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Monitoring and Assessment of Agricultural Drought Based on Solar-Induced Chlorophyll Fluorescence During Growing Season in North China Plain

Zhaoxu Zhang,Wei Xu,Q. Qin,Yujia Chen

Published 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

ABSTRACT

Drought is a frequent global phenomenon that has the most significant influence on agriculture. Solar-induced chlorophyll fluorescence (SIF) is a by-product of photosynthesis that can be used to monitor vegetation growth and agricultural drought. This study aims to monitor and assess monthly agricultural drought using SIF data with 0.05-degree spatial resolution. The scaled SIF index was calculated during the crop-growing season (March–October 2000–2017) in agricultural areas of North China Plain (NCP), and the monthly agricultural drought spatial distribution and severity were mapped. Results indicated that NCP experienced mild to severe drought during the study period, the severe drought (proportion more than 50%) affected months including March (2000, 2001, 2003, 2005, 2006, and 2010–2012), April (2000, 2001, 2003, 2010, and 2011), May (2000–2002 and 2004), June (2000 and 2001), September (2002), and October (2001 and 2002). By statistics, the average drought areas decreased from 2000 to 2017 in NCP. For frequency analysis, the frequencies of mild, moderate, and severe droughts were less than 0.4 in most areas of NCP, but severe drought frequency exceeded 0.6 in some areas. The monthly correlation analysis showed that the scaled SIF index had a significant positive correlation with precipitation and crop yield (wheat and corn); the maximum correlation coefficients (R) were 0.53 (September), 0.76 (May), and 0.77 (October). These results indicate that the scaled SIF index is suitable for region agricultural drought monitoring.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

  • Publication date

    Unknown publication date

  • Fields of study

    Agricultural and Food Sciences, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1109/JSTARS.2020.3032414
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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