scholarly journals Enhanced chlorophyll-a concentration in the wake of Sable Island, eastern Canada, revealed by two decades of satellite observations: a response to grey seal population dynamics?

2021 ◽  
Author(s):  
Emmanuel Devred ◽  
Andrea Hilborn ◽  
Cornelia den Heyer
Keyword(s):  
Time Series ◽  
Chlorophyll A ◽  
Phytoplankton Biomass ◽  
Satellite Observations ◽  
Eastern Canada ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Seal Population ◽  
Good Agreement

Abstract. Elevated surface chlorophyll-a concentration, an index of phytoplankton biomass, has been previously observed and documented by remote sensing in the waters to the southwest of Sable Island (SI) on the Scotian Shelf in eastern Canada. Here, we present a detailed analysis of this phenomenon using a 20-year time series of satellite-derived chlorophyll-a concentration (chl-a), paired with information on the particle backscattering coefficient at 443 nm (bbp(443)) and the detritus/gelbstoff absorption coefficient at 443 nm (adg(443) ) in an attempt to explain the possible mechanisms that lead to the increase in surface biomass in the surroundings of SI. We compared the seasonal cycle, climatology and trends of surface waters near SI to two control regions located both upstream and downstream of the island, away from terrigenous inputs. Application of the self-organizing maps approach (SOMs) to the time series of satellite-derived chl-a over the Scotian Shelf revealed the annual spatio-temporal patterns around SI and, in particular, persistently high phytoplankton biomass during winter and spring in the leeward side of SI, a phenomenon that is not observed in the control boxes. Time series analysis of the satellite archive evidenced a long-term increase in chl-a and adg(443), and a long-term decrease in bbp(443) in all regions. In the close vicinity of SI, the increase of chl-a and adg(443) during the winter months occurred at a rate twice that of the ones observed in the control boxes. In addition to the increase of the chl-a and adg(443) within the plume southward of SI, the surface area of the plume itself has also expanded by a factor of five over the last 20 years. While the island mass effect (IME) is certainly contributing to the enhanced biomass around SI, we hypothesize that the large increase in chl-a over the last 20 years is due to an injection of nutrients by the island’s grey seal colony, which has increased by about 300 % over the last twenty years. The contribution of nutrients from seals may sustain high phytoplankton biomass at a time of year when it is usually low. A conceptual model was developed to describe the annual variation of seal abundance on SI and estimate the standing stock of chl-a concentration that can be sustained by the release of nitrogen. Comparison between satellite observations and model simulations showed a very good agreement between the seal population increase on SI during the breeding season and the phytoplankton biomass increase during the winter. In addition, the 20-year satellite-derived trend in chlorophyll-a concentration showed a good agreement with the increasing trend in seal population on SI during the same time period. The satellite data analysis supports the concept of top-down control of marine mammals over lower trophic levels through a fertilisation mechanism, although these results could not be confirmed without in situ measurements for ground truthing. Our findings challenge the idea that the IME is restricted to islands with strong bathymetric slope located in oligotrophic waters of mid-latitudes and tropics, and demonstrate that enhanced marine production can occur in other oceanic regions, with potentially substantial implications for conservation and fisheries.

scholarly journals Enhanced chlorophyll-<i>a</i> concentration in the wake of Sable Island, eastern Canada, revealed by two decades of satellite observations: a response to grey seal population dynamics?

2021 ◽  
Vol 18 (23) ◽  
pp. 6115-6132
Author(s):  
Emmanuel Devred ◽  
Andrea Hilborn ◽  
Cornelia Elizabeth den Heyer
Keyword(s):  
Time Series ◽  
Phytoplankton Biomass ◽  
Standing Stock ◽  
Satellite Observations ◽  
Trophic Levels ◽  
Leeward Side ◽  
Grey Seal ◽  
Scotian Shelf ◽  
Eastern Canada ◽  
Chl A

