the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2020
Muyi Li
Abstract. Global products of remote sensing Normalized Difference Vegetation Index (NDVI) are critical to assessing the vegetation dynamic and its impacts and feedbacks on climate change from local to global scales. The previous versions of the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI product derived from the Advanced Very High Resolution Radiometer (AVHRR) provide global biweekly NDVI data starting from the 1980s, being a reliable long-term NDVI time series that has been widely applied in Earth and environmental sciences. However, the GIMMS NDVI products have several limitations (e.g., orbital drift and sensor degradation) and cannot provide continuous data for the future. In this study, we presented a machine learning model that employed massive high-quality and global-wide Landsat NDVI samples and a data consolidation method to generate a new version of the GIMMS NDVI product, i.e., PKU GIMMS NDVI (1982−2020), based on AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. A total of 3.6 million Landsat NDVI samples that well spread across the globe were extracted for vegetation biomes in all seasons. The PKU GIMMS NDVI exhibits higher accuracy than its predecessor (GIMMS NDVI3g) in terms of R2 (0.975 over 0.942), mean absolute error (MAE: 0.033 over 0.074), and mean absolute percentage error (MAPE: 9 % over 20 %). Notably, PKU GIMMS NDVI effectively eliminates the evident orbital drift and sensor degradation effects in tropical areas. The consolidated PKU GIMMS NDVI has a high temporal consistency with MODIS NDVI in describing vegetation trends (R2 = 0.962, MAE = 0.032, and MAPE = 6.5 %). The PKU GIMMS NDVI product can potentially provide a more solid data basis for global change studies. The theoretical framework that employs Landsat data samples can facilitate the generation of remote sensing products for other land surface parameters.
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Muyi Li et al.
Status: closed
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RC1: 'Comment on essd-2023-1', Anonymous Referee #1, 12 Apr 2023
For the past decades, NDVI has been one of the most frequently used and verified remote sensing indices to monitor vegetation dynamics at regional and global scales, with its unique advantages including the simple form, a low sensor requirement, robustness to changes in plant canopies, insensitiveness to atmospheric effects and the sun-sensor geometry, etc. The developments of global long-term NDVI products have thus been critical. The manuscript presented a new version of GIMMS NDVI products (denominated as PKU GIMMS NDVI in the manuscript), (I) to address the issues of orbital drift and sensor degradation effects in AVHRR data using massive Landsat samples and (II) to extend the temporal coverage to recent using MODIS NDVI. All parts of the manuscript were well organized and written. The methodology is feasible and reliable to me, and the results provide relatively comprehensive evidence on the spatial and temporal accuracies of the products. The product could potentially benefit many future vegetation studies. I have an overall positive attitude on the product and basically recommend the manuscript for publication, as long as the authors address the following concerns.
My major concern is on the data consolidation with MODIS NDVI. I am happy that a pixel-scale linear fusion method was adopted, as the relationship between AVHRR NDVI and MODIS NDVI could be spatially different. However, the relationship might also be driven by some other factors such as the plant function type, phenological cycle, and sensor design, which I believe are beyond the capacity of the simple linear function used in the study (Mao et al., 2012). This could be confirmed by the failure of the method in some EBF (Figure 10). Also, the phrases ‘before consolidation’ and ‘after consolidation’ have frequently confused me when reading. To me, the data before consolidation are an intermediate product that certainly needs a discussion but is improper and unnecessary to compare with other NDVI products, for example, in trend analysis. It is not an individual product.
The second issue is related to NDVI products used for comparison. I have noticed that only GIMMS NDVI3g was involved; meanwhile there exists some other global long-term NDVI products such as the LTDR4 and VIP3 (as the authors also mentioned in Introduction). So why were they excluded in the data comparison? Please clarify.
Last, the data published in ZENODO (https://zenodo.org/record/7441559#.Y7J7y3ZByCo) have not been well structured and the data size is way larger than the analogs. I suggest the authors, if possible, compress the data and stack the monthly NDVI to preferably every 10 years before uploading.
Specific comments
line 17-19
Better specify how were R2, MAE, and MAPE calculated.
Line 44-46
This is particularly severe when estimating long-term changes (e.g., Shen et al 2022).
