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    An LSTM approach to deciphering irrigation operations from remote sensing and groundwater levels records by Shiqi Wei, Tianfang Xu

    Published 2025-03-01
    “…We trained the LSTM model to detect irrigation events based on groundwater level changes in the High Plains region of Nebraska and Kansas from 2001 to 2020. Using Integrated Gradients, an Explainable AI (XAI) technique, we identified that precipitation, MODIS evapotranspiration (ET), and Near-Infrared NIR reflectance are critical factors in detecting irrigation, with antecedent rainfall reducing irrigation likelihood. …”
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