Abstract. Elevated surface chlorophyll-a (chl-a) concentration ([chl-a]), an index of phytoplankton biomass, has been previously observed and documented by remote sensing in the waters to the southwest of Sable Island (SI) on the Scotian Shelf in eastern Canada. Here, we present an analysis of this phenomenon using a 21-year time series of satellite-derived [chl-a], paired with information on the particle backscattering coefficient at 443 nm (bbp(443), a proxy for particle suspension) and the detritus/gelbstoff absorption coefficient at 443 nm (adg(443), a proxy to differentiate water masses and presence of dissolved organic matter) in an attempt to explain some possible mechanisms that lead to the increase in surface biomass in the surroundings of SI. We compared the seasonal cycle, 8 d climatology and seasonal trends of surface waters near SI to two control regions located both upstream and downstream of the island, away from terrigenous inputs. Application of the self-organising map (SOM) approach to the time series of satellite-derived [chl-a] over the Scotian Shelf revealed the annual spatio-temporal patterns around SI and, in particular, persistently high phytoplankton biomass during winter and spring in the leeward side of SI, a phenomenon that was not observed in the control boxes. In the vicinity of SI, a significant increase in [chl-a] and adg(443) during the winter months occurred at a rate twice that of the ones observed in the control boxes, while no significant trends were found for the other seasons. In addition to the increase in [chl-a] and adg(443) within the plume southwest of SI, the surface area of the plume itself expanded by a factor of 5 over the last 21 years. While the island mass effect (IME) explained the enhanced biomass around SI, we hypothesised that the large increase in [chl-a] over the last 21 years was partly due to an injection of nutrients by the island's grey seal colony, which has increased by 200 % during the same period. This contribution of nutrients from seals may sustain high phytoplankton biomass at a time of year when it is usually low following the fall bloom. A conceptual model was developed to estimate the standing stock of chl-a that can be sustained by the release of nitrogen (N) by seals. Comparison between satellite observations and model simulations showed a good temporal agreement between the increased abundance of seal on SI during the breeding season and the phytoplankton biomass increase during the winter. We found that about 20 % of chl-a standing stock increase over the last 21 years could be due to seal N fertilisation, the remaining being explained by climate forcing and oceanographic processes. Although without in situ measurements for ground truthing, the satellite data analysis provided evidence of the impact of marine mammals on lower trophic levels through a fertilisation mechanism that is coupled with the IME with potential implications for conservation and fisheries.

scholarly journals Seasonal Cycles of Phytoplankton Expressed by Sine Equations Using the Daily Climatology from Satellite-Retrieved Chlorophyll-a Concentration (1997–2019) Over Global Ocean

2020 ◽  
Vol 12 (16) ◽  
pp. 2662 ◽  
Author(s):  
Zexi Mao ◽  
Zhihua Mao ◽  
Cédric Jamet ◽  
Marc Linderman ◽  
Yuntao Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Chlorophyll A ◽  
Seasonal Variations ◽  
Global Ocean ◽  
Boreal Winter ◽  
Seasonal Cycles ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Global Oceans ◽  
Sine Equation

The global coverage of Chlorophyll-a concentration (Chl-a) has been continuously available from ocean color satellite sensors since September 1997 and the Chl-a data (1997–2019) were used to produce a climatological dataset by averaging Chl-a values at same locations and same day of year. The constructed climatology can remarkably reduce the variability of satellite data and clearly exhibit the seasonal cycles, demonstrating that the growth and decay of phytoplankton recurs with similarly seasonal cycles year after year. As the shapes of time series of the climatology exhibit strong periodical change, we wonder whether the seasonality of Chl-a can be expressed by a mathematic equation. Our results show that sinusoid functions are suitable to describe cyclical variations of data in time series and patterns of the daily climatology can be matched by sine equations with parameters of mean, amplitude, phase, and frequency. Three types of sine equations were used to match the climatological Chl-a with Mean Relative Differences (MRD) of 7.1%, 4.5%, and 3.3%, respectively. The sine equation with four sinusoids can modulate the shapes of the fitted values to match various patterns of climatology with small MRD values (less than 5%) in about 90% of global oceans. The fitted values can reflect an overall pattern of seasonal cycles of Chl-a which can be taken as a time series of biomass baseline for describing the state of seasonal variations of phytoplankton. The amplitude images, the spatial patterns of seasonal variations of phytoplankton, can be used to identify the transition zone chlorophyll fronts. The timing of phytoplankton blooms is identified by the biggest peak of the fitted values and used to classify oceans as different bloom seasons, indicating that blooms occur in all four seasons with regional features. In global oceans within latitude domains (48°N–48°S), blooms occupy approximately half of the ocean (50.6%) during boreal winter (December–February) in the northern hemisphere and more than half (58.0%) during austral winter (June–August) in the southern hemisphere. Therefore, the sine equation can be used to match the daily Chl-a climatology and the fitted values can reflect the seasonal cycles of phytoplankton, which can be used to investigate the underlying phenological characteristics.

scholarly journals Temporal variability of 10-year global SeaWiFS time-series of phytoplankton chlorophyll a concentration

