Spatial Variability and Detection Levels for Chlorophyll-A Estimates in High Latitude Lakes Using Landsat Imagery

Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. Satellite imagery can monitor algae biomass over large areas. The detection of chlorophyll a (chl-a) concentrations in small lakes is hindered by the low spatial resolution of conventional ocean colour satellites. The short time-series of the newest generation of space-borne sensors (e.g. Sentinel-2) is a bottleneck for assessing long-term trends. Although previous studies have evaluated the use of high-resolution sensors for assessing lakes' chl-a, it is still unclear how the spatial and temporal variability of chl-a concentration affect the performance of satellite estimates. We discuss the suitability of Landsat (LT) 30-m resolution imagery to assess lakes' chl-a concentrations under varying trophic conditions, across extensive high-latitude areas in Finland. We use in situ data obtained from field campaigns in 19 lakes and generate remote sensing estimates of chl-a, taking advantage of the long-time span of the LT 5 and 7 archives, from 1984 to 2017. Our results show that linear models based on LT data can explain approximately 50 % of the chl-a interannual variability. However, we demonstrate that the accuracy of the estimates is dependent on the lake's trophic state, with models performing in average twice as better in lakes with higher chl-a concentration (> 20 µg/l) in comparison with less eutrophic lakes. Finally, we demonstrate that linear models based on LT data can achieve high accuracy (R2 = 0.9; p-value < 0.05) in determining lakes' annual mean chl-a concentration, allowing the mapping of the trophic state of lakes across large regions. Given the long time-series and high spatial resolution, LT-based estimates of chl-a provide a tool for assessing the impacts of environmental change.

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Spatial Variability and Detection Levels for Chlorophyll-a Estimates in High Latitude Lakes Using Landsat Imagery

Remote Sensing ◽

10.3390/rs12182898 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2898

Author(s):

Filipe Lisboa ◽

Vanda Brotas ◽

Filipe Duarte Santos ◽

Sakari Kuikka ◽

Laura Kaikkonen ◽

...

Keyword(s):

Time Series ◽

Chlorophyll A ◽

Spatial Resolution ◽

High Latitude ◽

Trophic State ◽

Linear Models ◽

P Value ◽

Spatial And Temporal Variability ◽

Eutrophic Lakes ◽

Long Time

Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. While satellite imagery can monitor phytoplankton biomass using chlorophyll a (Chl) as a proxy over large areas, detection of Chl in small lakes is hindered by the low spatial resolution of conventional ocean color satellites. The short time-series of the newest generation of space-borne sensors (e.g., Sentinel-2) is a bottleneck for assessing long-term trends. Although previous studies have evaluated the use of high-resolution sensors for assessing lakes’ Chl, it is still unclear how the spatial and temporal variability of Chl concentration affect the performance of satellite estimates. We discuss the suitability of Landsat (LT) 30 m resolution imagery to assess lakes’ Chl concentrations under varying trophic conditions, across extensive high-latitude areas in Finland. We use in situ data obtained from field campaigns in 19 lakes and generate remote sensing estimates of Chl, taking advantage of the long-time span of the LT-5 and LT-7 archives, from 1984 to 2017. Our results show that linear models based on LT data can explain approximately 50% of the Chl interannual variability. However, we demonstrate that the accuracy of the estimates is dependent on the lake’s trophic state, with models performing in average twice as better in lakes with higher Chl concentration (>20 µg/L) in comparison with less eutrophic lakes. Finally, we demonstrate that linear models based on LT data can achieve high accuracy (R2 = 0.9; p-value < 0.05) in determining lakes’ mean Chl concentration, allowing the mapping of the trophic state of lakes across large regions. Given the long time-series and high spatial resolution, LT-based estimates of Chl provide a tool for assessing the impacts of environmental change.

