Optical Spectra of Phytoplankton Cultures for Remote Sensing Applications: Focus on Harmful Algal Blooms

Warner, Robert A.; Fan, Chunlei

doi:10.7763/ijesd.2013.v4.312

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…This general influence on spectral reflectance by phytoplankton pigments (i.e. chlorophyll) is also consistent with previous studies (Olmanson, Brezonik et al, 2013, Warner andFan, 2013). The upper Patuxent River represents sediment-dominated water with high TSS concentrations of 50 mg/L.…”

Section: Spectral Features Of Water Reflectance R(λ)supporting

confidence: 91%

Spectral Analysis of Water Reflectance for Hyperspectral Remote Sensing of Water Quailty in Estuarine Water

Fan¹

2014

GEP

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Hyperspectral remote sensing offers an effective approach for frequent, synoptic water quality measurements over a large spatial extent. However, the optical complexity of case 2 water makes the water quality monitoring by remote sensing in estuarine water a challenge. The prime objective of this study was to develop algorithms for hyperspectral remote sensing of water quality based on in situ spectral measurement of water reflectance. In this study, water reflectance spectra R(λ) were acquired by a pair of Ocean Optic 2000 spectroradiometers during the summers from 2008 to 2011 at Patuxent River, a tributary of Chesapeake Bay, USA. Simultaneously, concentrations of chlorophyll a and total suspended solids (TSS), as well as absorption of colored dissolved organic matter (CDOM) were measured. Empirical models that based on spectral features of water reflectance generally showed good correlations with water quality parameters. The retrieval model that using spectral bands at red/NIR showed a high correlation with chlorophyll a concentration (R 2 = 0.81). The ratio of green to blue spectral bands is the best predictor for TSS (R 2 = 0.75), and CDOM absorption is best correlated with spectral features at blue and NIR regions (R 2 = 0.85). These empirical models were further applied to the ASIA Eagle hyperspectral aerial imagery to demonstrate the feasibility of hyperspectral remote sensing of water quality in the optical complex estuarine waters.

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Section: Spectral Features Of Water Reflectance R(λ)supporting

confidence: 91%

Spectral Analysis of Water Reflectance for Hyperspectral Remote Sensing of Water Quailty in Estuarine Water

Fan¹

2014

GEP

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“…The specific recognition results are shown in Table 4. Unlike other lakes, Lake Taihu, as a eutrophic lake, has been seriously affected by water bloom [56], resulting in changes in the optical properties of the water body including an increase in turbidity, a change in water color, a decrease in transparency, and a change in spectral reflectance, ultimately leading to color confusion and spatial information distortion in remote sensing data [57,58]. This situation makes it difficult to effectively segment the water color and aquatic vegetation of Lake Taihu using traditional decision tree methods.…”

Section: Spatial and Temporal Changes In Aquatic Vegetation In Lake H...mentioning

confidence: 99%

Deep-Learning-Based Automatic Extraction of Aquatic Vegetation from Sentinel-2 Images—A Case Study of Lake Honghu

Gao,

Li,

Shen

et al. 2024

Remote Sensing

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Aquatic vegetation is an important component of aquatic ecosystems; therefore, the classification and mapping of aquatic vegetation is an important aspect of lake management. Currently, the decision tree (DT) classification method based on spectral indices has been widely used in the extraction of aquatic vegetation data, but the disadvantage of this method is that it is difficult to fix the threshold value, which, in turn, affects the automatic classification effect. In this study, Sentinel-2 MSI data were used to produce a sample set (about 930 samples) of aquatic vegetation in four inland lakes (Lake Taihu, Lake Caohai, Lake Honghu, and Lake Dongtinghu) using the visual interpretation method, including emergent, floating-leaved, and submerged vegetation. Based on this sample set, a DL model (Res-U-Net) was used to train an automatic aquatic vegetation extraction model. The DL model achieved a higher overall accuracy, relevant error, and kappa coefficient (90%, 8.18%, and 0.86, respectively) compared to the DT method (79%, 23.07%, and 0.77) and random forest (78%,10.62% and 0.77) when utilizing visual interpretation results as the ground truth. When utilizing measured point data as the ground truth, the DL model exhibited accuracies of 59%, 78%, and 91% for submerged, floating-leaved, and emergent vegetation, respectively. In addition, the model still maintains good recognition in the presence of clouds with the influence of water bloom. When applying the model to Lake Honghu from January 2017 to October 2023, the obtained temporal variation patterns in the aquatic vegetation were consistent with other studies. The study in this paper shows that the proposed DL model has good application potential for extracting aquatic vegetation data.

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“…Recent studies use libraries of high-resolution R rs features to classify phytoplankton involved in harmful algal blooms. These methods hold promise for wide-area mapping by orbital data [Warner and Fan, 2013]. For such applications, fine-scale perturbations are equally significant to the more commonly studied smooth effects.…”

Section: Introductionmentioning

confidence: 99%

Optimizing irradiance estimates for coastal and inland water imaging spectroscopy

Thompson

Seidel

Gao

et al. 2015

Geophysical Research Letters

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Next generation orbital imaging spectrometers, with advanced global remote sensing capabilities, propose to address outstanding ocean science questions related to coastal and inland water environments. These missions require highly accurate characterization of solar irradiance in the critical 380–600 nm spectral range. However, the irradiance in this spectral region is temporally variable and difficult to measure directly, leading to considerable variance between different models. Here we optimize an irradiance estimate using data from the NASA airborne Portable Remote Imaging Spectrometer (PRISM), leveraging spectrally smooth in‐scene targets. We demonstrate improved retrievals for both PRISM and the Next Generation Airborne Visible Infrared Imaging Spectrometer.

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Optical Spectra of Phytoplankton Cultures for Remote Sensing Applications: Focus on Harmful Algal Blooms

Cited by 6 publications

References 23 publications

Spectral Analysis of Water Reflectance for Hyperspectral Remote Sensing of Water Quailty in Estuarine Water

Spectral Analysis of Water Reflectance for Hyperspectral Remote Sensing of Water Quailty in Estuarine Water

Deep-Learning-Based Automatic Extraction of Aquatic Vegetation from Sentinel-2 Images—A Case Study of Lake Honghu

Optimizing irradiance estimates for coastal and inland water imaging spectroscopy

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