2009 ◽  
Vol 66 (7) ◽  
pp. 1547-1556 ◽  
Author(s):  
V. Vantrepotte ◽  
F. Mélin
Keyword(s):  
Time Series ◽  
Chlorophyll A ◽  
Temporal Variability ◽  
Global Scale ◽  
Wide Field ◽  
Seasonal Component ◽  
Ocean Colour ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
The Stability

Abstract Vantrepotte, V., and Mélin, F. 2009. Temporal variability of 10-year global SeaWiFS time-series of phytoplankton chlorophyll a concentration. – ICES Journal of Marine Science, 66: 1547–1556. The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) global dataset now offers a 10-year time-series of a consistent, well-calibrated, ocean colour record suitable to analyse temporal variability. The relative importance of the seasonal term in the chlorophyll a (Chl a) concentration signal is first assessed using statistical techniques of temporal decomposition. The emphasis is on the Census method II (X-11) approach, which allows year-to-year variations in the seasonal component. The seasonality detected in the SeaWiFS Chl a record is analysed through a generic province-based classification of marine ecosystems and at global scale and is found very variable spatially. Working with 5′-resolution gridded Chl a products, the contribution of the seasonal component derived from X-11 amounts to 64% of the total variance, compared with only 36% if a fixed annual cycle is assumed. The capacity of X-11 to capture interannual variations in seasonality is used to diagnose the stability of the Chl a seasonal cycle. Finally, linear changes in Chl a concentration observed after a decade of continuous ocean colour record agree globally with previous observations on shorter series. Significant changes of both signs are detected in various regions of the world’s oceans, but primarily a general decrease of Chl a in the mid-ocean gyres.

scholarly journals Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi-sensor ocean color and sea surface temperature products

2021 ◽  
Author(s):  
Hongyan Xi ◽  
Svetlana N. Losa ◽  
Antoine Mangin ◽  
Philippe Garnesson ◽  
Marine Bretagnon ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Input Data ◽  
Ocean Color ◽  
Satellite Observations ◽  
Sea Surface ◽  
Chl A ◽  
Functional Types

&lt;p&gt;With the extensive use of ocean color (OC) satellite products, diverse algorithms have been developed in the past decades to observe the phytoplankton community structure in terms of functional types, taxonomic groups and size classes. There is a need to combine satellite observations and biogeochemical modelling to enable comprehensive phytoplankton groups time series data and predictions under the changing climate. A prerequisite for this is continuous long-term satellite observations from past and current OC sensors with quantified uncertainties are essential to ensure their application. Previously we have configured an approach, namely OLCI-PFT (v1), to globally retrieve total chlorophyll a concentration (TChl-a), and chlorophyll a concentration (Chl-a) of multiple phytoplankton functional types (PFTs). This algorithm is developed based on empirical orthogonal functions (EOF) using satellite remote sensing reflectance (Rrs) products from the GlobColour archive (https://www.globcolour.info/). The algorithm can be applied to both, merged OC products and Sentinel 3A OLCI data. Global PFT Chl-a products of OLCI-PFT v1 are available on CMEMS under Ocean Products since July 2020. Lately we have updated the approach and established the OLCI-PFT v2 by including sea surface temperature (SST) as input data. The updated version delivers improved global products for the aforementioned PFT quantities. The per-pixel uncertainty of the retrieved TChl-a and PFT Chl-a products is estimated and validated by taking into account the uncertainties from both input data (satellite Rrs and SST) and model parameters through Monte Carlo simulations and analytical error propagation. The uncertainty of the OLCI-PFT products v2 was assessed on a global scale. For PFT Chl-a products this has been done for the first. The uncertainty of OLCI-PFT v2 TChl-a product is in general much lower than that of the TChl-a product generated in the frame of the ESA Ocean Colour Climate Change Initiative project (OC-CCI). The OLCI-PFT algorithm v1 and v2 have also been further adapted to use a merged MODIS-VIRRS input. Good consistency has been found between the OLCI-PFT products derived from using input data from the different OC sensors. This sets the ground to realize long-term continuous satellite global PFT products from OLCI-PFT. Satellite PFT uncertainty, as provided for our products, is essential to evaluate and improve coupled ecosystem-ocean models which simulate PFTs, and furthermore can be used to improve these models directly via data assimilation.&lt;/p&gt;

scholarly journals Phytoplankton Phenology in the Coastal Zone of Cyprus, Based on Remote Sensing and In Situ Observations

2021 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Monica Demetriou ◽  
Dionysios E. Raitsos ◽  
Antonia Kournopoulou ◽  
Manolis Mandalakis ◽  
Spyros Sfenthourakis ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Phytoplankton Biomass ◽  
Trophic Levels ◽  
Data Availability ◽  
Ocean Colour ◽  
Chl A ◽  
In Situ Data

