A New Global Solar-induced Chlorophyll Fluorescence (SIF) Data Product from TanSat Measurements

Abstract

The Chinese Carbon Dioxide Observation Satellite Mission (TanSat) is the third satellite for global CO2 monitoring and is capable of detecting weak solar-induced chlorophyll fluorescence (SIF) signals with its advanced technical characteristics. Based on the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing (IAPCAS) platform, we successfully retrieved the TanSat global SIF product spanning the period of March 2017 to February 2018 with a physically based algorithm. This paper introduces the new TanSat SIF dataset and shows the global seasonal SIF maps. A brief comparison between the IAPCAS TanSat SIF product and the data-driven SVD (singular value decomposition) SIF product is also performed for follow-up algorithm optimization. The comparative results show that there are regional biases between the two SIF datasets and the linear correlations between them are above 0.73 for all seasons. The future SIF data product applications and requirements for SIF space observation are discussed.

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Acknowledgements

This study was supported by the National Key R&D Program of China (No. 2016YFA0600203), the Key Research Program of the Chinese Academy of Sciences (ZDRW-ZS-2019-1 & ZDRW-ZS-2019-2), and the Youth Program of the National Natural Science Foundation of China (41905029). The TanSat L1B data service was provided by the International Reanalysis Cooperation on Carbon Satellite Data (IRCSD) (131211KYSB20180002) and the Cooperation on the Analysis of Carbon Satellite Data (CASA). The authors thank the OCO-2 team for providing the Level-2 SIF data products.

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Correspondence to Jing Wang.

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Yao, L., Yang, D., Liu, Y. et al. A New Global Solar-induced Chlorophyll Fluorescence (SIF) Data Product from TanSat Measurements. Adv. Atmos. Sci. 38, 341–345 (2021). https://doi.org/10.1007/s00376-020-0204-6

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Key words

  • TanSat
  • solar-induced chlorophyll fluorescence
  • retrieval algorithm
  • remote sensing