将SAR和光学遥感数据结合起来,用于在密集的植被覆盖面积上检索土壤水分
Jiahao Shi1,2, Huan Yang1,2, Xinli Hou1
1North China University of Water Resources and Electric Power, Zhengzhou, China.
PloS one
|January 17, 2025
概括
这项研究引入了使用Sentinel-1 SAR和Sentinel-2光学数据的结合方法,以准确地检索植被地区的土壤水分. 该方法通过估计植被含水量和纠正SAR信号来提高准确性,优于传统方法.
科学领域:
- 地球观测 地球观测
- 遥感 遥感 遥感 遥感
- 水文学的水文学
背景情况:
- 土壤湿度对于陆地与大气的相互作用至关重要,影响干旱监测和作物管理.
- 植被显著影响合成孔径雷达 (SAR) 信号,使植被地区的土壤水分回收复杂化.
研究的目的:
- 开发和验证一种结合式遥感方法,用于在植被密集的地区准确地检索表面土壤水分.
- 通过计算植被水含量 (VWC) 以及其对SAR反散的影响来改善土壤湿度估计.
主要方法:
- 使用 Sentinel-1 的SAR和 Sentinel-2 的光学数据.
- 开发了一个VWC估计模型,使用来自Sentinel-2数据的植被指数 (NDVI,NDWI2,NDGI,FVI).
- 集成的VWC模型与水云模型 (WCM) 进行SAR信号的植被校正.
- 应用了Oh模型和查看表 (LUT) 算法来检索土壤水分.
主要成果:
- 多种植被指数有效估计了VWC (R2=0.709,RMSE=0.30 kg·m−2).
- 植被校正显著提高了土壤湿度检索准确度,无论是VV (R2从0.659到0.802) 和VH (R2从0.398到0.509) 两极化.
- 与VV偏振相结合的方法实现了高精度 (R2=0.672,RMSE=0.048m3·m-3),超过了直接检索.
结论:
- 结合的方法有效地恢复了密集植被地区的土壤水分.
- 在这些条件下,VV两极化比VH两极化更适合用于土壤取水.
- 这种方法提供了一个可行的解决方案,用于精确地监测植被景观中的土壤湿度.
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