Intercomparison of ocean colour band-ratio algorithms for chlorophyll concentration in the Subtropical Front east of New Zealand

This study intercompared the performance of eight band-ratio chlorophyll-a algorithms which together can be used to process measurements from the ocean colour satellite sensors CZCS, OCTS, SeaWiFS, MODIS, MERIS, and GLI. The study area included Subtropical, Subtropical Front and Subantarctic waters east of New Zealand, and Case 1 waters of the New Zealand northeast continental shelf. Over 170 co-incident measurements of spectral normalised water-leaving radiance and near-surface concentration of chlorophyll-a were made on nine research voyages between 1998 and 2000. The studentised bootstrap method was used to identify statistically significant bias in algorithm products relative to in situ measurements. The band-ratio algorithms used by CZCS, OCTS and SeaWiFS missions systematically underestimated chlorophyll-a concentration in the offshore regions by between 21% and 45%, but showed no systematic bias in the continental shelf waters. The band-ratio algorithms applicable to the MODIS and MERIS sensors had no clear bias with respect to in situ measurements in offshore waters, but had a positive bias of 20% over the continental shelf. The proposed GLI band-ratio algorithm led to estimates that were negatively biased with respect to in situ measurement offshore (− 30%), and positively biased over the continental shelf (20%). The results were consistent with unusually high values of absorption in the blue part of the spectrum (443-490 nm) compared to the green part (∼ 550 nm) by phytoplankton pigments in the offshore waters, and high chlorophyll-specific absorption over the continental shelf.     

Regional and seasonal variability of chlorophyll-a in Chesapeake Bay as observed by SeaWiFS and MODIS-Aqua

Concentrations of the phytoplankton pigment chlorophyll-a (C_a) provide indicators of nutrient over-enrichment that has negatively affected Chesapeake Bay, U.S.A. C_a time-series from the National Aeronautics and Space Administration (NASA) Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer aboard the Aqua spacecraft (MODIS-Aqua) provide observations on temporal and spatial scales that far exceed current field and aircraft sampling strategies. These sensors provide consistent, frequent, and high density data to potentially complement ongoing Bay monitoring activities. We used the in situ Water Quality Monitoring Data set of the Chesapeake Bay Program to evaluate decade-long time-series of SeaWiFS and MODIS-Aqua C_a retrievals in the Bay. The accuracy of the retrievals generally degraded with increasing latitude as the optical complexity increases northward. C_a derived using empirical (“band ratio”) algorithms overestimated in situ measurements by 10-50 and 40-100% for SeaWiFS and MODIS-Aqua, respectively, but with limited variability. C_a derived using spectral-matching algorithms showed less bias for both sensors, but with significant variability and sensitivity to radiometric errors. Regionally-tuned empirical algorithms performed best throughout the Bay, offering a combination of reasonable accuracy and high spatial coverage. The radiometric spectral resolution used as input to the algorithms strongly influenced the quality of C_a retrievals from both sensors. These results establish a baseline quantification of algorithm and sensor performance in a variable and stressed ecosystem against which novel approaches might be compared.     

Remote sensing of phytoplankton community composition along the northeast coast of the United States

Satellite imagery has proven to be a powerful tool for measuring chlorophyll a in surface waters. While this provides an estimate of total phytoplankton biomass, it does not distinguish between phytoplankton groups, many of which have functional differences and therefore affect biogeochemical cycles differently. Phytoplankton pigment analysis has been used to quantify a wide range of photosynthetic and accessory pigments, and chemotaxonomic analysis (e.g. CHEMTAX) has been used to successfully quantify functional taxonomic groups in nature based on pigment distributions. Here, we combine CHEMTAX analysis with satellite-derived distributions of specific phytoplankton pigments to describe the distributions of particular components of the phytoplankton community in the northeast coast of the United States from space. The spatial and seasonal variations in phytoplankton community structure elucidated through satellite remote sensing methods generally agreed with observations of abundance estimates of cell counts. Diatoms were generally the most abundant phytoplankton in this region, especially during Winter-Spring and in the inner shelf, but phytoplankton populations shifted to increasing abundance of other taxa during Summer, especially offshore. While still preliminary, satellite-derived taxa-specific information with proper regional controls holds promise for providing information on phytoplankton abundance to a taxonomic group level which would greatly improve our understanding of the impacts of human activity and climate change on ecosystems.     

