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| DOI：10.13522/j.cnki.ggps.2025279 |
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| Applications and advances in multi-source remote sensing spatiotemporal data fusion for irrigation district water management: A review |
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SONG Peng, ZHANG Yuxiang, GU Tao, CHANG Mingqi, XU Lei, LEI Bo, LU Wenhong, HOU Peng
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1. China Irrigation and Drainage Development Center, Beijing 100054, China; 2. Zhengzhou University, Zhengzhou 450001, China
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| Abstract: |
| Irrigation districts play a crucial role in food security and sustaining agricultural development. In recent decades, remote sensing technologies have been increasingly used to improve water management in irrigation districts. However, differences in satellite platforms and their operational mechanisms often lead to inconsistencies in spatiotemporal resolution, observation scale, and sensitivity to environmental changes among datasets derived from different remote sensing sources, thereby limiting the accuracy, consistency and timeliness of information acquisition. To overcome these limitations and support more effective irrigation water management, this paper systematically reviews recent advances in multi-source remote sensing spatiotemporal fusion technologies and their applications in irrigation district management over the past decade. The review synthesizes representative methods and their underlying principles, including statistical fusion, pixel reconstruction, spatiotemporal fusion models, machine learning, deep learning, and physics-data hybrid modeling. The strengths and limitations of these approaches are comparatively analysed in terms of accuracy, stability, and applicability. Building on this synthesis, we further examine the applications of multi-source data fusion in irrigation district management, including irrigation area mapping, soil moisture monitoring, evapotranspiration estimation, irrigation water demand analysis, water use efficiency assessment, and dynamic irrigation scheduling. Through this comprehensive review, several key challenges are identified, including data registration consistency, limited model generalization across regions and sensors, sensitivity to environmental interference, and insufficient uncertainty quantification in remote sensing-based irrigation management. Finally, future research directions are proposed, emphasizing the integration of advanced spatiotemporal fusion algorithms, improved uncertainty assessment, and the development of smart irrigation districts supported by intelligent remote sensing systems. |
| Key words: remote sensing data fusion; smart irrigation management; evapotranspiration estimation; irrigation area identification; soil moisture monitoring |
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