the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 31 Mar 2022
Vegetation photosynthetic phenology metrics in northern terrestrial ecosystems: a dataset derived from a gross primary productivity product based on solar-induced chlorophyll fluorescence
Abstract. Vegetation phenology can profoundly modulate the climate-biosphere interactions and thus plays a key role in regulating the terrestrial carbon cycle and the climate. However, most previous phenology studies are based on the traditional vegetation indices, which are inadequate to characterize the seasonal activity of photosynthesis. Here, we generated an annual vegetation photosynthetic phenology dataset with a spatial resolution of 0.05 degree from 2001 to 2020, using the latest gross primary productivity product based on solar-induced chlorophyll fluorescence (GOSIF-GPP). We combined smoothing splines with multiple change-point detection to retrieve the phenology metrics: start of the growing season (SOS), end of the growing season (EOS), and length of growing season (LOS) for terrestrial ecosystems in the Northern Hemisphere. We found that the derived phenology metrics agreed better with in situ observations from the flux tower sites than vegetation indices and MODIS-GPP. Our phenology metrics captured the spatial-temporal patterns of the single and double growing season in the Northern Hemisphere. The double season was mainly from the cropland rotation and ecosystems having two different phenological cycles. In addition, we observed a trend toward advanced SOS in about 62.98 % of the land area, with a mean rate of 0.14 ± 0.01 days year-1, a trend toward delayed EOS in about 61.87 % of the area, with a mean rate of 0.19 ± 0.16 days year-1, and a trend toward extended LOS in about 70.52 % of the area, with a mean rate of 0.33 ± 0.17 days year-1. Our phenology product can be used for validating and developing phenology models or carbon cycle models, for evaluating satellite remote sensing phenology, and for monitoring climate change impacts on terrestrial ecosystems. The data are available at https://doi.org/10.6084/m9.figshare.17195009.v2 (Fang et al. 2021).
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RC1: 'Comment on essd-2021-452', Anonymous Referee #1, 10 May 2022
Fang et al. generated a 0.05 degree, annual dataset of vegetation photosynthetic phenology metrics (i.e., SOS, EOS, LOS) in North Hemisphere (NH) terrestrial ecosystems during 2001-2020. There are two major innovations in this study. First, a solar-induced chlorophyll fluorescence (SIF) derived GPP (GOSIF-GPP) product was utilized to derive phenology metrics, because SIF has been demonstrated to be a better proxy for photosynthesis than other vegetation indices (VIs). Second, a method that combined smoothing splines with multiple change-point detection was employed, so that phenology metrics, especially with multiple growing seasons, could be derived accurately. Fang et al. found SIF derived GPP could provide a better phenology estimation than other VIs, when validating with the metrics inferred from flux tower GPP. Further, the new dataset showed a trend of advanced SOS, delayed EOS, and extended LOS over 60-70% of the land area in NH. I believe this dataset will be useful for future studies to examine vegetation phenology under climate change and benchmark Earth System Models.
The data is well archived and the analysis is generally solid. However, this manuscript still requires some revision work, especially to clarify some ambiguous expressions so that readers will not be confused about. Please see my comments below.Major comments:
1. In Section 4.1, the authors utilized R and RMSE to evaluate the phenology metrics estimated from different datasets. I suggest the authors checking the mean bias as well. In addition, I suggest the authors making a comparison separately for each land cover to evaluate the performance of each biophysical variable.
2. For figures, the North Pole with no vegetation was in the center, while the vegetated continents were kind of clumped and hard to recognize. Especially, it is not straightforward to find California and North China Plain mentioned in the text. I suggest the authors using a different projection to present the results.Some minor suggestions:
Line 13 Specify which vegetation indices were used for comparison in this study.
Line 54 Clarify “better” compared to what, or just use “well”.
Line 56 “instantaneous” refers to changes happening in a short time (e.g., diurnal), therefore may be not accurate to be used here. The point should be: these traditional VIs can mainly detect structural changes but are less sensitive to physiological changes.
Line 64 Clarify “more accurately” compared to what.
