OC4 Algorithm Research Articles

Features of Empirical Bio-Optical Algorithms for Estimating Chlorophyll-a Concen tration from Satellite Ocean Color Data in Waters around the Antarctic Peninsula

The features of the empirical bio-optical algorithm operation in the waters around the Antarctic Peninsula are analyzed based on a comparison of calibrated data from the shipborne flow fluorimeter and satellite data from the OLCI radiometer on Senti nel-3A and Sentinel-3B satellites during the Antarctic summers of January-February 2020 and 2022. It is shown that the standard OC4 bio-optical algorithm significantly underestimates satellite estimates of Chl-a concentration from ~1.5 to ~9 times (on aver age by a factor of ~3.1). The known regional OC4-SO algorithm provides acceptable errors of Chl-a concentration estimates and can be used for studies related to the analysis of Chl-a concentration in the waters around the Antarctic Peninsula. The developed in this work new regional algorithm OC4-AP has significantly lower error in comparison with the known standard and regional algorithms. It can be used if it is necessary to obtain a remote estimate of the concentration of Chl-a, as close as possible to the accumulated world experience in determining this value by standard extract spectrophotometric and fluorimetric methods. The observed underestimation of satellite estimates of Chl-a concentration using the standard empirical bio-optical OC4 algorithm can be attributed to at least three reasons typical for the studied water area: low relative CDOM content, high phycoerythrin content, and stronger effect of pigment packing in phytoplankton cells compared to the average values in the World Ocean.

Retrieval algorithm of chlorophyll-a concentration for coastal waters based on ridge regression

Abstract. In this paper, we use the multiple scattering near-infrared aerosol correction model of SeaDAS to execute the atmospheric correction for Terra/Aqua MODIS remote sensing data, and also use three standard operational chlorophyll-a concentration inversion algorithms built in SeaDAS, named OC2, OC3, and OC4, to carry out chlorophyll-a concentration inversion in the Yellow Sea and East China Sea. We validate the inversion results using in-situ measured chlorophyll-a concentration data collected from the Yellow Sea and East China Sea in 2003. The results show that the inversion results of OC3 and OC4 algorithm are significantly larger than in-situ measured values, and the results of the OC2 algorithm are closer to the measured values. In view of the shortcomings of these algorithms, we proposes a chlorophyll-a concentration inversion model based on ridge regression, and carry out chlorophyll-a concentration inversion test using MODIS images covering the Yellow Sea and East China Sea 2003. The results indicate that, the new inversion model can effectively overcome the deficiencies of OC2, OC3, and OC4 algorithms. The inversion model can effectively overcome the covariance problem of OC2, OC3 and OC4 algorithms on the multivariate linear regression model, and the model passed the F-test (F=25.893, p=0.000<0.05), the mean absolute percentage error (MAPE) between the inversion values and the in-situ measured values was 21.8%, the root mean squared error (RMSE) was 0.325, and the coefficient of determination (R2) was 0.847. The accuracy and the fit degree of the new model were significantly better than those of the OC2, OC3 and OC4 algorithms. Therefore, the chlorophyll-a concentration inversion model based on ridge regression can effectively invert the chlorophyll-a concentration in the offshore.

Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network

A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (Rrs) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of Rrs565 and Rrs520/Rrs443, Rrs565/Rrs490, Rrs520/Rrs490, Rrs490/Rrs443, and Rrs670/Rrs565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m3, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively.

Global Ocean Chlorophyll-a Concentrations Derived From COCTS Onboard the HY-1C Satellite and Their Preliminary Evaluation

The Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite was launched on September 7, 2018, and has been providing global multispectral Earth observation data as a new spaceborne sensor for ocean color detection since September 10, 2018. In this study, an atmospheric correction algorithm using a composite of the algorithms developed by Wang and Gordon (1994) and He <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2004) and a chlorophyll-a concentration retrieval method that is a composite of the OC4 and color index (CI) algorithms are used to derive the global chlorophyll-a concentration from COCTS. The retrieval products are validated against <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data measured in the East China Sea and the South China Sea during the in-orbit testing activity for the HY-1C satellite with an unbiased percentage difference (UPD) of 39%, and the data are also validated against the Aerosol Robotic Network-Ocean Color (AERONET-OC) data with a UPD of 39%. The daily global chlorophyll-a concentration from COCTS with a gridded resolution of 9.2 km and a time period from September 10, 2018 to February 29, 2020 is compared with the same products from MODIS and VIIRS. The UPDs of COCTS against MODIS onboard Terra are approximately 20%, and the mean biases (in logarithm form) are approximately zero. Examples of chlorophyll-a concentration retrieval results in specific cases along the eastern coast of China and the Kuroshio Current are also presented to show the performance of the algorithms used in this study. The retrieval and evaluation results show that COCTS onboard HY-1C demonstrates satisfactory performance for global chlorophyll-a concentration observations.

