Optical water types found in Brazilian waters

Silva, Edson Filisbino Freire da; Novo, Evlyn Márcia Leão de Moraes; Lobo, Felipe de Lucia; Barbosa, Cláudio Clemente Faria; Noernberg, Maurício Almeida; Rotta, Luiz; Cairo, Carolline Tressmann; Maciel, Daniel Andrade; Júnior, Rogério Flores

doi:10.1007/s10201-020-00633-z

Cited by 17 publications

(13 citation statements)

References 34 publications

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“…In addition, the OWT total membership can be used in the uncertainty assessment of in situ spectra or remote sensing images [6,18,22]. This study also showed that the U-OWT was highly coordinated and com-patible with the TSI and Forel-Ule Scale, therefore, the dominant OWT of the U-OWT can be regarded as an indicator of water quality assessment [20,23,25,28,29]. What is more, the OWT schemes were helpful in the biogeochemical province partition of the oceans [61][62][63], the marine mesoscale features (water mass, eddy, plume, etc.)…”

Section: Future Prospects Of the U-owtmentioning

confidence: 64%

“…Some previous AOP-based OWT schemes were compared with the U-OWT. However, because of the difficulty in obtaining the raw OWT spectra data, some OWT schemes proposed by previous literatures were not included in such comparisons, such as Vantrepotte et al [11], Shi et al [12], Mélin and Vantrepotte [14], Ye et al [16], Hieronymi et al [18], Monolisha et al [20], Spyrakos et al [21], Uudeberg et al [25], Xue et al [26], Zhang et al [27], Balasubramanian et al [28], and da Silva et al [29] The adaptability of the U-OWT in inland waters also deserves to be explored, and some unique OWT classes for inland waters may be added to the present U-OWT scheme in the future.…”

Section: How Many Optical Water Types In the World?mentioning

confidence: 99%

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A Universal Fuzzy Logic Optical Water Type Scheme for the Global Oceans

2021

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The classification of natural waters is a way to generalize and systematize ocean color science. However, there is no consensus on an optimal water classification template in many contexts. In this study, we conducted an unsupervised classification of the PACE (Plankton, Aerosols, Cloud, and Ocean Ecosystem) synthetic hyperspectral data set, divided the global ocean waters into 15 classes, then obtained a set of fuzzy logic optical water type schemes (abbreviated as the U-OWT in this study) that were tailored for several multispectral satellite sensors, including SeaWiFS, MERIS, MODIS, OLI, VIIRS, MSI, and OLCI. The consistency analysis showed that the performance of U-OWT on different satellite sensors was comparable, and the sensitivity analysis demonstrated the U-OWT could resist a certain degree of input disturbance on remote sensing reflectance. Compared to existing ocean-aimed optical water type schemes, the U-OWT can distinguish more mesotrophic and eutrophic water classes. Furthermore, the U-OWT was highly compatible with other water classification taxonomies, including the trophic state index, the multivariate absorption combinations, and the Forel-Ule Scale, which indirectly demonstrated the potential for global applicability of the U-OWT. This finding was also helpful for the further conversion and unification of different water type taxonomies. As the fundamental basis, the U-OWT can be applied to many oceanic fields that need to be explored in the future. To promote the reproducibility of this study, an IDL®-based standalone U-OWT calculation tool is freely distributed.

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Section: Future Prospects Of the U-owtmentioning

confidence: 64%

Section: How Many Optical Water Types In the World?mentioning

confidence: 99%

A Universal Fuzzy Logic Optical Water Type Scheme for the Global Oceans

2021

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“…Unsupervised classification can be used when a priori knowledge is not available, and R rs data is used to define the clusters, thereby being less subject to user errors [59]. The k-means and fuzzy c-means are known techniques among the unsupervised classification methods [12,13,[20][21][22]24,31,57]. When comparing k-means versus fuzzy c-means and other unsupervised methods (e.g., Ward algorithm), the clustering results can be very similar [20,22,59].…”

Section: Optical Water Type Identificationmentioning

confidence: 99%

“…In inland waters, the same data collection is more substantial in the northern hemisphere supporting more reliable classifications [20,21]. In tropical regions, especially in the southern hemisphere, efforts have been made in the Amazon region [13,20,24], but much remains to be done in terms of classifying tropical water types.…”

