基于一种新的方法建立NDVI长期数据集的研究
1China Meteorological Administration Training Center, Beijing, 100081, People's Republic of China. fandl@cma.gov.cn.
Scientific reports
|June 17, 2023
概括
这项研究纠正了云卫星的错误.
科学领域:
- 遥感 遥感 遥感 遥感
- 地球观测 地球观测
- 机器学习应用 机器学习应用
背景情况:
- 不同的卫星传感器提供规范差异植被指数 (NDVI) 数据,包括AVHRR (NDVIa),MODIS (NDVIm) 和VIRR (NDVIv).
- 现有的NDVI数据集显示不同的关系和准确性,需要相互比较和校准.
研究的目的:
- 开发和验证一种基于机器学习的方法,用于纠正Fengyun卫星的可见和红外辐射仪 (VIRR) NDVI数据.
- 提高Fengyun卫星NDVI的准确性和产品质量,使其与MODIS NDVI可比.
主要方法:
- 利用机器学习,特别是线性回归算法,来建模不同NDVI数据集之间的关系.
- 构建了一个线性回归模型,以根据已建立的NDVI数据集纠正Fengyun卫星VIRR NDVI值.
主要成果:
- 在NDVIa和NDVIm之间以及在NDVIv和NDVIa之间建立了显著的相关性,NDVIv < NDVIa < NDVIm.
- 线性回归模型显著改善了修正的相关系数 (R2),所有置信级都显示出显著的相关性 (p < 0.01).
- 经过校正的Fengyun卫星NDVI实现了与MODIS NDVI可比的准确性.
结论:
- 机器学习,特别是线性回归,对于校准和纠正来自卫星的NDVI数据是有效的.
- 开发的Fengyun卫星NDVI校正方法提高了数据准确性和产品质量,使其更可靠地用于植被监测.
- 修正后的云卫星NDVI显示了与MODIS NDVI等高质量数据集的更好的一致性和可比性.
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