A hyperspectral view of the nearshore Mississippi River Delta: Characterizing suspended particles in coastal wetlands using imaging spectroscopy

Harringmeyer, Joshua P.; Ghosh, Nilotpal; Weiser, Matthew W.; Thompson, David R.; Simard, Marc; Lohrenz, Steven E.; Fichot, Cédric G.

doi:10.1016/j.rse.2023.113943

Cited by 7 publications

(4 citation statements)

References 88 publications

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“…According to this analysis, the Direct model coefficients trained in the Gulf of Maine appear less transferable than those of the Two-Step model , presumably because of its entire reliance on empirical parametrizations that can be local. Differences in the water temperature-DOM composition relationship, in salinity and turbidity ranges, and in suspended particle type/properties between systems ,, are all factors that can limit the transferability of the MLR parametrizations. Although the Direct and Two-Step models provide accurate estimates of [DOC] from DOM fluorescence and ancillary measurements, they do not replace laboratory-based analytical measurements of water samples (e.g., high-temperature combustion), which remain the most accurate means of measuring [DOC].…”

Section: Discussionmentioning

confidence: 99%

“…The system is directly connected to the Atlantic Ocean through tidal channels bisecting the lagoons . Samples were also collected during the NASA Earth Venture Suborbital (EVS)-3 Delta-X field campaign during Fall 2021 in the Mississippi River Deltaic Plain in south-central Louisiana, specifically in Atchafalaya Bay and Terrebonne Bay (Figure E). Atchafalaya Bay is dominated by turbid freshwater inputs from the Atchafalaya River.…”

Section: Methodsmentioning

confidence: 99%

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Improving Estimates of Dissolved Organic Carbon (DOC) Concentration from In Situ Fluorescence Measurements across Estuaries and Coastal Wetlands

Weiser,

Swanson,

Ghosh

et al. 2024

Environ. Sci. Technol.

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The use of optical proxies is essential to the sustained monitoring of dissolved organic carbon (DOC) in estuaries and coastal wetlands, where dynamics occur on subhour time scales. In situ dissolved organic matter (DOM) fluorescence, or FDOM, is now routinely measured along with ancillary water-quality indicators by commercial sondes. However, its reliability as an optical proxy of DOC concentration is often limited by uncertainties caused by in situ interferences and by variability in DOM composition and water matrix (ionic strength, pH) that are typical at the land-ocean interface. Although corrections for in situ interferences already exist, validated strategies to account for changes in the DOM composition and water matrix in these systems are still lacking. The transferability of methods across systems is also poorly known. Here, we used a comprehensive data set of laboratory-based excitation–emission matrix fluorescence and DOC concentration matched to in situ sonde measurements to develop and compare approaches that leverage ancillary water-quality indicators to improve estimates of DOC concentration from FDOM. Our analyses demonstrated the validity of in situ interference correction schemes, the importance of ancillary water-quality indicators to account for DOM composition and water matrix change, and the good transferability of the proposed methods.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Improving Estimates of Dissolved Organic Carbon (DOC) Concentration from In Situ Fluorescence Measurements across Estuaries and Coastal Wetlands

Weiser,

Swanson,

Ghosh

et al. 2024

Environ. Sci. Technol.

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“…In turbid conditions (Spring), we observed a peak near the red-edge (700 nm), associated with suspended sediment. Conversely, during fall, under clear waters with low turbidity but higher organic content (as indicated by elevated Chl-a in Table 1) due to seasonal bioproductivity (Harringmeyer et al, 2024), the maximum values are around the green (550 nm).…”

Section: Spectral Characteristics Of Wldmentioning

confidence: 99%

Depth Mapping in Turbid and Deep Waters Using AVIRIS-NG Imagery: A Study in Wax Lake Delta, Louisiana, USA

Kwon,

Passalacqua,

Soloy

et al. 2024

Preprint

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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), LA. 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 turbid 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 the attenuation as the key process for deep-depth retrievals. The depth maps of WLD captured by this model distinctly represent 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.

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“…UAV hyperspectral images have already found extensive applications in agriculture [15][16][17], with various strategies involving spectral preprocessing, feature selection, and regression modeling [18]. However, the utilization of UAV-borne hyperspectral images for water quality inversion has just witnessed gradual advancement in recent years [19,20]. Limited water quality samples, synchronized UAV images, and the complexity of spectral features in water bodies have emerged as obstacles [14,21,22].…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Framework for Retrieving Water Quality Parameters in Urban Rivers Using UAV Hyperspectral Images

Liu,

2024

Remote Sensing

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Efficient monitoring of water quality parameters (WQPs) is crucial for environmental health. Drone hyperspectral images have offered the potential for the flexible and accurate retrieval of WQPs. However, a machine learning (ML)-based multi-process strategy for WQP inversion has yet to be established. Taking a typical urban river in Guangzhou city, China, as the study area, this paper proposes a machine learning-based strategy combining spectral preprocessing and ML regression models with ground truth WQP data. Fractional order derivation (FOD) and discrete wavelet transform (DWT) methods were used to explore potential spectral information. Then, multiple methods were applied to select sensitive features. Three modeling strategies were constructed for retrieving four WQPs, including the Secchi depth (SD), turbidity (TUB), total phosphorus (TP), and permanganate index (CODMn). The highest R2s were 0.68, 0.90, 0.70, and 0.96, respectively, with corresponding RMSEs of 13.73 cm, 6.50 NTU, 0.06 mg/L, and 0.20 mg/L. Decision tree regression (DTR) was found to have the potential with the best performance for the first three WQPs, and eXtreme Gradient Boosting Regression (XGBR) for the CODMn. Moreover, tailored feature selection methods emphasize the importance of fitting processing strategies for specific parameters. This study provides an effective framework for WQP inversion that combines spectra mining and extraction based on drone hyperspectral images, supporting water quality monitoring and management in urban rivers.

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A hyperspectral view of the nearshore Mississippi River Delta: Characterizing suspended particles in coastal wetlands using imaging spectroscopy

Cited by 7 publications

References 88 publications

Improving Estimates of Dissolved Organic Carbon (DOC) Concentration from In Situ Fluorescence Measurements across Estuaries and Coastal Wetlands

Improving Estimates of Dissolved Organic Carbon (DOC) Concentration from In Situ Fluorescence Measurements across Estuaries and Coastal Wetlands

Depth Mapping in Turbid and Deep Waters Using AVIRIS-NG Imagery: A Study in Wax Lake Delta, Louisiana, USA

A Machine-Learning-Based Framework for Retrieving Water Quality Parameters in Urban Rivers Using UAV Hyperspectral Images

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