the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A grid dataset of leaf age-dependent LAI seasonality product (Lad-LAI) over tropical and subtropical evergreen broadleaved forests
Xueqin Yang
Xiuzhi Chen
Jiashun Ren
Wenping Yuan
Liyang Liu
Juxiu Liu
Dexiang Chen
Yihua Xiao
Shengbiao Wu
Lei Fan
Xiaoai Dai
Yongxian Su
Abstract. Quantification of large-scale leaf age-dependent leaf area index has been lacking in tropical and subtropical evergreen broadleaved forests (TEFs) despite the recognized importance of leaf age in influencing leaf photosynthetic capacity in this region. Here, we simplified the canopy leaves of TEFs into three age cohorts, i.e., young, mature and old one, with different photosynthesis capacity (Vc,max) and produced a first grid dataset of leaf age-dependent LAI product (referred to as Lad-LAI) over the continental scale from satellite observations of TROPOMI (the TROPOspheric Monitoring Instrument) sun-induced chlorophyll fluorescence (SIF) as a proxy of leaf photosynthesis. The seasonality of three LAI cohorts from the new Lad-LAI products agree well at the three sites (one in subtropical Asia and two in Amazon) with very fine collections of monthly LAI of young, mature and old leaves. Continental-scale comparisons with independent Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) products and 53 samples of in situ measurements of seasonal litterfall data also demonstrate the robustness of the LAI seasonality of the three leaf age cohorts. The spatial patterns clustered from the three LAI cohorts coincides with those clustered from climatic variables. And the young and mature LAI cohorts perform well in capturing a dry-season green-up of canopy leaves across the wet Amazonia areas where mean annual precipitation exceeds 2,000 mm yr−1, consistent with previous satellite data analysis. The new Lad-LAI products are primed to diagnose the adaption of tropical and subtropical forest to climate change; and will also help improve the development of phenology modules in Earth System Models. The proposed satellite-based approaches can provide reference for mapping finer temporal and spatial resolution LAI products with different leaf age cohorts. The Lad-LAI products are available at https://doi.org/10.6084/m9.figshare.21700955.v2 (Yang et al., 2022).
- Preprint
(2065 KB) -
Supplement
(825 KB) - BibTeX
- EndNote
Xueqin Yang et al.
Status: closed
-
RC1: 'Comment on essd-2022-436', Anonymous Referee #1, 02 Feb 2023
- Summary
Yang et al's work mapped the seasonal leaf area index (LAI) of three leaf age cohorts (i.e., young, mature, and old leaves) to interpret the phenological seasonality in tropical and subtropical forests. They accomplished this by calculating gross primary productivity (GPP) from TROPOMI solar-induced chlorophyl fluorescence (SIF) observations. They validated the results with ground-based observations of leaf dynamics, and with a satellite-based vegetation index (EVI). They obtained good agreement between simulated and observed LAI.
- Overall evaluation
The global relevance of the study is incontestable and is underscored by the novelty of such dataset. When published, it will be an important contribution for the understanding of tropical forests phenology. However, the manuscript needs substantial review of the English style as there are numerous language mistakes, which makes the comprehension of the text difficult.
- Minor comments
Besides extensive review of the English style that I am not listing here, a few minor points need to be observed:
- Line 39: “very fine collections of monthly LAI”. What does fine collections mean? Fine-scale?
- Line 94-95: GPP is not the same thing as photosynthesis!
- Figure 5: Improve figure caption by clarifying which panels (left or right) represent the simulated and observed LAIs;
- The authors provided the reference to Keller et al 2001 as a source for in situ samples of VCmax, but I don’t thin the citation is accurate. The referred paper is about biomass estimates and allometric equations;
- A key reference that should be included in the manuscript: https://doi.org/10.1111/nph.15726
Citation: https://doi.org/10.5194/essd-2022-436-RC1 - AC1: 'Reply on RC1', Xueqin Yang, 10 Apr 2023
-
RC2: 'Comment on essd-2022-436', Anonymous Referee #2, 12 Feb 2023
This work produced the first grid dataset of leaf age-dependent LAI product that is classified into young, mature, and old types, over the tropical evergreen broadleaved forests from satellite observations. It is an interesting work, and the overall framework is clear. The topic fits the ESSD, but there are still some major issues in this work that need to be addressed before this manuscript can be published. Some overall and point-to-point are provided below. I hope these comments are useful and constructive to improve this manuscript.
