Remote sensing of cyanobacteria-dominant algal blooms and water quality parameters in Zeekoevlei, a small hypertrophic lake, using MERIS

Eutrophication and cyanobacterial algal blooms present an increasing threat to the health of freshwater ecosystems and to humans who use these resources for drinking and recreation. Remote sensing is being used increasingly as a tool for monitoring these phenomena in inland and near-coastal waters. This study uses the Medium Resolution Imaging Spectrometer (MERIS) to view Zeekoevlei, a small hypertrophic freshwater lake situated on the Cape Flats in Cape Town, South Africa, dominated by Microcystis cyanobacteria. The lake's small size, highly turbid water, and covariant water constituents present a challenging case for both algorithm development and atmospheric correction. The objectives of the study are to assess the optical properties of the lake, to evaluate various atmospheric correction procedures, and to compare the performance of empirical and semi-analytical algorithms in hypertrophic water. In situ water quality parameter and radiometric measurements were made simultaneous to MERIS overpasses. Upwelling radiance measurements at depth 0.66 m were corrected for instrument self-shading and processed to water-leaving reflectance using downwelling irradiance measurements and estimates of the vertical attenuation coefficient for upward radiance, K_u, generated from a simple bio-optical model estimating the total absorption, a(λ), and backscattering coefficients, b_b(λ). The normalised water-leaving reflectance was used for assessing the accuracy of image-based Dark Object Subtraction and 6S Radiative Transfer Code atmospheric correction procedures applied to MERIS. Empirical algorithms for estimating chlorophyll a (Chl a), Total Suspended Solids (TSS), Secchi Disk depth (z_SD) and absorption by CDOM (a_CDOM) were derived from simultaneously collected in situ and MERIS measurements. The empirical algorithms gave high correlation coefficient values, although they have a limited ability to separate between signals from covariant water constituents. The MERIS Neural Network algorithms utilised in the standard Level 2 Case 2 waters product and Eutrophic Lakes processor were also used to derive water constituent concentrations. However, these failed to produce reasonable comparisons with in situ measurements owing to the failure of atmospheric correction and divergence between the optical properties and ranges used to train the algorithms and those of Zeekoevlei. Maps produced using the empirical algorithms effectively show the spatial and temporal variability of the water quality parameters during April 2008. On the basis of the results it is argued that MERIS is the current optimal sensor for frequent change detection applications in inland waters. This study also demonstrates the considerable potential value for simple TOA algorithms for hypertrophic systems. It is recommended that regional algorithm development be prioritized in southern Africa and that remote sensing be integrated into future operational water quality monitoring systems.     

A two-step optimization procedure for assessing water constituent concentrations by hyperspectral remote sensing techniques: An application to the highly turbid Venice lagoon waters

Over the past few years, the increased spectral and spatial resolution of remote sensing equipment has promoted the investigation of new techniques for inland and coastal water monitoring. The availability of new high-resolution data has allowed improvements in models based on the radiative transfer theory for assessing optical water quality parameters. In this study, we fine-tuned a physical model for the highly turbid Venice lagoon waters and developed an inversion technique based on a two-step optimization procedure appropriate for hyperspectral data processing to retrieve water constituent concentrations from remote data. In the first step, the solution of a linearized analytical formulation of the radiative transfer equations was found. In the second step, this solution was used to provide the initial values in a non-linear least squares-based method. This effort represents a first step in the construction of a feasible and timely methodology for Venice lagoon water quality monitoring by remote sensing, especially in view of the existing experimental hyperspectral satellite (Hyperion) and the future missions such as PRISMA, EnMap and HyspIRI. The optical properties of the water constituents were assessed on the basis of sea/lagoon campaigns and data from the literature. The water light field was shaped by an analytical formulation of radiative transfer equations and the application of numerical simulations (Hydrolight software). Once the optical properties of the Venice lagoon bio-optical model were validated, the inverse procedure was applied to local radiometric spectra to retrieve concentrations of chlorophyll, colored dissolved organic matter and tripton. The inverse procedure was validated by comparing these concentrations with those measured in the laboratory from in situ water samples, then it was applied to airborne (CASI and MIVIS) and satellite (Hyperion) sensors to derive water constituent concentration maps. The consistent results encourage the use of this procedure using future missions satellite (PRISMA, EnMap and HyspIRI).     

