Chlorophyll-a Algorithms Research Articles

Evaluation of MODIS—Aqua Chlorophyll-a Algorithms in the Basilicata Ionian Coastal Waters

Standard chlorophyll-a (chl-a) algorithms, which rely on Moderate Resolution Imaging Spectro-radiometer (MODIS) data aboard the Aqua satellite, usually show different performances depending on the area under consideration. In this paper, we assessed their accuracy in retrieving the chl-a concentration in the Basilicata Ionian Coastal waters (Ionian Sea, South of Italy). The outputs of one empirical (Med-OC3) and two semi-analytical algorithms, the Garver–Siegel–Maritorena (GSM) and the Generalized Inherent Optical Properties (GIOP) model, have been compared with ground measurements acquired during three different measurement campaigns. The achieved results prove the poor accuracy (adjusted R2 value of 0.12) of the investigated empirical algorithm and, conversely, the good performance of semi-analytical algorithms (adjusted R2 ranging from 0.74 to 0.79). The co-existence of Coloured Dissolved Organic Matter (CDOM) and Non-Algal Particles (NAP) has likely determined large errors in the reflectance ratios used in the OCx form algorithms. Finally, a local scale assessment of the bio-optical properties, on the basis of the in situ dataset, allowed for the definition of an operational local scale-tuned version of the MODIS chl-a algorithm, which assured increased accuracy (adjusted R2 value of 0.86). Such a tuned algorithm version can provide useful information which can be used by local authorities within regional management systems.

Evaluating the portability of satellite derived chlorophyll-a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations

This study evaluated the performances of twenty-nine algorithms that use satellite-based spectral imager data to derive estimates of chlorophyll-a concentrations that, in turn, can be used as an indicator of the general status of algal cell densities and the potential for a harmful algal bloom (HAB). The performance assessment was based on making relative comparisons between two temperate inland lakes: Harsha Lake (7.99 km2) in Southwest Ohio and Taylorsville Lake (11.88 km2) in central Kentucky. Of interest was identifying algorithm-imager combinations that had high correlation with coincident chlorophyll-a surface observations for both lakes, as this suggests portability for regional HAB monitoring. The spectral data utilized to estimate surface water chlorophyll-a concentrations were derived from the airborne Compact Airborne Spectral Imager (CASI) 1500 hyperspectral imager, that was then used to derive synthetic versions of currently operational satellite-based imagers using spatial resampling and spectral binning. The synthetic data mimics the configurations of spectral imagers on current satellites in earth’s orbit including, WorldView-2/3, Sentinel-2, Landsat-8, Moderate-resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS). High correlations were found between the direct measurement and the imagery-estimated chlorophyll-a concentrations at both lakes. The results determined that eleven out of the twenty-nine algorithms were considered portable, with r2 values greater than 0.5 for both lakes. Even though the two lakes are different in terms of background water quality, size and shape, with Taylorsville being generally less impaired, larger, but much narrower throughout, the results support the portability of utilizing a suite of certain algorithms across multiple sensors to detect potential algal blooms through the use of chlorophyll-a as a proxy. Furthermore, the strong performance of the Sentinel-2 algorithms is exceptionally promising, due to the recent launch of the second satellite in the constellation, which will provide higher temporal resolution for temperate inland water bodies. Additionally, scripts were written for the open-source statistical software R that automate much of the spectral data processing steps. This allows for the simultaneous consideration of numerous algorithms across multiple imagers over an expedited time frame for the near real-time monitoring required for detecting algal blooms and mitigating their adverse impacts.