Line 72 Explain a bit more about “predetermined thresholds or inflection points”.
Line 74 Explain what kind of “reconstruct” is referred to here, smoothing?
Line 78 “of” -> “which combined”
Line 80 “The strength of this method is not limited by… and can also be applied…” -> “This method has great strength in two aspects: (1) it is not limited by…; (2) it can also be applied…”
Line 83 “needs to be extended” -> “can be further extended”
Line 89 “constructed” -> ”adopted”/”developed”
Line 94 “SIF-GPP” -> “GOSIF-GPP”
Line 110 Clarify which classification type was used, IGBP?
Line 117 Clarify at which time scales the maximum GPP was calculated, e.g., 8-day maximum over 2001-2020?
Line 122 Clarify which type of partitioning approach was used for FLUXNET 2015 GPP?
Line 125 Clarify how the sites were determined as “relatively homogeneous”?
Line 136 Briefly describe how the MODIS GPP dataset was derived.
Line 136 Clarify why the time period “from 2001 to 2014” was selected for comparison?
Line 152-153 I suggest separately describing why (1) smoothing splines and (2) change points were used in this study. It seems the smoothing splines is to minimize the influence of outliers (as mentioned later in the paragraph) and not specifically aim to resolve multiple cycles.
Line 163 Clarify what “change characteristics” refers to, seasonal cycle?
Line 166 How about the case when ratio is larger than one standard deviation ABOVE the mean ratio?
Line 169 “by reconstructing the data time series by estimating parameters in the double logistic model” -> “if reconstructing the data time series with a double logistic model”
Line 176 Briefly explain what the “penalty factor” was used for.
Line 182 Explain what the “baselines” were used for.
Line 185-186 Explain what “amplitude thresholds” mean.
Line 188 I am not sure what “the most tightly-constrained transition dates” means.
Eq1, 2. Does this assume Bottom1 and Bottom2 are roughly zero? Fig 1 showed above-zero values. How would this affect your threshold calculation?
Line 195 “smoothing splines” -> “smoothed splines”?
Line 210-211 The current expression about uncertainty quantification is ambiguous. I am not sure if I fully understand. Could you elaborate more on this?
Line 229 Was the correlation coefficient calculated across all the site-years?
Line 255 “that” -> “which showed that”. And “uncertainty occurred” is not accurate, as phenology estimation from GOSIF-GPP also present uncertainties (Section 4.4).
Line 262 “the method” -> “the proposed method”
Line 264-265 “part of the cropland” -> “some croplands”
Line 284-286 I am not sure what information the authors would like to convey here.
Line 296 “10% SOS” -> ”SOS10%” to be consistent with Line 223
Line 299 “two different mixed grids” -> “two different kinds of mixed grids”
Line 300 “another” -> “the other”
Line 338 The analysis seems still long-term trend, not “interannual variation”.
Line 359 How did you define “re-modeling of the GPP time series”? Is the smoothing procedure employed in this study not a kind of “re-modeling”?
Line 362 “most appropriate for their specific application” is ambiguous and confusing.
Line 368-369 Describe the “spatially explicit pattern”.
Fig 2 Figure labels are confusing. X and y variables should be evaluated at the same threshold. Only adding the threshold to y axis label is misleading. “GOSIF-GPP”, “NDVI”, etc., in the top of the figure are also confusing, shouldn’t they be added to the y axis labels?Two suggestions for archiving the data:
1. The authors can set the data type as integer to reduce the file size.
2. Set NA values for the ocean.Citation: https://doi.org/10.5194/essd-2021-452-RC1 - AC1: 'Reply on RC1', Jing Fang, 06 Jul 2022
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RC2: 'Comment on essd-2021-452', Anonymous Referee #2, 20 Jun 2022
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L107ï¼åèæç®ä¸ä¸å å« Li å Xiao 2019bãæçè¿éåºè¯¥æ ¹æ®å¥åçææå¼ç¨ Li and Xiao (2019)ã
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Citation: https://doi.org/10.5194/essd-2021-452-RC2 -
RC3: 'Reply on RC2', Anonymous Referee #2, 20 Jun 2022
This study developed a photosynthetic phenology metric dataset from 2001 to 2020 with SIF-based GPP and the retrieval of phenology. This has important implications for the modeling and analysis of the global carbon cycle. However, I believe the comparison and validation approach proposed is flawed in this manuscript, making a reliable assessment challenging. Consequently, the current manuscript is not suitable for publication in the ESSD journal.