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.     

Satellite-based water quality monitoring in Lake Vänern,Sweden

Lake Vänern, Sweden, is one of Europe’s largest lakes and has a historical, cultural, ecological as well as economic importance. Lake water quality monitoring is required by national and international legislations and directives, but present programmes are insufficient to meet the requirements. To complement in situ based monitoring, the possibility to obtain reliable information about spatial and temporal water quality trends in Lake Vänern from the ENVISAT mission’s MERIS instrument was evaluated. The complete archive (2002–2012) of MERIS (Medium Resolution Imaging Spectrometer) full resolution data was processed using the water processor developed by Free University Berlin (FUB) to derive aerosol optical thickness (AOT), remote-sensing reflectance (R_rs) and water quality parameters: chlorophyll-a (chl-a) concentration, coloured dissolved organic matter absorption at 443 nm (CDOM), and total suspended matter (TSM) concentration. The objective was to investigate if, either, FUB reflectance products in combination with potential lake-specific band ratio algorithms for water quality estimation, or directly, FUB water quality products, could complement the existing monitoring programme.

Application of lake-specific band ratio algorithms requires high-quality reflectance products based on correctly estimated AOT. The FUB reflectance and AOT products were evaluated using Aerosol Robotic Network – Ocean Color (AERONET-OC) match-up data measured at station Pålgrunden in Lake Vänern. The mean absolute percentage differences (MAPDs) of the final reflectance retrievals at 413, 443, 490, 555, and 665 nm were 510%, 48%, 33%, 34%, and 33%, respectively, corresponding to a large positive bias in 413 nm, positive bias in 443–555 nm, and a negative bias in 665 nm. AOT was strongly overestimated in all bands.

The FUB water quality products were evaluated using match-up in situ data of chl-a, filtered absorbance (Abs_F(420)) and turbidity as Abs_F(420) is related to CDOM and turbidity is strongly related to TSM. The in situ data was collected within the Swedish national and regional monitoring programmes. In order to widen the range of water constituents and add more data to the analysis, data from four large Swedish lakes (Vänern, Vättern, Mälaren, and Hjälmaren) was included in the analysis. High correlation (r ≥ 0.85) between in situ data and MERIS FUB derived water quality estimates were obtained, but the absolute levels were over- (chl-a) or under- (CDOM) estimated. TSM was retrieved without bias.

Calibration algorithms were established for chl-a and CDOM based on the match-up data from all four lakes. After calibration of the MERIS FUB data, realistic time series could be derived that were well in line with in situ measurements. The MAPDs of the final retrievals of chl-a, Abs_F(420) and Turbidity in Lake Vänern were 37%, 15%, and 35%, respectively, corresponding to mean absolute differences (MADs) of 0.9 µg l^?1, 0.17 m^?1, and 0.32 mg l^?1 in absolute values.

The partly inaccurate reflectance estimations in combination with both positive and negative bias imply that successful application of band ratio algorithms is unlikely. The high correlation between MERIS FUB water quality products and in situ data, on the other hand, shows a potential to complement present water quality monitoring programmes and improve the understanding and representability of the temporally and spatially sparse in situ observations. The monitoring potential shown in this study is applicable to the Sentinel-3 mission’s OLCI (Ocean Land Colour Instrument), which was launched by the European Space Agency (ESA) in February 2016 as a part of the EC Copernicus programme.     

