Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight Simulations
نویسندگان
چکیده
The bottom depth of coastal benthic habitats plays a vital role in the ecological environment and navigation. In optically shallow waters (OSWs), seafloor reflectance has an impact on remotely sensed data, thus, water can be retrieved from remote sensing (Rrsλ) values provided by satellite imagery. Empirical methods for estimation are mainly limited field measurements coverage. addition, owing to diverse range bio-optical properties regions, high-precision models that could applied all OSWs insufficient. this study, we developed novel bottom-depth retrieval method based Hydrolight simulated datasets, which Rrsλ were generated radiative transfer theory instead actual data. Additionally, takes into consideration variable conditions depth, chlorophyll concentrations, reflectance. derived using data-driven machine learning random forest (RF) model. determination coefficient (R2) was greater than 0.98, root mean squared error (RMSE) less 0.4 m training validation datasets. This model shows promise use different regions while also broadening applications utilize Specifically, three areas South China Sea, i.e., Wenchang city, Xincun Bay, Huaguang Reef, Sentinel-2 depths validated bathymetric data acquired spaceborne photon-counting lidar ICESat-2, able penetrate clean sufficient detection. predicted showed good agreement with true large-scale mapping compensated limitations resulting along-track ICESat-2 Under variety circumstances, general-purpose effectively high spatial resolution imagery (such as Sentinel-2) waters.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14184590