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基于MODIS和Landsat数据融合的中国长期重建植被指数数据集。

Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data.

作者信息

Li Xiangqian, Peng Qiongyan, Shen Ruoque, Xu Wenfang, Qin Zhangcai, Lin Shangrong, Ha Si, Kong Dongdong, Yuan Wenping

机构信息

College of Science, Shihezi University, Shihezi, 832000, Xinjiang, China.

International Research Center of Big Data for Sustainable Development Goals, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 510245, Guangdong, China.

出版信息

Sci Data. 2025 Jan 26;12(1):152. doi: 10.1038/s41597-025-04497-9.

DOI:10.1038/s41597-025-04497-9
PMID:39865066
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11770109/
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.

摘要

植被指数是一种基于卫星的关键变量，用于监测全球植被分布和生长情况。然而，现有的植被指数数据集在实现高空间分辨率和高时间分辨率方面面临限制，从而限制了它们的应用潜力。本研究对一种机器学习时空融合模型（InENVI）进行了修订，以生成一个高分辨率归一化植被指数（NDVI）数据集，其时间分辨率为8天，空间分辨率为30米，覆盖了2001年至2020年的中国。共处理了432,230景陆地卫星影像，提高了数据质量和准确性。该数据集使用跨越6个地理区域的255,000个样本进行了验证，在捕捉时空NDVI变化方面表现出色。此外，该数据集有效解决了陆地卫星7号影像中的扫描线校正器关闭条纹问题。该数据集能够以30米的分辨率可靠地估算中国的年度NDVI，并且可通过一个开放数据存储库进行再利用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28fd/11770109/1f2717ea8d55/41597_2025_4497_Fig12_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28fd/11770109/3d5940656a2e/41597_2025_4497_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28fd/11770109/dab1ee28de23/41597_2025_4497_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28fd/11770109/8f5f2ebc82fd/41597_2025_4497_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28fd/11770109/9982997c94cd/41597_2025_4497_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28fd/11770109/1f1eb9ffec74/41597_2025_4497_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28fd/11770109/a9a315e1223c/41597_2025_4497_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28fd/11770109/e6836899bb02/41597_2025_4497_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28fd/11770109/1f2717ea8d55/41597_2025_4497_Fig12_HTML.jpg

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本文引用的文献

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Increased atmospheric vapor pressure deficit reduces global vegetation growth.大气水汽压亏缺减少了全球植被生长。
Sci Adv. 2019 Aug 14;5(8):eaax1396. doi: 10.1126/sciadv.aax1396. eCollection 2019 Aug.