Shen et al 2022, Plant phenology changes and drivers on the Qinghai–Tibetan Plateau. Nature Reviews Earth & Environment, 3, 633–651.
Line 126-127
Could the authors provide more details here?
Line 149
How was the spatial aggregation used? By highest frequency?
Line 196-197
would 9 30m-resolution landsat pixels represent 8km GIMMS pixel?
and was this done separately for each time step? How was the data temporally matched?
Or did I miss something here?
Line 201 section 3.2.2
was there any procedure performed to ensure the data quality of the two NDVI datasets?
Line 203-204
were all the currently available landsat data included here? or just for the sample locations?
Line 207-208
Please consider if it is necessary to build more than one models for a given biome due to large spatial variability within the same biome (not mandatory).
Line 219
2016 or 2015?
Line 220-222
But some studies have shown that modis VI also has quality issues due to, such as, sensor degradation.
Line 226
Which is the dependent variable here? MODIS NDVI or GIMMS NDVI?
Line 228-229
May be a little more detail about the spatial and temporal matching between modis and pku gimms ndvi would help.
And was there any procedure performed to ensure the data quality of the two NDVI datasets?
Line 229
2004-2015 or 2001-2015?
Line 252
does landsat have an orbit shift problem?
Figure 5
Please delete ‘unitless’ in the figure.
Citation: https://doi.org/10.5194/essd-2023-1-RC1 -
AC1: 'Reply on RC1', Muyi Li, 18 Jun 2023
Dear reviewer,
We are very pleased to finish a revised version of the manuscript essd-2023-1 entitled “Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022”. In preparing this revision we have considered all your comments and incorporated most of the suggestions. Temporal coverage of PKU GIMMS NDVI has been extended from 2020 to 2022. We greatly appreciate your time and effort spent in reviewing this manuscript, which have improved the revised version of the manuscript.
Substantial improvements have been made based on your comments, including:
- Random forests regressor with a moving window and extra explanatory variables (longitude and latitude) was employed to consolidate PKU GIMMS NDVI (1982−2015) with MODIS NDVI (2003−2022).
- We explained the reason why only the previous version of GIMMS NDVI (GIMMS NDVI3g) was used for comparison.
- We have re-structured the PKU GIMMS NDVI4g published in ZENODO.
Point-to-Point Responses can be found in the Supplements (.pdf). All the changes have been marked by red in the revised manuscript.
Sincerely yours,
Zaichun Zhu, Ph. D. (on behalf of the author team)
School of Urban Planning and Design
Peking University
Tel: 86 185 0042 6608
Email: zhu.zaichun@pku.edu.cn
-
AC1: 'Reply on RC1', Muyi Li, 18 Jun 2023
-
RC2: 'Comment on essd-2023-1', Anonymous Referee #2, 21 Apr 2023
This study proposes PKU GIMMS NDVI, a new global long-term NDVI time series data that covers 1982 to 2020 based on AVHRR and MODIS sensors onboard satellite platforms. The PKU GIMMS NDVI extends the GIMMS NDVI3g data and has better data quality. It has better agreement with Landsat NDVI compared to GIMMS NDVI3g, alleviating the orbital drift problem in the AVHRR sensors. The method proposed in this study could be used to generate consistent global NDVI data in the future, which would help study global terrestrial biosphere dynamics.
Detailed comments:
Line 14: “global-wide” may be simplified as “global”
Line 26: When introducing NDVI, please cite the original NDVI paper:
Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W., 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ, 351(1), p.309.
Line 97: Please provide literature references to the data sources.
Line 126: How is the time-weighted aggregation performed? Please explain in detail.
Line 127: Maybe the authors want to say “upscale” instead of “downscale.”
Line 139: How were the Landsat NDVI samples aggregated to 1/12˚? Please explain in detail.
Line 144: “temporal” should be “temporally.”
Line 226: could the authors elaborate more on how they spliced the PKU GIMMS NDVI and MODIS NDVI?
Line 254: How can seasonal fluctuations in the time series of NDVI bias be removed via the multi-year averaging method? Please explain.
Line 257: Maybe it should be “… was evaluated at 1,000 random points …”
Figure 2: maybe the authors could also show the regression line and equation in each panel?
