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https://doi.org/10.5194/essd-2022-277
https://doi.org/10.5194/essd-2022-277
16 Aug 2022
 | 16 Aug 2022
Status: this discussion paper is a preprint. It has been under review for the journal Earth System Science Data (ESSD). The manuscript was not accepted for further review after discussion.

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

Xiaojin Qian, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen, and Qi Sun

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).

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Xiaojin Qian, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen, and Qi Sun

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-277', Anonymous Referee #1, 12 Sep 2022
  • RC2: 'Comment on essd-2022-277', Anonymous Referee #2, 13 Oct 2022
  • RC3: 'Comment on essd-2022-277', Anonymous Referee #3, 07 Nov 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-277', Anonymous Referee #1, 12 Sep 2022
  • RC2: 'Comment on essd-2022-277', Anonymous Referee #2, 13 Oct 2022
  • RC3: 'Comment on essd-2022-277', Anonymous Referee #3, 07 Nov 2022
Xiaojin Qian, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen, and Qi Sun

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, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen, and Qi Sun

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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.
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