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  • Presentation | H51Q: Improving Agricultural Water and Soil Moisture Monitoring with Earth Observations and Machine Learning: Innovations in Data-Driven Approaches II Poster
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  • H51Q-0573: Detecting Irrigation Signals from SMAP L3 and L4 Soil Moisture: A Case Study in California's Central Valley
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  • Board 0573‚ Hall EFG (Poster Hall)
    NOLA CC
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Author(s):
Xin Huang, The University of Tokyo (First Author, Presenting Author)
Qing He, The University of Tokyo
Naota Hanasaki, National Institute for Environmental Studies (NIES)
Rolf Reichle, NASA Goddard Space Flight Center
Taikan Oki, The University of Tokyo (UTokyo)


Satellite observations of soil moisture recently offer valuable information about irrigation practices. In this study, we used data from NASA's Soil Moisture Active and Passive (SMAP) products to identify irrigation activities in California's Central Valley. We compared two levels of SMAP products: Level 3 product directly captures soil moisture changes due to irrigation, while Level 4 excludes these effects. By looking at the difference between these two products during the growing season, after removing unrelated systematic differences, we isolated the irrigation signals. This method highlights the usefulness of SMAP satellite data for detecting irrigation in areas with limited rainfall during the growing season. Importantly, it requires minimal additional information or complex adjustments, making it a practical and straightforward tool for monitoring irrigation from space.



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