Satellite-derived parameters for biological modelling in coastal waters: Illustration over the eastern continental shelf of the Bay of Biscay

In biological modelling of the coastal phytoplankton dynamics, the light attenuation coefficient is often expressed as a function of the concentrations of chlorophyll and mineral suspended particulate matter (SPM). In order to estimate the relationship between these parameters over the continental shelf of the northern Bay of Biscay, a set of in situ data has been gathered for the period 1998-2003 when SeaWiFS imagery is available. These data comprise surface measurements of the concentrations of total SPM, chlorophyll, and irradiance profiles from which is derived the attenuation coefficient of the photosynthetically available radiation, K_PAR. The performance of the IFREMER look-up table used to retrieve the chlorophyll concentration from the SeaWiFS radiance is evaluated on this new set of data. The quality of the estimated chlorophyll concentration is assessed from a comparison of the variograms of the in situ and satellite-derived chlorophyll concentrations. Once the chlorophyll concentration is determined, the non living SPM, which is defined as the SPM not related to the dead or alive endogenous phytoplankton, is estimated from the radiance at 555 nm by inverting a semi-analytic model. This method provides realistic estimations of concentrations of chlorophyll and SPM over the continental shelf all over the year. Finally, a relationship, based on non living SPM and chlorophyll, is proposed to estimate K_PAR on the continental shelf of the Bay of Biscay. The same formula is applied to non living SPM and chlorophyll concentrations, observed in situ or derived from SeaWiFS radiance.     

Comparison of MERIS,MODIS, SeaWiFS-derived particulate organic carbon,and in situ measurements in the South China Sea

Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) particulate organic carbon (POC) concentration products for the South China Sea (SCS) were compared with in situ data collected from October 2007 to December 2013. Spectral remote-sensing reflectance (R_rs,λ) was also measured to help understand POC algorithm performance. A strict comparison of the satellite-derived POC and in situ measurements showed that MERIS, MODIS, and SeaWiFS underestimated in situ values by 29.1, 11.7, and 31.5%, respectively. Similar results were obtained with a relaxed matching criterion. Through analysis of the causes of product uncertainty, the results suggested that satellite retrieval of R_rs,λ and the global POC algorithm both have an impact on inversion accuracy. However, the formulation of the POC algorithm seems to be more critical. When a regional algorithm was developed to obtain satellite-derived POC, both the strict and relaxed comparison results showed significant improvement, but for coastal waters, both algorithms had larger errors. Other factors affecting the comparison are also discussed.     

Space-borne study of seasonal,multi-year,and decadal phytoplankton dynamics in the Bay of Biscay

Seasonal and inter-annual variations in phytoplankton community abundance in the Bay of Biscay are studied. Preliminarily processed by the National Aeronautics and Space Administration (NASA) to yield normalized water-leaving radiance and the top-of-the-atmosphere solar radiance, Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Coastal Zone Color Scanner (CZCS) data are further supplied to our dedicated retrieval algorithms to infer the sought for parameters. By applying the National Oceanic and Atmospheric Administration's (NOAA's) Advanced Very High Resolution Radiometer (AVHRR) data, the surface reflection coefficient in the only band in the visible spectrum is derived and employed for analysis. Decadal bridged time series of variations of diatom-dominated phytoplankton and green dinoflagellate Lepidodinium chlorophorum within the shelf zone and the coccolithophore Emiliania huxleyi in the pelagic area of the Bay are documented and analysed in terms of impacts of some biogeochemical and geophysical forcing factors.

It is shown that in the shelf zone of the Bay, the diatom-dominated phytoplankton community variations are predominantly controlled by river discharge variations, by water column stratification conditions (forming in winter–early spring), and by wind action (resulting in such phenomena as up-wellings and sediment re-suspension).

Satellite data indicate that while in river deltas and adjoining waters the L. chlorophorum blooming events occur annually, in the Iroise Sea and near the Bailiwick of Guernsey, they happen irregularly. It is thought that such an irregular pattern, possibly, arises from L. chlorophorum competing with other phytoplankton species for nutrients.