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Simulation and Assessment of the Capabilities of Orbita Hyperspectral (OHS) Imagery for Remotely Monitoring Chlorophyll-a in Eutrophic Plateau Lakes

Remote Sensing ◽

10.3390/rs13142821 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2821

Author(s):

Runfei Zhang ◽

Zhubin Zheng ◽

Ge Liu ◽

Chenggong Du ◽

Chao Du ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Chlorophyll A ◽

Spatial Resolution ◽

Atmospheric Correction ◽

Dianchi Lake ◽

Inland Waters ◽

Eutrophic Lakes ◽

Correction Model ◽

Chl A

The chlorophyll-a (Chl-a) concentration of eutrophic lakes fluctuates significantly due to the disturbance of wind and anthropogenic activities on the water body. Consequently, estimation of the Chl-a concentration has become an immense challenge. Due to urgent demand and rapid development in high-resolution earth observation systems, it has become crucial to assess hyperspectral satellite imagery capabilities on inland water monitoring. The Orbita hyperspectral (OHS) satellite is the latest hyperspectral sensor with both high spectral and spatial resolution (2.5 nm and 10 m, respectively), which could provide great potential for remotely estimating the concentration of Chl-a for inland waters. However, there are still some deficiencies that are mainly manifested in the Chl-a concentration remote sensing retrieval model assessment and accuracy validation, as well as signal-to-noise ratio (SNR) estimation of OHS imagery for inland waters. Therefore, the radiometric performance of OHS imagery for water quality monitoring is evaluated in this study by comparing different atmospheric correction models and the SNR with several remote sensing images. Several crucial findings can be drawn: (1) the three-band model ((1/B15-1/B17)B19) developed by OHS imagery is most suitable for estimating the Chl-a concentration in Dianchi Lake, with the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of 15.55 µg/L and 16.31%, respectively; (2) the applicability of the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model for OHS imagery in a eutrophic plateau lake (Dianchi Lake) was better than the 6S (Second Simulation of Satellite Signal in the Solar Spectrum) model, and QUAC (Quick Atmospheric Correction) model, as well as the dark pixel method; (3) the SNR of the OHS imagery was similar to that of Hyperion imagery and was significantly higher than SNR of the HSI imagery; (4) the spatial resolution showed slight influence on the SNR of the OHS imagery. The results show that OHS imagery could be applied to remote sensing retrieval of Chl-a in eutrophic plateau lakes and presents a new tool for dynamic hyperspectral monitoring of water quality.

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Analysis of within- and between-day chlorophyll-a dynamics in Mantua Superior Lake, with a continuous spectroradiometric measurement

Marine and Freshwater Research ◽

10.1071/mf12229 ◽

2013 ◽

Vol 64 (4) ◽

pp. 303 ◽

Cited By ~ 27

Author(s):

M. Bresciani ◽

M. Rossini ◽

G. Morabito ◽

E. Matta ◽

M. Pinardi ◽

...

Keyword(s):

Chlorophyll A ◽

Continuous Monitoring ◽

Phytoplankton Growth ◽

Key Factors ◽

Eutrophic Lakes ◽

Chl A ◽

Metabolic Adaptations ◽

Horizontal Migration ◽

Radiometric System ◽

Growth Distribution

Eutrophic lakes display unpredictable patterns of phytoplankton growth, distribution, vertical and horizontal migration, likely depending on environmental conditions. Monitoring chlorophyll-a (Chl-a) concentration provides reliable information on the dynamics of primary producers if monitoring is conducted frequently. We present a practical approach that allows continuous monitoring of Chl-a concentration by using a radiometric system that measures optical spectral properties of water. We tested this method in a shallow, nutrient-rich lake in northern Italy, the Mantua Superior Lake, where the radiometric system collected data all throughout the day (i.e. every 5 min) for ~30 days. Here, specifically developed algorithms were used to convert water reflectance to Chl-a concentration. The best performing algorithm (R2 = 0.863) was applied to a larger dataset collected in September 2011. We characterised intra- and inter-daily Chl-a concentration dynamics and observed a high variability; during a single day, Chl-a concentration varied from 20 to 130 mg m–3. Values of Chl-a concentration were correlated with meteo-climatic parameters, showing that solar radiance and wind speed are key factors regulating the daily phytoplankton growth and dynamics. Such patterns are usually determined by vertical migration of different phytoplankton species within the water column, as well as by metabolic adaptations to changes in light conditions.

