New simple statistical formulas for estimating surface concentrations of suspended particulate matter (SPM) and particulate organic carbon (POC) from remote-sensing reflectance in the southern Baltic Sea

Woźniak, Sławomir B.; Darecki, Mirosław; Zabłocka, Monika; Burska, Dorota; Dera, Jerzy

doi:10.1016/j.oceano.2016.03.002

Cited by 32 publications

(23 citation statements)

References 20 publications

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“…The relationship between POC and suspended particulate matter concentration (C TSM ) ( Figure 8B) is very similar to studies that show that POC is highly positively correlated to suspended particulate matter (Ni et al, 2008;Cetinić et al, 2012;Woźniak et al, 2016;325 Yang et al, 2016). The linear relationship between POC (μm/L) and C TSM (mg/L) is shown in Figure 7B .…”

Section: Productivity and Migrationsupporting

confidence: 85%

Variation pattern of particulate organic carbon and nitrogen in oceans and inland waters

Huang¹,

Ye²,

Hao³

et al. 2017

Preprint

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Section: Productivity and Migrationsupporting

confidence: 85%

Variation pattern of particulate organic carbon and nitrogen in oceans and inland waters

Huang¹,

Ye²,

Hao³

et al. 2017

Preprint

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“…Table 3. In situ data of chlorophyll a (Chl a), phycocyanin, total suspended matter (TSM), the inorganic fraction of suspended matter (SPIM), the organic fraction of suspended matter (SPOM), turbidity, a CDOM (412), Secchi disc depth (Secchi), total absorption at 443 nm (a tot ), total attenuation at 443 nm (c tot ), total scattering at 443 nm (b tot ), and total backscattering at 470 nm (bb tot ) collected during three different field campaigns on 24 …”

Section: In Situ Datamentioning

confidence: 99%

“…Different remote sensing products (chlorophyll a, CDOM, suspended matter, diffuse attenuation coefficient, etc.) have been developed or adopted for the Baltic Sea [3,4,6,8,11,[17][18][19][20][21][22][23][24][25][26][27][28]. Many of the recent studies rely on MERIS data and the results can probably be implemented on Sentinel-3 OLCI data as well.…”

Section: Introductionmentioning

confidence: 99%

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

et al. 2017

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Abstract:The launch of Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A in 2016 is the beginning of a new era in long time, continuous, high frequency water quality monitoring of coastal waters. Therefore, there is a strong need to validate the OLCI products to be sure that the technical capabilities provided will be used in the best possible way in water quality monitoring and research. The Baltic Sea is an optically complex waterbody where many ocean colour products, performing well in other waterbodies, fail. We tested the performance of standard Case-2 Regional/Coast Colour (C2RCC) processing chain in retrieving water reflectance, inherent optical properties (IOPs), and water quality parameters such as chlorophyll a, total suspended matter (TSM) and coloured dissolved organic matter (CDOM) in the Baltic Sea. The reflectance spectra produced by the C2RCC are realistic in both shape and magnitude. However, the IOPs, and consequently the water quality parameters estimated by the C2RCC, did not have correlation with in situ data. On the other hand, some tested empirical remote sensing algorithms performed well in retrieving chlorophyll a, TSM, CDOM and Secchi depth from the reflectance produced by the C2RCC. This suggests that the atmospheric correction part of the processor performs relatively well while IOP retrieval part of the neural network needs extensive training with actual IOP data before it can produce reasonable estimates for the Baltic Sea.