E miliania huxleyi blooms are found to occur nearly every year in the northern part of the Bay, whereas in the central area, this phenomenon occurs very irregularly. Satellite data indicate that variations in the water chemistry (variations in the nitrogen : phosphorus ratio due to preceding blooms of diatoms), and the incident irradiance level (degree of cloudiness), are important factors controlling the occurrence of E. huxleyi blooming in the central part of the Bay. Covering a 30 year period, the bridged data from CZCS, AVHRR, SeaWiFS, and MODIS imply that climate change might be responsible for the observed increase in E. huxleyi blooming events in the Bay since 1979.     

A new regional algorithm for estimating chlorophyll-a in the Alboran Sea (Mediterranean Sea) from MODIS-Aqua satellite imagery

The utility of three different algorithms for retrieving surface chlorophyll-a values from satellite images of MODIS-Aqua is tested in the northern Alboran Sea. The available global algorithm to calculate chlorophyll-a from reflectance of MODIS-Aqua (OC3M) overestimates the surface chlorophyll-a in the study area. Another regional algorithm specifically developed for the Mediterranean Sea (MedOC3) improves the estimates although the best outcome is obtained with OC5, which was developed for Atlantic coastal waters. The three tested algorithms perform worse at in situ chlorophyll-a concentrations higher than 1 mg m^?3 and exhibit uncertainty levels higher than 35% for this range of concentrations. A new algorithm (ALBOC3) is proposed which produces a good estimation of the in situ chlorophyll-a for the whole range of concentrations normally registered in the study area (0.1–3.5 mg m^?3). We hypothesize that the particular bio-optical features of the northern Alboran Sea phytoplankton explain the poor functioning of the published algorithms that have been tested in this work.     

Inter-comparison of OC-CCI chlorophyll-a estimates with precursor data sets

Ocean colour is the only essential climate variable that targets a biological variable (chlorophyll-a concentration (chl-a)) and is also amenable to remote sensing at the global scale. However, the finite lifetime of individual ocean-colour sensors, and the differences in their characteristics increase the difficulty of creating a long-term, consistent, ocean-colour time series that meets the requirements of climate studies. The Ocean Colour Climate Change Initiative (OC-CCI), a European Space Agency programme, has recently produced a time series of satellite-based ocean-colour products at the global scale, merging data from three sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer on the Aqua Earth Observing System (MODIS-Aqua), and Medium Resolution Imaging Spectrometer (MERIS), while attempting to reduce inter-sensor biases.In this work we present a comparison between the OC-CCI chlorophyll-a product and precursor satellite-derived data sets, from both single missions (SeaWiFS, MODIS-Aqua, and MERIS) and multi-mission products (global ocean colour (GlobColour) and Making Earth Science Data Records for Use in Research Environments (MEaSUREs)). To this end, OC-CCI global monthly composites are compared to the similar products offered by single-mission and multi-mission records. Our results indicate that the OC-CCI product provides a higher number of observations. Comparing the observations that match with precursors, the OC-CCI product was generally most similar to the single-mission products. Relationships between OC-CCI and other precursors did not change significantly during a common and continuous period, and, on average the root-mean-square differences between log-transformed chlorophyll-a concentration are below or equal to 0.11. Further, when considering variability that could arise when merging data from different sources, it is shown that the OC-CCI product is a longer term constant than those from other multi-mission initiatives studied here.     

The effect of phytoplankton pigment composition and packaging on the retrieval of chlorophyll-a concentration from satellite observations in the Southern Ocean

