the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Aug 2022
The global leaf chlorophyll content dataset over 2003–2012 and 2018–2020 derived from MERIS/OLCI satellite data (GLCC): algorithm and validation
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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RC1: 'Comment on essd-2022-277', Anonymous Referee #1, 12 Sep 2022
In the manuscript “The global leaf chlorophyll content dataset over 2003–2012 and 2018–2020 derived from MERIS/OLCI satellite data (GLCC): algorithm and validation”, the authors used MERIS and Sentinel-3 data, and inversed radiative transfer model to generate a new global dataset of leaf chlorophyll content from 20003-2012, 2018-2020. The authors did a good job of making their data publicly available. Though the study could be a good addition to the line of research on remote sensing of new metrics such as leaf chlorophyll content, I concern the manuscript in its current form does not demonstrate much progress in science (e.g., more novel and robust algorithm, theoretical exploration on chlorophyll signals, uncertainty attribution) than the previous Croft study. The validation result does not support that the new estimates is more advanced. The longer time series also seem to lose its attractiveness in light a recent leaf chlorophyll product based on MODIS (https://ieeexplore.ieee.org/abstract/document/9875366/authors#authors).
- Specifically, I would suggest the authors to highlight their advances from the previous Croft study. I think both studies used similar set of radiative transfer models, though the choice of parameters might be different. Regarding the parameter’s choice, I hope the authors would provide more justification. E.g. it was not clear why Table 3 and 4 gave two sets of parameters for models (N, LCC) though they are use jointly for LCC derivation.
- The authors suggest that they used 4-scale for their modelling process, but I could not find detailed description on how they implement the model (I understand table 3 and 4 both include some parameters, but the workflow was not very clear). Importantly, I cannot figure out its difference to the Croft algorithm and advantages. Also note that LAI and clumping index are important variables describing canopy structure in 4-Scale, and their sources were not clearly mentioned. In particular LAI, as it contributes to a large proportion of the uncertainty in the LCC estimates, which might justify some uncertainty analysis.
- The validation result is a bit unexpected (which means some interesting discoveries might be there!), as the Croft product generally has a good performance for DBF, since LCC is likely to have larger seasonal variation, it would be easier to acquire a higher R2. Surprisingly, the new LCC product has better performance in EBF, not DBF than the Croft product. I wonder why.
Figure 4 is not very clear. Why would we see one measurement corresponds to many estimates, and what are those synthetic measurements?
Considering the issues above, I apologize that I cannot be supportive here. I encourage the authors to clearly demonstrate their progress in the LCC derivation algorithm, and get to the bottom on the difference between their products and the others, which can guide future users and developers of LCC. Nevertheless, I applaud the authors’ effort to make their data publicly available.
Citation: https://doi.org/10.5194/essd-2022-277-RC1 -
RC2: 'Comment on essd-2022-277', Anonymous Referee #2, 13 Oct 2022
The authors focused on mapping global weekly leaf chlorophyll across nearly 20 years based on radiative transfer models and improved LUT methods, which considered different kinds of canopy structure, PFTs, and soil backgrounds. This is an interesting and meaningful research, which has a profound impact on modulating ecosystem biophysical and biochemical dynamics. However, at present, there is no great breakthrough in either dataset or methodological innovation compared to previous studies. Besides that, the validation method results have not been greatly improved. I am sorry that I cannot support this manuscript for publication here. The main comments are as follows:
- Detailed description of how to use 4SAIL and 4-Scale model need to be added in manuscript. Now it is not clear for readers to figure out how you applied these models and what are the differences between other studies.
- This study didn’t consider the covariance between different functional traits and just set a fixed value for N, Cw, Cm, Canth and Cbrw at a global scale. Moreover, only a simple linear relationship between LCC and Cxc was set instead of considering the nonlinear change of the relationship between them with time. Above all can definitely improve the computational efficiency, but will cause large uncertainties when using the ill-posed radiative transfer models.
- Detailed description of validation method should be added. For the validation datasets: Firstly, I think such a limited validation dataset is insufficient to validate global LCC inversion results. Second, the validation dataset didn’t consider the seasonal variations of LCC.
Citation: https://doi.org/10.5194/essd-2022-277-RC2 -
RC3: 'Comment on essd-2022-277', Anonymous Referee #3, 07 Nov 2022
The manuscript from Qian et al. provided a study to quantify global leaf chlorophyll content over 2003-2012 and 2018-2020 through MERIS and Sentinel-3 OLCI satellite data. This 500-m and weekly leaf chlorophyll data set was generated by using a look-up table-based inversion of soil-vegetation radiative transfer modeling approach (PROSAIL-D and PROSPECT-D+4-scale models). By validating against 161 sampling measurements, this leaf chlorophyll content product achieved an accuracy of R2 = 0.41 and RMSE = 8.94 μg cm−2. Overall, this study is quite similar to the previous study by Croft (2020) in retrieval algorithms, model performance, and satellite data sources. The major differences occur in using Sentinel-3 OLCI satellite data. Regarding the strength of quantifying long-term global leaf chlorophyll content, a recent study by (Xu et al., 2022) used MODIS data to quantify even longer time series chlorophyll data from 2000 to 2020. Given these major concerns, this study needs to strengthen its innovations. For example, this study could leverage advanced machine learning surrogate modeling approaches for leaf chlorophyll content retrieval instead of look-up table approaches. This study can also consider integrating multi-source satellite data from Sentinel-2 or Landsat to quantify high-resolution (e.g., 30-m) leaf chlorophyll. In addition, this study could also be strengthened by collecting more comprehensive ground data for product validation.
