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An Operational Downscaling Method of Solar-Induced Chlorophyll Fluorescence (SIF) for Regional Drought Monitoring

Author

Listed:
  • Zhiming Hong

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China)

  • Yijie Hu

    (National Disaster Reduction Center of China, Beijing 100124, China)

  • Changlu Cui

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China)

  • Xining Yang

    (Department of Geography and Geology, Eastern Michigan University, Ypsilanti, MI 48197, USA)

  • Chongxin Tao

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
    School of Management, Xinjiang Agricultural University, Urumqi 830052, China)

  • Weiran Luo

    (School of Water Conservancy Sciences and Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Wen Zhang

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China)

  • Linyi Li

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China)

  • Lingkui Meng

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China)

Abstract

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and a promising indicator of drought monitoring, but the ability of high-resolution satellite-derived SIF for drought monitoring has not been widely investigated due to a lack of data. The lack of high spatiotemporal resolution satellite SIF hinders the resolution enhancement of SIF derived by downscaling or reconstruction algorithms. The TROPOspheric Monitoring Instrument (TROPOMI) SIF provides an alternative with finer spatiotemporal resolution. We present an operational downscaling method to generate 500 m 16-day SIF (TSIF) using Neural Networks over a local spatiotemporal window. The results showed that our method is very robust against overfitting, and TSIF has a strong spatiotemporal consistency with TROPOMI SIF (TROPOSIF) with R 2 = 0.956 and RMSE = 0.054 mWm − 2 sr − 1 nm − 1 . Comparison with another SIF product (CASIF) showed a spatiotemporal consistency with TSIF. Comparison with tower gross primary productivity (GPP) from AmeriFlux in California showed a strong correlation with R 2 for multiple ecosystems ranging from 0.58 to 0.88. We explored the capacity of TSIF for monitoring a drought event in Henan, China, showing that TSIF is more sensitive to drought and precipitation compared to the Enhanced Vegetation Index. Our TSIF is a very promising indicator for regional drought monitoring.

Suggested Citation

  • Zhiming Hong & Yijie Hu & Changlu Cui & Xining Yang & Chongxin Tao & Weiran Luo & Wen Zhang & Linyi Li & Lingkui Meng, 2022. "An Operational Downscaling Method of Solar-Induced Chlorophyll Fluorescence (SIF) for Regional Drought Monitoring," Agriculture, MDPI, vol. 12(4), pages 1-21, April.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:4:p:547-:d:791738
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    References listed on IDEAS

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    1. Prashant Srivastava & Dawei Han & Miguel Ramirez & Tanvir Islam, 2013. "Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 3127-3144, June.
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