy-limiting water stress) while the leaf has abundant carbon dioxide for photosynthesis. Once photosynthesis becomes limited by carbon dioxide (carbon-dioxide-limiting water stress), then yield reduction would occur. In this field experiment, measured response variables included the integrated crop water stress index (iCWSI), ET, and crop yield for maize and soybean during the 2020 and 2021 growing seasons. The irrigation was applied at four different refill levels: rainfed (0%), deficit (50%), full (100%), and over (150%). The irrigation depth was prescribed using four different irrigation methods. The field was irrigated with a center pivot irrigation system, which was also used as a platform to mount IRT sensors. The iCWSI thresholds required for irrigation management were determined using the iCWSI dataset collected in 2020. The low, medium, and high iCWSI thresholds were 120, 150, and 180, respectively for maize and 110, 130, and 150, respectively for soybean. These thresholds should be updated with iCWSI data from future studies in this region to increase the credibility of the thresholds for irrigation management. The mean iCWSI values for consecutive days after a wetting event substantially increased with time for each irrigation level and a larger range in iCWSI values was observed among the irrigation levels after three days from a wetting event. The seasonal iCWSI for different levels were found to be negatively correlated with seasonal evapotranspiration for both years. The correlations between seasonal ET and crop yield were significant with the rainfed and deficit levels for maize (p-value \u3c 0.001) and soybean (p-value = 0.04) in 2020. The iCWSI and yield data for the fully watered plots indicated that thermal stress was detected using the sensing system without incurring yield loss (i.e., energy-limiting water stress). The ET and yield data for 2021 indicated that reduction in seasonal crop ET did not result in yield loss which also supported the hypothesis. Future studies should investigate whether this phenomenon of detecting crop water stress in an early stage without yield loss is observed in other climates and locations

Feasibility assessment on use of proximal geophysical sensors to support precision management

A study was conducted at three sites in North Dakota to strengthen understanding of the usefulness of different proximal geophysical data types in agricultural contexts of varying pedology. This study hypothesizes that electromagnetic induction (EMI), gamma-ray sensor (GRS), cosmic-ray neutron sensor (CRNS), and elevation data layers are all useful in multiple linear regression (MLR) predictions of soil properties that meet expert criteria at three agricultural sites. In addition to geophysical data collection with vehicle-mounted sensors, 15 soil samples were collected at each site and analyzed for nine soil properties of interest. A set of model training data was compiled by pairing the sampled soil property measurements with the nearest geophysical data. Eleven models passed expert-defined uncertainty criteria at Site 1, 16 passed at Site 2, and 14 passed at Site 3. Electrical conductivity (EC), organic matter (OM), available water holding capacity, silt, and clay were predicted at Site 1 with an R-squared of prediction (2 ) \u3e .50 and acceptable root mean square error of prediction (RMSEP). Bulk density (BD), OM, available water capacity, silt, and clay were predicted with 2\u3e .50 and acceptable RMSEP at Site 2. At Site 3, no soil properties were predicted with acceptable RMSEP and an 2\u3e .50. These results confirm feasibility of our method, and the authors recommend the prioritization of EMI data collection if geophysical data collection is limited to a single mapping effort and calibration soil samples are few

SIG pour l

Une base de données des utilisateurs d’eau est un outil essentiel pour assurer la bonne gestion des systèmes d’irrigation dans de nombreux pays ACP. L'existence d’informations mises à jour sur la localisation des zones irriguées et des utilisateurs d’eau permet d’évaluer avec précision la demande et l’offre en eau d’irrigation dans une zone donnée, voire dans tout un pays. Une base de données rassemblant les utilisateurs d’eau permet au personnel des districts locaux d’irrigation et des associations d’utilisateurs d’eau d’organiser la distribution de l’eau et de planifier les travaux de maintenance, ainsi que de fixer les prix et d’attribuer les crédits d’eau pour l’utilisation des ressources publiques en eau. En République dominicaine, l’Instituto Nacional de Recursos Hidráulicos (INDRHI), l’agence nationale de développement des ressources en eau, est actuellement en train de décentraliser l’exploitation et la gestion des systèmes d’irrigation de l’île qui seront confiées à des associations locales d’utilisateurs d’eau. Pour mettre enEn République dominicaine, une base de données issues de systèmes d’informations géographiques (SIG) permet aux communautés d’améliorer la gestion des syst