the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A global daily seamless 9-km Vegetation Optical Depth (VOD) product from 2010 to 2021
Abstract. Vegetation optical depth (VOD) products provide information on vegetation water content and correlate with vegetation growth status, which are closely related to the global water and carbon cycles. The L-band signal penetrates deeper into the vegetation canopy than the higher frequency bands used for many previous VOD retrievals. Currently, there are only two operational L-band sensors aboard satellites, namely the SMOS satellite launched in 2010 and the SMAP satellite launched in 2015. The former has the limitation of a low spatial resolution of only 25 km, while the latter has improved the resolution to 9 km but has a shorter usable time range. Due to the influence of sensor and atmospheric conditions, as well as the observation methods of polar-orbiting satellites (such as scan gaps and observation revisit times), the daily data provided by both satellites suffer from varying degrees of missing data. In summary, the existing L-VOD products suffer from the defects of missing data and coarse resolution of historical data. There is few research on filling gaps and reconstructing 9-km long-term data for L-VOD products. To solve this problem, our study depends on a penalized least square regression based on three-dimensional discrete cosine transform to firstly generate the seamless global daily L-VOD products. Subsequently, the non-local filtering idea is applied to spatiotemporal fusion between high- and low-resolution data, resulting in a global daily seamless 9-km L-VOD product from 1 January 2010 to 31 July 2021. In order to validate the quality of the products, time series validation and simulated missing regions validation are used for the reconstructed data. The fusion products are validated both temporally and spatially, and also compared numerically with the original 9-km data during the overlapping period. Results show that the seamless SMOS (SMAP) dataset is evaluated with a coefficient of determination R2 of 0.855 (0.947), and root mean squared error (RMSE) of 0.094 (0.073) for the simulated real missing masks. The temporal consistency of the reconstructed daily L-VOD products is ensured with the original time-series distribution of valid values. The spatial information of the fusion product and the original 9-km data in the overlapping period is basically consistent (R2: 0.926–0.958, RMSE: 0.072–0.093, MAE: 0.047–0.064). The temporal variations between the fusion product and the original product are largely synchronized. Our dataset can provide timely vegetation information during natural disasters (e.g., floods, droughts, and forest fires), supporting early disaster warning and real-time response. This dataset can be downloaded at https://doi.org/10.5281/zenodo.13334757 (Hu et al.,2024).
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Status: closed
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RC1: 'Comment on essd-2024-411', Anonymous Referee #1, 14 Feb 2025
- AC1: 'Reply on RC1', Qianqqiang Yuan, 15 Mar 2025
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RC2: 'Comment on essd-2024-411', Anonymous Referee #2, 25 Feb 2025
The manuscript develops a method for generating a global seamless daily 9-km L-VOD product from 2010 to 2021. This product effectively fills gaps in the original VOD data from SMOS and SMAP while maintaining temporal and spatial consistency with the original L-VOD products. The topic is well suited for ESSD, and the results are of significant interest. However, there are some concerns regarding the performance of the reconstructed VOD. The manuscript could be suitable for publication after the authors address the general and specific comments provided below.
General comments
- A more detailed discussion of the results would strengthen the manuscript. In particular, further analysis of the performance of the new product across different land cover types would be beneficial. Additionally, evaluating VOD against vegetation-related parameters, such as aboveground biomass (AGB), NDVI, and LAI, would enhance clarity.
- It may be helpful to consider measures to reduce the bias between SMOS VOD and SMAP VOD, as this discrepancy could impact the accuracy of the fused product. A more detailed analysis of this uncertainty would be valuable.
- The overall readability of the manuscript could be improved, particularly in terms of phrasing, organization, and paragraph structure. The authors may wish to have the manuscript reviewed by a native English speaker to refine grammar, style, and syntax.
Specific comments
- Page 4, line 191. Define “IGBP, UMD and LAI” before their first use.
- Page 5, line 220. Define “DCT-PLS” before its first use
- Page 7, line 288. The bias between SMOS and SMAP products should be considered (e.g., 10.1016/j.rse.2022.113272). This addition is relevant and could significantly broaden the manuscript's appeal.
- Page 7, Line 313. Please consider adding a more detailed description of the spatiotemporal fusion experiment. This should include the relationship between the reconstructed SMOS VOD and reconstructed SMAP VOD, the division of time segments, and the relationship between the fusion product and the reconstructed SMOS VOD and SMAP VOD.
- Page 12, Fig. 7. The text in this figure is too small.
- Page 13, Fig. 9. Please explain why the reconstructed products were more blurred than the original product.
- Page 19, Fig. 16. Discuss the reseason of white pixels over land in VOD_st winter.
Citation: https://doi.org/10.5194/essd-2024-411-RC2 - AC2: 'Reply on RC2', Qianqqiang Yuan, 15 Mar 2025
Status: closed
-
RC1: 'Comment on essd-2024-411', Anonymous Referee #1, 14 Feb 2025
- AC1: 'Reply on RC1', Qianqqiang Yuan, 15 Mar 2025
-
RC2: 'Comment on essd-2024-411', Anonymous Referee #2, 25 Feb 2025
The manuscript develops a method for generating a global seamless daily 9-km L-VOD product from 2010 to 2021. This product effectively fills gaps in the original VOD data from SMOS and SMAP while maintaining temporal and spatial consistency with the original L-VOD products. The topic is well suited for ESSD, and the results are of significant interest. However, there are some concerns regarding the performance of the reconstructed VOD. The manuscript could be suitable for publication after the authors address the general and specific comments provided below.
General comments
- A more detailed discussion of the results would strengthen the manuscript. In particular, further analysis of the performance of the new product across different land cover types would be beneficial. Additionally, evaluating VOD against vegetation-related parameters, such as aboveground biomass (AGB), NDVI, and LAI, would enhance clarity.
- It may be helpful to consider measures to reduce the bias between SMOS VOD and SMAP VOD, as this discrepancy could impact the accuracy of the fused product. A more detailed analysis of this uncertainty would be valuable.
- The overall readability of the manuscript could be improved, particularly in terms of phrasing, organization, and paragraph structure. The authors may wish to have the manuscript reviewed by a native English speaker to refine grammar, style, and syntax.
Specific comments
- Page 4, line 191. Define “IGBP, UMD and LAI” before their first use.
- Page 5, line 220. Define “DCT-PLS” before its first use
- Page 7, line 288. The bias between SMOS and SMAP products should be considered (e.g., 10.1016/j.rse.2022.113272). This addition is relevant and could significantly broaden the manuscript's appeal.
- Page 7, Line 313. Please consider adding a more detailed description of the spatiotemporal fusion experiment. This should include the relationship between the reconstructed SMOS VOD and reconstructed SMAP VOD, the division of time segments, and the relationship between the fusion product and the reconstructed SMOS VOD and SMAP VOD.
- Page 12, Fig. 7. The text in this figure is too small.
- Page 13, Fig. 9. Please explain why the reconstructed products were more blurred than the original product.
- Page 19, Fig. 16. Discuss the reseason of white pixels over land in VOD_st winter.
Citation: https://doi.org/10.5194/essd-2024-411-RC2 - AC2: 'Reply on RC2', Qianqqiang Yuan, 15 Mar 2025
Data sets
A global daily seamless 9-km Vegetation Optical Depth (VOD) product from 2010 to 2021 Die Hu et al. https://doi.org/10.5281/zenodo.13334756
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