My specific comments:
- The manuscript need to be thoroughly polished.
- Abstract cannot summarize this work well, particularly for describing results, accuracy, and performance (Lines 37-48). Alternatively, add some quantitative metrics in Abstract, e.g., how much accuracy can be reached for the site- and continental-scale validation and comparison (Lines 38-43), and how LAI cohort perform well with satellite data analysis (Lines 45-48), and also, using concise language to shorten Lines 49-52.
- I just concerned the results were validated by only three sites (one in subtropical Asia and two in Amazon). Can not find more sites to validate? For example, eddy covariance data and may find more details from papers (DOI: 10.1126/science.aad506; https://doi.org/10.1016/j.agrformet.2013.04.031). More ground validation can show the robustness and accuracy of this dataset.
- Introduction can be considered to re-organize, as the current version seems lack some logics and useful information.
- It would be better to add a Study area and data used session to introduce some relevant information and Figure 1.
- Authors used a constant value (LAI = 7) of total LAI in tropical and subtropical EBFs., but the valid range of LAI is generally 0 to 10. Thus, I expect to see more evidence for selecting 7 or a sensitivity analysis of threshold can also be implemented.
- The format of Equation (1) should be: GPP = 𝐿𝐴𝐼𝑦𝑜𝑢𝑛𝑔 × 𝐴𝑛𝑦𝑜𝑢𝑛𝑔 + 𝐿𝐴𝐼𝑚𝑎𝑡𝑢𝑟𝑒 × 𝐴𝑛𝑚𝑎𝑡𝑢𝑟𝑒 + 𝐿𝐴𝐼𝑜𝑙𝑑 × 𝐴𝑛𝑜𝑙𝑑.
- It is weird why all R values are 0.99 in Fig.3?
- Fig.3 is not supposed to place at Method part, can move it into results or supplementary materials; and Fig.4 is not a contribution of this work, can move it into supplementary materials.
- Lines 351-355, can provide some scatterplots between Lad-LAI products and sites observations, rather than providing quantified accuracy metrics only.
Citation: https://doi.org/10.5194/essd-2022-436-RC2 - AC2: 'Reply on RC2', Xueqin Yang, 10 Apr 2023
-
RC3: 'Comment on essd-2022-436', Anonymous Referee #3, 13 Feb 2023
This paper introduces a novel dataset of age-dependent LAI for tropical and subtropic evergreen forests. Such a dataset is highly valuable and much in need to understand the dynamics of tropical canopy structure under climate change and improve the robustness of Earth System Models in reconstructing past dynamics and projecting future scenarios.
The study estimated three LAI age cohorts based on a neighbor-based decomposition model and SIF-derived GPP data. The seasonality of leaf demography and its spatial variations is evaluated against ground-based measurements, and satellite observations, and analyzed with regard to other independent studies from climate controls. Results suggested a robust representation of the spatial variability in seasonality, which will be useful for improving Earth System Models.
Overall, I find the dataset to be valuable and significant. I especially appreciate the authors’ efforts in collecting and synthesizing ground-based observations globally to evaluate the products. However, I have some concerns regarding the robustness of the neighbor-based decomposition approach, the absence of evaluation regarding interannual dynamics, and the uncertainties in GPP estimations. I hope the authors will consider these points and provide further clarification in their responses and/or revisions. Please find my major comments and minor for clarification below.
Major comments:
- The approach using spatially adjacent GPP information to solve the leaf age composition is interesting but needs more justification on its robustness. With four observations (from four neighboring pixels) to solve three unknowns (LAI cohorts), the system does not have much space or tolerance for observation uncertainties (that is GPP, please see a related comment below). I suggest providing goodness-of-fit metrics from the least squares to evaluate the model performance. However, this still may not be informative due to a limited number of observations and lack of variations between the neighboring cells. Ideally, one solution would be to include more observations (for example, by increasing the number neighboring pixels from 4 to 8) to improve the robustness and accuracy of the models, but that also means a decrease in the spatial resolution of the product.