Improved Chlorophyll-a Algorithm for the Satellite Ocean Color Data in the Northern Bering Sea and Southern Chukchi Sea

The Bering and Chukchi seas are an important conduit to the Arctic Ocean and are reported to be one of the most productive regions in the world’s oceans in terms of high primary productivity that sustains large numbers of fishes, marine mammals, and sea birds as well as benthic animals. Climate-induced changes in primary production and production at higher trophic levels also have been observed in the northern Bering and Chukchi seas. Satellite ocean color observations could enable the monitoring of relatively long term patterns in chlorophyll-a (Chl-a) concentrations that would serve as an indicator of phytoplankton biomass. The performance of existing global and regional Chl-a algorithms for satellite ocean color data was investigated in the northeastern Bering Sea and southern Chukchi Sea using in situ optical measurements from the Healy 2007 cruise. The model-derived Chl-a data using the previous Chl-a algorithms present striking uncertainties regarding Chl-a concentrations – for example, overestimation in lower Chl-a concentrations or systematic overestimation in the northeastern Bering Sea and southern Chukchi Sea. Accordingly, a simple two band ratio (Rrs(443)/Rrs(555)) algorithm of Chl-a for the satellite ocean color data was devised for the northeastern Bering Sea and southern Chukchi Sea. The MODIS-derived Chl-a data from July 2002 to December 2014 were produced using the new Chl-a algorithm to investigate the seasonal and interannual variations of Chl-a in the northern Bering Sea and the southern Chukchi Sea. The seasonal distribution of Chl-a shows that the highest (spring bloom) Chl-a concentrations are in May and the lowest are in July in the overall area. Chl-a concentrations relatively decreased in June, particularly in the open ocean waters of the Bering Sea. The Chl-a concentrations start to increase again in August and become quite high in September. In October, Chl-a concentrations decreased in the western area of the Study area and the Alaskan coastal waters. Strong interannual variations are shown in Chl-a concentrations in all areas. There is a slightly increasing trend in Chl-a concentrations in the northern Bering Strait (SECS). This increasing trend may be related to recent increases in the extent and duration of open waters due to the early break up of sea ice and the late formation of sea ice in the Chukchi Sea.

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands

Numerous algorithms have been proposed to retrieve chlorophyll-a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m−3, 16.25 mg·m−3, and 19.05 mg·m−3, respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll-a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m−3), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m−3).

Fluorescence-Based Approach to Estimate the Chlorophyll-A Concentration of a Phytoplankton Bloom in Ardley Cove (Antarctica)

A phytoplankton bloom occurred in Ardley Cove, King George Island in January 2016, during which maximum chlorophyll-a reached 9.87 mg/m3. Records show that blooms have previously not occurred in this area prior to 2010 and the average chlorophyll-a concentration between 1991 and 2009 was less than 2 mg/m3. Given the lack of in situ measurements and the poor performance of satellite algorithms in the Southern Ocean and Antarctic waters, we validate and assess several chlorophyll-a algorithms and apply an improved baseline fluorescence approach to examine this bloom event. In situ water properties including in vivo fluorescence, water leaving radiance, and solar irradiance were collected to evaluate satellite algorithms and characterize chlorophyll-a concentration, as well as dominant phytoplankton groups. The results validated the nFLH fluorescence baseline approach, resulting in a good agreement at this high latitude, high chlorophyll-a region with correlation at 59.46%. The dominant phytoplankton group within the bloom was micro-phytoplankton, occupying 79.58% of the total phytoplankton community. Increasing sea ice coverage and sea ice concentration are likely responsible for increasing phytoplankton blooms in the recent decade. Given the profound influence of climate change on sea-ice and phytoplankton dynamics in the region, it is imperative to develop accurate methods of estimating the spatial distribution and concentrations of the increasing occurrence of bloom events.