Citation: https://doi.org/10.5194/essd-2022-277-RC3
Status: closed
-
RC1: 'Comment on essd-2022-277', Anonymous Referee #1, 12 Sep 2022
In the manuscript “The global leaf chlorophyll content dataset over 2003–2012 and 2018–2020 derived from MERIS/OLCI satellite data (GLCC): algorithm and validation”, the authors used MERIS and Sentinel-3 data, and inversed radiative transfer model to generate a new global dataset of leaf chlorophyll content from 20003-2012, 2018-2020. The authors did a good job of making their data publicly available. Though the study could be a good addition to the line of research on remote sensing of new metrics such as leaf chlorophyll content, I concern the manuscript in its current form does not demonstrate much progress in science (e.g., more novel and robust algorithm, theoretical exploration on chlorophyll signals, uncertainty attribution) than the previous Croft study. The validation result does not support that the new estimates is more advanced. The longer time series also seem to lose its attractiveness in light a recent leaf chlorophyll product based on MODIS (https://ieeexplore.ieee.org/abstract/document/9875366/authors#authors).
- Specifically, I would suggest the authors to highlight their advances from the previous Croft study. I think both studies used similar set of radiative transfer models, though the choice of parameters might be different. Regarding the parameter’s choice, I hope the authors would provide more justification. E.g. it was not clear why Table 3 and 4 gave two sets of parameters for models (N, LCC) though they are use jointly for LCC derivation.
- The authors suggest that they used 4-scale for their modelling process, but I could not find detailed description on how they implement the model (I understand table 3 and 4 both include some parameters, but the workflow was not very clear). Importantly, I cannot figure out its difference to the Croft algorithm and advantages. Also note that LAI and clumping index are important variables describing canopy structure in 4-Scale, and their sources were not clearly mentioned. In particular LAI, as it contributes to a large proportion of the uncertainty in the LCC estimates, which might justify some uncertainty analysis.
- The validation result is a bit unexpected (which means some interesting discoveries might be there!), as the Croft product generally has a good performance for DBF, since LCC is likely to have larger seasonal variation, it would be easier to acquire a higher R2. Surprisingly, the new LCC product has better performance in EBF, not DBF than the Croft product. I wonder why.
Figure 4 is not very clear. Why would we see one measurement corresponds to many estimates, and what are those synthetic measurements?
Considering the issues above, I apologize that I cannot be supportive here. I encourage the authors to clearly demonstrate their progress in the LCC derivation algorithm, and get to the bottom on the difference between their products and the others, which can guide future users and developers of LCC. Nevertheless, I applaud the authors’ effort to make their data publicly available.
Citation: https://doi.org/10.5194/essd-2022-277-RC1 -
RC2: 'Comment on essd-2022-277', Anonymous Referee #2, 13 Oct 2022
The authors focused on mapping global weekly leaf chlorophyll across nearly 20 years based on radiative transfer models and improved LUT methods, which considered different kinds of canopy structure, PFTs, and soil backgrounds. This is an interesting and meaningful research, which has a profound impact on modulating ecosystem biophysical and biochemical dynamics. However, at present, there is no great breakthrough in either dataset or methodological innovation compared to previous studies. Besides that, the validation method results have not been greatly improved. I am sorry that I cannot support this manuscript for publication here. The main comments are as follows:
- Detailed description of how to use 4SAIL and 4-Scale model need to be added in manuscript. Now it is not clear for readers to figure out how you applied these models and what are the differences between other studies.
- This study didn’t consider the covariance between different functional traits and just set a fixed value for N, Cw, Cm, Canth and Cbrw at a global scale. Moreover, only a simple linear relationship between LCC and Cxc was set instead of considering the nonlinear change of the relationship between them with time. Above all can definitely improve the computational efficiency, but will cause large uncertainties when using the ill-posed radiative transfer models.
- Detailed description of validation method should be added. For the validation datasets: Firstly, I think such a limited validation dataset is insufficient to validate global LCC inversion results. Second, the validation dataset didn’t consider the seasonal variations of LCC.
Citation: https://doi.org/10.5194/essd-2022-277-RC2 -
RC3: 'Comment on essd-2022-277', Anonymous Referee #3, 07 Nov 2022
The manuscript from Qian et al. provided a study to quantify global leaf chlorophyll content over 2003-2012 and 2018-2020 through MERIS and Sentinel-3 OLCI satellite data. This 500-m and weekly leaf chlorophyll data set was generated by using a look-up table-based inversion of soil-vegetation radiative transfer modeling approach (PROSAIL-D and PROSPECT-D+4-scale models). By validating against 161 sampling measurements, this leaf chlorophyll content product achieved an accuracy of R2 = 0.41 and RMSE = 8.94 μg cm−2. Overall, this study is quite similar to the previous study by Croft (2020) in retrieval algorithms, model performance, and satellite data sources. The major differences occur in using Sentinel-3 OLCI satellite data. Regarding the strength of quantifying long-term global leaf chlorophyll content, a recent study by (Xu et al., 2022) used MODIS data to quantify even longer time series chlorophyll data from 2000 to 2020. Given these major concerns, this study needs to strengthen its innovations. For example, this study could leverage advanced machine learning surrogate modeling approaches for leaf chlorophyll content retrieval instead of look-up table approaches. This study can also consider integrating multi-source satellite data from Sentinel-2 or Landsat to quantify high-resolution (e.g., 30-m) leaf chlorophyll. In addition, this study could also be strengthened by collecting more comprehensive ground data for product validation.
Citation: https://doi.org/10.5194/essd-2022-277-RC3
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
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