Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data
Abstract The vegetation index is a key satellite-based variable used to monitor global vegetation distribution and growth. However, existing vegetation index datasets face limitations in achieving both high spatial and temporal resolution, restricting their application potential. This study revised...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04497-9 |
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| author | Xiangqian Li Qiongyan Peng Ruoque Shen Wenfang Xu Zhangcai Qin Shangrong Lin Si Ha Dongdong Kong Wenping Yuan |
| author_facet | Xiangqian Li Qiongyan Peng Ruoque Shen Wenfang Xu Zhangcai Qin Shangrong Lin Si Ha Dongdong Kong Wenping Yuan |
| author_sort | Xiangqian Li |
| collection | DOAJ |
| description | Abstract The vegetation index is a key satellite-based variable used to monitor global vegetation distribution and growth. However, existing vegetation index datasets face limitations in achieving both high spatial and temporal resolution, restricting their application potential. This study revised a machine learning spatiotemporal fusion model (InENVI) to produce a high-resolution NDVI dataset with 8-day temporal and 30 m spatial resolution, covering China from 2001 to 2020. A total of 432,230 Landsat scenes were processed, enhancing data quality and accuracy. The dataset was validated using 255,000 samples across 6 geographical regions, showing strong performance in capturing spatiotemporal NDVI variations. Additionally, the dataset effectively addresses Scan Line Corrector-off stripes in Landsat 7 imagery. This dataset enables reliable annual NDVI estimates for China at a 30-m resolution and is available for reuse through an open data repository. |
| format | Article |
| id | doaj-art-101943e268e44b7ebb776b9a8919eaab |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-101943e268e44b7ebb776b9a8919eaab2025-01-26T12:14:48ZengNature PortfolioScientific Data2052-44632025-01-0112111310.1038/s41597-025-04497-9Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat dataXiangqian Li0Qiongyan Peng1Ruoque Shen2Wenfang Xu3Zhangcai Qin4Shangrong Lin5Si Ha6Dongdong Kong7Wenping Yuan8College of Science, Shihezi UniversityInternational Research Center of Big Data for Sustainable Development Goals, School of Atmospheric Sciences, Sun Yat-sen UniversityInternational Research Center of Big Data for Sustainable Development Goals, School of Atmospheric Sciences, Sun Yat-sen UniversityKey Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of SciencesInternational Research Center of Big Data for Sustainable Development Goals, School of Atmospheric Sciences, Sun Yat-sen UniversityCarbon-Water Research Station in Karst Regions of Northern Guangdong, School of Geography and Planning, Sun Yat-Sen UniversityNational Climate Center, China Meteorological AdministrationSchool of Environmental Studies, China University of GeosciencesInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking UniversityAbstract The vegetation index is a key satellite-based variable used to monitor global vegetation distribution and growth. However, existing vegetation index datasets face limitations in achieving both high spatial and temporal resolution, restricting their application potential. This study revised a machine learning spatiotemporal fusion model (InENVI) to produce a high-resolution NDVI dataset with 8-day temporal and 30 m spatial resolution, covering China from 2001 to 2020. A total of 432,230 Landsat scenes were processed, enhancing data quality and accuracy. The dataset was validated using 255,000 samples across 6 geographical regions, showing strong performance in capturing spatiotemporal NDVI variations. Additionally, the dataset effectively addresses Scan Line Corrector-off stripes in Landsat 7 imagery. This dataset enables reliable annual NDVI estimates for China at a 30-m resolution and is available for reuse through an open data repository.https://doi.org/10.1038/s41597-025-04497-9 |
| spellingShingle | Xiangqian Li Qiongyan Peng Ruoque Shen Wenfang Xu Zhangcai Qin Shangrong Lin Si Ha Dongdong Kong Wenping Yuan Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data Scientific Data |
| title | Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data |
| title_full | Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data |
| title_fullStr | Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data |
| title_full_unstemmed | Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data |
| title_short | Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data |
| title_sort | long term reconstructed vegetation index dataset in china from fused modis and landsat data |
| url | https://doi.org/10.1038/s41597-025-04497-9 |
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