评估 Sentinel-2 填补缺口技术,用于移除云和数据重建
Said Grich1, Jamal Elfarkh2, Nadia Ouaadi2
1Center for Remote Sensing Applications (CRSA), University Mohammed VI Polytechnic (UM6P), Benguerir, 43150, Morocco. said.grich@um6p.ma.
Scientific reports
|February 17, 2026
概括
这项研究评估了填补Sentinel-2卫星图像中云层造成的空白的方法. 时空方法,特别是集群和线性回归 (CLR),在各种场景中显示出可靠数据恢复的最佳性能.
科学领域:
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
- 数据科学数据科学数据科学
背景情况:
- 云层覆盖经常导致 Sentinel-2 图像中的数据缺口,影响水资源管理和作物产量预测等时间敏感应用程序.
- 现有的填补差距的方法缺乏全面的性能比较,阻碍了最佳技术选择.
研究的目的:
- 建立一个评估框架,用于比较Sentinel-2图像的空间,时间,空间-时间和空间-光谱填空方法.
- 评估这些方法在修复云产生的数据缺口时的有效性,仅使用卫星衍生数据.
主要方法:
- 模拟的云场景被用来评估Sentinel-2可见,近红外和短波红外频段的填空方法.
- 使用确定系数 (R2),rRMSE和偏差来评估性能.
- 方法包括空间,时间,空间-时间 (例如,CLR,DL) 和空间-光谱 (例如,SSRF) 的方法.
主要成果:
- 时空方法,特别是集群和线性回归 (CLR),在各种差距场景中表现出最高的准确性和稳定性.
- 时空深度学习 (DL) 的表现很好,但需要大量的训练,并显示出有限的概括性.
- 空间光谱方法 (SSRF) 在可见和近射频段显示出强的结果,而像 kriging 这样的空间方法则在较大的差距上扎.
结论:
- 空间时空填补技术,特别是CLR,对于恢复Sentinel-2数据中由云引起的空白是最有效的.
- 该研究提供了比较评估,以指导选择适当的填补差距的方法,并确定未来研究的领域.
- 所有代码都是公开可用的,以确保可重复性,并促进卫星数据填补缺口的进一步进展.
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