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

Remote Sensing ◽

10.3390/rs12162662 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2662 ◽

Cited By ~ 2

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.

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Comparison of Chlorophyll a Extractions with Ethanol and Dimethyl Sulfoxide/Acetone, and a Concern about Spectrophotometric Phaeopigment Correction

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f92-256 ◽

1992 ◽

Vol 49 (11) ◽

pp. 2331-2336 ◽

Cited By ~ 23

Author(s):

D. J. Webb ◽

B. K. Burnison ◽

A. M. Trimbee ◽

E. E. Prepas

Keyword(s):

High Pressure ◽

Dimethyl Sulfoxide ◽

Chlorophyll A ◽

Green Algae ◽

Natural Populations ◽

Eutrophic Lakes ◽

North Central ◽

Chl A ◽

Blue Green Algae ◽

Hplc Analyses

Chlorophyll a (Chl a) in water samples from three mesotrophic to eutrophic lakes in north-central Alberta was extracted with one of three solvents (95% ethanol, 90% ethanol, or a 2:3 mixture of dimethyl sulfoxide and 90% acetone (DMSO/acetone)) and analyzed by two techniques (spectrophotometry and high pressure liquid chromatography (HPLC). The dominant phytoplankton were blue-green algae and diatoms. Total Chl a concentrations (i.e. no correction for phaeopigments (Pha)) were not significantly different among solvents (P > 0.5). Total Chl a concentrations from spectrophotometric analyses were significantly higher than those from HPLC analyses (4.2 ± 0.88 and 2.6 ± 0.50 μg∙L−1 respectively, P < 0.05). Pha concentrations derived by spectrophotometry were 64 times higher than those derived by HPLC (1.7 ± 0.52 and 0.025 ± 0.01 μg∙L−1 respectively, P < 0.005). Thus, spectrophotometry appears to dramatically overestimate Pha concentrations and may overestimate total Chl a (i.e. no correction for Pha). Therefore, ethanol and DMSO/acetone are equally suitable for Chl a extraction from natural populations dominated by blue-green algae and/or diatoms, but if information on Pha and/or accessory pigments is required, HPLC analyses are the appropriate route rather than spectrophotometry.

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Are drought occurrence and severity aggravating? A study on SPI drought class transitions using log-linear models and ANOVA-like inference

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3011-2012 ◽

2012 ◽

Vol 16 (8) ◽

pp. 3011-3028 ◽

Cited By ~ 23

Author(s):

E. E. Moreira ◽

J. T. Mexia ◽

L. S. Pereira

Keyword(s):

Time Series ◽

Linear Models ◽

Standardized Precipitation Index ◽

Drought Severity ◽

Drought Frequency ◽

Long Duration ◽

Long Time ◽

Log Linear ◽

Drought Occurrence ◽

Drought Classes

Abstract. Long time series (95 to 135 yr) of the 12-month time scale Standardized Precipitation Index (SPI) relative to 10 locations across Portugal were studied with the aim of investigating if drought frequency and severity are changing through time. Considering four drought severity classes, time series of drought class transitions were computed and later divided into several sub-periods according to the length of SPI time series. Drought class transitions were calculated to form a 2-dimensional contingency table for each sub-period, which refer to the number of transitions among drought severity classes. Two-dimensional log-linear models were fitted to these contingency tables and an ANOVA-like inference was then performed in order to investigate differences relative to drought class transitions among those sub-periods, which were considered as treatments of only one factor. The application of ANOVA-like inference to these data allowed to compare the sub-periods in terms of probabilities of transition between drought classes, which were used to detect a possible trend in droughts frequency and severity. Results for a number of locations show some similarity between alternate sub-periods and differences between consecutive ones regarding the persistency of severe/extreme and sometimes moderate droughts. In global terms, results do not support the assumption of a trend for progressive aggravation of drought occurrence during the last century, but rather suggest the existence of long duration cycles.

