A 30-meter resolution global land productivity dynamics dataset from 2013 to 2022
Abstract Land degradation is one of the most severe environmental challenges globally. To address its adverse impacts, the United Nations endorsed the Land Degradation Neutrality (SDG 15.3) within the Sustainable Development Goals in 2015. Trends in land productivity is a key sub-indicator for repor...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04883-3 |
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| author | Xiaosong Li Tong Shen Cesar Luis Garcia Ingrid Teich Yang Chen Jin Chen Amos Tiereyangn Kabo-Bah Ziyu Yang Xiaoxia Jia Qi Lu Mandakh Nyamtseren |
| author_facet | Xiaosong Li Tong Shen Cesar Luis Garcia Ingrid Teich Yang Chen Jin Chen Amos Tiereyangn Kabo-Bah Ziyu Yang Xiaoxia Jia Qi Lu Mandakh Nyamtseren |
| author_sort | Xiaosong Li |
| collection | DOAJ |
| description | Abstract Land degradation is one of the most severe environmental challenges globally. To address its adverse impacts, the United Nations endorsed the Land Degradation Neutrality (SDG 15.3) within the Sustainable Development Goals in 2015. Trends in land productivity is a key sub-indicator for reporting the progress toward SDG 15.3. Currently, the highest spatial resolution of global land productivity dynamics (LPD) products is 250-meter, which seriously hamper the SDG 15.3 reporting and intervention at the fine scale. Generating higher spatial resolution product faces significant challenges, including massive data processing, image cloud pollution, incompatible spatiotemporal resolution. This study, leveraging Google Earth Engine platform and utilizing Landsat-8 and MODIS imagery, employed the Gap-filling and Savitzky–Golay filtering algorithm and advanced spatiotemporal filtering method to obtain a high-quality 30-meter NDVI dataset, then the global 30-meter LPD product from 2013 to 2022 was generated by using the FAO-WOCAT methodology and compared against multiple datasets. This is the first global scale 30-meter LPD dataset, which provides essential data support for SDG 15.3 monitoring and reporting globally. |
| format | Article |
| id | doaj-art-d6f4fefa260b4af08fa0c151479d4088 |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-d6f4fefa260b4af08fa0c151479d40882025-08-20T01:53:11ZengNature PortfolioScientific Data2052-44632025-04-0112111510.1038/s41597-025-04883-3A 30-meter resolution global land productivity dynamics dataset from 2013 to 2022Xiaosong Li0Tong Shen1Cesar Luis Garcia2Ingrid Teich3Yang Chen4Jin Chen5Amos Tiereyangn Kabo-Bah6Ziyu Yang7Xiaoxia Jia8Qi Lu9Mandakh Nyamtseren10International Research Center of Big Data for Sustainable Development GoalsInternational Research Center of Big Data for Sustainable Development GoalsFood and Agriculture Organization of the United NationsFood and Agriculture Organization of the United NationsState Key Laboratory of Earth Surface Processes and Resource Ecology, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal UniversityState Key Laboratory of Earth Surface Processes and Resource Ecology, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal UniversityDepartment of Civil and Environmental Engineering, University of Energy and Natural ResourcesInternational Research Center of Big Data for Sustainable Development GoalsUnited Nations Convention to Combat DesertificationInstitute of Desertification Studies, Chinese Academy of ForestryInstitute of Geography and Geoecology, Mongolia Academy of SciencesAbstract Land degradation is one of the most severe environmental challenges globally. To address its adverse impacts, the United Nations endorsed the Land Degradation Neutrality (SDG 15.3) within the Sustainable Development Goals in 2015. Trends in land productivity is a key sub-indicator for reporting the progress toward SDG 15.3. Currently, the highest spatial resolution of global land productivity dynamics (LPD) products is 250-meter, which seriously hamper the SDG 15.3 reporting and intervention at the fine scale. Generating higher spatial resolution product faces significant challenges, including massive data processing, image cloud pollution, incompatible spatiotemporal resolution. This study, leveraging Google Earth Engine platform and utilizing Landsat-8 and MODIS imagery, employed the Gap-filling and Savitzky–Golay filtering algorithm and advanced spatiotemporal filtering method to obtain a high-quality 30-meter NDVI dataset, then the global 30-meter LPD product from 2013 to 2022 was generated by using the FAO-WOCAT methodology and compared against multiple datasets. This is the first global scale 30-meter LPD dataset, which provides essential data support for SDG 15.3 monitoring and reporting globally.https://doi.org/10.1038/s41597-025-04883-3 |
| spellingShingle | Xiaosong Li Tong Shen Cesar Luis Garcia Ingrid Teich Yang Chen Jin Chen Amos Tiereyangn Kabo-Bah Ziyu Yang Xiaoxia Jia Qi Lu Mandakh Nyamtseren A 30-meter resolution global land productivity dynamics dataset from 2013 to 2022 Scientific Data |
| title | A 30-meter resolution global land productivity dynamics dataset from 2013 to 2022 |
| title_full | A 30-meter resolution global land productivity dynamics dataset from 2013 to 2022 |
| title_fullStr | A 30-meter resolution global land productivity dynamics dataset from 2013 to 2022 |
| title_full_unstemmed | A 30-meter resolution global land productivity dynamics dataset from 2013 to 2022 |
| title_short | A 30-meter resolution global land productivity dynamics dataset from 2013 to 2022 |
| title_sort | 30 meter resolution global land productivity dynamics dataset from 2013 to 2022 |
| url | https://doi.org/10.1038/s41597-025-04883-3 |
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