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March 8, 2026SHILAP Revista de lepidopterologíaOpen Access

Generation of a global, daily, spatiotemporally seamless solar-induced chlorophyll fluorescence dataset from 2018 to 2021

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Authors

JLJingbo LiTongji UniversityQWQunming WangTongji UniversityPAPeter M. AtkinsonUniversity of Southampton

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Overview

A global dataset addresses solar-induced chlorophyll fluorescence data gaps, enhancing ecological monitoring and productivity estimates.

Key Points

  • This research aims to create a comprehensive solar-induced chlorophyll fluorescence dataset that fills data gaps for improved ecological monitoring.
  • Developed a Categorical Boosting model to reconstruct missing solar-induced chlorophyll fluorescence data.
  • Utilized high-revisit SIF samples and vegetation photosynthesis variables for model training.
  • Validations included correlation coefficients and root mean square error for both training and testing data.
  • Achieved a correlation coefficient of 0.885 and RMSE of 0.114 for training dataset evaluation.
  • Produced a global daily S2DSIF dataset that seamlessly fills temporal gaps in existing SIF data.
  • Demonstrated superior accuracy in S2DSIF compared to other solar-induced chlorophyll fluorescence products.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/69ada804bc08abd80d5bb325https://doi.org/10.1080/17538947.2026.2640801
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