Normalized Solar-Induced Fluorescence Responds Earlier Than Vegetation Indices to the 2019 North China Plain Drought
Recently, solar-induced chlorophyll fluorescence (SIF) from satellites has shown potential for evaluating vegetation status and stress responses. Fluorescence quantum yield (Φ<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub>) is essentially linked to vegetation stress. However, the complex physiological and structural responses of SIF and Φ<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> to drought need further study. This study normalized SIF as SIF<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> to account for angular variations and fluctuations in photosynthetically active radiation (PAR), aiming for more accurate drought monitoring. SIF<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> anomalies were compared to historical baselines (2019–2021 averages) of vegetation indices (VIs), raw SIF, and Φ<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> during a 2019 drought in the North China Plain (NCP). The results show SIF<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> provides an effective method for drought monitoring, showing the earliest decline compared to raw SIF, VIs, and Φ<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub>. In the first two weeks of drought, SIF<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> decreased by 8.2%, 7.0%, 12.5%, and 8.2% across the four NCP subdivisions. SIF<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> outperformed other indicators, proving sensitive to early drought detection. SIF<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> was also examined for tracking drought alleviation by rainfall. The uncertainty under different viewing geometries was quantified. SIF<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> anomalies showed a strong correlation with rainfall anomalies (R: 0.45 ~ 0.52) and meteorological factors like PAR (R: 0.80 ~ 0.84) and relative humidity (R:0.52 ~ 0.54). The correlation of near-infrared reflectance (NIRv) and Φ<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> anomalies with SIF was weak during drought onset (R: 0.16 ~ 0.32) but strong at the end (R: 0.83 ~ 0.87). These suggest both canopy structure (mainly characterized by NIRv) and vegetation chlorophyll (Φ<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub>) are impacted by drought and influence SIF at different stages.
Authors
- Xuecao Li (ORCID: https://orcid.org/0000-0002-6942-0746)
- Anne Gobin (ORCID: https://orcid.org/0000-0002-3742-7062)
- Yongyuan Gao (ORCID: https://orcid.org/0009-0004-1231-0564)
- Wei Su (ORCID: https://orcid.org/0000-0002-8726-5858)
- Si Gao (ORCID: https://orcid.org/0000-0003-3036-6508)
- Shuangxi Miao (ORCID: https://orcid.org/0000-0001-5308-1551)
- Jianxi Huang (ORCID: https://orcid.org/0000-0003-0341-1983)
- N N Voropay (ORCID: https://orcid.org/0000-0003-4179-4225)
- Bingbo Gao (ORCID: https://orcid.org/0000-0002-4315-4873)
- Kai Yan (ORCID: https://orcid.org/0000-0003-4262-1772)
- Yelu Zeng (ORCID: https://orcid.org/0000-0003-4267-1841)
- Dalei Hao (ORCID: https://orcid.org/0000-0003-3497-9774)
- Wendi Lu
- Liang Zhu (ORCID: https://orcid.org/0000-0002-1970-6914)
- Zhe Liu (ORCID: https://orcid.org/0000-0002-8214-8345)
- Huiren Tian
- Yachang He (ORCID: https://orcid.org/0009-0007-5783-171X)
Institutions
- Xi'an University of Science and Technology (CN)
- Pacific Northwest National Laboratory (US)
- Chinese Academy of Sciences (CN)
- Beijing Normal University (CN)
- Aerospace Information Research Institute (CN)
- Institute of Monitoring of Climatic and Ecological Systems (RU)
- State Key Laboratory of Remote Sensing Science (CN)
- Southwest Jiaotong University (CN)
- China Agricultural University (CN)
- Flemish Institute for Technological Research (BE)
Publication Details
- Journal
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tgrs.2025.3561216
- Citations
- 1
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 1.72
Funders
- National Natural Science Foundation of China