scholarly journals A Model-Based Assessment of Canopy-Scale Primary Productivity for the Baltic Sea Benthic Vegetation Using Environmental Variables and Spectral Indices

2021 ◽  
Vol 14 (1) ◽  
pp. 158
Author(s):  
Ele Vahtmäe ◽  
Jonne Kotta ◽  
Laura Argus ◽  
Mihkel Kotta ◽  
Ilmar Kotta ◽  
...  
Keyword(s):  
Primary Production ◽  
Aquatic Vegetation ◽  
Meteorological Data ◽  
Boosted Regression Trees ◽  
Model Description ◽  
Spectral Indices ◽  
Blue Band ◽  
Chl A ◽  
The Baltic

This study investigated the potential to predict primary production in benthic ecosystems using meteorological variables and spectral indices. In situ production experiments were carried out during the vegetation season of 2020, wherein the primary production and spectral reflectance of different communities of submerged aquatic vegetation (SAV) were measured and chlorophyll (Chl a+b) concentration was quantified in the laboratory. The reflectance of SAV was measured both in air and underwater. First, in situ reflectance spectra of each SAV class were used to calculate different spectral indices, and then the indices were correlated with Chl a+b. Indices using red and blue band combinations such as 650/450 and 650/480 nm explained the largest part of variability in Chl a+b for datasets measured in air and underwater. Subsequently, the best-performing indices were used in boosted regression trees (BRT) models, together with meteorological data to predict the community photosynthesis of different SAV classes. The predictive power (R2) of production models were very high, estimated at the range of 0.82-0.87. The variable contributing the most to the model description was SAV class, followed in most cases by the water temperature. Nevertheless, the inclusion of spectral indices significantly improved BRT models, often by over 20%, and surprisingly their contribution mostly exceeded that of photosynthetically active radiation.

scholarly journals Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

2019 ◽  
Vol 11 (6) ◽  
pp. 617 ◽  
Author(s):  
Sidrah Hafeez ◽  
Man Wong ◽  
Hung Ho ◽  
Majid Nazeer ◽  
Janet Nichol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Satellite Data ◽  
Coastal Waters ◽  
Machine Learning Techniques ◽  
Blue Band ◽  
Chl A ◽  
Learning Techniques ◽  
Reflectance Data

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.

scholarly journals Estimation of Size-Fractionated Primary Production from Satellite Ocean Colour in UK Shelf Seas

2018 ◽  
Vol 10 (9) ◽  
pp. 1389 ◽  
Author(s):  
Kieran Curran ◽  
Robert Brewin ◽  
Gavin Tilstone ◽  
Heather Bouman ◽  
Anna Hickman
Keyword(s):  
Primary Production ◽  
Ocean Model ◽  
Open Ocean ◽  
Photosynthetic Parameters ◽  
Ocean Colour ◽  
Chl A ◽  
In Situ Data ◽  
Shelf Seas ◽  
Shelf Sea

Satellite ocean-colour based models of size-fractionated primary production (PP) have been developed for the oceans on a global level. Uncertainties exist as to whether these models are accurate for temperate Shelf seas. In this paper, an existing ocean-colour based PP model is tuned using a large in situ database of size-fractionated measurements from the Celtic Sea and Western English Channel of chlorophyll-a (Chl a) and the photosynthetic parameters, the maximum photosynthetic rate ( P m B ) and light limited slope ( α B ). Estimates of size fractionated PP over an annual cycle in the UK shelf seas are compared with the original model that was parameterised using in situ data from the open ocean and a climatology of in situ PP from 2009 to 2015. The Shelf Sea model captured the seasonal patterns in size-fractionated PP for micro- and picophytoplankton, and generally performed better than the original open ocean model, except for nanophytoplankton PP which was over-estimated. The overestimation in PP is in part due to errors in the parameterisation of the biomass profile during summer, stratified conditions. Compared to the climatology of in situ data, the shelf sea model performed better when phytoplankton biomass was high, but overestimated PP at low Chl a.

scholarly journals Synergy of Satellite, In Situ and Modelled Data for Addressing the Scarcity of Water Quality Information for Eutrophication Assessment and Monitoring of Swedish Coastal Waters