VIIRS-derived chlorophyll-a using the ocean color index method

Abstract An implementation approach using the ocean color index (OCI)-based chlorophyll-a (Chl-a) algorithm for the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) has been developed. The OCI Chl-a algorithm for satellite-derived Chl-a data was originally developed by Hu, Lee, and Franz (2012) (J. Geophys. Res., 117, C01011, doi: 01010.01029/02011JC007395) for the Moderate Resolution Imaging Spectroradiometer (MODIS). It uses two Chl-a algorithms, i.e., the color index (CI)-based (reflectance difference-based) algorithm for oligotrophic waters and the usual ocean chlorophyll-type (OCx)-based (reflectance ratio-based) algorithm (e.g., OC3M for MODIS and OC3V for VIIRS), and merges the two algorithms for different Chl-a range applications (named OCI algorithm). In this study, we use the in situ Marine Optical Buoy (MOBY) optics data to demonstrate conclusively that using the CI-based Chl-a algorithm can significantly improve VIIRS Chl-a data over oligotrophic waters with much reduced data noise from instrument calibration and the imperfect atmospheric correction. Using the VIIRS-measured global Chl-a data derived from the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system, we have developed the CI-based algorithm specifically for VIIRS, and further improved the two Chl-a algorithms merging method using the blue-green reflectance ratio values. Extensive evaluation results show that the new OCI Chl-a algorithm for VIIRS can produce consistent Chl-a data compared with those from the OC3V algorithm. In particular, the data transition between the CI-based and OC3V-based Chl-a algorithm is quite smooth, and there are no obvious discontinuities in VIIRS-derived Chl-a data. The new OCI-based Chl-a algorithm has been implemented in MSL12 for routine production of VIIRS global Chl-a data.

Using Remote Sensing to Track Variation in Phosphorus and Its Interaction With Chlorophyll-a and Suspended Sediment

Treatment of eutrophication and algal blooms in large shallow lakes has been constrained by the endogenetic release of nutrients from sediment resuspension despite the wellcontrolled input of nutrients from surrounding land in Taihu Lake, China. In this study, we propose a method to simplify regionally empirical algorithms for total suspended matter (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSM</sub> ), chlorophyll-a (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chl-a</sub> ), and phosphorus concentration (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> ) for the Geostationary Ocean Color Imager (GOCI) for Taihu Lake. The results show that the GOCI-derived C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSM</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chl-a</sub> , and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> match well with the in situ measured data. Based on the validated synchronous and timely observation data (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSM</sub> , C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chl-a</sub> and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> ) from GOCI level-1b data, the interaction of phosphorus with algae and suspended sediment were analyzed from August 6 to August 9, 2013. The results indicate that phosphorus released from sediment resuspension is significant in Taihu Lake, with a high spatial-temporal variation. The effect of the restriction of nutrients (nitrogen and phosphorus) on algal growth varied temporally and spatially with the hydrodynamic characteristics of the lake. In most cases, restricted phosphorus in the lake is the primary reason for the algal growth, especially in open regions such as Center Lake (CL), Northwestern Lake (NWL), and Southwestern Lake (SWL). Both nitrogen and phosphorus are the restrictive factors to algal growth in Meiliang and Gonghu Bays. More attention should thus be focused on the endogenetic release of phosphorus in Taihu Lake when treating eutrophication and algal blooms in addition to controlling the input of nitrogen and phosphorus from surrounding land.

Remote sensing of phytoplankton functional types in the coastal ocean from the HyspIRI Preparatory Flight Campaign

The 2013–2015 Hyperspectral Infrared Imager (HyspIRI) Preparatory Flight Campaign, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER), seeks to demonstrate appropriate sensor signal, spatial and spectral resolution, and orbital pass geometry for a global mission to reveal ecological and climatic gradients expressed in the selected California, USA study area. One of the awarded projects focused on the flight transects covering the coastal ocean to demonstrate that the AVIRIS data can be used to infer phytoplankton functional types at the land–sea interface. Specifically, this project directly assesses whether HyspIRI can provide adequate signal in the complex aquatic environment of the coastal zone to address questions of algal bloom dynamics, water quality, transient responses to human disturbance, river runoff, and red tides. Phytoplankton functional type (PFT), or biodiversity, can be determined from ocean color using the Phytoplankton Detection with Optics (PHYDOTax) algorithm and this information can be used to detect and monitor for harmful algal blooms. PHYDOTax is sensitive to spectral shape and accurate retrievals of ocean color across the visible spectral range is needed. The specific goal of this paper is to address the challenges of sensor capabilities and atmospheric correction in coastal environments by assessing two atmospheric correction methods using AVIRIS data for the retrieval of ocean color for use in derived products of chlorophyll-a and phytoplankton functional type. The atmospheric correction algorithms Atmospheric Removal (ATREM) and Tafkaa were applied to AVIRIS imagery of Monterey Bay, CA collected on 10 April 2013 and 31 October 2013. Data products from the imagery were compared with shipboard measurements including chlorophyll-a from whole-water samples and phytoplankton community structure estimated from diagnostic pigment markers using CHEMical TAXonomy (CHEMTAX). Using ATREM and Tafkaa and a selected set of input parameters for the scenes, we were unable to produce accurate retrievals of ocean color for the determination of chlorophyll-a and phytoplankton diversity. A modified ATREM correction produced science-quality data in which chlorophyll-a was accurately estimated using the Ocean Color 3 (OC3) chlorophyll-a algorithm, but biodiversity using PHYDOTax was not accurately estimated. Improvements in sensor calibration, sensitivity, and atmospheric correction of the HyspIRI imagery data set is needed in order to adequately estimate biogeochemically meaningful data products for the ocean such as chlorophyll-a, inherent optical properties, or PFTs. The HyspIRI Science Team is seeking improvements so the HyspIRI Airborne Campaign data set can be used for algorithm development to understand biodiversity and ecosystem function of coastal habitats that are facing increasing threats of human impact and climate change.