Line 301: The section title could be “Validation of PKU GIMMS NDVI and GIMMS NDVI3g”
Figure 6: please explain in the figure caption how the R2 was computed in detail.
Line 337: The section title could be “Comparison with MODIS NDVI”？Citation: https://doi.org/10.5194/essd-2023-1-RC2 -
AC2: 'Reply on RC2', Muyi Li, 18 Jun 2023
Dear reviewer,
We are very pleased to finish a revised version of the manuscript essd-2023-1 entitled “Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022”. In preparing this revision we have considered all your comments and incorporated most of the suggestions. Temporal coverage of PKU GIMMS NDVI has been extended from 2020 to 2022. We greatly appreciate your time and effort spent in reviewing this manuscript, which have improved the revised version of the manuscript.
Substantial improvements have been made based on your comments, including:
- We have provided more details on how we performed the time-weighted aggregation method to convert the temporal resolution of the MODIS NDVI product (MOD13C1) from 16 days to half-month.
- We have also elaborated the method used to splice the PKU GIMMS NDVI and MODIS NDVI.
Point-to-Point Responses can be found in the Supplements (.pdf). All the changes have been marked by red in the revised manuscript.
Sincerely yours,
Zaichun Zhu, Ph. D. (on behalf of the author team)
School of Urban Planning and Design
Peking University
Tel: 86 185 0042 6608
Email: zhu.zaichun@pku.edu.cn
-
AC2: 'Reply on RC2', Muyi Li, 18 Jun 2023
-
RC3: 'Comment on essd-2023-1', Anonymous Referee #3, 24 Apr 2023
Overall Comments:
A relaiable long-term vegetation time series is critical to understand the dynamic of vegeataion and its feedback to the climate. This study by Li et al reconstructs a spatiotemporally consistent global NDVI dataset for 1982-2020 integrating Back Propagation Neural Network and a total of 3.6 million Landsat NDVI samples that well spread across the globe as input. This product, along with its predecessor (the GIMMS NDVI3g dataset), has been evaluated with the Landsat NDVI samples, showing substantial improvement. This study has originality and significance in uniqueness and usefulness.
Below are some comments that may help to further improve the manuscript. First, it seems that the golden truth of NDVI is Landsat NDVI samples. I recommend providing details in section 3.2.1 of Landsat NDVI samples to illustraluate: 1) Why Landsat NDVI is more accurate than other products? 2) Does Landsat NDVI have any limitations (e.g. the influence of clouds)? 3) Adding a plot to show the distribution of these samples (time and space).
Second, this dataset extends the time-span of 1982-2015 for its predecessor to 1982-2020, but there is no figure or analysis of this extension. It would be great to show more details of this extension (e.g. a long time series spanning from 1982 to 2015). In addition, it would be great to explain the results of consolidating the PKU dataset with MODIS NDVI data for the years after 2016. A comparison between MODIS and PKU NDVI datasets from 2017 to 2020 would be helpful (e.g. Figure 12).
Some minor comments: The Landsat NDVI samples were used both to train and validate the PKU GIMMS NDVI dataset. How the Landsat NDVI samples were seperated into two groups?
The figure caption for Figure 6 is confusing: comparison of R2 between the GIMMS NDVI4g and PKU GIMMS NDVI products. Is it R2 between the GIMMS NDVI4g and PKU GIMMS NDVI products? Or R2 between the GIMMS NDVI4g and Landsat NDVI, and that between PKU GIMMS NDVI and Landsat NDVI?
Why MOD13C1, not MOD13Q1, MOD13A3 or MOD13C2, is used in this study?
Figure 11: In the PKU dataset, most tropical regions show greening during 2004-2015, which may be unreasonable.
Figure 4: S5 shows no significant improvement compared to S4.
Figure 8: A color bar is missing.
Citation: https://doi.org/10.5194/essd-2023-1-RC3 -
AC3: 'Reply on RC3', Muyi Li, 18 Jun 2023
Dear reviewer,
We are very pleased to finish a revised version of the manuscript essd-2023-1 entitled “Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022”. In preparing this revision we have considered all your comments and incorporated most of the suggestions. Temporal coverage of PKU GIMMS NDVI has been extended from 2020 to 2022. We greatly appreciate your time and effort spent in reviewing this manuscript, which have improved the revised version of the manuscript.
