Leveraging AI for predicting fallow land and resource allocation in the San Joaquin Valley of California

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Abstract California’s San Joaquin Valley (SJV) faces persistent water scarcity, climate stress, and environmental degradation. Land fallowing, or idling cropland, is one management response used to reduce irrigation demand during dry or restricted-water years. Because fallowing changes unevenly across space and time, it complicates water management, strategic land-use planning, and multi-benefit land repurposing. This study forecasts the spatial and temporal distribution of fallowed lands in the SJV using a hybrid machine-learning and deep-learning framework coupled with a cellular automata. Using observed LandIQ-based cropland/fallow maps (training period (2014 to 2020) for the 2020-2022 validation period, the Deep Learning based Fallow Prediction Framework (DL-FPF; F1 ≈ 0.81; accuracy ≈ 0.94) outperformed classical machine-learning models (F1 ≈ 0.65-0.67; accuracy ≈ 0.88-0.89). Across the full SJV, DL-FPF identified 70,586.89 ha of gross crop-to-fallow transition from WY2022 to WY2024, equivalent to ≈25% of the WY2022 mapped fallow baseline. The final WY2024 map predicted 265,657.60 ha of Idle/Fallow land, 20,343.04 ha lower than WY2022 (-7.11%). An independent WY2024 LandIQ overlay showed moderate agreement with the DL-FPF prediction, with 10-class accuracy of 0.73, Cohen’s Kappa of 0.66, binary fallow/non-fallow F1 of 0.56, and binary IoU of 0.39; DL-FPF overestimated WY2024 fallow area by 13.34% relative to LandIQ. Gross transition pressure was highest in GW-only GSAs (31.67%), followed by SW-only GSAs (28.87%) and SW+GW GSAs (23.77%), while SW+GW GSAs had the largest absolute gross transition area. By separating transition magnitude, mapped WY2024 fallow area, and reference-map agreement, DL-FPF outputs can better support water allocation, cropping decisions, and multi-benefit land repurposing under the Sustainable Groundwater Management Act (SGMA).

Links and sources

• PDF from original source

Comments