Optical properties of waters in the Australasian sector of the Southern Ocean

Clementson, Lesley A.; Parslow, John; Turnbull, Alison; McKenzie, Donald C.; Rathbone, Christopher E.

doi:10.1029/2000jc000359

Cited by 61 publications

(44 citation statements)

References 38 publications

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“…Based on the removed continuum, it is possible to obtain measurements that determine absorption features such as the wavelength depth, feature absorption area, wavelength range width and asymmetry [33]. These features are associated with the shape of the absorption wavelength range and were used as input data for the classification by cluster analysis.…”

Section: Continuum-removal Techniquementioning

confidence: 99%

“…Among the approaches currently used, the inversion of semi-analytical models and hyperspectral imaging, commonly seen in studies in ocean waters, stand out the most. These models allow the simultaneous retrieval of the water quality optical parameters concentrations [33,34], the bathymetry [17,35,36], and the substrate composition of the aquatic environment [2].…”

Section: Introductionmentioning

confidence: 99%

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Signal Classification of Submerged Aquatic Vegetation Based on the Hemispherical–Conical Reflectance Factor Spectrum Shape in the Yellow and Red Regions

et al. 2013

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Abstract:The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical-conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the Remote Sens. 2013, 5 1857 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing.

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Section: Continuum-removal Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Signal Classification of Submerged Aquatic Vegetation Based on the Hemispherical–Conical Reflectance Factor Spectrum Shape in the Yellow and Red Regions

et al. 2013

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“…In general, the waters north of the APF have typical characteristic of low dissolved iron (DFe), Si(OH) 4 , chlorophyll-a (Chl-a) concentrations, and they are dominated by haptophytes, especially in areas remote from continental influence (e.g. Banse, 1996;Clementson et al, 2001;Hutchins et al, 2001). Surface waters south of the APF are usually rich in macronutrients (NO 3 , PO 4 , Si(OH) 4 ) with a phytoplankton community dominated by diatoms .…”

Section: Introductionmentioning

confidence: 99%

Importance of deep mixing and silicic acid in regulating phytoplankton biomass and community in the iron-limited Antarctic Polar Front region in summer

Cheah

Soppa

Wiegmann

et al. 2017

Deep Sea Research Part II: Topical Studies in Oceanography

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a b s t r a c tPhytoplankton community structure and their physiological response in the vicinity of the Antarctic Polar Front (APF; 44°S to 53°S, centred at 10°E) were investigated as part of the ANT-XXVIII/3 Eddy-Pump cruise conducted in austral summer 2012. Our results show that under iron-limited ð o 0:3 μmol m À 3 Þ conditions, high total chlorophyll-a (TChl-a) concentrations ð 40:6 mg m À 3 Þ can be observed at stations with deep mixed layer ð 4 60 mÞ across the APF. In contrast, light was excessive at stations with shallower mixed layer and phytoplankton were producing higher amounts of photoprotective pigments, diadinoxanthin (DD) and diatoxanthin (DT), at the expense of TChl-a, resulting in higher ratios of (DD þDT)/ TChl-a. North of the APF, significantly lower silicic acid (Si(OH) 4 ) concentrations ð o 2 mmol m À 3 Þ lead to the domination of nanophytoplankton consisting mostly of haptophytes, which produced higher ratios of (DD þDT)/TChl-a under relatively low irradiance conditions. The Si(OH) 4 replete ð 4 5 mmol m À 3 Þ region south of the APF, on the contrary, was dominated by microphytoplankton (diatoms and dinoflagellates) with lower ratios of (DD þDT)/TChl-a, despite having been exposed to higher levels of irradiance. The significant correlation between nanophytoplankton and (DD þDT)/TChl-a indicates that differences in taxon-specific response to light are also influencing TChl-a concentration in the APF during summer. Our results reveal that provided mixing is deep and Si(OH) 4 is replete, TChl-a concentrations higher than 0:6 mg m À 3 are achievable in the iron-limited APF waters during summer.

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“…CDOM absorption was determined by measuring the absorbance of water samples filtered through 0.2 µm nylon filters and converting to absorption coefficients as in Clementson et al [50] and Oubelkheir et al [8]: …”

Section: Water Constituent Siopsmentioning

confidence: 99%

Optimization of a Semi-Analytical Algorithm for Multi-Temporal Water Quality Monitoring in Inland Waters with Wide Natural Variability

Bramante

Sin

2015

Remote Sensing

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Current spectrometer design and the increasingly affordable price of field hyperspectral sensors are making feasible their use for water quality monitoring. In this study, we parameterized a semi-analytical algorithm to derive constituent concentrations from field spectroradiometer measurements in ten freshwater reservoirs over two years. In contrast to algorithms parameterized for single airborne or satellite sensor deployments, we optimized the algorithm for robust performance across all reservoirs and for multi-temporal application. Our algorithm produced chlorophyll-a concentration estimates with a root mean squared error (RMSE) of 7.7 mg¤m ¡3 over a range of 4-135 mg¤m ¡3 . The model also produced estimates of total suspended solids (TSS) concentration with an RMSE of 4.0 g¤m ¡3 over a range of 0-25 g¤m ¡3 . Choosing a non-linear objective function during inversion reduced variance of residuals in chlorophyll-a and TSS estimates by 20 and 18 percentage points, respectively. Application of our algorithm to two years of data and over ten study sites allowed us to specify sources of suboptimal parameterization and measure the non-stationarity of algorithm performance, analyses difficult for short or single deployments. Suboptimal parameterization, especially of backscatter properties between reservoirs, was the greatest source of error in our algorithm, accounting for 17%-20% of all error. In only one reservoir was time-dependent error apparent. In this reservoir, decreases in TSS over time resulted in less TSS estimate error due to imperfect model parameterization. For future applications, especially with ground-based sensors, model performance can easily be improved by using non-linear inversion procedures and replicating spectral measurements.

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Optical properties of waters in the Australasian sector of the Southern Ocean

Cited by 61 publications

References 38 publications

Signal Classification of Submerged Aquatic Vegetation Based on the Hemispherical–Conical Reflectance Factor Spectrum Shape in the Yellow and Red Regions

Signal Classification of Submerged Aquatic Vegetation Based on the Hemispherical–Conical Reflectance Factor Spectrum Shape in the Yellow and Red Regions

Importance of deep mixing and silicic acid in regulating phytoplankton biomass and community in the iron-limited Antarctic Polar Front region in summer

Optimization of a Semi-Analytical Algorithm for Multi-Temporal Water Quality Monitoring in Inland Waters with Wide Natural Variability

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