Section: Introductionmentioning

confidence: 98%

“…However, the Amazon optical data utilized to develop the aforementioned classification scheme were limited to a collection of spectra and bio-optical constituents sampled at the Great Lake of Curuai floodplain, while no information about seasonal variability was presented for the region. Similarly, Silva et al [24] performed a classification on overall Brazilian waters, including Amazonian lakes (e.g., Great Lake Curuai floodplain) and tributary rivers (e.g., Tapajós River), but still did not consider the seasonal variability. Seasonal variability of Amazon OWTs was considered in a study presented by Martinez et al [13].…”

Section: Introductionmentioning

confidence: 99%

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Optical Classification of Lower Amazon Waters Based on In Situ Data and Sentinel-3 Ocean and Land Color Instrument Imagery

et al. 2021

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Optical water types (OWTs) were identified from an in situ dataset of concomitant biogeochemical and optical parameters acquired in the Amazon River and its tributaries, in the Lower Amazon region, at different hydrological conditions from 2014 to 2017. A seasonal bio-optical characterization was performed. The k-means classification was applied to the in situ normalized reflectance spectra (rn(λ)), allowing the identification of four OWTs. An optical index method was also applied to the rn(λ) defining the thresholds of the OWTs. Next, level-3 Sentinel-3 Ocean and Land Color Instrument images representative of the seasonal discharge conditions were classified using the identified in situ OWTs as reference. The differences between Amazon River and clearwater tributary OWTs were dependent on the hydrological dynamics of the Amazon River, also showing a strong seasonal variability. Each OWT was associated with a specific bio-optical and biogeochemical environment assessed from the corresponding absorption coefficient values of colored dissolved organic matter (aCDOM) and particulate matter (ap), chlorophyll-a and suspended particulate matter (SPM) concentrations, and aCDOM/ap ratio. The rising water season presented a unique OWT with high SPM concentration and high relative contribution of ap to total absorption compared to the other OWTs. This bio-optical characterization of Lower Amazon River waters represents a first step for developing remote sensing inversion models adjusted to the optical complexity of this region.

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Nonlinear multidecadal trends in organic matter dynamics in Midwest reservoirs are a function of variable hydroclimate

Bhattacharya

Jones

Graham

et al. 2022

Limnology & Oceanography

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Dissolved organic matter (DOM) and particulate organic matter (POM) can influence biogeochemical processes in aquatic systems. An understanding, however, of the source, composition, and processes driving inland reservoir organic matter (OM) cycling at a regional scale over the long term is currently unexplored. Here, we quantify decadal patterns (> 20 yr) of DOM quantity and composition and POM in 40 reservoirs in the midcontinent United States. We built 184 Random Forest models to identify how the relative influence of watershed characteristics and limnological parameters on OM dynamics may vary over time and in synchrony with hydroclimatic anomalies. The reservoir OM quantity and composition varied nonmonotonically through time and in contrast to lake browning observed in the northern hemisphere. Reservoir DOM composition switched from humic and aromatic during wet summers to aliphatic, potentially autochthonous DOM during particularly prolonged dry summers in the mid-2000s. The shift in reservoir DOM quantity and composition could be attributed to the change in time-varying control of watershed and limnological factors mediated by the hydroclimatic conditions. Watershed control (e.g., percent crops) was predominant during wet summers, while the effect of reservoir morphology (e.g., maximum depth) and water quality parameters (e.g., Secchi depth, chlorophyll a) were evident during dry summers. Thus, future predictions of drier conditions may promote "greening" with negative implications for reservoir water quality and treated drinking water. Considering the nonlinear nature of reservoir OM dynamics and its controls will help to better mitigate water quality issues in these constructed systems increasingly impacted by global changes.

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Optical water types found in Brazilian waters

Cited by 17 publications

References 34 publications

A Universal Fuzzy Logic Optical Water Type Scheme for the Global Oceans

A Universal Fuzzy Logic Optical Water Type Scheme for the Global Oceans

Optical Classification of Lower Amazon Waters Based on In Situ Data and Sentinel-3 Ocean and Land Color Instrument Imagery

Nonlinear multidecadal trends in organic matter dynamics in Midwest reservoirs are a function of variable hydroclimate

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