The specific suggestions are as follows:
Main comments
(1) the comparison for phenology metrics: The vegetation greenness and photosynthesis are not always coupled (this is mentioned in the Introduction section). This study conducted a comparison between GPP-based and VI-based phenology metrics to prove that the GPP-based metrics outperform. In my opinion, this is not directly comparable. In contrast, GPP-based phenology metrics are based on vegetation photosynthesis activity, whereas VI-based phenology metrics (NDVI, EVI) are based on vegetation morphology, structure, and greenness. NDVI/EVI (greenness index) cannot well account for most productivity variation than GPP products. In addition, the remote-sensing VIs are derived from observation while GPP is derived from simulation. So, I suggest authors can replace VIs with multiply GPP products (excepting MODIS-GPP in this manuscript), and further comparing with SIF-based GPP.
(2) validation: The derivative datasets from EC-GPP were used for the validation. The derivative datasets from EC-GPP fall into the category of photosynthetic phenology. Hence, a tendentious validation generates a bias toward phenology metrics in the two categories. This verification is more suitable for photosynthetic phenology than for structure. The results that the accuracy of GPP-based phenology metrics outperforms the VI-based ones are not solid. Suggest authors validate photosynthetic phenology results using photosynthetic phenology observation.
(3) writing: The English writing doesn’t meet the requirement of ESSD, an international academic top journal. Many redundant sentences need to be reorganized.
Specific comments:
L100: Li and Xiao (2019) noted the global SIF products, not the GPP products. Do you want to cite this article?
Li, X.; Xiao, J. Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2. Remote Sens. 2019, 11, 2563. https://doi.org/10.3390/rs11212563
L107: the references do not contain Li and Xiao 2019b. I guess Li and Xiao (2019) should be cited here according to the meaning of the sentence.
L108: Also, Li and Xiao (2019) should be cited here
Li, X.; Xiao, J. Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2. Remote Sens. 2019, 11, 2563. https://doi.org/10.3390/rs11212563
L109-111: The original spatial resolution of LULC is 500m. Please provide the details of up-scaling.
L128-L129: The distribution of the EC tower can be shown on the map.
L134-135: It is to clarify whether NDVI, EVI, and NIRv have been synthesized to 8d resolution to match GOSIF-SPP and MODIS-GPP.
L155-156: Please provide the detailed methods for time series interpolation.
L162-163: Need reference.
L164-166: (3) and (4) can be merged into one category.
L172-174: Need reference.
L174-175: Please provide necessary the details of the data processing, parameters setting, and model description. More details can be pointed to a particular article.
L176: Are “penalty factor and the minimum segment” the parameters of the PELT model? What do they affect? As mentioned above, an overview of the model and its parameters are necessary.
L197-199: Is the same method applied to EC-GPP?
L206-207: This sentence is too vague. Please clarify the specific objectives of uncertainty analysis. Moreover, please add the details of R and RMSE, the critical statistical indicators.
L216: As an innovation of this study, the difference in phenology retrieving methods for phenological identification should be discussed.
L268: “artificial crop rotation pattern” is not a specialist vocabulary.
Table 1: clarify three thresholds. Figures and tables are able to “stand alone” from the body of the paper.
Fig1: clarify all abbreviations
Fig2: Add significant test and sample sizes, and clarify all abbreviations. Additionally, correct the dotted line as the solid line in the caption.
Fig3: Add the necessary latitude such as the North Pole and tropic of cancer. Add a brief explanation of the calculation method in the caption.
Fig4: clarify the abbreviations, legend unit, and x-axis label.
Fig5: Clarify the abbreviations and legend unit. Moreover, suggest author put this figure into the supplementary material due to its less information and the similarity to Fig4.
