An LSTM approach to deciphering irrigation operations from remote sensing and groundwater levels records
Agricultural irrigation, the largest consumptive water user, significantly impacts terrestrial energy and water cycle, atmospheric boundary layer and the sustainability of water resources management. However, irrigation records usually lack the necessary detail in terms of amount, location, time and...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377424006097 |
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| author | Shiqi Wei Tianfang Xu |
| author_facet | Shiqi Wei Tianfang Xu |
| author_sort | Shiqi Wei |
| collection | DOAJ |
| description | Agricultural irrigation, the largest consumptive water user, significantly impacts terrestrial energy and water cycle, atmospheric boundary layer and the sustainability of water resources management. However, irrigation records usually lack the necessary detail in terms of amount, location, time and source with adequate spatial and temporal resolution that are required for understanding farmers’ irrigation behavior and representing irrigation in hydrologic models. This study addresses the irrigation scheduling gap by leveraging in situ groundwater level records of index wells and multi-source remote sensing observations. We used a Bi-directional Long Short-Term Memory (LSTM) network to capture the temporal relationship between groundwater fluctuations and land surface responses to irrigation. 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. This framework enables allocation of long-term irrigation amounts to individual events, allows hydrologic models to assimilate irrigation dataset to assess irrigation impacts, and improves irrigation behavior representation in water resources management. |
| format | Article |
| id | doaj-art-d8160804351e4afd982fc0bb622cc8be |
| institution | Kabale University |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-d8160804351e4afd982fc0bb622cc8be2025-01-25T04:10:42ZengElsevierAgricultural Water Management1873-22832025-03-01308109273An LSTM approach to deciphering irrigation operations from remote sensing and groundwater levels recordsShiqi Wei0Tianfang Xu1School of Sustainable Engineering and The Built Environment, Arizona State University, USACorresponding author.; School of Sustainable Engineering and The Built Environment, Arizona State University, USAAgricultural irrigation, the largest consumptive water user, significantly impacts terrestrial energy and water cycle, atmospheric boundary layer and the sustainability of water resources management. However, irrigation records usually lack the necessary detail in terms of amount, location, time and source with adequate spatial and temporal resolution that are required for understanding farmers’ irrigation behavior and representing irrigation in hydrologic models. This study addresses the irrigation scheduling gap by leveraging in situ groundwater level records of index wells and multi-source remote sensing observations. We used a Bi-directional Long Short-Term Memory (LSTM) network to capture the temporal relationship between groundwater fluctuations and land surface responses to irrigation. 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. This framework enables allocation of long-term irrigation amounts to individual events, allows hydrologic models to assimilate irrigation dataset to assess irrigation impacts, and improves irrigation behavior representation in water resources management.http://www.sciencedirect.com/science/article/pii/S0378377424006097High plainsIrrigationLSTMExplainable AIGroundwater |
| spellingShingle | Shiqi Wei Tianfang Xu An LSTM approach to deciphering irrigation operations from remote sensing and groundwater levels records Agricultural Water Management High plains Irrigation LSTM Explainable AI Groundwater |
| title | An LSTM approach to deciphering irrigation operations from remote sensing and groundwater levels records |
| title_full | An LSTM approach to deciphering irrigation operations from remote sensing and groundwater levels records |
| title_fullStr | An LSTM approach to deciphering irrigation operations from remote sensing and groundwater levels records |
| title_full_unstemmed | An LSTM approach to deciphering irrigation operations from remote sensing and groundwater levels records |
| title_short | An LSTM approach to deciphering irrigation operations from remote sensing and groundwater levels records |
| title_sort | lstm approach to deciphering irrigation operations from remote sensing and groundwater levels records |
| topic | High plains Irrigation LSTM Explainable AI Groundwater |
| url | http://www.sciencedirect.com/science/article/pii/S0378377424006097 |
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