Identifying Agricultural Consumptive-Use Patterns To Support Adaptive Water Management In California’s Santa Clara Valley Via Remote Sensing And Machine Learning
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https://doi.org/10.1371/journal.pwat.0000416 <-- shared paper
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H/T @Guillaume Wright | Executive Editor, PLOS
“💧 With drought [and high temperatures] gripping many areas of the world right now... [the H/T] wanted to highlight a new paper in PLOS Water this week with a very timely focus on hydroclimatic stresses and what can be done to mitigate this through water management practices when it comes to agriculture.
[The authors] investigate[d] adaptive water management practices in California’s Santa Clara Valley via remote sensing and machine learning techniques. They [found] good evidence for use of customized agricultural water-management plans for irrigation monitoring, conservation planning, and adaptive water management in groundwater-dependent regions such as is found in California…”
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“Site-specific agricultural water management is particularly important in highly productive and diverse agricultural regions such as California’s Santa Clara Valley (SCV), where broad crop categories can obscure substantial parcel-scale differences in consumptive use. This study used unsupervised machine learning on remotely sensed data from 2019-2023 to develop operationally distinct, agricultural consumptive-use groups for major crop types. Time series of Sentinel-2 normalized difference vegetation index (NDVI), OpenET ensemble actual evapotranspiration (ETa, used as a proxy for consumptive use), and PRISM precipitation were analyzed at the parcel level for truck crops, vineyards, and hay crops. Across 2,189 parcels (~7,483 ha), the only-NDVI and NDVI+ETa clustering approaches identified 13 consumptive-use clusters and revealed substantial within-crop heterogeneity hidden by conventional crop averages. Six clusters were detected in truck crops (TC), four in vineyards (VC), and three in hay crops (HC). Adding ETa magnitude and trend information generally reduced average within-cluster coefficient of variation (CV) relative to only-NDVI clustering and increased separation among cluster means. The ratios of between-cluster to mean within-cluster CV were lower under only-NDVI (TC = 0.30, VC = 0.46, HC = 0.74) than under NDVI+ETa (TC = 1.35, VC = 1.06, HC = 1.07), compared with crop-wide CVs of 17%, 24%, and 20%, respectively. Truck crops showed the strongest seasonal and interannual heterogeneity, vineyards exhibited clearer spatial differentiation and a modest decline in consumptive use, and hay crops separated into distinct pasture and grain-forage systems with contrasting delivery needs and climate sensitivity. Across all three crop types, observed consumptive-use patterns were consistent with hydroclimatic stresses and management practices, including cover cropping, irrigation technology, deficit irrigation, and seasonal fallowing, although direct attribution would require independent field, permit, or metering data. These findings support customized agricultural water-management plans for irrigation monitoring, conservation planning, and adaptive water management in groundwater-dependent regions…”
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