2019 ◽  
Vol 11 (17) ◽  
pp. 2051 ◽  
Author(s):  
Susanne Kratzer ◽  
Dmytro Kyryliuk ◽  
Moa Edman ◽  
Petra Philipson ◽  
Steve Lyon
Keyword(s):  
Satellite Data ◽  
Secchi Depth ◽  
Monitoring Data ◽  
Zone Model ◽  
Monthly Basis ◽  
Chl A ◽  
In Situ Data ◽  
Baltic Proper ◽  
The Baltic

Monthly CHL-a and Secchi Depth (SD) data derived from the full mission data of the Medium Resolution Imaging Spectrometer (MERIS; 2002–2012) were analysed along a horizontal transect from the inner Bråviken bay and out into the open sea. The CHL-a values were calibrated using an algorithm derived from Swedish lakes. Then, calibrated Chl-a and Secchi Depth (SD) estimates were extracted from MERIS data along the transect and compared to conventional monitoring data as well as to data from the Swedish Coastal zone Model (SCM), providing physico-biogeochemical parameters such as temperature, nutrients, Chlorophyll-a (CHL-a) and Secchi depth (SD). A high negative correlation was observed between satellite-derived CHL-a and SD (ρ = −0.91), similar to the in situ relationship established for several coastal gradients in the Baltic proper. We also demonstrate that the validated MERIS-based estimates and data from the SCM showed strong correlations for the variables CHL-a, SD and total nitrogen (TOTN), which improved significantly when analysed on a monthly basis across basins. The relationship between satellite-derived CHL-a and modelled TOTN was also evaluated on a monthly basis using least-square linear regression models. The predictive power of the models was strong for the period May-November (R2: 0.58–0.87), and the regression algorithm for summer was almost identical to the algorithm generated from in situ data in Himmerfjärden bay. The strong correlation between SD and modelled TOTN confirms that SD is a robust and reliable indicator to evaluate changes in eutrophication in the Baltic proper which can be assessed using remote sensing data. Amongst all three assessed methods, only MERIS CHL-a was able to correctly depict the pattern of phytoplankton phenology that is typical for the Baltic proper. The approach of combining satellite data and physio-biogeochemical models could serve as a powerful tool and value-adding complement to the scarcely available in situ data from national monitoring programs. In particular, satellite data will help to reduce uncertainties in long-term monitoring data due to its improved measurement frequency.

scholarly journals Link or sink: a modelling interpretation of the open Baltic biogeochemistry

2004 ◽  
Vol 1 (1) ◽  
pp. 219-274 ◽  
Author(s):  
M. Vichi ◽  
P. Ruardij ◽  
J. W. Baretta
Keyword(s):  
Organic Matter ◽  
Primary Production ◽  
Water Column ◽  
Nutrient Dynamics ◽  
Net Primary Production ◽  
Meteorological Data ◽  
Euphotic Zone ◽  
Nutrient Loads ◽  
Intermediate Waters ◽  
The Baltic

Abstract. A 1-D model system, consisting of the 1-D version of the Princeton Ocean Model (POM) coupled with the European Regional Seas Ecosystem Model (ERSEM) has been applied to a sub-basin of the Baltic Proper, the Bornholm basin. The model has been forced with 3h meteorological data for the period 1979-1990, producing a 12-year hindcast validated with datasets from the Baltic Environmental Database for the same period. The model results demonstrate the model to hindcast the time-evolution of the physical structure very well, confirming the view of the open Baltic water column as a three layer system of surface, intermediate and bottom waters. Comparative analyses of modelled hydrochemical components with respect to the independent data have shown that the long-term system behaviour of the model is within the observed ranges. Also primary production processes, deduced from oxygen (over)saturation are hindcast correctly over the entire period and the annual net primary production is within the observed range. The largest mismatch with observations is found in simulating the biogeochemistry of the Baltic intermediate waters. Modifications in the structure of the model (addition of fast-sinking detritus and polysaccharide dynamics) have shown that the nutrient dynamics is linked to the quality and dimensions of the organic matter produced in the euphotic zone, highlighting the importance of the residence time of the organic matter within the microbial foodweb in the intermediate waters. Experiments with different scenarios of riverine nutrient loads, assessed in the limits of a 1-D setup, have shown that the external input of organic matter makes the open Baltic model more heterotrophic. The characteristics of the inputs also drive the dynamics of nitrogen in the bottom layers leading either to nitrate accumulation (when the external sources are inorganic), or to coupled nitrification-denitrification (under strong organic inputs). The model indicates the permanent stratification to be the main feature of the system as regulator of carbon and nutrient budgets. The model predicts that most of the carbon produced in the euphotic zone is also consumed in the water column and this enhances the importance of heterotrophic benthic processes as final closures of carbon and nutrient cycles in the open Baltic.