Validation of Envisat MERIS algorithms for chlorophyll retrieval in a large, turbid and optically-complex shallow lake

The 10-year archive of MEdium Resolution Imaging Spectrometer (MERIS) data is an invaluable resource for studies on lake system dynamics at regional and global scales. MERIS data are no longer actively acquired but their capacity for global scale monitoring of lakes from satellites will soon be re-established through the forthcoming Sentinel-3 Ocean and Land Colour Instrument (OLCI). The development and validation of in-water algorithms for the accurate retrieval of biogeochemical parameters is thus of key importance if the potential of MERIS and OLCI data is to be fully exploited for lake monitoring. This study presents the first extensive validation of algorithms for chlorophyll-a (chl-a) retrieval by MERIS in the highly turbid and productive waters of Lake Balaton, Hungary. Six algorithms for chl-a retrieval from MERIS over optically complex Case 2 waters, including band-difference and neural network architectures, were compared using the MERIS archive for 2007–2012. The algorithms were locally-tuned and validated using in situ chl-a data (n=289) spanning the five year processed image time series and from all four lake basins. In general, both band-difference algorithms tested (Fluorescence Line Height (FLH) and Maximum Chlorophyll Index (MCI)) performed well, whereas the neural network processors were generally found to much less accurately retrieve in situ chl-a concentrations. The Level 1b FLH algorithm performed best overall in terms of chl-a retrieval (R2=0.87; RMSE=4.19mgm−3; relative RMSE=30.75%) and particularly at chl-a concentrations of ≥10mgm−3 (R2=0.85; RMSE=4.81mgm−3; relative RMSE=20.77%). However, under mesotrophic conditions (i.e., chl-a<10mgm−3) FLH was outperformed by the locally-tuned FUB/WeW processor (relative FLH RMSE<10mgm−3=57.57% versus relative FUB/WeW RMSE<10mgm−3=46.96%). An ensemble selection of in-water algorithms is demonstrated to improve chl-a retrievals.

An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters

Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll-a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll-a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll-a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll-a algorithms—the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands—with RMS error of 0.416 and 0.437 for each in log chlorophyll-a units, respectively. However, it is clear that each algorithm performs better at different chlorophyll-a ranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll-a product compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie.

Spectral effects of atmospheric dust and clouds on estimation of chlorophyll-a concentration from radiation measurements

