从合并的MODIS和Landsat数据中,长期重建了中国的植被指数数据集
Xiangqian Li1,2, Qiongyan Peng2, Ruoque Shen2
1College of Science, Shihezi University, Shihezi, 832000, Xinjiang, China.
Scientific data
|January 26, 2025
概括
通过使用机器学习的融合模型,为中国 (2001-2020) 创建了一个新的高分辨率规范差异植被指数 (NDVI) 数据集. 这种增强的NDVI数据集提供了30m的空间分辨率和8天的时间分辨率,以改善植被监测.
科学领域:
- 地球观测 地球观测
- 遥感 遥感 遥感 遥感
- 地理空间科学 地理空间科学
背景情况:
- 植被指数对于监测全球植被至关重要,但往往缺乏足够的空间和时间分辨率.
- 现有的数据集阻碍了对植被分布和生长模式的详细分析.
研究的目的:
- 为中国开发一个高分辨率 (30米空间,8天时间) 规范差异植被指数 (NDVI) 数据集,涵盖2001-2020年.
- 提高植被监测数据的准确性和适用性.
主要方法:
- 修改了一种机器学习的时空融合模型 (InENVI) 来处理432,230个Landsat场景.
- 提高了数据质量和准确性,特别是解决了Landsat 7扫描线校正器的缺陷.
主要成果:
- 为中国生成了一个全面的NDVI数据集,具有前所未有的空间和时间细节.
- 使用6个地区255,000个样本验证了数据集,证实了在捕捉时空变化的强大表现.
- 成功纠正了Landsat 7图像中的扫描线校正线条.
结论:
- 新的数据集使得可靠的年度NDVI估计在中国的30m分辨率.
- 这种公开可用的数据集显著提高了植被监测能力.
- 促进中国各地关于植被动态和陆地表面过程的研究.
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