Substantial improvements have been made based on your comments, including:
- We have provided more details on how we performed the time-weighted aggregation method to convert the temporal resolution of the MODIS NDVI product (MOD13C1) from 16 days to half-month.
- We have also elaborated the method used to splice the PKU GIMMS NDVI and MODIS NDVI
Point-to-Point Responses (.pdf). All the changes have been marked by red in the revised manuscript.
Sincerely yours,
Zaichun Zhu, Ph. D. (on behalf of the author team)
School of Urban Planning and Design
Peking University
Tel: 86 185 0042 6608
Email: zhu.zaichun@pku.edu.cn
-
AC3: 'Reply on RC3', Muyi Li, 18 Jun 2023
Status: closed
-
RC1: 'Comment on essd-2023-1', Anonymous Referee #1, 12 Apr 2023
For the past decades, NDVI has been one of the most frequently used and verified remote sensing indices to monitor vegetation dynamics at regional and global scales, with its unique advantages including the simple form, a low sensor requirement, robustness to changes in plant canopies, insensitiveness to atmospheric effects and the sun-sensor geometry, etc. The developments of global long-term NDVI products have thus been critical. The manuscript presented a new version of GIMMS NDVI products (denominated as PKU GIMMS NDVI in the manuscript), (I) to address the issues of orbital drift and sensor degradation effects in AVHRR data using massive Landsat samples and (II) to extend the temporal coverage to recent using MODIS NDVI. All parts of the manuscript were well organized and written. The methodology is feasible and reliable to me, and the results provide relatively comprehensive evidence on the spatial and temporal accuracies of the products. The product could potentially benefit many future vegetation studies. I have an overall positive attitude on the product and basically recommend the manuscript for publication, as long as the authors address the following concerns.
My major concern is on the data consolidation with MODIS NDVI. I am happy that a pixel-scale linear fusion method was adopted, as the relationship between AVHRR NDVI and MODIS NDVI could be spatially different. However, the relationship might also be driven by some other factors such as the plant function type, phenological cycle, and sensor design, which I believe are beyond the capacity of the simple linear function used in the study (Mao et al., 2012). This could be confirmed by the failure of the method in some EBF (Figure 10). Also, the phrases ‘before consolidation’ and ‘after consolidation’ have frequently confused me when reading. To me, the data before consolidation are an intermediate product that certainly needs a discussion but is improper and unnecessary to compare with other NDVI products, for example, in trend analysis. It is not an individual product.
The second issue is related to NDVI products used for comparison. I have noticed that only GIMMS NDVI3g was involved; meanwhile there exists some other global long-term NDVI products such as the LTDR4 and VIP3 (as the authors also mentioned in Introduction). So why were they excluded in the data comparison? Please clarify.
Last, the data published in ZENODO (https://zenodo.org/record/7441559#.Y7J7y3ZByCo) have not been well structured and the data size is way larger than the analogs. I suggest the authors, if possible, compress the data and stack the monthly NDVI to preferably every 10 years before uploading.
Specific comments
line 17-19
Better specify how were R2, MAE, and MAPE calculated.
Line 44-46
This is particularly severe when estimating long-term changes (e.g., Shen et al 2022).
Shen et al 2022, Plant phenology changes and drivers on the Qinghai–Tibetan Plateau. Nature Reviews Earth & Environment, 3, 633–651.
Line 126-127
Could the authors provide more details here?
Line 149
How was the spatial aggregation used? By highest frequency?
Line 196-197
would 9 30m-resolution landsat pixels represent 8km GIMMS pixel?
and was this done separately for each time step? How was the data temporally matched?
Or did I miss something here?
Line 201 section 3.2.2
was there any procedure performed to ensure the data quality of the two NDVI datasets?
Line 203-204
were all the currently available landsat data included here? or just for the sample locations?
Line 207-208
Please consider if it is necessary to build more than one models for a given biome due to large spatial variability within the same biome (not mandatory).
Line 219
2016 or 2015?
Line 220-222
But some studies have shown that modis VI also has quality issues due to, such as, sensor degradation.
Line 226
Which is the dependent variable here? MODIS NDVI or GIMMS NDVI?
