Remote estimation of chl-a concentration in turbid productive waters — Return to a simple two-band NIR-red model?

Today the water quality of many inland and coastal waters is compromised by cultural eutrophication in consequence of increased human agricultural and industrial activities. Remote sensing is widely applied to monitor the trophic state of these waters. This study investigates the performance of near infrared-red models for the remote estimation of chlorophyll-a concentrations in turbid productive waters and evaluates several near infrared-red models developed within the last 34 years. Three models were calibrated for a dataset with chlorophyll-a concentrations from 0 to 100 mg m⁻³ and validated for independent and statistically different datasets with chlorophyll-a concentrations from 0 to 100 mg m⁻³ and 0 to 25 mg m⁻³ for the spectral bands of the MEdium Resolution Imaging Spectrometer (MERIS) and MODerate resolution Imaging Spectroradiometer (MODIS). The MERIS two-band model estimated chlorophyll-a concentrations slightly more accurately than the more complex models, with mean absolute errors of 2.3 mg m⁻³ for chlorophyll-a concentrations from 0 to 100 mg m⁻³ and 1.2 mg m⁻³ for chlorophyll-a concentrations from 0 to 25 mg m⁻³. Comparable results from several near infrared-red models with different levels of complexity, calibrated for inland and coastal waters around the world, indicate a high potential for the development of a simple universally applicable near infrared-red algorithm.     

Relationships between suspended mineral concentrations and red-waveband reflectances in moderately turbid shelf seas

This paper considers the uncertainties that arise in estimating the concentration of suspended minerals by optical remote sensing in waters which contain unknown concentrations of other optically significant constituents. Relationships between suspended mineral concentrations and remote sensing reflectance were calculated by radiative transfer modelling using representative specific inherent optical properties (SIOPs) for phytoplankton (CHL), suspended mineral particles of terrigenous origin (MSS_ter) and coloured dissolved organic matter (CDOM) that were derived from measurements at 173 stations in UK shelf seas. When only suspended minerals were present, remote sensing reflectance (R_rs) was related to MSS_ter by a family of saturation curves whose shape depended strongly on wavelength. However the addition of CHL and CDOM made this relationship considerably more complex. Polynomial expressions were therefore derived for the maximum and minimum values of MMS_ter consistent with a given R_rs667 in the presence of independently varying concentrations of CHL and CDOM. For CHL ranging from 0 to 10 mg m⁻³ and CDOM from 0 to 1 m⁻¹, for example, an R_rs667 observation 0.01 sr⁻¹ could corresponded to MSS_ter values between 7 and 12 g m⁻³. The presence of biogenic minerals in the form of diatom frustules, MSS_dia had little influence on the accuracy of MSS_ter retrieval. The degree of variability in the relationship between MSS_ter and Rrs667 predicted by the model was confirmed by measurements of radiometric profiles and mineral concentrations at 110 Irish Sea stations. Uncertainties in the remote sensing of MSS_ter in coastal waters are more appropriately indicated by upper and lower limits set according to the likely ranges of other optically significant constituents than by percentage errors. Moreover, the influence of these constituents should be eliminated before variations in the relationship between MSS_ter and Rrs are attributed to qualitative changes in mineral particle characteristics.     

Two-component modeling of the optical properties of a diatom bloom in the Southern Ocean

