Depth Mapping in Turbid and Deep Waters Using AVIRIS‐NG Imagery: A Study in Wax Lake Delta, Louisiana, USA
Abstract Remote sensing has been widely applied to investigate fluvial processes, but depth retrievals face significant constraints in deep and turbid conditions. This study evaluates the potential for depth retrievals under such challenging conditions using NASA's Airborne Visible/Infrared Ima...
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| Format: | Article |
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Wiley
2024-11-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR036875 |
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| author | Siyoon Kwon Paola Passalacqua Antoine Soloy Daniel Jensen Marc Simard |
| author_facet | Siyoon Kwon Paola Passalacqua Antoine Soloy Daniel Jensen Marc Simard |
| author_sort | Siyoon Kwon |
| collection | DOAJ |
| description | Abstract Remote sensing has been widely applied to investigate fluvial processes, but depth retrievals face significant constraints in deep and turbid conditions. This study evaluates the potential for depth retrievals under such challenging conditions using NASA's Airborne Visible/Infrared Imaging Spectrometer‐Next Generation (AVIRIS‐NG) imagery. We employ interpretable machine learning to construct a hyperspectral regressor for water depth and explore the spectral characteristics of deep and turbid waters in Wax Lake Delta (WLD), Louisiana, USA. The reflectance spectra of WLD show minor effects from depth differences due to turbidity. Nevertheless, a Random Forest with Recursive Feature Elimination (RF‐RFE) effectively generalizes high and low turbidity cases in a single model, achieving a R2 of 0.94±0.005. Moreover, this model shows a maximum detectable depth of approximately 30 m, outperforming other methods. A spectral analysis using Shapley additive explanations (SHAP) points out the importance of learning various spectral bands and non‐linear relationships between depth and reflectance. Specifically, the short blue and Near‐InfraRed (NIR) bands, with high attenuation coefficients, play a crucial role. This finding highlights attenuation as the key process for deep‐depth retrievals. The depth maps of WLD produced by this model accurately capture the spatial distribution of deep river and shallow delta regions. However, the high dependency on short blue and NIR bands leads to discontinuous areas due to the noise sensitivity of these bands. This result highlights a drawback of remote sensing using empirical models. Future research will focus on correcting such discontinuities by integrating data from multiple remote sensing sources. |
| format | Article |
| id | doaj-art-c63a6abe560249c99aaaf6f49e9da409 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-c63a6abe560249c99aaaf6f49e9da4092025-08-23T13:05:51ZengWileyWater Resources Research0043-13971944-79732024-11-016011n/an/a10.1029/2023WR036875Depth Mapping in Turbid and Deep Waters Using AVIRIS‐NG Imagery: A Study in Wax Lake Delta, Louisiana, USASiyoon Kwon0Paola Passalacqua1Antoine Soloy2Daniel Jensen3Marc Simard4Fariborz Maseeh Department of Civil, Architectural and Environmental Engineering The University of Texas at Austin Austin TX USAFariborz Maseeh Department of Civil, Architectural and Environmental Engineering The University of Texas at Austin Austin TX USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USAJet Propulsion Laboratory California Institute of Technology Pasadena CA USAAbstract Remote sensing has been widely applied to investigate fluvial processes, but depth retrievals face significant constraints in deep and turbid conditions. This study evaluates the potential for depth retrievals under such challenging conditions using NASA's Airborne Visible/Infrared Imaging Spectrometer‐Next Generation (AVIRIS‐NG) imagery. We employ interpretable machine learning to construct a hyperspectral regressor for water depth and explore the spectral characteristics of deep and turbid waters in Wax Lake Delta (WLD), Louisiana, USA. The reflectance spectra of WLD show minor effects from depth differences due to turbidity. Nevertheless, a Random Forest with Recursive Feature Elimination (RF‐RFE) effectively generalizes high and low turbidity cases in a single model, achieving a R2 of 0.94±0.005. Moreover, this model shows a maximum detectable depth of approximately 30 m, outperforming other methods. A spectral analysis using Shapley additive explanations (SHAP) points out the importance of learning various spectral bands and non‐linear relationships between depth and reflectance. Specifically, the short blue and Near‐InfraRed (NIR) bands, with high attenuation coefficients, play a crucial role. This finding highlights attenuation as the key process for deep‐depth retrievals. The depth maps of WLD produced by this model accurately capture the spatial distribution of deep river and shallow delta regions. However, the high dependency on short blue and NIR bands leads to discontinuous areas due to the noise sensitivity of these bands. This result highlights a drawback of remote sensing using empirical models. Future research will focus on correcting such discontinuities by integrating data from multiple remote sensing sources.https://doi.org/10.1029/2023WR036875depth mappingimaging spectroscopyWax Lake Deltaremote sensingmachine learning |
| spellingShingle | Siyoon Kwon Paola Passalacqua Antoine Soloy Daniel Jensen Marc Simard Depth Mapping in Turbid and Deep Waters Using AVIRIS‐NG Imagery: A Study in Wax Lake Delta, Louisiana, USA Water Resources Research depth mapping imaging spectroscopy Wax Lake Delta remote sensing machine learning |
| title | Depth Mapping in Turbid and Deep Waters Using AVIRIS‐NG Imagery: A Study in Wax Lake Delta, Louisiana, USA |
| title_full | Depth Mapping in Turbid and Deep Waters Using AVIRIS‐NG Imagery: A Study in Wax Lake Delta, Louisiana, USA |
| title_fullStr | Depth Mapping in Turbid and Deep Waters Using AVIRIS‐NG Imagery: A Study in Wax Lake Delta, Louisiana, USA |
| title_full_unstemmed | Depth Mapping in Turbid and Deep Waters Using AVIRIS‐NG Imagery: A Study in Wax Lake Delta, Louisiana, USA |
| title_short | Depth Mapping in Turbid and Deep Waters Using AVIRIS‐NG Imagery: A Study in Wax Lake Delta, Louisiana, USA |
| title_sort | depth mapping in turbid and deep waters using aviris ng imagery a study in wax lake delta louisiana usa |
| topic | depth mapping imaging spectroscopy Wax Lake Delta remote sensing machine learning |
| url | https://doi.org/10.1029/2023WR036875 |
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