Atmospheric dust and clouds modify the spectral distribution of the incident solar radiation. The influence of these spectral effects on the determination of chlorophyll-a concentration from the sea surface and remotely sensed radiation measurements made from ships, aircrafts or satellites was studied in the region off Northwest Africa in the Atlantic Ocean. The chlorophyll-a algorithms that are typically used in ocean colour community are based on ratios of reflectance values of different wavelengths. This fact, together with the wavelength dependent effect of atmospheric dust and clouds, causes uncertainties in the estimation of chlorophyll-a concentration. The most frequently used Morel, OC4v4 and OC3M chlorophyll-a algorithms were included in the investigation. The highest and the smallest uncertainties were found for the Morel and the OC3M algorithms, respectively. For the first time, sky conditions with dust and clouds together in the atmosphere were studied. Overestimations of the chlorophyll-a concentration of up to 8.8% were observed in the case of dusty skies without clouds. The concentration was also overestimated between 7.6% and 14.3% for skies with mixtures of dust and clouds increasing the solar irradiance. Underestimations of up to 24.1% and overestimations of up to 12.2% were found for skies with mixtures of dust and clouds decreasing the solar irradiance. Compensations of the influences of spectral effects were observed at special ratios of clouds to dust. Earlier results in relation to the impact of clouds on the estimation of chlorophyll-a concentration were verified. The spectral effects of cloudy skies cannot be neglected because it may result in an error of up to 40% of the estimated value.

Assessment of MODIS-Aqua chlorophyll-a algorithms in coastal and shelf waters of the eastern Arabian Sea

The use of ocean colour remote sensing to facilitate the monitoring of phytoplankton biomass in coastal waters is hampered by the high variability in absorption and scattering from substances other than phytoplankton. The eastern Arabian Sea coastal shelf is influenced by river run-off, winter convection and monsoon upwelling. Bio-optical parameters were measured along this coast from March 2009 to June 2011, to characterise the optical water type and validate three Chlorophyll-a (Chla) algorithms applied to Moderate Resolution Imaging Spectroradiometer on Aqua (MODIS-Aqua) data against in situ measurements. Ocean Colour 3 band ratio (OC3M), Garver–Siegel–Maritorena Model (GSM) and Generalized Inherent Optical Property (GIOP) Chla algorithms were evaluated. OC3M performed better than GSM and GIOP in all regions and overall, was within 11% of in situ Chla. GSM was within 24% of in situ Chla and GIOP on average was 55% lower. OC3M was less affected by errors in remote sensing reflectance Rrs(λ) and by spectral variations in absorption coefficient (aCDOM(λ)) of coloured dissolved organic material (CDOM) and total suspended matter (TSM) compared to the other algorithms. A nine year Chla time series from 2002 to 2011 was generated to assess regional differences between OC3M and GSM. This showed that in the north eastern shelf, maximum Chla occurred during the winter monsoon from December to February, where GSM consistently gave higher Chla compared to OC3M. In the south eastern shelf, maximum Chla occurred in June to July during the summer monsoon upwelling, and OC3M yielded higher Chla compared to GSM. OC3M currently provides the most accurate Chla estimates for the eastern Arabian Sea coastal waters.

Light absorption properties of southeastern Bering Sea waters: Analysis, parameterization and implications for remote sensing

The absorption coefficients of phytoplankton (aPHY(λ)), non-algal particles (NAP) (aNAP(λ)) and colored dissolved organic matter (CDOM) (aCDOM(λ)) were investigated and parameterized in the southeastern Bering Sea during July 2008. The absorption coefficients were well structured with respect to hydrographic and biogeochemical characteristics of the shelf. The highest values of aPHY(443) were observed offshore and the lowest values of aPHY(443) were found in the coastal domain, a low productivity region associated with limited macronutrients. Values of aDG(λ) (aCDOM(λ)+aNAP(λ)) revealed an east–west gradient pattern with higher values in the coastal domain, and lower values in the outer domain. Lower chlorophyll specific aPHY(λ) (a*PHY(λ)) observed relative to middle and lower latitude waters indicated a change in pigment composition and/or package effect, which was consistent with phytoplankton community structure. aCDOM(λ) was the dominant light absorbing coefficient at all wavelengths examined except at 676nm. Modeling of remote-sensing reflectance (Rrs(λ)) and the diffuse attenuation coefficient (Kd(λ)) from inherent optical properties revealed the strong influence of aCDOM(λ) on Rrs(λ) and Kd(λ). Good optical closure was achieved between modeled and radiometer measured Rrs(λ) and Kd(λ) with average percent difference of less than 25% and 19% respectively, except at red wavelengths. The aCDOM(λ) accounted for >50% of Kd(λ) which was vertically variable. Chlorophyll-a calculated by the NASA standard chlorophyll-a algorithm (OC4.v6) was overestimated due to higher aCDOM(λ) and underestimated due to lower a*PHY(λ) at low and high concentrations of chlorophyll-a, respectively.