Diatom cells have distinctive optical characteristics, originating from their relatively large cell size, fucoxanthin content and silica cell wall. It has been proposed that diatom-dominated phytoplankton blooms can be identified by optical remote sensing and that specifically tuned chlorophyll and primary production algorithms should be applied in regions where these blooms are present. However there have been few studies on how the optical properties of diatom blooms change as they progress from active growth to senescence, and it is unlikely that measurements on laboratory cultures encompass the full range of physiological states found in natural waters. We have therefore examined the inherent optical properties (IOPs) of the waters around the island of South Georgia at the end of the spring diatom bloom. Considerable variability was found in the relationships between the inherent optical properties and analytically determined chlorophyll a concentrations even in the surface layer, which meant that the usual bio-optical assumptions for Case 1 waters did not apply. To account for this variability, phytoplankton absorption and scattering were modeled as a two-component mixture, with the components representing actively growing and senescent material. The specific inherent optical properties of the two components were derived by linear regression of total IOPs against chlorophyll concentration and a fraction of the suspended mineral concentration. These specific IOPs were used to develop radiative transfer models of diatom blooms in varying stages of growth and senescence. Remote sensing reflectances calculated using this technique confirmed the tendency of the standard algorithms employed in SeaWiFS, MODIS and MERIS data processing to under-estimate near-surface chlorophyll concentrations in diatom blooms. However the inclusion of increasing proportions of senescent material had a significant effect on algorithm performance only at chlorophyll concentrations below 10 mg m^− 3. Optical depths predicted by the model around South Georgia were 9 +/− 2 m at 512 nm, indicating that a large fraction of the phytoplankton biomass was located below the depth from which the remote sensing signals originated.     

Remote estimation of chlorophyll a in optically complex waters based on optical classification

Accurate assessment of phytoplankton chlorophyll a (Chla) concentration in turbid waters by means of remote sensing is challenging due to optically complexity and significant variability of case 2 waters, especially in inland waters with multiple optical types. In this study, a water optical classification algorithm is developed, and two semi-analytical algorithms (three- and four-band algorithm) for estimating Chla are calibrated and validated using four independent datasets collected from Taihu Lake, Chaohu Lake, and Three Gorges Reservoir. The optical classification algorithm is developed using the dataset collected in Taihu Lake from 2006 to 2009. This dataset is also used to calibrate the three- and four-band Chla estimation algorithms. The optical classification technique uses remote sensing reflectance at three bands: Rrs(G), Rrs(650), and Rrs(NIR), where G indicates the location of reflectance peak in the green region (around 560 nm), and NIR is the location of reflectance peak in the near-infrared region (around 700 nm). Optimal reference wavelengths of the three- and four-band algorithm are located through model tuning and accuracy optimization. The three- and four-band algorithm accuracy is further evaluated using other three independent datasets. The improvement of optical classification in Chla estimation is revealed by comparing the performance of the two algorithms for non-classified and classified waters.Using the slopes of the three reflectance bands, the 138 reflectance spectra samples in the calibration dataset are classified into three classes, each with a specific spectral shape character. The three- and four-band algorithm performs well for both non-classified and classified waters in estimating Chla. For non-classified waters, strong relationships are yielded between measured and predicted Chla, but the performance of the two algorithms is not satisfactory in low Chla conditions, especially for samples with Chla below 30 mg m^− 3. For classified waters, the class-specific algorithms perform better than for non-classified waters. Class-specific algorithms reduce considerable mean relative error from algorithms for non-classified waters in Chla predicting. Optical classification makes that there is no need to adjust the optimal position to estimate Chla for other waters using the class-specific algorithms. The findings in this study demonstrate that optical classification can greatly improve the accuracy of Chla estimation in optically complex waters.     

Feasibility of hyperspectral remote sensing for mapping benthic macroalgal cover in turbid coastal waters—a Baltic Sea case study

Quantitative analysis of coastal marine benthic communities enables to adequately estimate the state of coastal marine environment, provide better evidence for environmental changes and describe processes that are conditioned by anthropogenic forces. Remote sensing could provide a tool for mapping bottom vegetation if the substrates are spectrally resolvable. We measured reflectance spectra of green (Cladophora glomerata), red (Furcellaria lumbricalis), and brown (Fucus vesiculosus) macroalgae and used a bio-optical model in estimating whether these algae distinguish optically from each other, from sandy bottom or deep water in turbid water conditions of the Baltic Sea. The simulation was carried out for three different water types: (1) CDOM-rich coastal water, (2) coastal waters not directly impacted by high CDOM discharge from rivers but with high concentration of cyanobacteria, (3) open Baltic waters. Our modelling results indicate that the reflectance spectra of C. glomerata, F. lumbricalis, F. vesiculosus differ from each other and also from sand and deep water reflectance spectra. The differences are detectable by remote sensing instruments at spectral resolution of 10 nm and SNR better than 1000:1. Thus, the lowest depth limits where the studied macroalgae grow do not exceed the depth where such remote sensing instruments could potentially detect the spectral differences between the studied species.     