Validation of satellite ocean color primary products at optically complex coastal sites: Northern Adriatic Sea, Northern Baltic Proper and Gulf of Finland

The study presents and discusses the application of in situ data from the ocean color component of the Aerosol Robotic Network (AERONET-OC) to assess primary remote sensing products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the AQUA platform and from the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS) on the OrbView-2 spacecraft. Three AERONET-OC European coastal sites exhibiting different atmospheric and marine optical properties were considered for the study: the Acqua Alta Oceanographic Tower (AAOT) in the northern Adriatic Sea representing Case-1 and Case-2 moderately sediment dominated waters; and, the Gustaf Dalen Lighthouse Tower (GDLT) in the northern Baltic Proper and the Helsinki Lighthouse Tower (HLT) in the Gulf of Finland, both characterized by Case-2 waters dominated by colored dissolved organic matter (CDOM). The analysis of MODIS derived normalized water-leaving radiance at 551 nm, L_WN(551), has shown relatively good results for all sites with uncertainties of the order of 10% and biases ranging from − 1 to − 4%. Larger uncertainty and bias have been observed at 443 nm for the AAOT (i.e., 18 and − 7%, respectively). At the same center wavelength, results for GDLT and HLT have exhibited much larger uncertainties (i.e., 56 and 67%, respectively) and biases (i.e., 18 and 25%, respectively), which undermine the possibility of presently using remote sensing L_WN data at the blue center wavelengths for bio-optical investigations in the Baltic Sea. An evaluation of satellite derived aerosol optical thickness, τ_a, has shown uncertainties and biases of the order of tens of percent increasing with wavelength at all sites. Specifically, MODIS derived τ_a at 869 nm has shown an overestimate of 71% at the AAOT, 101% at GDLT and 91% at HLT, respectively. This result highlights the effects of a limited number of aerosol models for the atmospheric correction process, and might also indicate the need of applying a vicarious calibration factor to the remote sensing data at the 869 nm center wavelength to remove the effects of uncertainties in the atmospheric optical model and the space sensor radiometric calibration. Similar results have been obtained from the analysis of SeaWiFS data. Finally, in view of illustrating the possibility of increasing the accuracy of satellite regional radiometric products, AERONET-OC data have been applied to reduce systematic errors in MODIS and Medium Resolution Imaging Spectrometer (MERIS) L_WN data likely due to the atmospheric correction process. Results relying on MODIS match-ups for the Baltic Sites (i.e., GDLT and HLT) and MERIS matchups for the AAOT, have indicated a substantial reduction of both uncertainty and bias in the blue and red center wavelengths.     

Validation of MERIS ocean-color products in the Bohai Sea: A case study for turbid coastal waters

An extensive in situ data set in the Bohai Sea of China was collected to assess radiometric properties and concentrations of ocean constituents derived from Medium Resolution Imaging Spectrometer (MERIS). The data collected include spectral normalized water-leaving radiance L_wn(λ) and concentrations of suspended particulate matter (SPM) and chlorophyll a (Chl-a). A strict spatio-temporal match-up method was adopted in view of the complexity and variability of the turbid coastal area, resulting in 13, 48 and 18 match-ups for MERIS L_wn(λ), SPM and Chl-a estimates, respectively. For MERIS L_wn(λ), the match-ups showed mean absolute percentage differences (APD) of 17%-20% in the 412, 443, 620 and 665 nm bands, whereas L_wn(λ) at bands from 490 and 560 nm had better APD of 15-16%. The band ratio of L_wn(490) to L_wn(560) of the satellite data was in good agreement with in situ observations with an APD of 4%. MERIS SPM and Chl-a products overestimated the in situ values, with the APD of approximately 50% and 60%, respectively. When match-up criteria were relaxed, the assessment results degraded systematically. Hence, in turbid coastal areas where temporal variability and spatial heterogeneity of bio-optical properties may be pronounced as the result of terrestrial influences and local dynamics, the strict spatio-temporal match-up is recommended.     