Line 228-229
May be a little more detail about the spatial and temporal matching between modis and pku gimms ndvi would help.
And was there any procedure performed to ensure the data quality of the two NDVI datasets?
Line 229
2004-2015 or 2001-2015?
Line 252
does landsat have an orbit shift problem?
Figure 5
Please delete ‘unitless’ in the figure.
Citation: https://doi.org/10.5194/essd-2023-1-RC1 -
AC1: 'Reply on RC1', Muyi Li, 18 Jun 2023
Dear reviewer,
We are very pleased to finish a revised version of the manuscript essd-2023-1 entitled “Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022”. In preparing this revision we have considered all your comments and incorporated most of the suggestions. Temporal coverage of PKU GIMMS NDVI has been extended from 2020 to 2022. We greatly appreciate your time and effort spent in reviewing this manuscript, which have improved the revised version of the manuscript.
Substantial improvements have been made based on your comments, including:
- Random forests regressor with a moving window and extra explanatory variables (longitude and latitude) was employed to consolidate PKU GIMMS NDVI (1982−2015) with MODIS NDVI (2003−2022).
- We explained the reason why only the previous version of GIMMS NDVI (GIMMS NDVI3g) was used for comparison.
- We have re-structured the PKU GIMMS NDVI4g published in ZENODO.
Point-to-Point Responses can be found in the Supplements (.pdf). All the changes have been marked by red in the revised manuscript.
Sincerely yours,
Zaichun Zhu, Ph. D. (on behalf of the author team)
School of Urban Planning and Design
Peking University
Tel: 86 185 0042 6608
Email: zhu.zaichun@pku.edu.cn
-
AC1: 'Reply on RC1', Muyi Li, 18 Jun 2023
-
RC2: 'Comment on essd-2023-1', Anonymous Referee #2, 21 Apr 2023
This study proposes PKU GIMMS NDVI, a new global long-term NDVI time series data that covers 1982 to 2020 based on AVHRR and MODIS sensors onboard satellite platforms. The PKU GIMMS NDVI extends the GIMMS NDVI3g data and has better data quality. It has better agreement with Landsat NDVI compared to GIMMS NDVI3g, alleviating the orbital drift problem in the AVHRR sensors. The method proposed in this study could be used to generate consistent global NDVI data in the future, which would help study global terrestrial biosphere dynamics.
Detailed comments:
Line 14: “global-wide” may be simplified as “global”
Line 26: When introducing NDVI, please cite the original NDVI paper:
Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W., 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ, 351(1), p.309.
Line 97: Please provide literature references to the data sources.
Line 126: How is the time-weighted aggregation performed? Please explain in detail.
Line 127: Maybe the authors want to say “upscale” instead of “downscale.”
Line 139: How were the Landsat NDVI samples aggregated to 1/12˚? Please explain in detail.
Line 144: “temporal” should be “temporally.”
Line 226: could the authors elaborate more on how they spliced the PKU GIMMS NDVI and MODIS NDVI?
Line 254: How can seasonal fluctuations in the time series of NDVI bias be removed via the multi-year averaging method? Please explain.
Line 257: Maybe it should be “… was evaluated at 1,000 random points …”
Figure 2: maybe the authors could also show the regression line and equation in each panel?
Line 301: The section title could be “Validation of PKU GIMMS NDVI and GIMMS NDVI3g”
Figure 6: please explain in the figure caption how the R2 was computed in detail.
Line 337: The section title could be “Comparison with MODIS NDVI”？Citation: https://doi.org/10.5194/essd-2023-1-RC2 -
AC2: 'Reply on RC2', Muyi Li, 18 Jun 2023
Dear reviewer,
We are very pleased to finish a revised version of the manuscript essd-2023-1 entitled “Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022”. In preparing this revision we have considered all your comments and incorporated most of the suggestions. Temporal coverage of PKU GIMMS NDVI has been extended from 2020 to 2022. We greatly appreciate your time and effort spent in reviewing this manuscript, which have improved the revised version of the manuscript.
Substantial improvements have been made based on your comments, including:
- We have provided more details on how we performed the time-weighted aggregation method to convert the temporal resolution of the MODIS NDVI product (MOD13C1) from 16 days to half-month.
