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1. Monthly Crop Water Consumption of Irrigated Crops in the United States From 1981 to 2019

   https://doi.org/10.1029/2024WR038334  

   Lamsal, Gambhir; Marston, Landon_T (February 2025, Water Resources Research)

   Abstract Irrigated agriculture depends on surface water and groundwater, but we do not have a clear picture of how much water is consumed from these sources by different crops across the US over time. Current estimates of crop water consumption are insufficient in providing the spatial granularity and temporal depth required for comprehensive long‐term analysis. To fill this data gap, we utilized crop growth models to quantify the monthly crop water consumption ‐ distinguishing between rainwater, surface water, and groundwater ‐ of the 30 most widely irrigated crops in the US from 1981 to 2019 at 2.5 arc min. These 30 crops represent approximately 95% of US irrigated cropland. We found that the average annual total crop water consumption for these 30 irrigated crops in the US was 154.2 km³, 70% of which was from irrigation. Corn and alfalfa accounted for approximately 16.7 and 24.8 km³of average annual blue crop water consumption, respectively, which is nearly two‐fifths of the blue crop water consumed in the US. Surface water consumption decreased by 41.2%, while groundwater consumption increased by 6.8%, resulting in a 17.3% decline in blue water consumption between 1981 and 2019. We find good agreement between our results and existing modeled evapotranspiration (ET) products, remotely sensed ET estimates (OpenET), and water use data from the US Geological Survey and US Department of Agriculture. Our data set and model can help assess the impact of irrigation practices and water scarcity on crop production and sustainability. 

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2. Remote Sensing and Aerodynamic Temperature-Based Energy Balance Models to Estimate Crop Evapotranspiration Rates

   https://doi.org/10.5539/jas.v15n4p15  

   Chávez, José L. (March 2023, Journal of Agricultural Science)

   Different methods exist to measure or estimate actual crop evapotranspiration (ETa). However, some methods require a large number of data input or strict field conditions. Remote sensing based ETa algorithms based on extreme thermal pixels (hot and cold) have limitations when required extreme pixels are not present in the acquired thermal infra-red imagery. In addition, satellite overpass frequency and spatial pixel resolution may be a limitation for some agricultural fields and micro-climates. Surface energy balance methods that use surface radiometric temperatures often fail to perform well under drought, limited irrigation, salt affected soils, or under sparse vegetation conditions. One option is to measure or estimate the crop/surface sensible heat flux through the aerodynamic temperature approach, then calculate the available energy and solve the energy balance for latent heat flux. Thus, this study presents different published algorithms that characterize the crop or field surface aerodynamic temperature and then applies them to different conditions for evaluation. Determining spatial ETa continuously has the potential to improve the irrigation water management decision making. The aerodynamic temperature approach was initially developed with good results as a function of surface radiometric temperature, air temperature, crop leaf area index, and wind speed or surface aerodynamic resistance. However, the inclusion of the crop fractional percent cover and of a new resistance term (turbulent-mixing row resistance) greatly improved the estimation of the sensible heat and latent heat fluxes, when evaluated with heat flux data derived from eddy covariance energy balance towers. Results also indicate that the aerodynamic method has transferability potential to different regions, crops, and irrigation methods than the conditions encountered in the method development. 

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3. Assessing Remote Sensing-Based Maize Crop Biophysical Characteristics and Evapotranspiration Estimations

   https://doi.org/10.5539/jas.v16n10p11  

   Al-Majali, Zaid; Chávez, José Luis (September 2024, Journal of Agricultural Science)

   The rapid growth in population, climate variability, and decreasing water resources necessitate innovative agricultural practices to ensure food security and resource conservation. This study investigates the effectiveness of various multispectral imagery from remote sensing (RS) platforms, Unmanned Aerial Systems (UAS), PlanetDove microsatellites, Sentinel-2, Landsat 8/9, and proximal MSR-5 in assessing crop biophysical characteristics (CBPCs) and actual crop evapotranspiration (ETa) for maize fields in northeastern Colorado. The research aims to evaluate the accuracy of vegetation indices (VIs) derived from these platforms in estimating key CBPCs, including leaf area index (LAI), crop height (Hc), and fractional vegetation cover (Fc), as well as ETa. Field experiments were conducted during 2022 at the USDA-ARS Limited Irrigation Research Farm in Greeley, Colorado, U.S.A., using different irrigation strategies. Surface reflectance data collected using a handled sensor and observed LAI, Hc, and Fc values, served as ground truth for validating RS estimates. The study applied various statistical analyses to compare the performance of different RS platforms and models. Results indicate that higher-resolution platforms, particularly UAS, provided higher accuracy in estimating VIs and CBPCs than satellite platforms. The study also highlights the influence of environmental conditions on the accuracy of RS models, with locally calibrated models outperforming those developed in dissimilar conditions. The findings underscore the potential of advanced RS technologies in enhancing precision agriculture practices and optimizing water resource management. 

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4. Using Machine Learning to Integrate On-Farm Sensors and Agro-Meteorology Networks into Site-Specific Decision Support

   https://doi.org/10.13031/trans.13917  

   Kelley, Jason; McCauley, Dalyn; Alexander, G. Aaron; Gray, Wilton F.; Siegfried, Rylie; Oldroyd, Holly J. (January 2020, Transactions of the ASABE)

   null (Ed.)

   Highlights Machine learning can incorporate a variety of data from low-cost sensors and estimate actual ET by comparison with short-term, higher-cost measurements. On-farm weather monitoring can be leveraged to estimate site-specific crop-water requirements. Expanding spatial coverage of weather and actual ET through on-farm monitoring will facilitate localization and leverage publicly available weather data to guide irrigation decisions and improve irrigation water management. Abstract . One of the basic challenges to adopting science-based irrigation scheduling is providing reliable, site-specific estimates of actual crop water demand. While agro-meteorology networks cover most agricultural production areas in the U.S., widely spaced stations represent regionally specific, rather than site-specific, conditions. A variety of low to moderate cost commercial weather stations are available but do not provide directly useful information, such as actual evapotranspiration (ETa), or the ability to incorporate additional sensors. We demonstrate that machine learning methods can provide real-time, site-specific information about ETa and crop water demand using on-farm sensors and public weather information. Two years of field experiments were conducted at four irrigated field sites with crops including snap beans, alfalfa, and pasture. On-farm data were compared to publicly available data originating at nearby agro-meteorology network stations. The machine learning procedure can robustly estimate ETa using data from a few basic sensors, but the resulting estimate is sensitive to the range of conditions that are used as training data. The results demonstrate that machine learning can be used with affordable sensors and publicly available data to improve local estimates of crop water demand when high-quality measurements can be co-located for short periods of time. Supplementary sensors can also be integrated into a tailored monitoring plan to estimate crop stress and other operational considerations. Keywords: Agro-meteorology, Irrigation requirement, Machine learning, Site-specific Irrigation. 

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5. IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S.

   https://doi.org/10.3390/rs12142328  

   Ketchum, David; Jencso, Kelsey; Maneta, Marco P.; Melton, Forrest; Jones, Matthew O.; Huntington, Justin (July 2020, Remote Sensing)

   High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986–2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km 2 of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r 2 = 0.90), and high agreement when estimates are aggregated to the state level (r 2 = 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km 2 ) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California. 

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