The Antarctic waters are known to be optically unique and the standard empirical ocean colour algorithms applied to these waters may not address the regional bio-optical characteristics. This article sheds light on the performance of current empirical algorithms and a regionally optimized algorithm (ROA) for the retrieval of chlorophyll-a (chl-a) concentration from Aqua-Moderate Resolution Imaging Spectroradiometer (Aqua-MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) in the Indian Ocean Sector of Southern Ocean (IOSO). Analysis indicated that empirical algorithms used for the retrieval of chl-a concentration from Aqua-MODIS and SeaWiFS underestimate by a factor varying from 2 to 2.9, resulting in underestimation when in situ chl-a exceeds about 0.3 mg m^?3. To explain these uncertainties, a study was carried out to understand the effect of phytoplankton pigment composition and pigment packaging on remote-sensing reflectance (R_rs,λ), from the analysis of phytoplankton-specific absorption coefficient (a_ph,*_λ). The spatial variation of phytoplankton groups analysed using diagnostics pigments (DP) indicated shifting of the phytoplankton community structure from offshore to coastal Antarctic, with a significant increasing trend for diatoms and a decreasing trend for haptophytes population. The diatom-dominated population exhibits lower a_ph,*_λ in the 405–510 nm region (with relative flattening in 443–489 nm) compared with the a_ph,*_λ spectra of the haptophytes-dominated population that peaks near 443 nm. The flattening of a_ph,*_λ spectra for the diatom-dominated population was attributed to its larger cell size, which leads to pigment packaging (intracellular shading) and in turn results in higher R_rs,λ. The relationship between pigment composition (normalized by chl-a) and blue:green absorption band ratios (a_ph,*₄₄₃:a_ph,*₅₅₅ and a_ph,*₄₈₉:a_ph,*₅₅₅) corresponding to the Aqua-MODIS and SeaWiFS bands showed in-phase associations with most of the pigments such as 19?-hexanoyloxyfucoxanthin, 19?-butanoyloxyfucoxanthin, peridinin, and zeaxanthin. In contrast, the out-of-phase association observed between the blue:green absorption ratios and fucoxanthin indicated apparent deviations from the general pigment retrieval algorithms, which assumes that blue:green ratios vary in a systematic form with chl-a. The out-of-phase correspondence suggests that the increasing trend of fucoxanthin pigments towards the Antarctic coast was associated with the decreasing trend of blue:green absorption ratios and in turn results in higher R_rs,λ. Therefore, an increase in R_rs,λ leads to underestimation of chl-a from Aqua-MODIS and SeaWiFS in the IOSO region.     

Technical note Empirical algorithm using SeaWiFS hyperspectral bands: A simple test

The aim of this note is to test an empirical algorithm using spectral curvature theory at selected SeaWiFS hyerspectral bands, in order to estimate the chlorophyll pigment concentrations in the coastal waters of Baie des Chaleurs (Gulf of St. Lawrence). Simulated SeaWiFS algorithms require the measurement of reflectances with a high sensitive spectroradiometer as well as the responsitivity of SeaWiFS sensor at each band. Volume reflectances were taken using a hand held spectroradiometer simultaneously to measurements of in situ chlorophyll pigment concentrations. An empirical algorithm of the form: log (chlorophyll pigments) 0.92-0.46 Delta2 log R0sea(555) yields an estimate of chlorophyll pigment concentrations within a mean deltaC / C of 23%.     

Technical note The SeaWiFS Automatic Data Processing System (SeaAPS)

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) was designed to measure ocean colour, the spectral variation of water-leaving radiance that can be related to the concentrations of phytoplankton pigments, coloured dissolved organic material and suspended particulate matter. The Dundee Satellite Receiving Station records and archives 1-km imagery covering the European shelf-seas, north-east Atlantic Ocean and Mediterranean Sea, which is subsequently processed in near-real time by Plymouth Marine Laboratory using SeaWiFS Automatic Processing System (SeaAPS). SeaWiFS imagery is combined with contemporary Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data to provide products, supplied via the World Wide Web, that are used within many areas of oceanographic research.     

A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters

Chlorophyll-a concentration derived from the Sea-viewing Wide Fieldof-view Sensor (SeaWiFS) after applying the current SeaWiFS Data Analysis System (SeaDAS) processing tools appears to be higher than reality in coastal areas, particularly from late summer to early spring when optical properties of water are dominated by yellow substances and suspended matter. As a complement to the SeaWiFS standard procedure addressing clear water, empirical algorithms can bring immediate progress for observing the coastal domain. This paper proposes to modify the SeaWiFS Ocean Colour 4 band algorithm (OC4) by including the 412 and 555 channels. The effect of the suspended matter on the ratios used as inputs in OC4 is revealed by the 555 channel whereas the atmospheric over-correction and the absorption by yellow substances are related to the 412 channel. Based on a dataset located in the English Channel and on the continental shelf of the Bay of Biscay, a parametrization of the relationship between the OC4 ratio and the 412 and 555 bands has been empirically proposed for different chlorophyll concentrations. Application of a lookup table, relating triplets (OC4 band ratio, 412 and 555 bands) to chlorophyll-a concentration, provides realistic concentration maps likely to satisfy the needs of researchers involved in environmental surveying or fishery management.     

