Satellite mapping of Baltic Sea Secchi depth with multiple regression models

Stock, Andy

doi:10.1016/j.jag.2015.04.002

Cited by 25 publications

(22 citation statements)

References 27 publications

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“…Secchi depth is also relatively easy to measure compared to more laborious procedures associated with measuring Chl-a absorption and gravimetric measurements of TSM, thus leading to a higher number of match-ups in this study ( n = 35), especially when using small boats for validation. Secchi depth can also be modelled using a multiple regression approach [80].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

Kyryliuk

Kratzer

2019

Sensors

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In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the SentiNel Application Platform (SNAP) whilst adjusting the specific scatter of Total Suspended Matter (TSM) for the Baltic Sea in order to improve TSM retrieval. The remote sensing product “kd_z90max” (i.e., the depth of the water column from which 90% of the water-leaving irradiance are derived) from C2RCC-SNAP showed a good correlation with in situ Secchi depth (SD). Additionally, a regional in-water algorithm was applied to derive SD from the attenuation coefficient Kd(489) using a local algorithm. Furthermore, a regional in-water relationship between particle scatter and bench turbidity was applied to generate turbidity from the remote sensing product “iop_bpart” (i.e., the scattering coefficient of marine particles at 443 nm). The spectral shape of the remote sensing reflectance (Rrs) data extracted from match-up stations was evaluated against reflectance data measured in situ by a tethered Attenuation Coefficient Sensor (TACCS) radiometer. The L2 products were evaluated against in situ data from several dedicated validation campaigns (2016–2018) in the NW Baltic proper. All derived L2 in-water products were statistically compared to in situ data and the results were also compared to results for MERIS validation from the literature and the current S3 Level-2 Water (L2W) standard processor from EUMETSAT. The Chl-a product showed a substantial improvement (MNB 21%, RMSE 88%, APD 96%, n = 27) compared to concentrations derived from the Medium Resolution Imaging Spectrometer (MERIS), with a strong underestimation of higher values. TSM performed within an error comparable to MERIS data with a mean normalized bias (MNB) 25%, root-mean square error (RMSE) 73%, average absolute percentage difference (APD) 63% n = 23). Coloured Dissolved Organic Matter (CDOM) absorption retrieval has also improved substantially when using the product “iop_adg” (i.e., the sum of organic detritus and Gelbstoff absorption at 443 nm) as a proxy (MNB 8%, RMSE 56%, APD 54%, n = 18). The local SD (MNB 6%, RMSE 62%, APD 60%, n = 35) and turbidity (MNB 3%, RMSE 35%, APD 34%, n = 29) algorithms showed very good agreement with in situ data. We recommend the use of the SNAP C2RCC with regionally adjusted TSM-specific scatter for water product retrieval as well as the regional turbidity algorithm for Baltic Sea monitoring. Besides documenting the evaluation of the C2RCC processor, this paper may also act as a handbook on the validation of Ocean Colour data.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

Kyryliuk

Kratzer

2019

Sensors

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“…The SD is affected by various water quality parameters, such as chlorophyll, total phosphorus concentration, and color in addition to light scattering and light absorption ( Carlson, 1977 ; Fee et al, 1996 ; Brezonik et al, 2015 ). Despite the numerous variables affecting transparency, the TSI SD is commonly used as a key eutrophication indicator of different water body types ( Karabin, 1985 ; Pyhälä, Fleming-Lehtinen & Laamanen, 2014 ; Stock, 2015 ; Binding et al, 2015 ; Heddam, 2016 ; Alikas & Kratzer, 2017 ). Therefore, the Secchi depth (SD) is an important visual indicator of water clarity that can be used for the water quality index calculation ( Carlson, 1977 ; Alexakis et al, 2016 ; Heddam, 2016 ; Ochocka & Pasztaleniec, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

“…Water transparency also has an impact on lake processes and the behavior of freshwater organisms ( Fee et al, 1996 ; Berger, Diehl & Kunz, 2006 ). Transparency is considered a trophic state proxy (TSI SD ) in water management and especially for ecological assessments ( Carlson, 1977 ; Egan et al, 2009 ; Pyhälä, Fleming-Lehtinen & Laamanen, 2014 ; Stock, 2015 ; Binding et al, 2015 ; Heddam, 2016 ; Alikas & Kratzer, 2017 ). However, transparency is more than just an indicator.…”

