Xiangqian Li, Qiongyan Peng, Ruoque Shen, Wenfang Xu, Zhangcai Qin, Shangrong Lin, Si Ha, Dongdong Kong, Wenping Yuan
{"title":"基于MODIS和Landsat数据融合的中国植被指数长期重建数据集。","authors":"Xiangqian Li, Qiongyan Peng, Ruoque Shen, Wenfang Xu, Zhangcai Qin, Shangrong Lin, Si Ha, Dongdong Kong, Wenping Yuan","doi":"10.1038/s41597-025-04497-9","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"152"},"PeriodicalIF":6.9000,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11770109/pdf/","citationCount":"0","resultStr":"{\"title\":\"Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data.\",\"authors\":\"Xiangqian Li, Qiongyan Peng, Ruoque Shen, Wenfang Xu, Zhangcai Qin, Shangrong Lin, Si Ha, Dongdong Kong, Wenping Yuan\",\"doi\":\"10.1038/s41597-025-04497-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"152\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-01-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11770109/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-025-04497-9\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04497-9","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data.
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.
期刊介绍：
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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