Retrieval of oceanic chlorophyll concentration with relevance vector machines

In this communication, we evaluate the performance of the relevance vector machine (RVM) for the estimation of biophysical parameters from remote sensing data. For illustration purposes, we focus on the estimation of chlorophyll-a concentrations from remote sensing reflectance just above the ocean surface. A variety of bio-optical algorithms have been developed to relate measurements of ocean radiance to in situ concentrations of phytoplankton pigments, and ultimately most of these algorithms demonstrate the potential of quantifying chlorophyll-a concentrations accurately from multispectral satellite ocean color data. Both satellite-derived data and in situ measurements are subject to high levels of uncertainty. In this context, robust and stable non-linear regression methods that provide inverse models are desirable.Lately, the use of the support vector regression (SVR) has produced good results in inversion problems, improving state-of-the-art neural networks. However, the SVR has some deficiencies, which could be theoretically alleviated by the RVM. In this paper, performance of the RVM is evaluated in terms of accuracy and bias of the estimations, sparseness of the solutions, robustness to low number of training samples, and computational burden. In addition, some theoretical issues are discussed, such as the sensitivity to training parameters setting, kernel selection, and confidence intervals on the predictions.Results suggest that RVMs offer an excellent trade-off between accuracy and sparsity of the solution, and become less sensitive to the selection of the free parameters. A novel formulation of the RVM that incorporates prior knowledge of the problem is presented and successfully tested, providing better results than standard RVM formulations, SVRs, neural networks, and classical bio-optical models for SeaWIFS, such as Morel, CalCOFI and OC2/OC4 models.     

The colour of the Mediterranean Sea: Global versus regional bio-optical algorithms evaluation and implication for satellite chlorophyll estimates

In this paper, uncertainties in the retrieval of satellite surface chlorophyll concentrations in the Mediterranean Sea have been evaluated using both regional and global ocean colour algorithms. The rationale for this effort was to define the most suitable ocean colour algorithm for the reprocessing of the entire SeaWiFS archive over the Mediterranean region where standard algorithms were demonstrated to be inappropriate. Using a large dataset of coincident in situ chlorophyll and optical measurements, covering most of the trophic regimes of the basin, we validated two existing regional algorithms [Bricaud, A., E. Bosc, and D. Antoine, 2002. Algal biomass and sea surface temperature in the Mediterranean Basin — Intercomparison of data from various satellite sensors, and implications for primary production estimates. Remote Sensing of Environment, 81(2-3), 163-178.; D'Ortenzio, F., S. Marullo, M. Ragni, M. R. d'Alcala and R. Santoleri, 2002. Validation of empirical SeaWiFS algorithms for chlorophyll-alpha retrieval in the Mediterranean Sea — A case study for oligotrophic seas. Remote Sensing of Environment, 82(1), 79-94.] and the global algorithm OC4v4 used for standard NASA SeaWiFS products. The results of our analysis confirmed that the OC4v4 performs worse than the two existing regional algorithms. Nonetheless, these two regional algorithms do show uncertainties dependent on chlorophyll values. Then, we introduced a better tuned algorithm, the MedOC4. Using an independent set of in situ chlorophyll data, we quantified the uncertainties in SeaWiFS chlorophyll estimates using the existing and new regional algorithms. The results confirmed that MedOC4 is the best algorithm matching the requirement of unbiased satellite chlorophyll estimates and improving the percentage of the satellite uncertainty, and that the NASA standard chlorophyll products are affected by an uncertainty of the order of 100%. Moreover, the analysis suggests that the poor quality of the SeaWiFS chlorophyll in the Mediterranean is not due to the atmospheric correction term but to peculiarities in the optical properties of the water column. Finally the observed discrepancy between the global and the regional bio-optical algorithms has been discussed analysing the differences between the two in situ datasets used for tuning the algorithms (SeaBASS versus ours). The main results are that methodological differences in the two datasets cannot play a major role and the inherent bio-optical properties of the basin can explain the observed discrepancy. In particular the oligotrophic water of the Mediterranean Sea is less blue (30%) and greener (15%) than the global ocean.     