Alterations in phytoplankton biomass, community structure and timing of their growth (phenology), are directly implicated in the carbon cycle and energy transfer to higher trophic levels of the marine food web. Due to the lack of long-term in situ datasets, there is very little information on phytoplankton seasonal succession in Cyprus (eastern Mediterranean Sea). On the other hand, satellite-derived measurements of ocean colour can only provide long-term time series of chlorophyll (an index of phytoplankton biomass) up to the first optical depth (surface waters). The coupling of both means of observations is essential for understanding phytoplankton dynamics and their response to environmental change. Here, we use 23 years of remotely sensed, regionally tuned ocean-colour observations, along with a unique time series of in situ phytoplankton pigment composition data, collected in coastal waters of Cyprus during 2016. The satellite observations show an initiation of phytoplankton growth period in November, a peak in February and termination in April, with an overall mean duration of ~4 months. An in-depth exploration of in situ total Chl-a concentration and phytoplankton pigments revealed that pico- and nano-plankton cells dominated the phytoplankton community. The growth peak in February was dominated by nanophytoplankton and potentially larger diatoms (pigments of 19’ hexanoyloxyfucoxanthin and fucoxanthin, respectively), in the 0–20 m layer. The highest total Chl-a concentration was recorded at a station off Akrotiri peninsula in the south, where strong coastal upwelling has been reported. Another station in the southern part, located next to a fish farm, showed a higher contribution of picophytoplankton during the most oligotrophic period (summer). Our results highlight the importance of using available in situ data coupled to ocean-colour remote sensing, for monitoring marine ecosystems in areas with limited in situ data availability.

Environmental factors associated with long-term changes in chlorophyll-<b><i>a</i></b> concentration in the Amazon floodplain

2011 ◽  
Vol 8 (2) ◽  
pp. 3739-3770 ◽  
Author(s):  
E. Alcântara ◽  
E. M. Novo ◽  
C. F. Barbosa ◽  
M.-P. Bonnet ◽  
J. Stech ◽  
...  
Keyword(s):  
Time Series ◽  
Power Spectrum ◽  
Water Level ◽  
Chlorophyll A ◽  
Wave Radiation ◽  
Significant Variable ◽  
Chlorophyll A Concentration ◽  
Time Frequency ◽  
The Relationship

Abstract. Long-term environmental time series of continuously collected data are fundamental to identify and classify pulses and determine their role in aquatic systems. This paper presents in situ daily mean chlorophyll-a concentration time series, key information for the current understanding of carbon fluxes in and out of the Amazonian floodplain system. This paper also investigates how seasonal fluctuations in water level affect the relationship between chlorophyll-a concentration and some of its controlling limnological (water level, water surface temperature, pH and turbidity) and meteorological (wind intensity, relative humidity and short wave radiation) variables provided by an automatic monitoring system (Integrated System for Environmental Monitoring-SIMA) deployed at Curai Lake. The data are collected in preprogrammed time interval (1 h) and are transmitted by satellite in quasi-real time for any user in a range of 2500 km from the acquisition point. We used Pearson correlation to determine the quantitative relationship between chlorophyll-a time series and others environmental parameters. Fourier power spectrum analyses were applied to identify periods of high variability in chlorophyll-a time series and wavelet power spectrum analyses helped to characterize their time-frequency structure. To further investigate the relationship between chlorophyll-a and the statistically significant variable highlighted by Pearson's correlation, the set of time series was submitted to cross wavelet analysis. The time series of chlorophyll-a shows two high peaks (47 μg L−1 and 53.30 μg L−1) of concentration during a year: first during the rising water and second during the low water level. A small peak was observed during the high water level (10 μg L−1). For the most part of rising, high and receding water level, the chlorophyll-a concentration is often low (from 2.20 μg L−1 to 9.10 μg L−1). chlorophyll-a concentration displays periodicities ranging from 2–60 days, with a coherence of approximately 1 in phase with water level during the rising and low water period. Water level dynamics and turbidity explain 68% of the chlorophyll-a time series variability.

scholarly journals Spatiotemporal Variability of Surface Phytoplankton Carbon and Carbon-to-Chlorophyll a Ratio in the South China Sea Based on Satellite Data