Atmospheric correction of ENVISAT/MERIS data over case II waters: the use of black pixel assumption in oxygen and water vapour absorption bands

The Medium Resolution Imaging Spectrometer (MERIS) sensor, with its good physical design, can provide excellent data for water colour monitoring. However, owing to the shortage of shortwave-infrared (SWIR) bands, the traditional near-infrared (NIR)–SWIR algorithm for atmospheric correction in inland turbid case II waters cannot be extended to the MERIS data directly, which limits its applications. In this study, we developed a modified NIR black pixel method for atmospheric correction of MERIS data in inland turbid case II waters. In the new method, two special NIR bands provided by MERIS data, an oxygen absorption band (O₂ A-band, 761 nm) and a water vapour absorption band (vapour A-band, 900 nm), were introduced to keep the assumption of zero water-leaving reflectance valid according to the fact that both atmospheric transmittance and water-leaving reflectance are very small at these two bands. After addressing the aerosol wavelength dependence for the cases of single- and multiple-scattering conditions, we further validated the new method in two case lakes (Lake Dianchi in China and Lake Kasumigaura in Japan) by comparing the results with in situ measurements and other atmospheric correction algorithms, including Self-Contained Atmospheric Parameters Estimation for MERIS data (SCAPE-M) and the Basic ERS (European Remote Sensing Satellite) & ENVISAT (Environmental Satellite) (A)ATSR ((Advanced) Along-Track Scanning Radiometer) and MERIS (BEAM) processor. We found that the proposed method had acceptable accuracy in the bands within 560–754 nm (MERIS bands 5–10) (average absolute deviation (AAD) = 0.0081, average deviation (AD) = 0.0074), which are commonly used in the estimation models of chlorophyll-a (chl-a) concentrations. In addition, the performance of the new method was superior to that of the BEAM processor and only slightly worse than that of SCAPE-M in these bands. Considering its acceptable accuracy and simplicity both in principle and at implementation compared with the SCAPE-M method, the new method provides an option for atmospheric correction of MERIS data in inland turbid case II waters with applications aiming for chl-a estimation.     

Towards a practical remote-sensing model of suspended sediment concentrations in turbid waters using MERIS measurements

A new empirical index, termed the normalized suspended sediment index (NSSI), is proposed to predict total suspended sediment (TSS) concentrations in inland turbid waters using Medium Resolution Imaging Spectrometer (MERIS) full-resolution (FR) 300 m data. The algorithm is based on the normalized difference between two MERIS spectral bands, 560 and 760 nm. NSSI shows its potential in application to our study region – Poyang Lake – the largest freshwater lake in China. An exponential function (R² = 0.90, p < 0.01) accurately explained the variance in the in situ data and showed better performance for the TSS range 10–524 mg l^?1. The algorithm was then validated with TSS estimates using an atmospheric-corrected MERIS FR image. The validation showed that the NSSI algorithm was a more robust TSS algorithm than the band-ratio algorithms. Findings of this research imply that NSSI can be successfully used on MERIS images to obtain TSS in Poyang Lake. This work provided a practical remote-sensing approach to estimate TSS in the optically and hydrologically complex Poyang Lake and the method can be easily extended to other similar waters.     

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.     

Ocean transparency from space: Validation of algorithms estimating Secchi depth using MERIS, MODIS and SeaWiFS data

Ocean transparency, often measured using Secchi disk, is a useful index of water quality or productivity and is used in many environmental studies. The spaceborne ocean color sensors provide synoptic and regular radiometric data and can be used for applying environmental policies if the data is converted into relevant biogeochemical properties. We adapted and developed semi-analytical and empirical algorithms to estimate the Secchi depth from satellite ocean color data in both coastal and oceanic waters. The development of the algorithms is based on the use of a comprehensive in situ bio-optical dataset. The algorithms are validated using an extensive set of coincident satellite estimates and in situ measurements of the Secchi depth (so-called matchups). More than 400 matchups are compiled for the MERIS, MODIS and SeaWiFS sensors. The comparison between Secchi depth retrievals from remote sensing data and in situ measurements yields determination coefficients (R²) between 0.50 and 0.73, depending on the sensor and algorithm. The type II linear regression slopes and intercepts vary between 0.95 and 1.46, and between − 0.8 and 6.2 m, respectively. While semi-analytical algorithms provide the most promising results on in situ data, the empirical one proves to be more robust on remote sensing data because it is less sensitive to error due to erroneous atmospheric corrections. Using ocean color archives, one can derive maps of ocean transparency for different areas. Our climatology of the Secchi depth based on ocean color for the transition zone between the North Sea and Baltic Sea is compared to an historical dataset.     