Fig6：The similar hue in blue and green is difficult to distinguish the increase or decrease of phenology metrics.
Citation: https://doi.org/10.5194/essd-2021-452-RC3 - AC2: 'Reply on RC2', Jing Fang, 06 Jul 2022
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RC3: 'Reply on RC2', Anonymous Referee #2, 20 Jun 2022
Status: closed
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RC1: 'Comment on essd-2021-452', Anonymous Referee #1, 10 May 2022
Fang et al. generated a 0.05 degree, annual dataset of vegetation photosynthetic phenology metrics (i.e., SOS, EOS, LOS) in North Hemisphere (NH) terrestrial ecosystems during 2001-2020. There are two major innovations in this study. First, a solar-induced chlorophyll fluorescence (SIF) derived GPP (GOSIF-GPP) product was utilized to derive phenology metrics, because SIF has been demonstrated to be a better proxy for photosynthesis than other vegetation indices (VIs). Second, a method that combined smoothing splines with multiple change-point detection was employed, so that phenology metrics, especially with multiple growing seasons, could be derived accurately. Fang et al. found SIF derived GPP could provide a better phenology estimation than other VIs, when validating with the metrics inferred from flux tower GPP. Further, the new dataset showed a trend of advanced SOS, delayed EOS, and extended LOS over 60-70% of the land area in NH. I believe this dataset will be useful for future studies to examine vegetation phenology under climate change and benchmark Earth System Models.
The data is well archived and the analysis is generally solid. However, this manuscript still requires some revision work, especially to clarify some ambiguous expressions so that readers will not be confused about. Please see my comments below.Major comments:
1. In Section 4.1, the authors utilized R and RMSE to evaluate the phenology metrics estimated from different datasets. I suggest the authors checking the mean bias as well. In addition, I suggest the authors making a comparison separately for each land cover to evaluate the performance of each biophysical variable.
2. For figures, the North Pole with no vegetation was in the center, while the vegetated continents were kind of clumped and hard to recognize. Especially, it is not straightforward to find California and North China Plain mentioned in the text. I suggest the authors using a different projection to present the results.Some minor suggestions:
Line 13 Specify which vegetation indices were used for comparison in this study.
Line 54 Clarify “better” compared to what, or just use “well”.
Line 56 “instantaneous” refers to changes happening in a short time (e.g., diurnal), therefore may be not accurate to be used here. The point should be: these traditional VIs can mainly detect structural changes but are less sensitive to physiological changes.
Line 64 Clarify “more accurately” compared to what.
Line 72 Explain a bit more about “predetermined thresholds or inflection points”.
Line 74 Explain what kind of “reconstruct” is referred to here, smoothing?
Line 78 “of” -> “which combined”
Line 80 “The strength of this method is not limited by… and can also be applied…” -> “This method has great strength in two aspects: (1) it is not limited by…; (2) it can also be applied…”
Line 83 “needs to be extended” -> “can be further extended”
Line 89 “constructed” -> ”adopted”/”developed”
Line 94 “SIF-GPP” -> “GOSIF-GPP”
Line 110 Clarify which classification type was used, IGBP?
Line 117 Clarify at which time scales the maximum GPP was calculated, e.g., 8-day maximum over 2001-2020?
Line 122 Clarify which type of partitioning approach was used for FLUXNET 2015 GPP?
Line 125 Clarify how the sites were determined as “relatively homogeneous”?
Line 136 Briefly describe how the MODIS GPP dataset was derived.
Line 136 Clarify why the time period “from 2001 to 2014” was selected for comparison?
Line 152-153 I suggest separately describing why (1) smoothing splines and (2) change points were used in this study. It seems the smoothing splines is to minimize the influence of outliers (as mentioned later in the paragraph) and not specifically aim to resolve multiple cycles.
Line 163 Clarify what “change characteristics” refers to, seasonal cycle?
Line 166 How about the case when ratio is larger than one standard deviation ABOVE the mean ratio?