- While the age-dependent LAI product is produced at monthly time steps over 2001 – 2018, it has only been validated and evaluated in terms of its LAI seasonality (i.e. multi-year average climatology). The reliability and usefulness of this product in representing interannual variabilities of leaf demography are highly uncertain. Thus, I strongly encourage the authors to evaluate the interannual temporal dynamics, even if only limited, since ground observations are often insufficient. The reliability of this product in terms of representation seasonality vs. interannual variabilities should be explicitly stated in the abstract, and thoroughly discussed in the main text, to prevent misuse of the dataset. I also suggest providing LAI cohorts seasonality as the main product, and the temporal dynamics as a supplementary dataset with a clear note of usage provided along with the product.
- SIF-GPP relationships used to estimate GPP in this study were based on only four sites with ground observations, that may not fully represent the tropical areas over the globe. Therefore, GPP estimations from SIF are subject to high uncertainties with possibly large biases. Given that the analytical approach used to solve does not consider uncertainties, the impact of GPP estimation uncertainties on age-dependent LAI estimates should be carefully discussed.
- Please note that evaluation against EVI is not entirely independent, since the RT-SIF dataset was a reconstruction from MODIS NBAR surface reflectance data.
- The manuscript needs improvements in language and grammar. I suggest carefully revising it to improve clarity.
Specific comments
Abstract
Please specify the temporal span, temporal and spatial resolution of the LAI product.
L36: It should be noted that this is a SIF dataset that was reconstructed from MODIS and TROPOMI to avoid confusion.
L40-41: Since the RTSIF is reconstructed from MODIS surface reflectance data, the evaluation against EVI is not precisely “independent”.
Introduction
L103: The last paragraph of the Introduction should be shortened with a brief summary of the method and findings.
Method:
L132-133: How much are the spatial variations in the constant LAI value?
L147-168: Using GPP-SIF relationships based on only four sites is suspect to extrapolation issues over the entire areas.
L155: VPD data sources are different between Table S3 and Figure 2. ERA5-Land is at 0.1 degree instead of 0.05 deg? Can you double check?
L175: Could you please provide the GPP-SIF relationship equation and overall goodness-of-fit?
L270-271: Note that the RTSIF product is reconstructed from MODIS using the short-term TROPOMI data as a training set. Therefore, the evaluation against EVI is not independent.
L273: Can you please elaborate on how EVI reflects young and mature leaves, not old ones?
L274: Specify MSD
Figure S1: the figure is too blur to read.
L326: Please specify which variable (x,y) is estimated or observed
Result:
Figure 5: It’s not clear which is estimated versus observed data
L355-357: This sentence is a bit unclear. Can you elaborate on the “trade-off”?
L359-360: Should one of the “early wet season” be “dry season"?
L397: Chen et al., 2019 is not found in the reference list.
L395: Is it possible to keep a consistent number of clusters between the three datasets? For example, can you set eight clusters in Lad-LAI, so the southeast Asia area has three clusters consistent with plots d-f. This will make it easier to compare the datasets.
L413: I wonder if you have any hypothesis for the low performance in southeast Asia in comparison with other regions? (Figure 8a-c)
Figure 12: Please increase font size. It’s not clear which line represents site data. Can you also illustrate the meaning of the dots?
Citation: https://doi.org/10.5194/essd-2022-436-RC3 - AC3: 'Reply on RC3', Xueqin Yang, 10 Apr 2023
Status: closed
-
RC1: 'Comment on essd-2022-436', Anonymous Referee #1, 02 Feb 2023
- Summary
Yang et al's work mapped the seasonal leaf area index (LAI) of three leaf age cohorts (i.e., young, mature, and old leaves) to interpret the phenological seasonality in tropical and subtropical forests. They accomplished this by calculating gross primary productivity (GPP) from TROPOMI solar-induced chlorophyl fluorescence (SIF) observations. They validated the results with ground-based observations of leaf dynamics, and with a satellite-based vegetation index (EVI). They obtained good agreement between simulated and observed LAI.
- Overall evaluation
The global relevance of the study is incontestable and is underscored by the novelty of such dataset. When published, it will be an important contribution for the understanding of tropical forests phenology. However, the manuscript needs substantial review of the English style as there are numerous language mistakes, which makes the comprehension of the text difficult.
- Minor comments
Besides extensive review of the English style that I am not listing here, a few minor points need to be observed:
- Line 39: “very fine collections of monthly LAI”. What does fine collections mean? Fine-scale?
- Line 94-95: GPP is not the same thing as photosynthesis!
