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.

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Journal
IEEE Transactions on Geoscience and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/tgrs.2025.3561216
Citations
1
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Remote Sensing in Agriculture
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article
Field-Weighted Citation Impact
1.72

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article

Normalized Solar-Induced Fluorescence Responds Earlier Than Vegetation Indices to the 2019 North China Plain Drought

Xuecao Li, Anne Gobin, Yongyuan Gao, Wei Su et al.
1 citations
IEEE Transactions on Geoscience and Remote Sensing
Remote Sensing in Agriculture
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article

Normalized Solar-Induced Fluorescence Responds Earlier Than Vegetation Indices to the 2019 North China Plain Drought

Xuecao Li, Anne Gobin, Yongyuan Gao, Wei Su, Si Gao, Shuangxi Miao, Jianxi Huang, N N Voropay, Bingbo Gao, Kai Yan, Yelu Zeng, Dalei Hao, Wendi Lu, Liang Zhu, Zhe Liu, Huiren Tian, Yachang He
article en
1 citations

Abstract

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.

IEEE Transactions on Geoscience and Remote SensingVol. 63
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)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 18%
Remote Sensing in Agriculture
1.72
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