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.     

MERIS satellite chlorophyll mapping of oligotrophic and eutrophic waters in the Laurentian Great Lakes

Chlorophyll-a (Chla) concentrations and ‘water-leaving’ reflectance were assessed along transects in Keweenaw Bay (Lake Superior) and in Green Bay (Lake Michigan) (two of the Laurentian Great Lakes, USA), featuring oligotrophic (0.4–0.8 mg Chla m^? 3) and eutrophic to hyper-eutrophic waters (11–131 mg Chla m^? 3), respectively. A red-to-NIR band Chla retrieval algorithm proved to be applicable to Green Bay, but gave mostly negative values for Keweenaw Bay. An alternative algorithm could be based on Chla fluorescence, which in Keweenaw Bay was indicated by enhanced reflectance near 680 nm. Bands 7, 8 and 9 of the Medium Resolution Imaging Spectrometer (MERIS) have been specifically designed to detect phytoplankton fluorescence in coastal waters. A quite strong linear relationship was found between Chla concentration and fluorescence line height (FLH) computed with these MERIS bands. The same relationship held for observations on oligotrophic waters elsewhere, but not for Green Bay, where the FLH diminished to become negative as Chla increased. The remote sensing application of the algorithms could be tested because a MERIS scene was acquired coinciding with the day of the field observations in Keweenaw Bay and one day after those in Green Bay. For Green Bay the pixel values from the red-to-NIR band algorithm compared well to the steep Chla gradient in situ. This result is very positive from the perspective of satellite use in monitoring eutrophic inland and coastal waters in many parts of the world. Implementation of the FLH relationship in the scene of Keweenaw Bay produced highly variable pixel values. The FLH in oligotrophic inland waters like Lake Superior appears to be very close to or below the MERIS detection limit. An empirical algorithm incorporating three MERIS bands in the blue-to-green spectral region might be used as an alternative, but its applicability to other regions and seasons remains to be verified. Moreover, none of the algorithms will be suitable for mesotrophic water bodies. The results indicate that Chla mapping in oligotrophic and mesotrophic areas of the Great Lakes remains problematic for the current generation of satellite sensors.     

Atmospheric correction of ENVISAT/MERIS data over inland waters: Validation for European lakes

Traditional methods for aerosol retrieval and atmospheric correction of remote sensing data over water surfaces are based on the assumption of zero water reflectance in the near-infrared. Another type of approach which is becoming very popular in atmospheric correction over water is based on the simultaneous retrieval of atmospheric and water parameters through the inversion of coupled atmospheric and bio-optical water models. Both types of approaches may lead to substantial errors over optically-complex water bodies, such as case II waters, in which a wide range of temporal and spatial variations in the concentration of water constituents is expected. This causes the water reflectance in the near-infrared to be non-negligible, and that the water reflectance response under extreme values of the water constituents cannot be described by the assumed bio-optical models. As an alternative to these methods, the SCAPE-M atmospheric processor is proposed in this paper for the automatic atmospheric correction of ENVISAT/MERIS data over inland waters. A-priori assumptions on the water composition and its spectral response are avoided by SCAPE-M by calculating reflectance of close-to-land water pixels through spatial extension of atmospheric parameters derived over neighboring land pixels. This approach is supported by the results obtained from the validation of SCAPE-M over a number of European inland water validation sites which is presented in this work. MERIS-derived aerosol optical thickness, water reflectance and water pigments are compared to in-situ data acquired concurrently to MERIS images in 20 validation match-ups. SCAPE-M has also been compared to specific processors designed for the retrieval of lake water constituents from MERIS data. The performance of SCAPE-M to reproduce ground-based measurements under a range of water types and the ability of MERIS data to monitor chlorophyll-a and phycocyanin pigments using semiempirical algorithms after SCAPE-M processing are discussed. It has been found that SCAPE-M is able to provide high accurate water reflectance over turbid waters, outperforming models based on site-specific bio-optical models, although problems of SCAPE-M to cope with clear waters in some cases have also been identified.     