- Figure 5: Improve figure caption by clarifying which panels (left or right) represent the simulated and observed LAIs;
- The authors provided the reference to Keller et al 2001 as a source for in situ samples of VCmax, but I don’t thin the citation is accurate. The referred paper is about biomass estimates and allometric equations;
- A key reference that should be included in the manuscript: https://doi.org/10.1111/nph.15726
Citation: https://doi.org/10.5194/essd-2022-436-RC1 - AC1: 'Reply on RC1', Xueqin Yang, 10 Apr 2023
-
RC2: 'Comment on essd-2022-436', Anonymous Referee #2, 12 Feb 2023
This work produced the first grid dataset of leaf age-dependent LAI product that is classified into young, mature, and old types, over the tropical evergreen broadleaved forests from satellite observations. It is an interesting work, and the overall framework is clear. The topic fits the ESSD, but there are still some major issues in this work that need to be addressed before this manuscript can be published. Some overall and point-to-point are provided below. I hope these comments are useful and constructive to improve this manuscript.
My specific comments:
- The manuscript need to be thoroughly polished.
- Abstract cannot summarize this work well, particularly for describing results, accuracy, and performance (Lines 37-48). Alternatively, add some quantitative metrics in Abstract, e.g., how much accuracy can be reached for the site- and continental-scale validation and comparison (Lines 38-43), and how LAI cohort perform well with satellite data analysis (Lines 45-48), and also, using concise language to shorten Lines 49-52.
- I just concerned the results were validated by only three sites (one in subtropical Asia and two in Amazon). Can not find more sites to validate? For example, eddy covariance data and may find more details from papers (DOI: 10.1126/science.aad506; https://doi.org/10.1016/j.agrformet.2013.04.031). More ground validation can show the robustness and accuracy of this dataset.
- Introduction can be considered to re-organize, as the current version seems lack some logics and useful information.
- It would be better to add a Study area and data used session to introduce some relevant information and Figure 1.
- Authors used a constant value (LAI = 7) of total LAI in tropical and subtropical EBFs., but the valid range of LAI is generally 0 to 10. Thus, I expect to see more evidence for selecting 7 or a sensitivity analysis of threshold can also be implemented.
- The format of Equation (1) should be: GPP = 𝐿𝐴𝐼𝑦𝑜𝑢𝑛𝑔 × 𝐴𝑛𝑦𝑜𝑢𝑛𝑔 + 𝐿𝐴𝐼𝑚𝑎𝑡𝑢𝑟𝑒 × 𝐴𝑛𝑚𝑎𝑡𝑢𝑟𝑒 + 𝐿𝐴𝐼𝑜𝑙𝑑 × 𝐴𝑛𝑜𝑙𝑑.
- It is weird why all R values are 0.99 in Fig.3?
- Fig.3 is not supposed to place at Method part, can move it into results or supplementary materials; and Fig.4 is not a contribution of this work, can move it into supplementary materials.
- Lines 351-355, can provide some scatterplots between Lad-LAI products and sites observations, rather than providing quantified accuracy metrics only.
Citation: https://doi.org/10.5194/essd-2022-436-RC2 - AC2: 'Reply on RC2', Xueqin Yang, 10 Apr 2023
-
RC3: 'Comment on essd-2022-436', Anonymous Referee #3, 13 Feb 2023
This paper introduces a novel dataset of age-dependent LAI for tropical and subtropic evergreen forests. Such a dataset is highly valuable and much in need to understand the dynamics of tropical canopy structure under climate change and improve the robustness of Earth System Models in reconstructing past dynamics and projecting future scenarios.
The study estimated three LAI age cohorts based on a neighbor-based decomposition model and SIF-derived GPP data. The seasonality of leaf demography and its spatial variations is evaluated against ground-based measurements, and satellite observations, and analyzed with regard to other independent studies from climate controls. Results suggested a robust representation of the spatial variability in seasonality, which will be useful for improving Earth System Models.
Overall, I find the dataset to be valuable and significant. I especially appreciate the authors’ efforts in collecting and synthesizing ground-based observations globally to evaluate the products. However, I have some concerns regarding the robustness of the neighbor-based decomposition approach, the absence of evaluation regarding interannual dynamics, and the uncertainties in GPP estimations. I hope the authors will consider these points and provide further clarification in their responses and/or revisions. Please find my major comments and minor for clarification below.