Assessment of water quality in Lake Garda (Italy) using Hyperion

For testing the integration of the remote sensing related technologies into the water quality monitoring programs of Lake Garda (the largest Italian lake), the spatial and spectral resolutions of Hyperion and the capability of physics-based approaches were considered highly suitable. Hyperion data were acquired on 22nd July 2003 and water quality was assessed (i) defining a bio-optical model, (ii) converting the Hyperion at-sensor radiances into subsurface irradiance reflectances, and (iii) adopting a bio-optical model inversion technique. The bio-optical model was parameterised using specific inherent optical properties of the lake and light field variables derived from a radiative transfer numerical model. A MODTRAN-based atmospheric correction code, complemented with an air/water interface correction was used to convert Hyperion at-sensor radiances into subsurface irradiance reflectance values. These reflectance values were comparable to in situ reflectance spectra measured during the Hyperion overpass, except at longer wavelengths (beyond 700 nm), where reflectance values were contaminated by severe atmospheric adjacency effects. Chlorophyll-a and tripton concentrations were retrieved by inverting two Hyperion bands selected using a sensitivity analysis applied to the bio-optical model. The sensitivity analysis indicated that the assessment of coloured dissolved organic matter was not achievable in this study due to the limited coloured dissolved organic matter concentration range of the lake, resulting in reflectance differences below the environmental measurement noise of Hyperion. The chlorophyll-a and tripton image-products were compared to in situ data collected during the Hyperion overpass, both by traditional sampling techniques (8 points) and by continuous flow-through systems (32 km). For chlorophyll-a the correlation coefficient between in situ point stations and Hyperion-inferred concentrations was 0.77 (data range from 1.30 to 2.16 mg m^− 3). The Hyperion-derived chlorophyll-a concentrations also match most of the flow-through transect data. For tripton, the validation was constrained by variable re-suspension phenomena. The correlation coefficient between in situ point stations and Hyperion-derived concentrations increased from 0.48 to 0.75 (data range from 0.95 to 2.13 g m^− 3) if the sampling data from the re-suspension zone was avoided. The comparison of Hyperion-derived tripton concentrations and flow-through transect data exhibited a similar mismatch. The results of this research suggest further studies to address compatibilities of validation methods for water body features with a high rate of change, and to reduce the contamination by atmospheric adjacency effects on Hyperion data at longer wavelengths in Alpine environment. The transferability of the presented method to other sensors and the ability to assess water quality independent from in situ water quality data, suggest that management relevant applications for Lake Garda (and other subalpine lakes) could be supported by remote sensing.     

长江口及邻近海域悬浮颗粒物对叶绿素a遥感反演算法的影响分析

利用水介质光辐射传输数值模型Hydrolight,结合前人对长江口及邻近海域水体的生物—光学模型研究,模拟不同光学水体的遥感反射率,并分析遥感反射率对悬浮颗粒物(SPM)的敏感性以及SPM对4种叶绿素a(Chla)反演算法(二波段、三波段、荧光基线高度(FLH)和综合叶绿素指数(SCI)算法)的影响。结果表明:由Hydrolight模拟得到的遥感反射率与现场同步实测的遥感反射率的均方根误差小于0.01sr-1,其中可实现遥感反射率在550~725nm波段较精确的模拟。遥感反射率对SPM的敏感性随着Chla浓度的升高而降低。二波段、三波段算法适合低SPM浓度水体的Chla浓度反演,FLH算法反演Chla浓度时易受SPM的影响,而SCI算法在中、高SPM浓度水体中消除SPM的影响进而反演Chla的潜力较好。     