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.     

Remote sensing chlorophyll a of optically complex waters (rias Baixas, NW Spain): Application of a regionally specific chlorophyll a algorithm for MERIS full resolution data during an upwelling cycle

This study takes advantage of a regionally specific algorithm and the characteristics of Medium Resolution Imaging Spectrometer (MERIS) in order to deliver more accurate, detailed chlorophyll a (chla) maps of optically complex coastal waters during an upwelling cycle. MERIS full resolution chla concentrations and in situ data were obtained on the Galician (NW Spain) shelf and in three adjacent rias (embayments), sites of extensive mussel culture that experience frequent harmful algal events. Regionally focused algorithms (Regional neural network for rias Baixas or NNRB) for the retrieval of chla in the Galician rias optically complex waters were tested in comparison to sea-truth data. The one that showed the best performance was applied to a series of six MERIS (FR) images during a summer upwelling cycle to test its performance. The best performance parameters were given for the NN trained with high-quality data using the most abundant cluster found in the rias after the application of fuzzy c-mean clustering techniques (FCM). July 2008 was characterized by three periods of different meteorological and oceanographic states. The main changes in chla concentration and distribution were clearly captured in the images. After a period of strong upwelling favorable winds a high biomass algal event was recorded in the study area. However, MERIS missed the high chlorophyll upwelled water that was detected below surface in the ria de Vigo by the chla profiles, proving the necessity of in situ observations. Relatively high biomass “patches” were mapped in detail inside the rias. There was a significant variation in the timing and the extent of the maximum chla areas. The maps confirmed that the complex spatial structure of the phytoplankton distribution in the rias Baixas is affected by the surface currents and winds on the adjacent continental shelf. This study showed that a regionally specific algorithm for an ocean color sensor with the characteristics of MERIS in combination with in situ data can be of great help in chla monitoring, detection and study of high biomass algal events in an area affected by coastal upwelling such as the rias Baixas.     

Assessment of SeaWiFS,MODIS, and MERIS ocean colour products in the South China Sea

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS) remote-sensing radiometric and chlorophyll-a (chl-a) concentration products for the South China Sea (SCS) from October 2003 to May 2010 were assessed using in situ data. A strict spatiotemporal match-up method was used to minimize the temporal variability effects of atmosphere and seawater around the measurement site. A comparison of the remote-sensing reflectance (R_rs(λ)) of the three sensors with in situ values from the open waters of the SCS showed that the mean absolute percentage difference varied from 13% to 55% in the 412–560 nm spectral range. Generally, the MERIS radiometric products exhibited higher typical uncertainties and bias than the SeaWiFS and MODIS products. The R_rs(443) to R_rs(555/551/560) band ratios of the satellite data were in good agreement with in situ observations for these sensors. The SeaWiFS, MODIS, and MERIS chl-a products overestimated in situ values by 74%, 42%, and 120%, respectively. MODIS retrieval accuracy was better than those of the other sensors, with MERIS performing the worst. When the match-up criteria were relaxed, the assessment results degraded systematically. Therefore, strict spatiotemporal match-up is recommended to minimize the possible influences of small-scale variation in geophysical properties around the measurement site. Coastal and open-sea areas in the SCS should be assessed separately because their biooptical properties are different and the results suggest different atmospheric correction problems.     