- We have also elaborated the method used to splice the PKU GIMMS NDVI and MODIS NDVI.
Point-to-Point Responses can be found in the Supplements (.pdf). All the changes have been marked by red in the revised manuscript.
Sincerely yours,
Zaichun Zhu, Ph. D. (on behalf of the author team)
School of Urban Planning and Design
Peking University
Tel: 86 185 0042 6608
Email: zhu.zaichun@pku.edu.cn
-
AC2: 'Reply on RC2', Muyi Li, 18 Jun 2023
-
RC3: 'Comment on essd-2023-1', Anonymous Referee #3, 24 Apr 2023
Overall Comments:
A relaiable long-term vegetation time series is critical to understand the dynamic of vegeataion and its feedback to the climate. This study by Li et al reconstructs a spatiotemporally consistent global NDVI dataset for 1982-2020 integrating Back Propagation Neural Network and a total of 3.6 million Landsat NDVI samples that well spread across the globe as input. This product, along with its predecessor (the GIMMS NDVI3g dataset), has been evaluated with the Landsat NDVI samples, showing substantial improvement. This study has originality and significance in uniqueness and usefulness.
Below are some comments that may help to further improve the manuscript. First, it seems that the golden truth of NDVI is Landsat NDVI samples. I recommend providing details in section 3.2.1 of Landsat NDVI samples to illustraluate: 1) Why Landsat NDVI is more accurate than other products? 2) Does Landsat NDVI have any limitations (e.g. the influence of clouds)? 3) Adding a plot to show the distribution of these samples (time and space).
Second, this dataset extends the time-span of 1982-2015 for its predecessor to 1982-2020, but there is no figure or analysis of this extension. It would be great to show more details of this extension (e.g. a long time series spanning from 1982 to 2015). In addition, it would be great to explain the results of consolidating the PKU dataset with MODIS NDVI data for the years after 2016. A comparison between MODIS and PKU NDVI datasets from 2017 to 2020 would be helpful (e.g. Figure 12).
Some minor comments: The Landsat NDVI samples were used both to train and validate the PKU GIMMS NDVI dataset. How the Landsat NDVI samples were seperated into two groups?
The figure caption for Figure 6 is confusing: comparison of R2 between the GIMMS NDVI4g and PKU GIMMS NDVI products. Is it R2 between the GIMMS NDVI4g and PKU GIMMS NDVI products? Or R2 between the GIMMS NDVI4g and Landsat NDVI, and that between PKU GIMMS NDVI and Landsat NDVI?
Why MOD13C1, not MOD13Q1, MOD13A3 or MOD13C2, is used in this study?
Figure 11: In the PKU dataset, most tropical regions show greening during 2004-2015, which may be unreasonable.
Figure 4: S5 shows no significant improvement compared to S4.
Figure 8: A color bar is missing.
Citation: https://doi.org/10.5194/essd-2023-1-RC3 -
AC3: 'Reply on RC3', Muyi Li, 18 Jun 2023
Dear reviewer,
We are very pleased to finish a revised version of the manuscript essd-2023-1 entitled “Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022”. In preparing this revision we have considered all your comments and incorporated most of the suggestions. Temporal coverage of PKU GIMMS NDVI has been extended from 2020 to 2022. We greatly appreciate your time and effort spent in reviewing this manuscript, which have improved the revised version of the manuscript.
Substantial improvements have been made based on your comments, including:
- We have provided more details on how we performed the time-weighted aggregation method to convert the temporal resolution of the MODIS NDVI product (MOD13C1) from 16 days to half-month.
- We have also elaborated the method used to splice the PKU GIMMS NDVI and MODIS NDVI
Point-to-Point Responses (.pdf). All the changes have been marked by red in the revised manuscript.
Sincerely yours,
Zaichun Zhu, Ph. D. (on behalf of the author team)
School of Urban Planning and Design
Peking University
Tel: 86 185 0042 6608
Email: zhu.zaichun@pku.edu.cn
-
AC3: 'Reply on RC3', Muyi Li, 18 Jun 2023
Muyi Li et al.
Data sets
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2020 Muyi Li, Sen Cao, and Zaichun Zhu https://zenodo.org/record/7441559#.Y7J7y3ZByCo
Muyi Li et al.
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