Major comments:
- The approach using spatially adjacent GPP information to solve the leaf age composition is interesting but needs more justification on its robustness. With four observations (from four neighboring pixels) to solve three unknowns (LAI cohorts), the system does not have much space or tolerance for observation uncertainties (that is GPP, please see a related comment below). I suggest providing goodness-of-fit metrics from the least squares to evaluate the model performance. However, this still may not be informative due to a limited number of observations and lack of variations between the neighboring cells. Ideally, one solution would be to include more observations (for example, by increasing the number neighboring pixels from 4 to 8) to improve the robustness and accuracy of the models, but that also means a decrease in the spatial resolution of the product.
- While the age-dependent LAI product is produced at monthly time steps over 2001 – 2018, it has only been validated and evaluated in terms of its LAI seasonality (i.e. multi-year average climatology). The reliability and usefulness of this product in representing interannual variabilities of leaf demography are highly uncertain. Thus, I strongly encourage the authors to evaluate the interannual temporal dynamics, even if only limited, since ground observations are often insufficient. The reliability of this product in terms of representation seasonality vs. interannual variabilities should be explicitly stated in the abstract, and thoroughly discussed in the main text, to prevent misuse of the dataset. I also suggest providing LAI cohorts seasonality as the main product, and the temporal dynamics as a supplementary dataset with a clear note of usage provided along with the product.
- SIF-GPP relationships used to estimate GPP in this study were based on only four sites with ground observations, that may not fully represent the tropical areas over the globe. Therefore, GPP estimations from SIF are subject to high uncertainties with possibly large biases. Given that the analytical approach used to solve does not consider uncertainties, the impact of GPP estimation uncertainties on age-dependent LAI estimates should be carefully discussed.
- Please note that evaluation against EVI is not entirely independent, since the RT-SIF dataset was a reconstruction from MODIS NBAR surface reflectance data.
- The manuscript needs improvements in language and grammar. I suggest carefully revising it to improve clarity.
Specific comments
Abstract
Please specify the temporal span, temporal and spatial resolution of the LAI product.
L36: It should be noted that this is a SIF dataset that was reconstructed from MODIS and TROPOMI to avoid confusion.
L40-41: Since the RTSIF is reconstructed from MODIS surface reflectance data, the evaluation against EVI is not precisely “independent”.
Introduction
L103: The last paragraph of the Introduction should be shortened with a brief summary of the method and findings.
Method:
L132-133: How much are the spatial variations in the constant LAI value?
L147-168: Using GPP-SIF relationships based on only four sites is suspect to extrapolation issues over the entire areas.
L155: VPD data sources are different between Table S3 and Figure 2. ERA5-Land is at 0.1 degree instead of 0.05 deg? Can you double check?
L175: Could you please provide the GPP-SIF relationship equation and overall goodness-of-fit?
L270-271: Note that the RTSIF product is reconstructed from MODIS using the short-term TROPOMI data as a training set. Therefore, the evaluation against EVI is not independent.
L273: Can you please elaborate on how EVI reflects young and mature leaves, not old ones?
L274: Specify MSD
Figure S1: the figure is too blur to read.
L326: Please specify which variable (x,y) is estimated or observed
Result:
Figure 5: It’s not clear which is estimated versus observed data
L355-357: This sentence is a bit unclear. Can you elaborate on the “trade-off”?
L359-360: Should one of the “early wet season” be “dry season"?
L397: Chen et al., 2019 is not found in the reference list.
L395: Is it possible to keep a consistent number of clusters between the three datasets? For example, can you set eight clusters in Lad-LAI, so the southeast Asia area has three clusters consistent with plots d-f. This will make it easier to compare the datasets.
L413: I wonder if you have any hypothesis for the low performance in southeast Asia in comparison with other regions? (Figure 8a-c)
Figure 12: Please increase font size. It’s not clear which line represents site data. Can you also illustrate the meaning of the dots?
Citation: https://doi.org/10.5194/essd-2022-436-RC3 - AC3: 'Reply on RC3', Xueqin Yang, 10 Apr 2023
Xueqin Yang et al.
Data sets
Leaf age-dependent LAI seasonality product (Lad-LAI) over tropical and subtropical evergreen broadleaved forests Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yongxian Su https://doi.org/10.6084/m9.figshare.21700955.v2
Xueqin Yang et al.
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 557 | 148 | 21 | 726 | 46 | 8 | 9 |
- HTML: 557
- PDF: 148
- XML: 21
- Total: 726
- Supplement: 46
- BibTeX: 8
- EndNote: 9
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1