Line 169 “by reconstructing the data time series by estimating parameters in the double logistic model” -> “if reconstructing the data time series with a double logistic model”
Line 176 Briefly explain what the “penalty factor” was used for.
Line 182 Explain what the “baselines” were used for.
Line 185-186 Explain what “amplitude thresholds” mean.
Line 188 I am not sure what “the most tightly-constrained transition dates” means.
Eq1, 2. Does this assume Bottom1 and Bottom2 are roughly zero? Fig 1 showed above-zero values. How would this affect your threshold calculation?
Line 195 “smoothing splines” -> “smoothed splines”?
Line 210-211 The current expression about uncertainty quantification is ambiguous. I am not sure if I fully understand. Could you elaborate more on this?
Line 229 Was the correlation coefficient calculated across all the site-years?
Line 255 “that” -> “which showed that”. And “uncertainty occurred” is not accurate, as phenology estimation from GOSIF-GPP also present uncertainties (Section 4.4).
Line 262 “the method” -> “the proposed method”
Line 264-265 “part of the cropland” -> “some croplands”
Line 284-286 I am not sure what information the authors would like to convey here.
Line 296 “10% SOS” -> ”SOS10%” to be consistent with Line 223
Line 299 “two different mixed grids” -> “two different kinds of mixed grids”
Line 300 “another” -> “the other”
Line 338 The analysis seems still long-term trend, not “interannual variation”.
Line 359 How did you define “re-modeling of the GPP time series”? Is the smoothing procedure employed in this study not a kind of “re-modeling”?
Line 362 “most appropriate for their specific application” is ambiguous and confusing.
Line 368-369 Describe the “spatially explicit pattern”.
Fig 2 Figure labels are confusing. X and y variables should be evaluated at the same threshold. Only adding the threshold to y axis label is misleading. “GOSIF-GPP”, “NDVI”, etc., in the top of the figure are also confusing, shouldn’t they be added to the y axis labels?Two suggestions for archiving the data:
1. The authors can set the data type as integer to reduce the file size.
2. Set NA values for the ocean.Citation: https://doi.org/10.5194/essd-2021-452-RC1 - AC1: 'Reply on RC1', Jing Fang, 06 Jul 2022
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RC2: 'Comment on essd-2021-452', Anonymous Referee #2, 20 Jun 2022
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L100ï¼Li å Xiao (2019) 注æå°å ¨ç SIF 产åï¼èä¸æ¯ GPP 产åãä½ æ³å¼ç¨è¿ç¯æç« åï¼
æï¼Xãèï¼J. ä» ä»å¤ªé³è¯±å¯¼çå¶ç»¿ç´ è§å ä¸ç»å¶å åä½ç¨ï¼æºèª OCO-2 çå ¨çç²¾ç»å辨çæ°æ®éãRemote Sens. 2019, 11, 2563. https://doi.org/10.3390/rs11212563
L107ï¼åèæç®ä¸ä¸å å« Li å Xiao 2019bãæçè¿éåºè¯¥æ ¹æ®å¥åçææå¼ç¨ Li and Xiao (2019)ã
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Citation: https://doi.org/10.5194/essd-2021-452-RC2 -
RC3: 'Reply on RC2', Anonymous Referee #2, 20 Jun 2022
This study developed a photosynthetic phenology metric dataset from 2001 to 2020 with SIF-based GPP and the retrieval of phenology. This has important implications for the modeling and analysis of the global carbon cycle. However, I believe the comparison and validation approach proposed is flawed in this manuscript, making a reliable assessment challenging. Consequently, the current manuscript is not suitable for publication in the ESSD journal.
The specific suggestions are as follows:
Main comments
(1) the comparison for phenology metrics: The vegetation greenness and photosynthesis are not always coupled (this is mentioned in the Introduction section). This study conducted a comparison between GPP-based and VI-based phenology metrics to prove that the GPP-based metrics outperform. In my opinion, this is not directly comparable. In contrast, GPP-based phenology metrics are based on vegetation photosynthesis activity, whereas VI-based phenology metrics (NDVI, EVI) are based on vegetation morphology, structure, and greenness. NDVI/EVI (greenness index) cannot well account for most productivity variation than GPP products. In addition, the remote-sensing VIs are derived from observation while GPP is derived from simulation. So, I suggest authors can replace VIs with multiply GPP products (excepting MODIS-GPP in this manuscript), and further comparing with SIF-based GPP.