Mapping lake CDOM by satellite remote sensing

Given the importance of coloured dissolved organic matter (CDOM) for the structure and function of lake ecosystems, a method to estimate the amount of CDOM in lake waters over large geographic areas would be highly desirable. Advanced Land Imager (ALI) images were acquired in southern Finland (in 2002) and southern Sweden (in 2003) together with in situ measurements of bio-optical properties of 34 lakes (39 measuring stations). Based on this dataset, a band-ratio type algorithm was developed using ALI band 2 and band 3 for estimating CDOM content (absorption of filtrated water at 420 nm) in lakes. Correlation between in situ measured CDOM and the remote sensing estimate of CDOM was high, r²=0.73. The CDOM retrieval algorithm obtained on the basis of two images and in situ data was validated on a third ALI image (eastern Finland, 2002) that was available in the ALI image archive. In situ water-colour monitoring data from 22 lakes (27 measuring stations) in the third image were available in a database of the Finnish Environment Administration. The water-colour data were converted to CDOM absorption values, which were then compared to the results from a third ALI image. The correlation between remotely estimated and in situ CDOM values in the algorithm validation image was high, r²=0.83. These results support the conclusion that CDOM content in lakes over a wide range of concentrations (a_CDOM(420) between 0.68 and 11.13 m⁻¹) can be mapped using Advanced Land Imager data.     

An assessment of chlorophyll-a algorithms available for SeaWiFS in coastal and open areas of the Bay of Bengal and Arabian Sea

Three ocean colour algorithms, OC4v6, Carder and OC5 were tested for retrieving Chlorophyll-a (Chla) in coastal areas of the Bay of Bengal and open ocean areas of the Arabian Sea. Firstly, the algorithms were run using ~ 80 in situ Remote Sensing Reflectance, (R_rs(λ)) data collected from coastal areas during eight cruises from January 2000 to March 2002 and the output was compared to in situ Chla. Secondly, the algorithms were run with ~ 20 SeaWiFS R_rs(λ) and the results were compared with coincident in situ Chla. In both cases, OC5 exhibited the lowest log₁₀-RMS, bias, had a slope close to 1 and this algorithm appears to be the most accurate for both coastal and open ocean areas. Thirdly the error in the algorithms was regressed against Total Suspended Material (TSM) and Coloured Dissolved Organic Material (CDOM) data to assess the co-variance with these parameters. The OC5 error did not co-vary with TSM and CDOM. OC4v6 tended to over-estimate Chla > 2 mg m⁻³ and the error in OC4v6 co-varied with TSM. OC4v6 was more accurate than the Carder algorithm, which over-estimated Chla at concentrations > 1 mg m⁻³ and under-estimated Chla at values < 0.5 mg m⁻³. The error in Carder Chla also co-varied with TSM. The algorithms were inter-compared using > 5500 SeaWiFS R_rs(λ) data from coastal to offshore transects in the Northern Bay of Bengal. There was good agreement between OC4v6 and OC5 in open ocean waters and in coastal areas up to 2 mg m⁻³. There was a strong divergence between Carder and OC5 in open ocean and coastal waters. OC4v6 and Carder tended to over-estimate Chla in coastal areas by a factor of 2 to 3 when TSM > 25 g m⁻³. We strongly recommend the use of OC5 for coastal and open ocean waters of the Bay of Bengal and Arabian Sea. A Chla time series was generated using OC5 from 2000 to 2003, which showed that concentrations at the mouths of the Ganges reach a maxima (~ 5 mg m⁻³) in October and November and were 0.08 mg m⁻³ further offshore increasing to 0.2 mg m⁻³ during December. Similarly in early spring from February to March, Chla was 0.08 to 0.2 mg m⁻³ on the east coast of the Bay.     