Assessment of remotely-sensed sea-surface temperature and chlorophyll-a concentration in San Matías Gulf (Patagonia, Argentina)

Field measurements of surface temperature and chlorophyll-a concentration were used to evaluate for the first time the performance of the standard sea surface temperature and chlorophyll-a algorithms of the Moderate Resolution Imaging Spectroradiometer (MODIS) in San Matías Gulf, northern of the Argentine Patagonian Continental Shelf (between 40°47′S and 42°13′S). The fit of the temperature data to a Standard Major Axis (SMA) type II regression model indicated that a high proportion of the total variance (r2≥0.80) was explained by the model. For chlorophyll-a, the low correlation (r2=0.15) and high dispersion indicated a poor performance and a general overestimation of Chl-a by the OC3Mv5 algorithm as indicated by the Relative Percent Difference (RPD=113%). However, for stations located in the northern area of the gulf the result showed a higher correlation (r2=0.76) and less dispersion (RPD=31%). The feasible reasons and explanations for the performance of NLSST and OC3Mv5 algorithms are discussed. The results of the temporal variability analysis of SST and Chl-a in different areas of the gulf agreed with previous studies.

Validation of Chlorophyll-a Algorithms in the Coastal Waters of Bay of Bengal Initial Validation Results from OCM-2

In this paper we report chlorophyll measurements made during an ocean colour validation cruise in April 2011 of the research vessel, Sagar Paschimi in the coastal waters of Northern Bay of Bengal. The chlorophyll-a concentration in these waters range from 0.2 to 4.0 mg/m3. Chlorophyll-a concentration from OCM-2 was estimated using the global ocean colour algorithms namely, OC2, OC3, OC4 and Chl-a algorithms respectively. OCM data was processed using the global SeaWiFS Data Analysis System (SeaDAS) in which all the above mentioned algorithms are embedded for estimating the chlorophyll-a concentration. A comparative study was made between and in-situ and satellite derived chlorophyll-a concentration. Although the matchups between in-situ and satellite data from OCM-2 were sparse, it indicates that direct application of the standard SeaWiFS algorithm-the OC4-V4 algorithm—in the coastal waters of the Bay of Bengal will underestimate chlorophyll-a by up to 30%. The results show a good correlation with an R value of 0.61 using OC2 algorithm. However, all the other global algorithms over estimate the chlorophyll-a concentration even in low chlorophyll concentration range. The comparison between in-situ and all the existing chlorophyll algorithms shows the efficiency of these algorithms for quantification of chlorophyll in coastal waters and hence the need to develop regional algorithms and fluorescence based algorithms for better quantification.

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.

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 log 10-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.

An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe

Abstract. Nearly half of the earth's photosynthetically fixed carbon derives from the oceans. To determine global and region specific rates, we rely on models that estimate marine net primary productivity (NPP) thus it is essential that these models are evaluated to determine their accuracy. Here we assessed the skill of 21 ocean color models by comparing their estimates of depth-integrated NPP to 1156 in situ 14C measurements encompassing ten marine regions including the Sargasso Sea, pelagic North Atlantic, coastal Northeast Atlantic, Black Sea, Mediterranean Sea, Arabian Sea, subtropical North Pacific, Ross Sea, West Antarctic Peninsula, and the Antarctic Polar Frontal Zone. Average model skill, as determined by root-mean square difference calculations, was lowest in the Black and Mediterranean Seas, highest in the pelagic North Atlantic and the Antarctic Polar Frontal Zone, and intermediate in the other six regions. The maximum fraction of model skill that may be attributable to uncertainties in both the input variables and in situ NPP measurements was nearly 72%. On average, the simplest depth/wavelength integrated models performed no worse than the more complex depth/wavelength resolved models. Ocean color models were not highly challenged in extreme conditions of surface chlorophyll-a and sea surface temperature, nor in high-nitrate low-chlorophyll waters. Water column depth was the primary influence on ocean color model performance such that average skill was significantly higher at depths greater than 250 m, suggesting that ocean color models are more challenged in Case-2 waters (coastal) than in Case-1 (pelagic) waters. Given that in situ chlorophyll-a data was used as input data, algorithm improvement is required to eliminate the poor performance of ocean color NPP models in Case-2 waters that are close to coastlines. Finally, ocean color chlorophyll-a algorithms are challenged by optically complex Case-2 waters, thus using satellite-derived chlorophyll-a to estimate NPP in coastal areas would likely further reduce the skill of ocean color models.

