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Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.rse.2020.112035
Jianwei Wei , Menghua Wang , Zhongping Lee , Henry O. Briceño , Xiaolong Yu , Lide Jiang , Rodrigo Garcia , Junwei Wang , Kelly Luis

Abstract Polar-orbiting ocean color satellites such as Landsat-8, Suomi National Polar-orbiting Partnership (SNPP), and Sentinel-3 offer valuable image data for the derivation of water bathymetry in optically shallow environments. Because of the multi-spectral limitation, however, it is challenging to derive bathymetry over global shallow waters without reliable mechanistic algorithms. In this contribution, we present and test a physics-based algorithm for improved retrieval of bathymetry with multi-spectral sensors. The algorithm leverages the temporal variation of water-column optical properties in two satellite measurements. By incorporating two remote sensing reflectance spectra in an optimization procedure, it enhances the spectral constraining condition for the optimization, thus leading to improved retrieval accuracy. This scheme is evaluated using synthetic multi-spectral data. It is shown that the new approach can provide accurate estimation of water depths over 0–30 m range with three types of benthic substrates (corals, seagrass, and sand) and for a wide range of water column optical properties. Based on the degree of improvement, Landsat-8 appears to be benefited the most, followed by SNPP, and then Sentinel-3. The application of the new approach is demonstrated with satellite images over shallow waters (0–30 m) dominated with coral reefs, seagrass, and sand, respectively. This proof-of-concept study confirms the promise of multi-spectral satellite sensors for accurate water depth retrieval by accounting for the temporal characteristics in multiple measurements, suggesting a path forward for the derivation of bathymetry from the existing satellites over global shallow waters.

中文翻译:

使用多光谱卫星海洋颜色传感器进行浅水测深:利用图像数据的时间变化

摘要 Landsat-8、Suomi National Polar-orbiting Partnership (SNPP) 和 Sentinel-3 等极轨海洋彩色卫星为推导光学浅层环境中的水深测量提供了宝贵的图像数据。然而,由于多光谱的限制,如果没有可靠的机械算法,在全球浅水区推导水深测量是具有挑战性的。在这个贡献中,我们提出并测试了一种基于物理的算法,用于改进多光谱传感器的测深检索。该算法利用了两次卫星测量中水柱光学特性的时间变化。通过在优化过程中结合两个遥感反射光谱,它增强了优化的光谱约束条件,从而提高了检索精度。该方案使用合成的多光谱数据进行评估。结果表明,新方法可以准确估计 0-30 m 范围内的水深,包括三种类型的底栖基质(珊瑚、海草和沙子),并且具有广泛的水柱光学特性。根据改进程度,Landsat-8 似乎受益最多,其次是 SNPP,然后是 Sentinel-3。新方法的应用通过分别以珊瑚礁、海草和沙子为主的浅水区 (0-30 m) 的卫星图像进行了演示。这项概念验证研究通过考虑多次测量的时间特征,证实了多光谱卫星传感器在准确水深反演方面的前景,
更新日期:2020-12-01
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