scholarly journals Seasonal and Spatial Changes of Primary Production in the Baltic Sea (Europe) Based on in situ Measurements in the Period of 1993–2018

2021 ◽  
Vol 7 ◽  
Author(s):  
Agnieszka Zdun ◽  
Joanna Stoń-Egiert ◽  
Dariusz Ficek ◽  
Mirosława Ostrowska
Keyword(s):  
Primary Production ◽  
Baltic Sea ◽  
Spring Bloom ◽  
Gulf Of Gdańsk ◽  
The Baltic Sea ◽  
Production Values ◽  
Spatial Changes ◽  
Gulf Of Gdansk ◽  
The Baltic

The seasonal and spatial variability of primary production (PP) measured using 14C method in two regions: open waters of the Baltic Sea and the Gulf of Gdansk were discussed. The statistical analyses of 26-years dataset (from 1993 to 2018) allow to confirm some regularities of productivity and find some features resulting mainly from changing environmental conditions like solar insolation, temperature, and chlorophyll a concentration. In the dataset, production values varied from 0.005 to 7.8 g C m–2 day–1 in open waters and from 0.07 to 12.9 g C m–2 day–1 in the Gulf of Gdansk. Analysis showed that PP in open waters were 6–17% lower than in Gulf of Gdansk in most of the cases. In both regions, the periods of intense productivity in spring and autumn were observed, but vegetation begins a month earlier in the Gulf of Gdansk than in open waters. Probably the accumulation of nutrients after the winter causes the spring bloom (April–May) in both regions to be more intense (even two times higher) than the autumn bloom (September–October) associated with favorable hydrological conditions resulting from summer insolation. The presented results showed slight downward trends in productivity in both regions, the most visible in the spring in the Bay of Gdansk. This confirms the recent reports on a possible improvement in the eutrophication state of the Baltic Sea.

scholarly journals Link or sink: a modelling interpretation of the open Baltic biogeochemistry

2004 ◽  
Vol 1 (1) ◽  
pp. 79-100 ◽  
Author(s):  
M. Vichi ◽  
P. Ruardij ◽  
J. W. Baretta
Keyword(s):  
Organic Matter ◽  
Primary Production ◽  
Water Column ◽  
Nutrient Dynamics ◽  
Net Primary Production ◽  
Meteorological Data ◽  
Euphotic Zone ◽  
Nutrient Loads ◽  
Intermediate Waters ◽  
The Baltic

Abstract. A 1-D model system, consisting of the 1-D version of the Princeton Ocean Model (POM) coupled with the European Regional Seas Ecosystem Model (ERSEM) has been applied to a sub-basin of the Baltic Proper, the Bornholm basin. The model has been forced with 3h meteorological data for the period 1979-1990, producing a 12-year hindcast validated with datasets from the Baltic Environmental Database for the same period. The model results demonstrate the model to hindcast the time-evolution of the physical structure very well, confirming the view of the open Baltic water column as a three layer system of surface, intermediate and bottom waters. Comparative analyses of modelled hydrochemical components with respect to the independent data have shown that the long-term system behaviour of the model is within the observed ranges. Also primary production processes, deduced from oxygen (over)saturation are hindcast correctly over the entire period and the annual net primary production is within the observed range. The largest mismatch with observations is found in simulating the biogeochemistry of the Baltic intermediate waters. Modifications in the structure of the model (addition of fast-sinking detritus and polysaccharide dynamics) have shown that the nutrient dynamics are linked to the quality and dimensions of the organic matter produced in the euphotic zone, highlighting the importance of the residence time of the organic matter within the microbial foodweb in the intermediate waters. Experiments with different scenarios of riverine nutrient loads, assessed in the limits of a 1-D setup, have shown that the external input of organic matter makes the open Baltic model more heterotrophic. The characteristics of the inputs also drive the dynamics of nitrogen in the bottom layers leading either to nitrate accumulation (when the external sources are inorganic), or to coupled nitrification-denitrification (under strong organic inputs). The model indicates the permanent stratification to be the main feature of the system as regulator of carbon and nutrient budgets. The model predicts that most of the carbon produced in the euphotic zone is also consumed in the water column and this enhances the importance of heterotrophic benthic processes as final closure of carbon and nutrient cycles in the open Baltic.