(2) validation: The derivative datasets from EC-GPP were used for the validation. The derivative datasets from EC-GPP fall into the category of photosynthetic phenology. Hence, a tendentious validation generates a bias toward phenology metrics in the two categories. This verification is more suitable for photosynthetic phenology than for structure. The results that the accuracy of GPP-based phenology metrics outperforms the VI-based ones are not solid. Suggest authors validate photosynthetic phenology results using photosynthetic phenology observation.
(3) writing: The English writing doesn’t meet the requirement of ESSD, an international academic top journal. Many redundant sentences need to be reorganized.
Specific comments:
L100: Li and Xiao (2019) noted the global SIF products, not the GPP products. Do you want to cite this article?
Li, X.; Xiao, J. Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2. Remote Sens. 2019, 11, 2563. https://doi.org/10.3390/rs11212563
L107: the references do not contain Li and Xiao 2019b. I guess Li and Xiao (2019) should be cited here according to the meaning of the sentence.
L108: Also, Li and Xiao (2019) should be cited here
Li, X.; Xiao, J. Mapping Photosynthesis Solely from Solar-Induced Chlorophyll Fluorescence: A Global, Fine-Resolution Dataset of Gross Primary Production Derived from OCO-2. Remote Sens. 2019, 11, 2563. https://doi.org/10.3390/rs11212563
L109-111: The original spatial resolution of LULC is 500m. Please provide the details of up-scaling.
L128-L129: The distribution of the EC tower can be shown on the map.
L134-135: It is to clarify whether NDVI, EVI, and NIRv have been synthesized to 8d resolution to match GOSIF-SPP and MODIS-GPP.
L155-156: Please provide the detailed methods for time series interpolation.
L162-163: Need reference.
L164-166: (3) and (4) can be merged into one category.
L172-174: Need reference.
L174-175: Please provide necessary the details of the data processing, parameters setting, and model description. More details can be pointed to a particular article.
L176: Are “penalty factor and the minimum segment” the parameters of the PELT model? What do they affect? As mentioned above, an overview of the model and its parameters are necessary.
L197-199: Is the same method applied to EC-GPP?
L206-207: This sentence is too vague. Please clarify the specific objectives of uncertainty analysis. Moreover, please add the details of R and RMSE, the critical statistical indicators.
L216: As an innovation of this study, the difference in phenology retrieving methods for phenological identification should be discussed.
L268: “artificial crop rotation pattern” is not a specialist vocabulary.
Table 1: clarify three thresholds. Figures and tables are able to “stand alone” from the body of the paper.
Fig1: clarify all abbreviations
Fig2: Add significant test and sample sizes, and clarify all abbreviations. Additionally, correct the dotted line as the solid line in the caption.
Fig3: Add the necessary latitude such as the North Pole and tropic of cancer. Add a brief explanation of the calculation method in the caption.
Fig4: clarify the abbreviations, legend unit, and x-axis label.
Fig5: Clarify the abbreviations and legend unit. Moreover, suggest author put this figure into the supplementary material due to its less information and the similarity to Fig4.
Fig6：The similar hue in blue and green is difficult to distinguish the increase or decrease of phenology metrics.
Citation: https://doi.org/10.5194/essd-2021-452-RC3 - AC2: 'Reply on RC2', Jing Fang, 06 Jul 2022
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RC3: 'Reply on RC2', Anonymous Referee #2, 20 Jun 2022
Data sets
Vegetation photosynthetic phenology metrics in northern terrestrial ecosystems: a dataset derived from a gross primary productivity product based on solar-induced chlorophyll fluorescence Jing Fang, Xing Li, Jingfeng Xiao, Xiaodong Yan, Bolun Li, Feng Liu https://doi.org/10.6084/m9.figshare.17195009.v2
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