Remote sensing reflectance of Pomeranian lakes and the Baltic**The study was partially financed by MNiSW (Ministry of Science and Higher Education) as a research project N N306 066434 in the years 2008–2011. The partial support for this study was also provided by the SatBałtyk project (Satellite Monitoring of the Baltic Sea Environment) funded by the European Union from the European Regional Development Fund contract No. POIG 01.01.02-22-011/09.The paper was presented at the 6th International Conference ‘C

Ficek, Dariusz; Zapadka, Tomasz; Dera, Jerzy

doi:10.5697/oc.53-4.959

Cited by 46 publications

(26 citation statements)

References 7 publications

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“…Most previous studies on the origin, distribution and degradation of DOM and how it influences the optical properties of CDOM have been undertaken in oceans (Andrew et al, 2013;Matsuoka et al, 2014;Hancke et al, 2014;D'Sa et al, 2014), coastal waters (Stedmon et al, 2000;Vantrepotte et al, 2007;Kutser et al, 2009;Para et al, 2013) or in highlatitude lakes (Ficek et al, 2011;Ylöstalo et al, 2014). Understandably, the bias towards high-latitude systems partly reflects the fact that this region contains a high density of humic-rich lakes.…”

Section: Optical Properties Of Cdommentioning

confidence: 99%

Spatio-seasonal variability of chromophoric dissolved organic matter absorption and responses to photobleaching in a large shallow temperate lake

et al. 2017

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Abstract. The development and validation of remotesensing-based approaches for the retrieval of chromophoric dissolved organic matter (CDOM) concentrations requires a comprehensive understanding of the sources and magnitude of variability in the optical properties of dissolved material within lakes. In this study, spatial and seasonal variability in concentration and composition of CDOM and the origin of its variation was studied in Lake Balaton (Hungary), a large temperate shallow lake in central Europe. In addition, we investigated the effect of photobleaching on the optical properties of CDOM through in-lake incubation experiments. There was marked variability throughout the year in CDOM absorption in Lake Balaton (a CDOM (440) = 0.06-9.01 m −1 ). The highest values were consistently observed at the mouth of the main inflow (Zala River), which drains humic-rich material from the adjoining Kis-Balaton wetland, but CDOM absorption decreased rapidly towards the east where it was consistently lower and less variable than in the westernmost lake basins. The spectral slope parameter for the interval of 350-500 nm (S CDOM (350-500)) was more variable with increasing distance from the inflow (observed range 0.0161-0.0181 nm −1 for the mouth of the main inflow and 0.0158-0.0300 nm −1 for waters closer to the outflow). However, spatial variation in S CDOM was more constant exhibiting a negative correlation with a CDOM (440). Dissolved organic carbon (DOC) was strongly positively correlated with a CDOM (440) and followed a similar seasonal trend but it demonstrated more variability than either a CDOM or S CDOM with distance through the system. Photobleaching resulting from a 7-day exposure to natural solar UV radiation resulted in a marked decrease in allochthonous CDOM absorption (7.04 to 3.36 m −1 , 42 % decrease). Photodegradation also resulted in an increase in the spectral slope coefficient of dissolved material.

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Section: Optical Properties Of Cdommentioning

confidence: 99%

Spatio-seasonal variability of chromophoric dissolved organic matter absorption and responses to photobleaching in a large shallow temperate lake

et al. 2017

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“…Optically complex waters are independently influenced by Chl-a, TSM, and CDOM [8]. Therefore, these can be the reasons why standard remote sensing algorithms often fail in optically complex waters [72,73], and there are many regional or waterbody based [25,65,70,74,75] empirical algorithms. However, optical properties can vary strongly even inside one waterbody, for instance, during the one day in Lake Peipsi the concentration of Chl-a varied from 16.8 to 215.2 mg•m −3 , and in addition there is seasonal and interannual variability; therefore, studies have suggested, that more detailed approach would be beneficial [76,77].…”

Section: Discussionmentioning

confidence: 99%

“…For instance, the reflectance ratio of 665/560 nm was the best for OLCI and MSI; however, for Ramses, the model using the ratio of 560/660 nm was preferred. These ratios are quite commonly used for predictions; however, as shown in Table A1, they are used with different statistical techniques, such as linear regression [67,68,91,98,99], and power regression [67,75,98,99]. For Brown OWT, the log-transformed multiple linear regression model using the Brezonik et al [100] algorithm, using 488 nm and 830 nm, showed the highest results for all spectral ranges.…”