scholarly journals Validation of a Primary Production Algorithm of Vertically Generalized Production Model Derived from Multi-Satellite Data around the Waters of Taiwan

2020 ◽  
Vol 12 (10) ◽  
pp. 1627
Author(s):  
Kuo-Wei Lan ◽  
Li-Jhih Lian ◽  
Chun-Huei Li ◽  
Po-Yuan Hsiao ◽  
Sha-Yan Cheng
Keyword(s):  
Primary Production ◽  
Production Model ◽  
Dark Bottle ◽  
Kuroshio Water ◽  
Chl A ◽  
Basin Scale ◽  
Mean Square Difference ◽  
The Kuroshio ◽  
China Coast

Basin-scale sampling for high frequency oceanic primary production (PP) is available from satellites and must achieve a strong match-up with in situ observations. This study evaluated a regionally high-resolution satellite-derived PP using a vertically generalized production model (VGPM) with in situ PP. The aim was to compare the root mean square difference (RMSD) and relative percent bias (Bias) in different water masses around Taiwan. Determined using light–dark bottle methods, the spatial distribution of VGPM derived from different Chl-a data of MODIS Aqua (PPA), MODIS Terra (PPT), and averaged MODIS Aqua and Terra (PPA&T) exhibited similar seasonal patterns with in situ PP. The three types of satellite-derived PPs were linearly correlated with in situ PPs, the coefficients of which were higher throughout the year in PPA&T (r2 = 0.61) than in PPA (r2 = 0.42) and PPT (r2 = 0.38), respectively. The seasonal RMSR and bias for the satellite-derived PPs were in the range of 0.03 to 0.09 and −0.14 to −0.39, respectively, which suggests the PPA&T produces slightly more accurate PP measurements than PPA and PPT. On the basis of environmental conditions, the subareas were further divided into China Coast water, Taiwan Strait water, Northeastern upwelling water, and Kuroshio water. The VPGM PP in the four subareas displayed similar features to Chl-a variations, with the highest PP in the China Coast water and lowest PP in the Kuroshio water. The RMSD was higher in the Kuroshio water with an almost negative bias. The PPA exhibited significant correlations with in situ PP in the subareas; however, the sampling locations were insufficient to yield significant results in the China Coast water.

scholarly journals Preliminary Investigation on Phytoplankton Dynamics and Primary Production Models in an Oligotrophic Lake from Remote Sensing Measurements

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5072
Author(s):  
Ilaria Cesana ◽  
Mariano Bresciani ◽  
Sergio Cogliati ◽  
Claudia Giardino ◽  
Remika Gupana ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Preliminary Investigation ◽  
Fluorescence Emission ◽  
Phytoplankton Primary Production ◽  
Chl A ◽  
Production Models ◽  
Solar Noon ◽  
Lake Waters

The aim of this study is to test a series of methods relying on hyperspectral measurements to characterize phytoplankton in clear lake waters. The phytoplankton temporal evolutions were analyzed exploiting remote sensed indices and metrics linked to the amount of light reaching the target (EPAR), the chlorophyll-a concentration ([Chl-a]OC4) and the fluorescence emission proxy. The latter one evaluated by an adapted version of the Fluorescence Line Height algorithm (FFLH). A peculiar trend was observed around the solar noon during the clear sky days. It is characterized by a drop of the FFLH metric and the [Chl-a]OC4 index. In addition to remote sensed parameters, water samples were also collected and analyzed to characterize the water body and to evaluate the in-situ fluorescence (FF) and absorbed light (FA). The relations between the remote sensed quantities and the in-situ values were employed to develop and test several phytoplankton primary production (PP) models. Promising results were achieved replacing the FA by the EPAR or FFLH in the equation evaluating a PP proxy (R2 > 0.65). This study represents a preliminary outcome supporting the PP monitoring in inland waters by means of remote sensing-based indices and fluorescence metrics.