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“…These algorithms were developed for open ocean waters and rely on the dominance of phytoplankton biomass in the total POC concentration. More recently, some purely empirical approaches were developed from in situ measurements performed in offshore and coastal waters [19][20][21][22] or exclusively from measurements collected in coastal waters (mainly in river-dominated systems) [23]. These algorithms are all empirical because the use of a semi-analytical algorithm to estimate POC requires a perfect knowledge of the spectral specific POC-IOPs properties, which are not available as yet.…”

Section: Candidate Algorithmsmentioning

confidence: 99%

“…C in the first attenuation and euphotic layers, respectively), [15], such information is still not available for global coastal waters, which are more complex bio-optical environments [18]. To overcome this limitation on our understanding of the POC dynamics, some purely empirical approaches were recently developed from in situ measurements performed in offshore and coastal waters [19][20][21][22] or exclusively from measurements collected in coastal waters (mainly in river-dominated systems) [23] to estimate the surface POC concentration from OCR. However, these algorithms were almost all developed from limited datasets gathered in specific regions.…”

Section: Introductionmentioning

confidence: 99%

Deriving Particulate Organic Carbon in Coastal Waters from Remote Sensing: Inter-Comparison Exercise and Development of a Maximum Band-Ratio Approach

et al. 2019

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Recently, different algorithms have been developed to assess near-surface particulate organic matter (POC) concentration over coastal waters. In this study, we gathered an extensive in situ dataset representing various contrasted bio-optical coastal environments at low, medium, and high latitudes, with various bulk particulate matter chemical compositions (mineral-dominated, 50% of the data set, mixed, 40%, or organic-dominated, 10%). The dataset includes 606 coincident measurements of POC concentration and remote-sensing reflectance, R rs, with POC concentrations covering three orders of magnitude. Twelve existing algorithms have then been tested on this data set, and a new one was proposed. The results show that the performance of historical algorithms depends on the type of water, with an overall low performance observed for mineral-dominated waters. Furthermore, none of the tested algorithms provided satisfactory results over the whole POC range. A novel approach was thus developed based on a maximum band ratio of R rs (red/blue, red/yellow or red/green ratio). Based on the standard statistical metric for the evaluation of inverse models, the new algorithm presents the best performance. The root-mean square deviation for log-transformed data (RMSD log ) is 0.25. The mean absolute percentage difference (MAPD) is 37.48%. The mean bias (MB) and median ratio (MR) values are 0.54 µg L −1 and 1.02, respectively. This algorithm replicates quite well the distribution of in situ data. The new algorithm was also tested on a matchup dataset gathering 154 coincident MERIS (MEdium Resolution Imaging Spectrometer) R rs and in situ POC concentration sampled along the French coast. The matchup analysis showed that the performance of the new algorithm is satisfactory (RMSD log = 0.24, MAPD = 34.16%, MR = 0.92). A regional illustration of the model performance for the Louisiana continental shelf shows that monthly mean POC concentrations derived from MERIS with the new algorithm are consistent with those derived from the 2016 algorithm of Le et al. which was specifically developed for this region.3 of 29 were tested over this in situ dataset. A new empirical algorithm involving a maximum band ratio was then developed. Finally, the new approach was applied to a dataset of the medium resolution imaging spectrometer (MERIS) and a coastal region was selected, where the spatial changes of POC concentrations were discussed. Data and Methods In Situ DataThe in-situ dataset includes the concentration of biogeochemical parameters (POC, Chla, and suspended particulate matter, SPM) and remote sensing reflectance spectra R rs (λ) where λ is the wavelength. Measurements were collected by different contributors and instruments undoubtedly introducing uncertainties, which are not necessarily well characterized (Table 1). The field measurement protocols and data processing are described in detail in related papers listed in Table 1. Measurements were sampled between 1997 and 2015 in various coastal regions (Figure 1): European coastal waters ...

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New simple statistical formulas for estimating surface concentrations of suspended particulate matter (SPM) and particulate organic carbon (POC) from remote-sensing reflectance in the southern Baltic Sea

Cited by 32 publications

References 20 publications

Variation pattern of particulate organic carbon and nitrogen in oceans and inland waters

Variation pattern of particulate organic carbon and nitrogen in oceans and inland waters

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

Deriving Particulate Organic Carbon in Coastal Waters from Remote Sensing: Inter-Comparison Exercise and Development of a Maximum Band-Ratio Approach

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