Variability in measured and modelled remote sensing reflectance for coastal waters at LEO-15

A large database of in situ bio-optical measurements were collected at the LEO-15 (Long-term Ecosystem Observatory) off the southern coast of New Jersey, USA. The data were used to quantify the impact of coastal upwelling on near-shore bulk apparent (AOP) and inherent (IOP) optical properties. There was good qualitative agreement between the AOPs and IOPs in space and time. The measured IOPs were used as inputs to the Hydrolight radiative transfer model (RTE). Estimated spectral AOPs from the RTE were strongly correlated (generally R²>0.80) to measured AOPs. If optical closure between in-water measurements was achieved then the RTE was used to construct the spectral remote sensing reflectance. The modelled remote sensing reflectances were compared to satellite-derived reflectance estimates from four different algorithms. Quantitative agreement between the satellite-measured and in-water modelled remote sensing reflectance was good but results were variable between the different models. The strength of the correlation and spectral consistency was variable with space and time. Correlations were strongest in clear offshore waters and lowest in the near-shore turbid waters. In the near-shore waters, the correlation was strongest for blue wavelengths (400–555?nm) but lower for the red wavelengths of light.     

Bio-optical properties in waters influenced by the Mississippi River during low flow conditions

Spatial and temporal patterns of bio-optical properties were studied in the Northern Gulf of Mexico during cruises in April and October of 2000, a year during which the discharge volume from the Mississippi River was unusually low. Highly variable surface Chl a concentrations (0.1 to 17 mg m⁻³) and colored dissolved organic matter (CDOM) absorption (0.07 to 1.1 m⁻¹ at 412 nm) were observed in the study region that generally decreased with increasing salinity waters, being highest nearshore and decreasing at offshore stations. The optical properties of absorption, scattering, and diffuse attenuation coefficients reflected these distributions with phytoplankton particles and CDOM contributing to most of the spatial, vertical, and seasonal variability. The diffuse attenuation coefficient K_d(λ) and spectral remote sensing reflectance R_rs(λ) were linear functions of absorption and backscattering coefficients a(λ) and b_b(λ) through the downward average cosine μ_d and the ratio of variables f/Q at the SeaWiFS wavebands for waters with widely varying bio-optical conditions. Although various R_rs(λ) ratio combinations showed high correlation with surface Chl a concentrations and CDOM absorption at 412 nm, power law equations derived using the R_rs(490)/R_rs(555) and R_rs(510)/R_rs(555) ratios provided the best retrievals of Chl a concentrations and CDOM absorption from SeaWiFS reflectance data.     

A neural-network model to retrieve CDOM absorption from in situ measured hyperspectral data in an optically complex lake: Lake Taihu case study

Coloured dissolved organic matter (CDOM) is an important water component that affects water colour and ecological environment under water. The remote estimation of CDOM is always a challenge in the field of water-colour remote sensing owing to its weak signal. To further study the CDOM-retrieval approach, field experiments, including water-quality analysis and spectral measurements, were carried out in Lake Taihu waters from 8 to 21 November 2007. On the foundation of analysing water-inherent optical properties, sensitive spectral factors were selected, and then neural-network models were established for retrieving CDOM. The results show that the model with 10 nodes in the hidden layer performs best, yielding a correlation coefficient (R) of 0.887 and a root-mean-square error of 0.156 m^?1. Meanwhile, the predictive errors of the model developed here and the previously proposed algorithms were compared with each other. The mean value of the relative error of the former is 12.8% (standard deviation of 29.9%), and is much lower than its counterpart of other models, which indicates that the developed model has a higher accuracy for CDOM retrieval in Lake Taihu waters. Meanwhile, other datasets collected at different times were also imported into the model for applicability analysis; the derived errors suggest a relatively good performance of the model. This research firstly explores the CDOM retrieval in optically complex lake waters, and the corresponding findings support a technical framework for accurately extracting CDOM information in Lake Taihu waters, based on an adequate understanding of water optical properties.     

Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass

An extensive field campaign was carried out for the validation of a previously published reflectance ratio-based algorithm for quantification of the cyanobacterial pigment phycocyanin (PC). The algorithm uses band settings of the Medium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT, and should accurately retrieve the PC concentration in turbid, cyanobacteria-dominated waters. As algae and cyanobacteria often co-occur, the algorithm response to varying phytoplankton composition was explored. Remote sensing reflectance and reference pigment measurements were obtained in the period 2001-2005 in Spain and the Netherlands using field spectroradiometry and various pigment extraction methods. Additional field data was collected in Spain in May 2005 to allow intercalibration of spectroradiometry and pigment assessment methods. Two methods for extraction of PC from concentrated water samples, and in situ measured PC fluorescence, compared well. Reflectance measurements with different field spectroradiometers used in Spain and the Netherlands also gave similar results. Residual analysis of PC predicted by the algorithm showed that overestimation of PC mainly occurred in the presence of chlorophylls b and c, and phaeophytin. The errors were strongest at low PC relative to Chl a concentrations. A correction applied for absorption by Chl b markedly improved the prediction. Without such a correction, the quality of the PC prediction still increased markedly with estimates > 50 mg PC m^− 3, allowing monitoring of the cyanobacterial status of eutrophic waters. The threshold concentration may be lowered when a high intracellular PC:Chl a ratio or cyanobacterial dominance is expected. Below the limit, predicted PC concentrations should be considered as the highest estimate. We evaluated that remote sensing of both PC and Chl a would allow assessment of cyanobacterial risk to water quality and public health in over 70% of our cases.     

Mapping coloured dissolved organic matter concentration in coastal waters

Optical properties of the Baltic Sea are dominated by coloured dissolved organic matter (CDOM). High concentration of CDOM is probably one of the reasons why standard chlorophyll-retrieval algorithms fail badly in the Baltic Sea. Our aim was to test can CDOM be mapped by remote sensing instruments in coastal waters of relatively CDOM-rich environments like the Baltic Sea. The results show that sensors with high radiometric resolution, such as Advanced Land Imager (ALI), can be used for mapping CDOM in a wide concentration range. The ALI image also showed that optical properties of coastal waters are extremely variable. CDOM concentration may vary 4–5-fold in two adjacent 30 m pixels. This indicates a need for relatively high spatial resolution (30 m or less) remote sensing data in monitoring coastal environments.     

A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data

In order to retrieve bathymetry, substratum type and the concentrations of the optically active constituents of the water column, an integrated physics based mapping approach was applied to airborne hyperspectral data of Moreton Bay, Australia. The remotely sensed data were sub-optimal due to high and mid-level cloud covers. Critical to the correct interpretation of the resultant coastal bathymetry map was the development of a quality control procedure based on additional outputs of the integrated physics based mapping approach and the characteristics of the instrument. These two outputs were: an optical closure term which defines differences between the image and model based remote sensing signal; and an estimate of the relative contribution of the substratum signal to the remote sensing signal. This quality control procedure was able to identify those pixels with a reliable retrieval of depth and to detect thin and thick clouds and their shadows, which were subsequently masked out from further analysis. The derived coastal bathymetry in depths ranging 4-13 m for the mapped area was within ± 15% of boat-based multi-beam acoustic mapping survey of the same area. The agreement between the imaging spectrometry and the acoustic datasets varies as a function of the contribution of the bottom visibility to the remote sensing signal. As expected, there was greater agreement in shallower clear water (± 0.67 m) than quasi-optically deep water (± 1.35 m). The quantitative identification and screening of the optically deep waters and the quasi-optically deep waters led to improved precision in the depth retrieval. These results suggest that the physics based mapping approach adopted in this study performs well for retrieving water column depths in coastal waters in water depths ranging 4-13 m for the area and conditions studied, even with sub-optimal imagery.     