Estimating total suspended matter concentration in tropical coastal waters of the Berau estuary,Indonesia

This study presents the application of a semi-empirical approach, based on the Kubelka–Munk (K-M) model, to retrieve the total suspended matter (TSM) concentration of water bodies from ocean colour remote sensing. This approach is validated with in situ data sets compiled from the tropical waters of Berau estuary, Indonesia. Compared to a purely empirical approach, the K-M model provides better results in the retrieval of TSM concentration on both data sets (in situ and Medium Resolution Imaging Spectrometer (MERIS)). In this study, the K-M model was calibrated with in situ measurements of remote-sensing reflectance (R _rs) and TSM concentration. Next, the inverse K-M model was successfully applied to images taken by the MERIS instrument by generating regional maps of TSM concentration. MERIS top-of-atmosphere radiances were atmospherically corrected using the Moderate Spectral Resolution Atmospheric Transmittance (MODTRAN) radiative transfer model. The best correlation between R _rs measured in situ and R _rs MERIS was found to be at a wavelength of 620 nm. The TSM concentrations retrieved using the K-M model showed a lower root mean square error (RMSE), a higher coefficient of determination and a smaller relative error than those retrieved by the purely empirical approach.     

Using MERIS full resolution data to monitor coastal waters — A case study from Himmerfjärden, a fjord-like bay in the northwestern Baltic Sea

In this paper we investigate if MERIS full resolution (FR) data (300 m) is sufficient to monitor changes in optical constituents in Himmerfjärden, a fjord-like, north–south facing bay of about 30 km length and 4 km width. The MERIS FR products were derived using a coastal processor (FUB Case-2 Plug-In). We also compared the performance between FUB and standard processor (MEGS 7.4), using reduced resolution (RR) data (1 km resolution) from the open Baltic Sea, and compared the products to sea-truthing data. The optical variables measured for sea-truthing were chlorophyll, suspended particulate matter (SPM), as well as coloured dissolved organic matter (CDOM, also termed yellow substances), and the spectral diffuse attenuation coefficient, K_d(490). The comparison of the RR data to the sea-truthing data showed that, in the open Baltic Sea, the MERIS standard processor overestimated chlorophyll by about 59%, and SPM by about 28%, and underestimated yellow substance by about 81%, whereas the FUB processor underestimated SPM by about 60%, CDOM by about 78%, and chlorophyll a by about 56%. The FUB processor showed a relatively high precision for all optical components (standard deviation: 6–18%), whereas the precision for the MEGS 7.4 was rather low (standard deviation: 43–73%), except for CDOM (standard deviation: 13%). The analysis of the FR data showed that all FR level 2 water products derived from MERIS followed a polynomial decline in concentration when moving off-shore. The distribution of chlorophyll and SPM was best described by a 2nd order polynomial, and the distribution of CDOM by a 3rd order polynomial, verifying the diffusional model described in Kratzer and Tett [Kratzer, S. and Tett, P. (in press). Using bio-optics to investigate the extent of coastal waters — a Swedish case study. Hydrobiologia.]. A new K_d(490) and Secchi depth algorithm based on MERIS channel 3 (490 nm) and channel 6 (620 nm) each was derived from radiometric sea-truthing data (TACCS, Satlantic). Applying the K_d(490) algorithm to the MERIS FR data over Himmerfjärden, and comparing to sea-truthing data the results showed a strong correlation (r = 0.94). When comparing the FR data to the sea-truthing data CDOM and K_d(490) showed a low accuracy, but a high precision with a rather constant off-set. In summary, one may state that the precision of MERIS data improves by applying the FUB Case-2 processor and the accuracy improves with improved spatial resolution for chlorophyll and SPM. Furthermore, the FUB processor can be used off-the-shelf for open Baltic Sea monitoring, provided one corrects for the respective off-set from sea-truthing data which is most likely caused by an inaccuracy in the atmospheric correction. Additionally, the FR data can be used to derive CDOM, K_d(490) and Secchi depth in Himmmerfjärden if one corrects for the respective off-set. We will need to perform more comparisons between sea-truthing and MERIS FR data before the new K_d(490) algorithm can be made operational, including also scenes from other times of year. In order to provide a level 2 product that can be used reliably by the Baltic Sea user community, our recommendation to ESA is to include the spectral attenuation coefficient as a MERIS standard product.     