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Survival of Microcystis aeruginosa and Scenedesmus obliquus under dark anaerobic conditions

Marine and Freshwater Research ◽

10.1071/mf06212 ◽

2007 ◽

Vol 58 (7) ◽

pp. 634 ◽

Cited By ~ 11

Author(s):

X. L. Shi ◽

F. X. Kong ◽

Y. Yu ◽

Z. Yang

Keyword(s):

Chlorophyll A ◽

Microcystis Aeruginosa ◽

Metabolic Activity ◽

Scenedesmus Obliquus ◽

Anaerobic Conditions ◽

Eutrophic Lakes ◽

Cell Metabolic Activity ◽

Chl A ◽

Cell Concentrations ◽

Fda Hydrolysis

The cyanobacterium Microcystis aeruginosa and the green alga Scenedesmus obliquus were incubated individually and together in the dark and under anaerobic conditions created by adding the reducing agent cysteine. Flow cytometry was used to monitor cell concentrations, fluorescence of chlorophyll-a (chl-a), and cell metabolic activity measured with an esterase-sensitive probe to detect fluorescein diacetate (FDA) hydrolysis of the two species. M. aeruginosa showed a slight increase in cell metabolic activity, no conspicuous death of cells, and absence of decay of chlorophyll-a fluorescence in individual and competition cases under dark anaerobic conditions. Cell metabolic activity and fluorescence of S. obliquus, on the contrary, decreased sharply, and cell concentrations fluctuated markedly with time in the unialgal cultures, but showed only a slight decline in the mixed cultures. M. aeruginosa appeared to be more tolerant to dark anaerobic conditions than S. obliquus, which may arise in eutrophic lakes beneath thick surface scums in the water column, or in the bottom sediments. Tolerance of these conditions may be important to the dominance of M. aeruginosa in eutrophic lakes.

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The CMEMS GlobColour chlorophyll <i>a</i> product based on satellite observation: multi-sensor merging and flagging strategies

Ocean Science ◽

10.5194/os-15-819-2019 ◽

2019 ◽

Vol 15 (3) ◽

pp. 819-830 ◽

Cited By ~ 12

Author(s):

Philippe Garnesson ◽

Antoine Mangin ◽

Odile Fanton d'Andon ◽

Julien Demaria ◽

Marine Bretagnon

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Spatial Resolution ◽

Remote Sensing Data ◽

Satellite Observation ◽

Data Sets ◽

In Situ Data ◽

Long Time ◽

The One ◽

Monitoring Service

Abstract. This paper concerns the GlobColour-merged chlorophyll a products based on satellite observation (SeaWiFS, MERIS, MODIS, VIIRS and OLCI) and disseminated in the framework of the Copernicus Marine Environmental Monitoring Service (CMEMS). This work highlights the main advantages provided by the Copernicus GlobColour processor which is used to serve CMEMS with a long time series from 1997 to present at the global level (4 km spatial resolution) and for the Atlantic level 4 product (1 km spatial resolution). To compute the merged chlorophyll a product, two major topics are discussed: The first of these topics is the strategy for merging remote-sensing data, for which two options are considered. On the one hand, a merged chlorophyll a product computed from a prior merging of the remote-sensing reflectance of a set of sensors. On the other hand, a merged chlorophyll a product resulting from a combination of chlorophyll a products computed for each sensor. The second topic is the flagging strategy used to discard non-significant observations (e.g. clouds, high glint and so on). These topics are illustrated by comparing the CMEMS GlobColour products provided by ACRI-ST (Garnesson et al., 2019) with the OC-CCI/C3S project (Sathyendranath et al., 2018). While GlobColour merges chlorophyll a products with a specific flagging, the OC-CCI approach is based on a prior reflectance merging before chlorophyll a derivation and uses a more constrained flagging approach. Although this work addresses these two topics, it does not pretend to provide a full comparison of the two data sets, which will require a better characterisation and additional inter-comparison with in situ data.