Section: Discussionmentioning

confidence: 99%

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

et al. 2020

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Currently, water monitoring programs are mainly based on in situ measurements; however, this approach is time-consuming, expensive, and may not reflect the status of the whole water body. The availability of Multispectral Imager (MSI) and Ocean and Land Colour Instrument (OLCI) free data with high spectral, spatial, and temporal resolution has increased the potential of adding remote sensing techniques into monitoring programs, leading to improvement of the quality of monitoring water. This study introduced an optical water type guided approach for boreal regions inland and coastal waters to estimate optical water quality parameters, such as the concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM), the absorption coefficient of coloured dissolved organic matter at a wavelength of 442 nm (aCDOM(442)), and the Secchi disk depth, from hyperspectral, OLCI, and MSI reflectance data. This study was based on data from 51 Estonian and Finnish lakes and from the Baltic Sea coastal area, which altogether were used in 415 in situ measurement stations and covered a wide range of optical water quality parameters (Chl-a: 0.5–215.2 mg·m−3; TSM: 0.6–46.0 mg·L−1; aCDOM(442): 0.4–43.7 m−1; and Secchi disk depth: 0.2–12.2 m). For retrieving optical water quality parameters from reflectance spectra, we tested 132 empirical algorithms. The study results describe the best algorithm for each optical water type for each spectral range and for each optical water quality parameter. The correlation was high, from 0.87 up to 0.93, between the in situ measured optical water quality parameters and the parameters predicted by the optical water type guided approach.

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“…Based on the spectral absorption characteristic of CDOM, Del Castillo and Miller [14], D'Sa and Miller [18] and Ficek et al [13] established statistical relationships between a g and R rs ratio (using different wavelengths) in Mississippi River and the Baltic. Using R rs (λ) to calculate a and further to separate out a dg and a ph , semi-analytical models, such as GSM [23] and QAA [28], were developed based on IOCCG dataset and in situ data, while Brando and Dekker [5] and Hoge and Lyon [22] developed models using in situ data in Fitzroy Estuary and U.S. Middle Atlantic Bight.…”

Section: Discussionmentioning

confidence: 99%

“…Empirical algorithms [13][14][15][16][17][18][19] were mostly based on spectral reflectance ratios to calculate a g (λ), and these algorithms required adequate data to parameterize the model and may only be valid for specific locations. Algorithms based on statistical modeling [18,[20][21][22][23][24], such as optimization (Garver-Siegel-Maritorena, GSM), matrix inversion algorithm, artificial neural network (aNN) and Linear Matrix Inversion (LMI) algorithm, used some semi-analytical methodologies, but required knowledge about specific biochemical parameters [5].…”

Section: Introductionmentioning

confidence: 99%

Validation and Calibration of QAA Algorithm for CDOM Absorption Retrieval in the Changjiang (Yangtze) Estuarine and Coastal Waters

et al. 2017

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Distribution, migration and transformation of chromophoric dissolved organic matter (CDOM) in coastal waters are closely related to marine biogeochemical cycle. Ocean color remote sensing retrieval of CDOM absorption coefficient (a g (λ)) can be used as an indicator to trace the distribution and variation characteristics of the Changjiang diluted water, and further to help understand estuarine and coastal biogeochemical processes in large spatial and temporal scales. The quasi-analytical algorithm (QAA) has been widely applied to remote sensing inversions of optical and biogeochemical parameters in water bodies such as oceanic and coastal waters, however, whether the algorithm can be applicable to highly turbid waters (i.e., Changjiang estuarine and coastal waters) is still unknown. In this study, large amounts of in situ data accumulated in the Changjiang estuarine and coastal waters from 9 cruise campaigns during 2011 and 2015 are used to verify and calibrate the QAA. Furthermore, the QAA is remodified for CDOM retrieval by employing a CDOM algorithm (QAA_CDOM). Consequently, based on the QAA and the QAA_CDOM, we developed a new version of algorithm, named QAA_cj, which is more suitable for highly turbid waters, e.g., Changjiang estuarine and coastal waters, to decompose a g from a dg (CDOM and non-pigmented particles absorption coefficient). By comparison of matchups between Geostationary Ocean Color Imager (GOCI) retrievals and in situ data, it reveals that the accuracy of retrievals from calibrated QAA is significantly improved. The root mean square error (RMSE), mean absolute relative error (MARE) and bias of total absorption coefficients (a(λ)) are lower than 1.17, 0.52 and 0.66 m −1 , and a g (λ) at 443 nm are lower than 0.07, 0.42 and 0.018 m −1 . These results indicate that the calibrated algorithm has a better applicability and prospect for highly turbid coastal waters with extremely complicated optical properties. Thus, reliable CDOM products from the improved QAA_cj can advance our understanding of the land-ocean interaction process by earth observations in monitoring spatial-temporal distribution of the river plume into sea.

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Cited by 46 publications

References 7 publications

Spatio-seasonal variability of chromophoric dissolved organic matter absorption and responses to photobleaching in a large shallow temperate lake

Spatio-seasonal variability of chromophoric dissolved organic matter absorption and responses to photobleaching in a large shallow temperate lake

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

Validation and Calibration of QAA Algorithm for CDOM Absorption Retrieval in the Changjiang (Yangtze) Estuarine and Coastal Waters

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