A 30-meter resolution global land productivity dynamics dataset from 2013 to 2022
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.
Authors
- Ingrid Teich (ORCID: https://orcid.org/0000-0002-7305-2923)
- Mandakh Nyamtseren (ORCID: https://orcid.org/0000-0003-1658-1148)
- César Luis García (ORCID: https://orcid.org/0000-0001-8310-753X)
- Tong Shen (ORCID: https://orcid.org/0000-0001-7656-6017)
- Qi Lu (ORCID: https://orcid.org/0000-0002-8939-9390)
- Jin Chen (ORCID: https://orcid.org/0000-0002-6497-4141)
- Yang Chen (ORCID: https://orcid.org/0000-0002-5590-0810)
- Xiaoxia Jia
- Xiaosong Li (ORCID: https://orcid.org/0000-0002-6048-1577)
- Amos T. Kabo-Bah (ORCID: https://orcid.org/0000-0002-1361-4250)
- Ziyu Yang (ORCID: https://orcid.org/0000-0002-8847-1864)
Institutions
- University of Bern (CH)
- Food and Agriculture Organization of the United Nations (IT)
- Mongolian Academy of Sciences (MN)
- Chinese Academy of Sciences (CN)
- Beijing Normal University (CN)
- Albuquerque Public Schools (US)
- University of Energy and Natural Resources (GH)
- Beijing Institute of Big Data Research (CN)
- Chinese Academy of Forestry (CN)
- Beijing Great Wall Hospital (CN)
- Aerospace Information Research Institute (CN)
- United Nations University (DE)
- Institute of Desertification Studies (CN)
- University of Chinese Academy of Sciences (CN)
- State Key Laboratory of Remote Sensing Science (CN)
Publication Details
- Journal
- Scientific Data
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1038/s41597-025-04883-3
- Citations
- 1
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 1.72