Optical properties of Peter the Great Bay waters compared with satellite ocean colour data

The shipboard measurements of optical and hydrological properties of seawater were used for development of regional bio-optical algorithms of waters of Peter the Great Bay. The shipboard measurements of chlorophyll-a and dissolved organic matter (DOM) concentrations were obtained by flow-through laser fluorometer (LF-3) (Institute for Automation and Control Processes, Vladivostok, Russia) and Sea-Bird Profiling CTD SBE-19plus (Conductivity, Temperature and Depth Sea-Bird Electronics, Sea-Bird Electronics, Inc., Washington, USA) equipped with WetStar fluorometers (WET Labs, Oregon, USA). The measurements were optically weighted according to light attenuation by depth. Ocean colour Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua data were used for our comparative analysis. Chlorophyll-a concentrations from ocean colour data were retrieved by OC3M, Garver-Siegel-Maritorena (GSM) and Carder algorithms. Relationships of the shipboard and satellite sensor data were analysed comparatively in our work. The Carder model gives the most general results of estimation of chlorophyll-a concentrations for all observed water cases. OC3M and GSM algorithms show good performance in case I water but they have large errors in the Amur Bay waters because of a large impact of the Razdolnaya river and Vladivostok city anthropogenic activity. Two regional chlorophyll-a algorithms were offered and they improve significantly the accuracy of chlorophyll-a concentration estimation. The first one is based on empirical regression between chlorophyll-a concentration and remotely sensed reflectance ratio. The second one uses satellite-derived values of sun-induced chlorophyll-a fluorescence when chlorophyll-a concentrations are higher than 3 mg m−3. The last one gives the best results for all water cases and provides an approach for separation between DOM produced by phytoplankton and DOM not connected with phytoplankton activity. This allows a regional DOM algorithm to be made.

Estimation of chlorophyll-A concentration using an artificial neural network (ANN)-based algorithm with oceansat-I OCM data

An artificial neural network (ANN) based chlorophyll-a algorithm was developed to estimate chlorophyll-a concentration using OCEANSAT-I Ocean Colour Monitor (OCM) satellite-data. A multi-layer perceptron (MLP) type neural network was trained using simulated reflectances (~60,000 spectra) with known chlorophyll-a concentration, corresponding to the first five spectral bands of OCM. The correlation coefficient(r2) andRMSE for the log transformed training data was found to be 0.99 and 0.07, respectively. The performance of the developed ANN-based algorithm was tested with the global SeaWiFS Bio-optical Algorithm Mini Workshop (SeaBAM) data (~919 spectra), 0.86 and 0.13 were observed asr2 andRMSE for the test data set. The algorithm was further validated with thein-situ bio-optical data collected in the northeastern Arabian Sea (~215 spectra), ther2 andRMSE were observed as 0.87 and 0.12 for this regional data set. Chlorophyll-a images were generated by applying the weight and bias matrices obtained during the training, on the normalized water leaving radiances (nLW) obtained from the OCM data after atmospheric correction. The chlorophyll-a image generated using ANN based algorithm and global Ocean Chlorophyll-4 (OC4) algorithm was compared. Chlorophyll-a estimated using both the algorithms showed a good correlation for the open ocean regions. However, in the coastal waters the ANN algorithm estimated relatively smaller concentrations, when compared to OC4 estimated chlorophyll-a.