A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021
<p>Vegetation optical depth (VOD) products provide information on vegetation water content and correlate with vegetation growth status; these are closely related to the global water and carbon cycles. The L-band signal penetrates deeper into the vegetation canopy than the higher-frequency band...
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Copernicus Publications
2025-06-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/2849/2025/essd-17-2849-2025.pdf |
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| author | D. Hu Y. Wang H. Jing L. Yue Q. Zhang L. Fan Q. Yuan Q. Yuan Q. Yuan H. Shen L. Zhang |
| author_facet | D. Hu Y. Wang H. Jing L. Yue Q. Zhang L. Fan Q. Yuan Q. Yuan Q. Yuan H. Shen L. Zhang |
| author_sort | D. Hu |
| collection | DOAJ |
| description | <p>Vegetation optical depth (VOD) products provide information on vegetation water content and correlate with vegetation growth status; these 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, i.e., the Soil Moisture and Ocean Salinity (SMOS) satellite launched in 2010 and the Soil Moisture Active Passive (SMAP) satellite launched in 2015. The former has the limitation of a low spatial resolution of only 25 km, while the latter has improved this 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-band VOD (L-VOD) products suffer from the defects of missing data and coarse resolution of historical data. There is little 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 a three-dimensional discrete cosine transform to firstly generate the seamless global daily L-VOD products. Subsequently, the nonlocal filtering idea is applied to spatiotemporal fusion between high-resolution 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-region validation are used for the reconstructed data. The fusion products are validated both temporally and spatially and are 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 (<span class="inline-formula"><i>R</i><sup>2</sup></span>) of 0.855 (0.947) and a 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 (<span class="inline-formula"><i>R</i><sup>2</sup></span>: 0.926–0.958,<span id="page2850"/> RMSE: 0.072–0.093, and mean absolute error 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 responses. This dataset can be downloaded at <span class="uri">https://doi.org/10.5281/zenodo.13334757</span> <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx30">Hu et al.</a>, <a href="#bib1.bibx30">2024</a>)</span>.</p> |
| format | Article |
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| institution | OA Journals |
| issn | 1866-3508 1866-3516 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Copernicus Publications |
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| series | Earth System Science Data |
| spelling | doaj-art-2840ff136d6044a4b7757ade76f826642025-08-20T02:21:28ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162025-06-01172849287210.5194/essd-17-2849-2025A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021D. Hu0Y. Wang1H. Jing2L. Yue3Q. Zhang4L. Fan5Q. Yuan6Q. Yuan7Q. Yuan8H. Shen9L. Zhang10School of Geodesy and Geomatics, Wuhan University, Wuhan, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, ChinaEnergy Administration of Yunnan Province, Yunnan, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, ChinaCenter of Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian, ChinaChongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan, ChinaKey Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, ChinaKey Laboratory of Polar Environment Monitoring and Public Governance, Ministry of Education, Wuhan University, Wuhan, ChinaSchool of Resource and Environmental Science, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China<p>Vegetation optical depth (VOD) products provide information on vegetation water content and correlate with vegetation growth status; these 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, i.e., the Soil Moisture and Ocean Salinity (SMOS) satellite launched in 2010 and the Soil Moisture Active Passive (SMAP) satellite launched in 2015. The former has the limitation of a low spatial resolution of only 25 km, while the latter has improved this 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-band VOD (L-VOD) products suffer from the defects of missing data and coarse resolution of historical data. There is little 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 a three-dimensional discrete cosine transform to firstly generate the seamless global daily L-VOD products. Subsequently, the nonlocal filtering idea is applied to spatiotemporal fusion between high-resolution 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-region validation are used for the reconstructed data. The fusion products are validated both temporally and spatially and are 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 (<span class="inline-formula"><i>R</i><sup>2</sup></span>) of 0.855 (0.947) and a 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 (<span class="inline-formula"><i>R</i><sup>2</sup></span>: 0.926–0.958,<span id="page2850"/> RMSE: 0.072–0.093, and mean absolute error 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 responses. This dataset can be downloaded at <span class="uri">https://doi.org/10.5281/zenodo.13334757</span> <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx30">Hu et al.</a>, <a href="#bib1.bibx30">2024</a>)</span>.</p>https://essd.copernicus.org/articles/17/2849/2025/essd-17-2849-2025.pdf |
| spellingShingle | D. Hu Y. Wang H. Jing L. Yue Q. Zhang L. Fan Q. Yuan Q. Yuan Q. Yuan H. Shen L. Zhang A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021 Earth System Science Data |
| title | A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021 |
| title_full | A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021 |
| title_fullStr | A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021 |
| title_full_unstemmed | A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021 |
| title_short | A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021 |
| title_sort | global daily seamless 9 thinsp km vegetation optical depth vod product from 2010 to 2021 |
| url | https://essd.copernicus.org/articles/17/2849/2025/essd-17-2849-2025.pdf |
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