scholarly journals VERTICAL DISTRIBUTION OF CHLOROPHYLL-A BASED ON NEURAL NETWORK

2010 ◽  
Vol 2 (-) ◽  
Author(s):  
TAKAHIRO OSAWA ◽  
CHAO FANG ZHAO ◽  
I WAYAN Nuarsa ◽  
I KETUT SWARDIKA ◽  
YASUHIRO SUGIMORI
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vertical Distribution ◽  
Primary Production ◽  
Chlorophyll A ◽  
Ocean Color ◽  
Chl A ◽  
In Situ Data ◽  
Artificial Neural

An algorithm of estimating Vertical distribution of Chlorophyll-a (Chl-a) was evaluated based on Artificial Neural Networks (ANN) method in Hokkaido field in the northwest of Pacific Ocean. The algorithm applied to the data of SeaWiFS on OrbView-2 and AVHRR on NOAA off Hokkaido, has been applied on September 24, 1998 and September 28, 2001. Ocean color sensor provides the information of the photosynthetic pigment concentration for the upper 22% of the euphotic zone. In order to model a primary production in the water column derived from satellite, it is important to obtain the vertical profile of Chl-a distribution, because the maximum value of Chl-a concentration used to lie in the subsurface region. A shifted Gaussian model has been proposed to describe the variation of the chlorophyll-a (Chl-a) profile which consists of four parameters, i.e. background biomass (B0), maximum depth of Chl-a (zm), total biomass in the peak (h), and a measurement of the thickness or vertical scale of the peak (cr). However, these parameters are not easy to be determined directly from satellite data. Therefore, in the present study, an ANN methodology is used. Using in-situ data from 1974 to 1994 around Japan Islands, the above four parameters are calculated to derive the Chl-a concentration, sea surface temperature, mixed layer depth, latitude, longitude, and Julian days. The total of 6983 profiles of Chl-a and temperature are used for ANN. The correlation coefficients of these parameters are 0.79 (B0), 0.73 (h), 0.76 (cr) and 0.79 (zm) respectively. A site called A-linc off Hokkaido is used to evaluate Chl-a concentration in each depth. After comparing with in-situ data and ANN model, the results show good agreement relatively. Therefore, the ANN method is applicable and available tool to estimate primary production and fish resources from the space. Keywords : Ocean color, Chlorophyll-a (Chl-a), Vertical structure, Artificial Neural Networks (ANN).

scholarly journals Consistency between Satellite Ocean Colour Products under High Coloured Dissolved Organic Matter Absorption in the Baltic Sea

2021 ◽  
Vol 14 (1) ◽  
pp. 89
Author(s):  
Gavin H. Tilstone ◽  
Silvia Pardo ◽  
Stefan G. H. Simis ◽  
Ping Qin ◽  
Nick Selmes ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Atmospheric Correction ◽  
Relative Difference ◽  
Global Ocean ◽  
Ocean Colour ◽  
The Baltic Sea ◽  
Chl A ◽  
The Baltic

Ocean colour (OC) remote sensing is an important tool for monitoring phytoplankton in the global ocean. In optically complex waters such as the Baltic Sea, relatively efficient light absorption by substances other than phytoplankton increases product uncertainty. Sentinel-3 OLCI-A, Suomi-NPP VIIRS and MODIS-Aqua OC radiometric products were assessed using Baltic Sea in situ remote sensing reflectance (Rrs) from ferry tracks (Alg@line) and at two Aerosol Robotic Network for Ocean Colour (AERONET-OC) sites from April 2016 to September 2018. A range of atmospheric correction (AC) processors for OLCI-A were evaluated. POLYMER performed best with <23 relative % difference at 443, 490 and 560 nm compared to in situ Rrs and 28% at 665 nm, suggesting that using this AC for deriving Chl a will be the most accurate. Suomi-VIIRS and MODIS-Aqua underestimated Rrs by 35, 29, 22 and 39% and 34, 22, 17 and 33% at 442, 486, 560 and 671 nm, respectively. The consistency between different AC processors for OLCI-A and MODIS-Aqua and VIIRS products was relatively poor. Applying the POLYMER AC to OLCI-A, MODIS-Aqua and VIIRS may produce the most accurate Rrs and Chl a products and OC time series for the Baltic Sea.

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