Remote sensing of water quality in an Australian tropical freshwater impoundment using matrix inversion and MERIS images

The purpose of this study was to investigate how semi-analytical inversion techniques developed for the remote sensing of water quality parameters (chlorophyll a, tripton and coloured dissolved organic matter (CDOM)) in inland waters could be adapted or improved for application to Australian tropical and sub-tropical water bodies. The Matrix Inversion Method (MIM) with a semi-analytic model of the anisotropy of the in-water light field was applied to MERIS images of Burdekin Falls Dam, Australia, a tropical freshwater impoundment. Specific attention was required to improve the atmospheric correction of the MERIS data. The performance of the conventional three band exact solution of the MIM was compared to that of over-determined solutions that used constant and differential weighting for each sensor band.The results of the application of the MIM algorithm showed that the best weighting scheme had a mean chlorophyll a retrieval difference of 1.0 μgl^− 1, the three band direct matrix inversion scheme had a mean difference of 4.2 μgl^− 1 and the constant weight scheme had a mean difference of 5.5 μgl^− 1. For tripton, the best performed weighting scheme had a mean difference of 1.2 mgl^− 1, the three band scheme had a mean difference of 3.4 mgl^− 1 and the constant weight scheme had a mean difference of 1.8 mgl^− 1. For the CDOM retrieval, the mean difference was found to be 0.12 m^− 1 for the best performed weighting scheme, 0.25 m^− 1 for the three band scheme and 0.52 m^− 1 for the constant weight scheme. It was found that significant improvements in the accuracy and precision of retrieved water quality parameter values can be obtained by using differentially weighted, over-determined systems of equations, rather than exact solutions. These more reliable estimates of water quality parameters will allow water resource managers to improve their monitoring regimes.     

Bio-optical algorithm evaluation for MODIS for western Canada coastal waters: An exploratory approach using in situ reflectance

Biophysical and above-water reflectance measurements collected in 2006 were used to evaluate the OC3M, standard GSM01, and a modified version of the GSM01 algorithms for estimating chlorophyll-a (chl) concentrations in the Strait of Georgia, located off the southwest coast of Canada. The Strait was generally a case 2 water body, transitioning from chromophoric dissolved organic matter (CDOM) dominant in the central region to possibly particulate dominant in Fraser River plume regions. Results showed that the OC3M algorithm was somewhat effective (R² = 0.550) outside the most turbid areas of the Fraser River plume. However, a systematic overestimation of lower chl concentrations was found, which may have been related to the higher CDOM absorption observed throughout the Strait. The standard GSM01 algorithm had moderately good agreement with measured CDOM absorption (R² = 0.593) and total suspended solids (TSS) concentrations (R² = 0.888), but was ineffective at estimating chl concentrations. Localized characterization of the CDOM absorption, through a hyperbolic CDOM model, improved the modified GSM01 results with slightly better agreement with measure CDOM absorption (R² = 0.614) and TSS concentrations (R² = 0.933). When the modified GSM01 algorithm was limited to regions with lower combined CDOM and non-algal particulate absorption (a_dg (443) < 0.7 m^− 1), it was more effective then the OC3M algorithm at estimating chl concentrations. This suggests that a threshold value on the a_dg (443) or b_bp (443) estimated by the GSM01 algorithm may be beneficial for limiting turbidity influence on the algorithm. The further reinterpretation of phytoplankton absorption from the modified GSM01 algorithm with a two-component phytoplankton model resulted in a chl relationship with an R² = 0.677 and a linear slope closer to one.     