Measurement of water colour using AVIRIS imagery to assess the potential for an operational monitoring capability in the Pamlico Sound Estuary,USA

The monitoring of water colour parameters can provide an important diagnostic tool for the assessment of aquatic ecosystem condition. Remote sensing has long been used to effectively monitor chlorophyll concentrations in open ocean systems; however, operational monitoring in coastal and estuarine areas has been limited because of the inherent complexities of coastal systems, and the coarse spectral and spatial resolutions of available satellite systems. Data were collected using the National Aeronautics and Space Administration (NASA) Advanced Visible–Infrared Imaging Spectrometer (AVIRIS) flown at an altitude of approximately 20 000 m to provide hyperspectral imagery and simulate both MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectrometer (MODIS) data. AVIRIS data were atmospherically corrected using a radiative transfer modelling approach and analysed using band ratio and linear regression models. Regression analysis was performed with simultaneous field measurements data in the Neuse River Estuary (NRE) and Pamlico Sound on 15 May 2002. Chlorophyll a (Chl a) concentrations were optimally estimated using AVIRIS bands (9.5 nm) centred at 673.6 and 692.7 nm, resulting in a coefficient of determination (R ²) of 0.98. Concentrations of Chromophoric Dissolved Organic Matter (CDOM), Total Suspended Solids (TSS) and Fixed Suspended Solids (FSS) were also estimated, resulting in coefficients of determination of R ² = 0.90, 0.59 and 0.64, respectively. Ratios of AVIRIS bands centred at or near those corresponding to the MERIS and MODIS sensors indicated that relatively good satellite‐based estimates could potentially be derived for water colour constituents at a spatial resolution of 300 and 500 m, respectively.     

An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea

An extensive bio-optical data set from field measurements was used to evaluate the performance of standard Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color (in-water) algorithms in the Baltic Sea, which represents an example of optically complex Case 2 waters with high concentration of colored dissolved organic matter (CDOM). The data set includes coincident measurements of radiometric quantities, chlorophyll a concentration (Chl a), and absorption coefficient of CDOM, which were taken on 25 cruises between 1993 and 2001. The data cover a wide range of variability with Chl a in surface waters from about 0.3 to 100 mg m⁻³. All the MODIS pigment algorithms examined as well as the SeaWiFS OC4v4 algorithm showed a systematic and large overestimation in chlorophyll retrievals. The mean systematic and random errors based on our entire data set exceeded 150% or even 200% in some cases, making these standard algorithms inadequate for pigment determinations in the Baltic. Although new parameterization of the standard pigment algorithms based on our field measurements in the Baltic resulted in a significant reduction of errors, the overall performance of such regionally tuned algorithms remained unsatisfactory. For example, the mean normalized bias (MNB) for the regionally tuned MODIS chlor_a_2 algorithm was reduced to 26% (from over 200% for the standard algorithm), but the root mean square (RMS) error was still large (>100%). The MODIS K_490 algorithm for estimating the diffuse attenuation coefficient of downwelling irradiance showed the best performance among all the algorithms examined. With the new coefficients based on our field data, the regional version of this algorithm showed an acceptable level of errors, MNB=4% and RMS=30%. In addition to the apparent problems of the standard in-water bio-optical algorithms, we found that the atmospheric correction currently in use for MODIS and SeaWiFS imagery usually fails to retrieve upwelling radiances emerging from the Baltic Sea. The match-up comparisons of the coincident in situ and satellite determinations of normalized water-leaving radiances showed generally poor agreement, especially in the blue spectral region. It appears that new approaches for ocean color algorithms are required in the Baltic Sea.