Section: Introductionmentioning

confidence: 99%

Trophic state (TSI_SD) and mixing type significantly influence pelagic zooplankton biodiversity in temperate lakes (NW Poland)

Sługocki

Czerniawski

2018

PeerJ

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BackgroundLake depth and the consequent mixing regime and thermal structure have profound effects on ecosystem functioning, because depth strongly affects the availability of nutrients, light, and oxygen. All these conditions influence patterns of zooplankton diversity. Zooplankton are a key component of the aquatic environment and are essential to maintaining natural processes in freshwater ecosystems. However, zooplankton biodiversity can be different regard to depth, mixing type and trophic state. Therefore, the aim of this study was to examine how depth and mixing regime affect zooplankton diversity in lakes. We also investigated the vertical distribution of diversity across a trophic gradient of lakes.MethodsA total of 329 zooplankton samples from 79 temperate lakes (36 polymictic and 43 dimictic) were collected. The biodiversity of zooplankton was calculated using species richness (SR) and the Shannon index (SI). An index based on Secchi disc visibility was used to determine the trophic state index (TSISD) of lakes. The one-way ANOVA with Duncan’s post hoc test were used to determine differences in zooplankton biodiversity between mictic lake types and thermal layers. To find the best predictors for zooplankton biodiversity a multiple stepwise regression was used. The rarefaction method was used to evaluate the impact of mixing types, thermal layers, and the TSISDon zooplankton biodiversity indices. A Sørensen similarity analysis and nonmetric multidimensional scaling (NMDS) were performed to describe the similarity patterns in species composition among lakes.ResultsWe identified a total of 151 taxa from 36 polymictic and 43 dimictic lakes. Lake depth and the TSISD were significantly correlated with the biodiversity of lake zooplankton. The results of ANOVA and Duncan tests show that mictic type and thermal zones had a significant effect on zooplankton biodiversity. The rarefaction curve showed significant differences in zooplankton biodiversity, which was greater in lakes with lower trophic state. Ordination by NMDS showed clustering of different mictic types, thermal layers, and composition changes throughout the TSISDprofile. Moreover, we determined that polymictic lakes are more heterogeneous than dimictic lakes in regard to zooplankton similarities.DiscussionBoth mictic lake types were characterized by varying levels of zooplankton biodiversity, which is shaped by the communities’ response to lake depth, thermal layers and TSISD values. The zooplankton SR and SI (during daylight hours) depends greatly on the mixing type. Lake type also indicates the importance of the metalimnion in shaping zooplankton biodiversity in dimictic lakes. In addition, data from NW Polish lakes indicated that the increase of the TSISD leads to taxonomic shifts and has a negative effect on the diversity of all groups of zooplankton.

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“…Many in situ observations were clustered in space. Thus, models fitted to these data would in practice be optimized for performance in those locations with many close-by observations (Stock, 2015). We thus weighted the observations when training algorithms.…”

Section: Observation Weightsmentioning

confidence: 99%

Accuracy of Empirical Satellite Algorithms for Mapping Phytoplankton Diagnostic Pigments in the Open Ocean: A Supervised Learning Perspective

Stock¹,

Subramaniam²

2020

Front. Mar. Sci.

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Satellite mapping of Baltic Sea Secchi depth with multiple regression models

Cited by 25 publications

References 27 publications

Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

Trophic state (TSI_SD) and mixing type significantly influence pelagic zooplankton biodiversity in temperate lakes (NW Poland)

Accuracy of Empirical Satellite Algorithms for Mapping Phytoplankton Diagnostic Pigments in the Open Ocean: A Supervised Learning Perspective

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Satellite mapping of Baltic Sea Secchi depth with multiple regression models

Cited by 25 publications

References 27 publications

Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

Trophic state (TSISD) and mixing type significantly influence pelagic zooplankton biodiversity in temperate lakes (NW Poland)

Accuracy of Empirical Satellite Algorithms for Mapping Phytoplankton Diagnostic Pigments in the Open Ocean: A Supervised Learning Perspective

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Trophic state (TSI_SD) and mixing type significantly influence pelagic zooplankton biodiversity in temperate lakes (NW Poland)