Coupling remote sensing data with in-situ optical measurements to estimate suspended particulate matter under the Evros river influence (North-East Aegean sea,Greece)

ABSTRACT

Monitoring the riverine output of Suspended Particulate Matter (SPM) distribution in marine embayment is a crucial factor for the water quality of neighbouring coastal regions. This study presents satellite-derived SPM calculations against in-situ measurements in the continental shelf of North-East Aegean surrounding the transboundary Evros river mouth. Surface SPM, Inherent Optical Properties (IOPs) and remote sensing reflectance (R_rs) data were collected in a field campaign during low river discharge period (June 2016). The relationship between the optical backscattering coefficient (b_bp) and the in-situ SPM concentrations was investigated. Subsequently, an empirical single band model was applied for estimating SPM concentrations by using the Landsat-8 Operational Land Imager (L8/OLI) red band and the model was then locally tuned within the study area. Furthermore, a multi-band SPM-retrieval algorithm was developed using the in-situ surface reflectance R_rs for calibration and it was validated using the Leave-One-Out Cross Validation technique (LOOCV). The relationship between in-situ SPM and backscattering coefficient values showed good proportionality, thus, nominating the predominance of terrestrial mineral particles. Validation against field measurements indicated that the SPM concentrations derived from the newly-developed multi-band algorithm had an improved significance correlation (96%), compared to both the single band model (not-tuned) (coefficient of determination, R² = 0.82) and its locally tuned version (R² = 0.83). Most importantly, the generated multi-band model apart from exhibiting the best performance (R² = 0.93), it revealed high SPM spots which were not detected by the locally tuned single band model, indicating additional processes originating from river outflows, coastal erosion and subaqueous thermal springs in the area. In contrast, the locally tuned single band model overestimated SPM values in offshore waters, where low concentrations are encountered under the influence of the clear Black Sea Water (BSW).     

Absorption and scattering properties of water body in Taihu Lake,China: absorption

In order to acquire inherent optical properties to serve the lake water colour/quality remote sensing in Taihu Lake 67 samples were distributed almost all over the lake. Surface water samples were collected and returned to the laboratory for the subsequent processing and analysis. In the laboratory, the absorptions due to the total particulate matter, non‐algal particulate matter, phytoplankton pigment, and CDOM, together with their concentrations were measured and/or calculated, respectively. Then their absorption properties were analysed and compared with those of other lake waters and/or coastal/open waters. Some different and similar characteristics were uncovered. On the one hand, it provides not only a solid basement for the Taihu Lake water colour/quality remote sensing with semi‐analytical/analytical approach but also a typical case for inherent optical properties of case two water especially for inland freshwater lakes. On the other, it is very helpful to improve the practical and intensive application and development of remote sensing in monitoring lake water quality.     

Estimation of chlorophyll-a concentration in waters over the continental shelf of the Bay of Biscay: a comparison of remote sensing algorithms

Ocean colour imagery is used increasingly as a tool to assess water quality via chlorophyll-a concentration (chl-a) estimations in European waters. The Bay of Biscay is affected by major river discharges, which alter the constituents of the marine waters. Chlorophyll-a algorithms, designed for use at global scales, are less accurate due to the variability of optically active in-water constituents. Hence, regionally parameterized empirical algorithms are necessary. The main objective of the present study was to develop a regional algorithm to retrieve chl-a in surface water using in situ R _rs, for a subsequent application to Medium Resolution Imaging Spectrometer (MERIS) satellite images. To address this objective, a platform was developed initially and a measurement procedure adapted for the field HR4000CG Spectrometer. Subsequently, the procedure was tested during a survey over the south-eastern Bay of Biscay (North-East Atlantic Ocean), to establish a MERIS chl-a algorithm for the area, by comparing different global remote sensing chl-a algorithms, with band ratios. Results validated with the jackknife resampling procedure show a satisfactory relationship between the R _rs(510)/R _{r s}(560) and chl-a (R ² _jac?=?0.681). This ratio is better correlated to chl-a than those obtained with established chl-a remote sensing algorithms. High content in coloured dissolved organic matter (CDOM > 0.4 m^?1) and suspended particulate matter (SPM > 2.8 mg l^?1) influenced this relationship, with yellow substances having a stronger effect.