Preprints
https://doi.org/10.5194/essd-2022-277
https://doi.org/10.5194/essd-2022-277
 
16 Aug 2022
16 Aug 2022
Status: this preprint is currently under review for the journal ESSD.

The global leaf chlorophyll content dataset over 2003–2012 and 2018–2020 derived from MERIS/OLCI satellite data (GLCC): algorithm and validation

Xiaojin Qian1,2, Liangyun Liu3,4, Xidong Chen5, Xiao Zhang3,4, Siyuan Chen6, and Qi Sun7 Xiaojin Qian et al.
  • 1School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
  • 2State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences. Beijing, 100101, China
  • 3International Research Center of Big Data for Sustainable Development Goals, Beijing, 100094, China
  • 4Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • 5College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
  • 6College of Geological Engineering and Geomatics, Chang’an University, Xi’an, 710054, China
  • 7College of Surveying and Planning, Shangqiu Normal University, Shangqiu, 476000, China

Abstract. Leaf chlorophyll content (LCC), a prominent plant physiological trait and a proxy for leaf photosynthetic capacity, plays a crucial role in the monitoring of agriculture and carbon cycle modeling. In this study, global 500 m LCC weekly dataset (GLCC) for the period 2003–2012 to 2018–2020 were produced from ENVISAT MERIS and Sentinel-3 OLCI satellite data using a physically-based radiative transfer modeling approach. Firstly, five look-up-tables (LUTs) were generated using PROSAIL-D and PROSPECT-D+4-Scale models for woody and non-woody plants, respectively. For the four LUTs applicable to woody plants, each LUT contains three sub-LUTs corresponding to three types of crown height. For the one LUT applicable to non-woody vegetation type, it includes 25 sub-LUTs corresponding to five kinds of canopy structure and five kinds of soil background. The average of the LCC inversion results of all sub-LUTs for each plant function type (PFT) was considered as the retrieval. The LUT algorithm was validated using the synthetic dataset, which gave an R2 value higher than 0.79 and an RMSE value lower than 10.5 μg cm−2. Then, the GLCC dataset was generated using the MERIS/OLCI multispectral data over 2003–2012 and 2018–2020 at a spatial resolution of 500 m and temporal resolution of one week. The GLCC dataset was validated using 161 field measurements, covering six PFTs. The validation results yielded an overall accuracy of R2 = 0.41 and RMSE = 8.94 μg cm−2. Finally, the GLCC dataset presented acceptable consistency with the existing MERIS LCC dataset developed by Croft et al. (2020). OLCI, as the successor to MERIS data, was used for the first time to co-produce LCC data from 2003–2012 to 2018–2020 in conjunction with MERIS data. This new GLCC dataset spanning nearly 20 years will provide a valuable opportunity for the monitoring of vegetation growth and terrestrial carbon cycle modeling. The GLCC dataset is available at https://doi.org/10.25452/figshare.plus.20439351 (Qian et al., 2022b).

Xiaojin Qian et al.

Status: open (until 21 Oct 2022)

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  • RC1: 'Comment on essd-2022-277', Anonymous Referee #1, 12 Sep 2022 reply

Xiaojin Qian et al.

Data sets

GLCC: global leaf chlorophyll content dataset over 2003–2012 and 2018–2020 derived from MERIS/OLCI satellite data Qian, Xiaojin; Liu, Liangyun; Chen, Xidong; Zhang, Xiao; Chen, Siyuan; Sun, Qi https://doi.org/10.25452/figshare.plus.20439351

Xiaojin Qian et al.

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Short summary
Leaf chlorophyll content (LCC) is an important plant physiological trait and a proxy for leaf photosynthetic capacity. We generated a global LCC dataset from ENVISAT MERIS and Sentinel-3 OLCI satellite data for the period 2003–2012 to 2018–2020 using a physically-based radiative transfer modeling approach. This new LCC dataset spanning nearly 20 years will provide a valuable opportunity for the monitoring of vegetation growth and terrestrial carbon cycle modeling on a global scale.