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Spatial Variability Assessment on The High-resolution Chlorophyll-a Extraction from Landsat 8and Sentinel 2 Imageries in Johor Waters

Journal of Engineering Technology and Applied Physics ◽

10.33093/jetap.2020.x1.8 ◽

2020 ◽

Vol 2 ◽

pp. 38-43

Author(s):

Fatin Nabihah Syahira Ridzuan ◽

Mohd Nadzri Md Reba ◽

Monaliza Mohd Din ◽

Mazlan Hashim ◽

Po Teen Lim ◽

...

Keyword(s):

High Resolution ◽

Chlorophyll A ◽

Spatial Resolution ◽

Coastal Region ◽

Water Quality Monitoring ◽

Landsat 8 ◽

Statistical Regression ◽

Chl A ◽

In Situ Data ◽

Sentinel 2

High resolution Chlorophyll-a (Chl-a) can indicate the trophic status of the water and provide useful information on optical features of water body in water quality monitoring. Remote sensing has great potential to offer the spatial and temporal coverage needed. Over the last decades the Sea WIFS and MODIS were applied, but not suitable due to the low spatial resolution for monitoring Chl-a in coastal area. However, the retrieval of Chl-a in the coastal region is usually challenging due to the other in-water substances regardless of Chl-a, hence resulting in the satellite retrieved Chl-a overestimation. By the advancement of the Sentinel-2 and Landsat 8 satellites, continuous high resolution optical imageries have served for remarkable coastal mapping capability thanks to the spectroscopic capability certain spectral bands and as high as 10-meter spatial resolution. This paper aims to evaluate the performance of the SEADASS and SNAP processor for Chl-a estimation from the Operational Land Imager (OLI)and MultiSpectral Instrument(MSI) data in Johor waters. The representative models, in standard algorithm OC3and C2RCC, were adapted to retrieve Chl-a concentration. The statistical regression has shown that these algorithms give an acceptable Chl-a estimation at medium and high resolution with R2=0.44 from OC3and R2=0.55from C2RCC comparing to the in-situ data. Despite of the spatial, temporal and spectral variability, this paper shows that OLI and MSI could provide Chl-a mapping capability at suitable Chl-a estimation techniques.

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Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China

Water ◽

10.3390/w13172406 ◽

2021 ◽

Vol 13 (17) ◽

pp. 2406

Author(s):

Zhenmei Liao ◽

Nan Zang ◽

Xuan Wang ◽

Chunhui Li ◽

Qiang Liu

Keyword(s):

Machine Learning ◽

Water Quality ◽

Chlorophyll A ◽

Trophic State ◽

Water Transfer ◽

Support Vector ◽

Miyun Reservoir ◽

Chl A ◽

Svm Model

Although water transfer projects can alleviate the water crisis, they may cause potential risks to water quality safety in receiving areas. The Miyun Reservoir in northern China, one of the receiving reservoirs of the world’s largest water transfer project (South-to-North Water Transfer Project, SNWTP), was selected as a case study. Considering its potential eutrophication trend, two machine learning models, i.e., the support vector machine (SVM) model and the random forest (RF) model, were built to investigate the trophic state by predicting the variations of chlorophyll-a (Chl-a) concentrations, the typical reflection of eutrophication, in the reservoir after the implementation of SNWTP. The results showed that compared with the SVM model, the RF model had higher prediction accuracy and more robust prediction ability with abnormal data, and was thus more suitable for predicting Chl-a concentration variations in the receiving reservoir. Additionally, short-term water transfer would not cause significant variations of Chl-a concentrations. After the project implementation, the impact of transferred water on the water quality of the receiving reservoir would have gradually increased. After a 10-year implementation, transferred water would cause a significant decline in the receiving reservoir’s water quality, and Chl-a concentrations would increase, especially from July to August. This led to a potential risk of trophic state change in the Miyun Reservoir and required further attention from managers. This study can provide prediction techniques and advice on water quality security management associated with eutrophication risks resulting from water transfer projects.

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