Satellite-derived multi-year trend in primary production in the Arctic Ocean

Spaceborne one month averaged data, predominantly from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and partly from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used to investigate changes in primary production (PP) by phytoplankton in the Arctic Ocean from 1998 to 2010. Several PP retrieval algorithms were tested against the collected in situ data, and it was shown that the algorithm by Behrenfeld and Falkowski gave the best results (with the coefficient of correlation, r, equal to 0.8 and 0.75, respectively, for the pelagic and shelf zones). Based on the performed test, the Behrenfeld and Falkowski algorithm was further applied for determining both the annual PP in the Arctic and the PP trend over the above-mentioned time period. Results of our analysis indicate that PP in the Arctic has increased by ?15.9% over 13 years (1998–2010). This finding, as well as the absolute annual values of PP remotely quantified in the present study, is at odds with analogous numerical assessments by other workers. These disagreements are thought to be due to differences in the applied methodologies of satellite data processing such as cloud masking and determination of phytoplankton concentration within (1) overcast areas and (2) areas of massive growth of coccolithophores as well as (3) in the shelf zone prone to a significant influence of land and river run-off.     

Satellite observations of optical and biological properties in the Korean dump site of the Yellow Sea

With the standard near-infrared (NIR) atmospheric correction algorithm for ocean color data processing, a high chlorophyll-a concentration patch was consistently observed from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua platform in the middle of the Yellow Sea during the spring (end of March to early May). This prominent patch was not observed in the historical ocean color satellite imageries in late 1970s to early 1980s, and a location corresponding to this patch has been used as a Korean dump site since 1988. At the same time, MODIS chlorophyll-a concentrations derived using the shortwave infrared (SWIR) atmospheric correction algorithm developed for the ocean color satellite data in turbid coastal or high-productive ocean waters were significantly reduced.Comparison between in situ and MODIS chlorophyll-a measurements shows that the chlorophyll-a from the MODIS-Aqua products using the standard-NIR atmospheric correction algorithm is significantly overestimated. The images of the MODIS-derived normalized water-leaving radiance spectra and water diffuse attenuation coefficient data using the NIR-SWIR-based atmospheric correction approach show that absorption and scattering by organic and inorganic matter dumped in the Korean dump site have strongly influenced the satellite-derived chlorophyll-a data. Therefore, the biased high chlorophyll-a patch in the region is in fact an overestimation of chlorophyll-a values due to large errors from the standard-NIR atmospheric correction algorithm. Using the NIR-SWIR algorithm for MODIS-Aqua ocean color data processing, ocean color products from 2002 to 2008 for the Korean dump site region have been generated and used for characterizing the ocean optical and biological properties. Results show that there have been some important changes in the seasonal and interannual variations of phytoplankton biomass and other water optical and biological properties induced by colored dissolved organic matters, as well as suspended sediments.     

Evaluation of SeaWiFS chlorophyll‐a in the Black and Mediterranean Seas

The performance of NASA's OC2 and OC4 algorithms to estimate chlorophyll‐a concentrations from SeaWiFS radiometric measurements on the global scale was tested in two contrasted bio‐optical environments, the Black Sea and the Mediterranean Sea. The in situ bio‐optical measurements were made during October 1999 at 25 stations. Comparisons of the in situ measurements with the concurrent SeaWiFS retrievals indicate that the OC2 and OC4 algorithms are not working satisfactorily in both seas. Case 2 waters dominate the Black Sea and the failure of the algorithms is expected. On the other hand, failure of the algorithms in the case 1 waters of the Mediterranean Sea may be due to their specific optical properties. Modifying the OC4 algorithm to include SeaWiFS information at 412?nm yields a better performance in the Mediterranean Sea without degrading performance in the Black Sea. Combining a local algorithm adapted to oligotrophic waters of the Mediterranean Sea and OC4 provides the best results overall.     

