用Sentinel-2进行全球植被和水资源绘制的优化光谱指数
Charalambos Chrysostomou1, Stelios P Neophytides2, Michalis Mavrovouniotis2
1ERATOSTHENES Centre of Excellence, Limassol, Cyprus. charalambos.chrysostomou@eratosthenes.org.cy.
Scientific reports
|January 13, 2026
概括
新的卫星指数,象征回归植被指数 (SRVI) 和象征回归水指数 (SRWI),改善了植被和水的绘制. 这些数据驱动的指数在各种生态系统和季节中提供了更好的准确性.
科学领域:
- 地球观测 地球观测
- 遥感 遥感 遥感 遥感
- 地理空间分析是什么
背景情况:
- 传统的植物和水位测绘的光谱指数面临着诸如和,生态系统敏感性问题以及与其他表面特征混等局限性.
- 开发准确和强大的指数对于使用卫星数据进行全球环境监测至关重要.
研究的目的:
- 引入新的光谱指数,即符号回归植被指数 (SRVI) 和符号回归水指数 (SRWI),使用数据驱动的符号回归框架来推导.
- 评估SRVI和SRWI的表现,并将其与各种生物体和季节的植被和水资源测绘的已建立指数进行比较.
主要方法:
- 在 Sentinel-2 Level-2A 反射率数据上使用符号回归框架,以ESA WorldCover标签为指导.
- 从物理可解释的带组合 (可见,NIR,SWIR) 演变的索引表达式.
- 在11个独立地区使用杰弗里斯-马图斯塔距离评估指数表现,并与基准指数进行比较.
主要成果:
- 与基准相比,SRVI显示了植被和非植被之间更好的分离能力,以及植被类型之间的更高的歧视.
- SRWI提供了更一致的水分划分,减少了与建筑和阴影区域的混,优于现有的水分指数.
- 这两种指数都显示出在不同地区和季节的普遍性.
结论:
- 符号回归是一种有效的方法,用于发现用于遥感的紧,可解释和可概括的光谱指数.
- SRVI和SRWI为全球植被和地表水的绘制提供了实际的进展,解决了传统指数的局限性.
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