Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight Simulations
The bottom depth of coastal benthic habitats plays a vital role in the coastal ecological environment and navigation. In optically shallow waters (OSWs), seafloor reflectance has an impact on the remotely sensed data, and thus, water depth can be retrieved from the remote sensing reflectance (<in...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/18/4590 |
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| author | Yuxin Wang Xianqiang He Yan Bai Teng Li Difeng Wang Qiankun Zhu Fang Gong |
| author_facet | Yuxin Wang Xianqiang He Yan Bai Teng Li Difeng Wang Qiankun Zhu Fang Gong |
| author_sort | Yuxin Wang |
| collection | DOAJ |
| description | The bottom depth of coastal benthic habitats plays a vital role in the coastal ecological environment and navigation. In optically shallow waters (OSWs), seafloor reflectance has an impact on the remotely sensed data, and thus, water depth can be retrieved from the remote sensing reflectance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub><mfenced><mi>λ</mi></mfenced></mrow></semantics></math></inline-formula>) values provided by satellite imagery. Empirical methods for depth estimation are mainly limited by field measurements coverage. In addition, owing to the diverse range of water bio-optical properties in coastal regions, the high-precision models that could be applied to all OSWs are insufficient. In this study, we developed a novel bottom-depth retrieval method based on Hydrolight simulated datasets, in which <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub><mfenced><mi>λ</mi></mfenced></mrow></semantics></math></inline-formula> were generated from radiative transfer theory instead of actual satellite data. Additionally, this method takes into consideration the variable conditions of water depth, chlorophyll concentrations, and bottom reflectance. The bottom depth can be derived from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub><mfenced><mi>λ</mi></mfenced></mrow></semantics></math></inline-formula> using a data-driven machine learning method based on the random forest (RF) model. The determination coefficient (R<sup>2</sup>) was greater than 0.98, and the root mean squared error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula>) was less than 0.4 m for the training and validation datasets. This model shows promise for use in different coastal regions while also broadening the applications that utilize satellite data. Specifically, we derived the bottom depth in three areas in the South China Sea, i.e., the coastal regions of Wenchang city, Xincun Bay, and Huaguang Reef, based on Sentinel-2 imagery. The derived depths were validated by the bathymetric data acquired by spaceborne photon-counting lidar ICESat-2, which was able to penetrate clean shallow waters for sufficient bottom detection. The predicted bottom depth showed good agreement with the true depth, and large-scale mapping compensated for the limitations resulting from along-track ICESat-2 data. Under a variety of circumstances, this general-purpose depth retrieval model can be effectively applied to high spatial resolution imagery (such as that from Sentinel-2) for bottom depth mapping in optically shallow waters. |
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| last_indexed | 2024-03-09T22:38:12Z |
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| spelling | doaj.art-e8378f79c9494ae1ae9f70799ba093282023-11-23T18:45:12ZengMDPI AGRemote Sensing2072-42922022-09-011418459010.3390/rs14184590Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight SimulationsYuxin Wang0Xianqiang He1Yan Bai2Teng Li3Difeng Wang4Qiankun Zhu5Fang Gong6State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, ChinaThe bottom depth of coastal benthic habitats plays a vital role in the coastal ecological environment and navigation. In optically shallow waters (OSWs), seafloor reflectance has an impact on the remotely sensed data, and thus, water depth can be retrieved from the remote sensing reflectance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub><mfenced><mi>λ</mi></mfenced></mrow></semantics></math></inline-formula>) values provided by satellite imagery. Empirical methods for depth estimation are mainly limited by field measurements coverage. In addition, owing to the diverse range of water bio-optical properties in coastal regions, the high-precision models that could be applied to all OSWs are insufficient. In this study, we developed a novel bottom-depth retrieval method based on Hydrolight simulated datasets, in which <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub><mfenced><mi>λ</mi></mfenced></mrow></semantics></math></inline-formula> were generated from radiative transfer theory instead of actual satellite data. Additionally, this method takes into consideration the variable conditions of water depth, chlorophyll concentrations, and bottom reflectance. The bottom depth can be derived from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub><mfenced><mi>λ</mi></mfenced></mrow></semantics></math></inline-formula> using a data-driven machine learning method based on the random forest (RF) model. The determination coefficient (R<sup>2</sup>) was greater than 0.98, and the root mean squared error (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow></semantics></math></inline-formula>) was less than 0.4 m for the training and validation datasets. This model shows promise for use in different coastal regions while also broadening the applications that utilize satellite data. Specifically, we derived the bottom depth in three areas in the South China Sea, i.e., the coastal regions of Wenchang city, Xincun Bay, and Huaguang Reef, based on Sentinel-2 imagery. The derived depths were validated by the bathymetric data acquired by spaceborne photon-counting lidar ICESat-2, which was able to penetrate clean shallow waters for sufficient bottom detection. The predicted bottom depth showed good agreement with the true depth, and large-scale mapping compensated for the limitations resulting from along-track ICESat-2 data. Under a variety of circumstances, this general-purpose depth retrieval model can be effectively applied to high spatial resolution imagery (such as that from Sentinel-2) for bottom depth mapping in optically shallow waters.https://www.mdpi.com/2072-4292/14/18/4590bottom depthsatellite remote sensingHydrolight simulationrandom forestSentinel-2ICESat-2 |
| spellingShingle | Yuxin Wang Xianqiang He Yan Bai Teng Li Difeng Wang Qiankun Zhu Fang Gong Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight Simulations Remote Sensing bottom depth satellite remote sensing Hydrolight simulation random forest Sentinel-2 ICESat-2 |
| title | Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight Simulations |
| title_full | Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight Simulations |
| title_fullStr | Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight Simulations |
| title_full_unstemmed | Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight Simulations |
| title_short | Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight Simulations |
| title_sort | satellite derived bottom depth for optically shallow waters based on hydrolight simulations |
| topic | bottom depth satellite remote sensing Hydrolight simulation random forest Sentinel-2 ICESat-2 |
| url | https://www.mdpi.com/2072-4292/14/18/4590 |
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