2020 ◽  
Vol 13 (1) ◽  
pp. 30
Author(s):  
Wenlong Xu ◽  
Guifen Wang ◽  
Long Jiang ◽  
Xuhua Cheng ◽  
Wen Zhou ◽  
...  
Keyword(s):  
South China Sea ◽  
Chlorophyll A ◽  
South China ◽  
Satellite Data ◽  
Phytoplankton Biomass ◽  
The South China Sea ◽  
Spatiotemporal Variability ◽  
The South ◽  
Chl A ◽  
China Sea

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.

scholarly journals Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 664
Author(s):  
Yun Xue ◽  
Lei Zhu ◽  
Bin Zou ◽  
Yi-min Wen ◽  
Yue-hong Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Chlorophyll A ◽  
Language Processing ◽  
Water Bodies ◽  
Inversion Effect ◽  
Least Squares Regression ◽  
Chlorophyll A Concentration ◽  
Chl A

For Case-II water bodies with relatively complex water qualities, it is challenging to establish a chlorophyll-a concentration (Chl-a concentration) inversion model with strong applicability and high accuracy. Convolutional Neural Network (CNN) shows excellent performance in image target recognition and natural language processing. However, there little research exists on the inversion of Chl-a concentration in water using convolutional neural networks. Taking China’s Dongting Lake as an example, 90 water samples and their spectra were collected in this study. Using eight combinations as independent variables and Chl-a concentration as the dependent variable, a CNN model was constructed to invert Chl-a concentration. The results showed that: (1) The CNN model of the original spectrum has a worse inversion effect than the CNN model of the preprocessed spectrum. The determination coefficient (RP2) of the predicted sample is increased from 0.79 to 0.88, and the root mean square error (RMSEP) of the predicted sample is reduced from 0.61 to 0.49, indicating that preprocessing can significantly improve the inversion effect of the model.; (2) among the combined models, the CNN model with Baseline1_SC (strong correlation factor of 500–750 nm baseline) has the best effect, with RP2 reaching 0.90 and RMSEP only 0.45. The average inversion effect of the eight CNN models is better. The average RP2 reaches 0.86 and the RMSEP is only 0.52, indicating the feasibility of applying CNN to Chl-a concentration inversion modeling; (3) the performance of the CNN model (Baseline1_SC (RP2 = 0.90, RMSEP = 0.45)) was far better than the traditional model of the same combination, i.e., the linear regression model (RP2 = 0.61, RMSEP = 0.72) and partial least squares regression model (Baseline1_SC (RP2 = 0.58. RMSEP = 0.95)), indicating the superiority of the convolutional neural network inversion modeling of water body Chl-a concentration.

scholarly journals Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2192
Author(s):  
Xujie Yang ◽  
Yan Jiang ◽  
Xuwei Deng ◽  
Ying Zheng ◽  
Zhiying Yue
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll A ◽  
Dynamic Monitoring ◽  
Water Quality Parameter ◽  
Landsat Images ◽  
Chl A ◽  
Donghu Lake ◽  
Cubic Model

Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 − B3)/(B4 + B3) [(Green − Red)/(Green + Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 μg/L, respectively and those of the validation model were 0.831, 6.509% and 19.846μg/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that the TLI (∑) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005–2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 μg/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future.

scholarly journals Assessing the potential of integrating Landsat sensors for estimating chlorophyll-a concentration in a reservoir

2017 ◽  
Vol 49 (5) ◽  
pp. 1608-1617 ◽  
Author(s):  
Matias Bonansea ◽  
Claudia Rodriguez ◽  
Lucio Pinotti
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Landsat Imagery ◽  
Quality Parameters ◽  
Landsat 8 ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Sensing Applications ◽  
Wide Range ◽  
Quality Assessments

Abstract Landsat satellites, 5 and 7, have significant potential for estimating several water quality parameters, but to our knowledge, there are few investigations which integrate these earlier sensors with the newest and improved mission of Landsat 8 satellite. Thus, the comparability of water quality assessing across different Landsat sensors needs to be evaluated. The main objective of this study was to assess the feasibility of integrating Landsat sensors to estimate chlorophyll-a concentration (Chl-a) in Río Tercero reservoir (Argentina). A general model to retrieve Chl-a was developed (R2 = 0.88). Using observed versus predicted Chl-a values the model was validated (R2 = 0.89) and applied to Landsat imagery obtaining spatial representations of Chl-a in the reservoir. Results showed that Landsat 8 can be combined with Landsat 5 and 7 to construct an empirical model to estimate water quality characteristics, such as Chl-a in a reservoir. As the number of available and upcoming sensors with open access will increase with time, we expect that this trend will certainly further promote remote sensing applications and serve as a valuable basis for a wide range of water quality assessments.

Sign in / Sign up

Export Citation Format

Share Document