Remote sensing of phytoplankton pigments: A comparison of empirical and theoretical approaches

Algorithms that have been used on a routine basis for remote sensing of the phytoplankton pigment, chlorophyll- a, from ocean colour data from satellite sensors such as the CZCS (Coastal Zone Color Scanner), SeaWiFS (Sea Viewing Wide Field-of-View Sensor) and OCTS (Ocean Colour and Temperature Scanner) are all of an empirical nature. However, there exist theoretical models that allow ocean colour to be expressed as a function of the inherent optical properties of seawater, such as the absorption coefficient and the backscattering coefficient. These properties can in turn be expressed as functions of chlorophyll- a, at least for the so-called Case 1 waters in which phytoplankton may be considered to be the single, independent variable responsible for most of the variations in the marine optical properties. Here, we use such a theoretical approach to model variations in ocean colour as a function of chlorophyll- a concentration, and compare the results with some empirical models in routine use. The parameters of phytoplankton absorption necessary for the implementation of the ocean colour model are derived from our database of over 700 observations of phytoplankton absorption spectra and concurrent measurements of phytoplankton pigments by HPLC (High Performance Liquid Chromatography) techniques. Since there are reports in the literature that significant differences exist in the performance of the algorithms in polar regions compared with lower latitudes, the model is first implemented using observations made at latitudes less than 50. It is then applied to the Labrador Sea, a high-latitude environment. Our results show that there are indeed differences in the performance of the algorithm at high latitudes, and that these differences may be attributed to changes in the optical characteristics of phytoplankton that accompany changes in the taxonomic composition of their assemblages. The sensitivities of the model to assumptions made regarding absorption by coloured dissolved organic matter (or yellow substances) and backscattering by particles are examined. The importance of Raman scattering on ocean colour and its influence on the algorithms are also investigated.     

Preliminary results for SeaWiFS vicarious calibration coefficients in the Baltic Sea

The technique of vicarious calibration is used in connection with an atmospheric correction to improve the Sea viewing Wide Field of view Sensor (SeaWiFS) normalized water-leaving radiance by the first determination of mean vicarious calibration coefficients from in situ measurements in the Baltic Sea. A necessary adjustment of the SeaWiFS pre-flight calibration slope was found to be +3.5%, +0.3%, ?1.7%, ?0.4%, +0.8% and ?1.3% for the first six SeaWiFS channels. The derived mean vicarious calibration coefficients are higher than the coefficients in the standard SeaWiFS Data Analysis System (SeaDAS) software but with similar shape and good agreement with other research results. The coefficients were used to obtain better normalized water-leaving radiance from SeaWiFS measurements in the Baltic Sea. The deviations of calculated to measured radiances in the open Baltic Sea are between 3% and 47% in the channels 412 to 670?nm, with the trend of higher deviations in the blue channels. The objective of radiance determination in all SeaWiFS channels with a maximum uncertainty of 5% in clear water regions is probably not reachable in the turbid water of the Baltic Sea.     

Evaluation of SeaWiFS and MODIS chlorophyll‐a products in the Argentinean Patagonian Continental Shelf (38° S–55° S)

Field measurements of surface chlorophyll‐a concentration were used to evaluate for the first time the performance of the standard Moderate Resolution Imaging Spectroradiometer (MODIS) and both standard and regional Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) ocean colour algorithms in the Patagonian Continental Shelf (PCS) between 38° S and 55° S. The results showed that the regional algorithms did not significantly improve the global algorithm estimates. Moreover, the SeaWiFS OC4v4 algorithm, National Aeronautics and Space Administration (NASA) standard chlorophyll product, showed the best performance among all the algorithms examined. Nonetheless, all the global and local algorithms analysed showed uncertainties dependent on chlorophyll concentration. Low chlorophyll‐a concentration values tended to be overestimated and high values tended to be underestimated. A regional analysis within the PCS showed that higher uncertainties are found in the homogeneous side of the tidal fronts present in the PCS, in areas suggested to be optically complex case 2 waters, while a better result (less bias) was obtained in the southern mid‐shelf region. We discuss the probable reasons and provide possible explanations of the regional differences in the performance of the algorithms.