Light Absorption by Phytoplankton in the Upper Mixed Layer of the Black Sea: Seasonality and Parametrization

Standard NASA ocean color algorithm OC4 was developed on the basis of ocean optical data and while appropriate for Case 1 oceanic waters could not be adequately applied for the Black Sea waters due to its different bio-optical properties. OC4 algorithm is shown to overestimate chlorophyll concentration (Chl-a) in summer and underestimate Chl-a during early spring phytoplankton blooms in the Black Sea. For correct conversion of satellite data to Chl-a, primary production and other indicators regional algorithms should be developed taking into account bio-optical properties of the Black Sea waters. Light absorption by phytoplankton pigments – a_ph (λ) have been measured in open sea and shelf Black Sea waters in different seasons since 1998. It was shown that the first optical depth was located within the upper mixed layer (UML) for most of the year with the exception of the spring when seasonal stratification was developing. As a result spectral features of water leaving radiance were determined by optical properties of the UML. Significant seasonal differences in Chl-a specific light absorption coefficients of phytoplankton within UML have been revealed. These differences were caused by adaptive changes of composition and intracellular pigment concentration due to variable environment conditions – mainly light intensity. Empirical relationships between a_ph (λ) and Chl-a were derived by least squares fitting to power functions for different seasons. Incorporation of these results will refine the regional ocean color models and provide improved and seasonally adjusted estimates of chlorophyll a concentration, downwelling radiance and primary production in the Black Sea based on satellite data.

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.

Development of an explicit algorithm for remote sensing estimation of chlorophyll a using symbolic regression

The primary mission of ocean color remote sensing is to provide accurate marine bio-optical properties from satellite data. We propose a new algorithm that uses symbolic regression to estimate chlorophyll a (chl a) concentrations from remote sensing reflectance. We compared the accuracy and computational efficiency of the new algorithm to that of the explicit empirical algorithms (OC4v4 and OC4v6), and implicit algorithms based on neural networks or support vector machines (SVM). Results show that the accuracy of the symbolic regression algorithm is higher than that of the OC4 algorithms and comparable to that of implicit algorithms. The improvement is particularly important for high biomass areas (chl a ≥ 3 mg m(-3)) that are often found in optically complex waters. The computational efficiency of the explicit algorithm developed by symbolic regression is comparable to that of the two versions of OC4 algorithms and better than that of implicit algorithms based on SVM. With its good precision and fast processing, the symbolic regression algorithm is a powerful tool for remote sensing of chl a that could be used advantageously in the reprocessing of large datasets.

Impact of phytoplankton community size on a linked global ocean optical and ecosystem model

We isolated the effect phytoplankton cell size has on varying remote sensing reflectance spectra (R rs(λ)) in the presence of optically active constituents by using optical and radiative transfer models linked in an offline diagnostic calculation to a global biogeochemical/ecosystem/circulation model with explicit phytoplankton size classes. Two case studies were carried out, each with several scenarios to isolate the effects of chlorophyll concentration, phytoplankton cell size, and size-varying phytoplankton absorption on R rs(λ). The goal of the study was to determine the relative contribution of phytoplankton cell size and chlorophyll to overall R rs(λ) and to understand where a standard band ratio algorithm (OC4) may under/overestimate chlorophyll due to R rs(λ) being significantly affected by phytoplankton size. Phytoplankton cell size was found to contribute secondarily to R rs(λ) variability and to amplify or dampen the seasonal cycle in R rs(λ), driven by chlorophyll. Size and chlorophyll were found to change in phase at low to mid-latitudes, but were anti-correlated or poorly correlated at high latitudes. Phytoplankton size effects increased model calculated R rs(443) in the subtropical ocean during local spring through early fall months in both hemispheres and decreased R rs(443) in the Northern Hemisphere high latitude regions during local summer to fall months. This study attempts to tease apart when/where variability about the OC4 relationship may be associated with cell size variability. The OC4 algorithm may underestimate [Chl] when the fraction of microplankton is elevated, which occurs in the model simulations during local spring/summer months at high latitudes in both hemispheres.

Evaluation of the SeaWiFS and MODIS Chlorophyll a Algorithms Used for the Northern South China Sea during the Summer Season

The present study made evaluations of SeaWiFS-derived and MODIS-derived Chlorophyll a (Chl a) concentrations in the Northern South China Sea (NSCS), using in situ data collected during two research cruises which were conducted during the summer of 2004 (September 18 to October 8) and 2007 (August 10 to 29). The data of ±48 h and 3 × 3 pixels were used for the comparison between satellite and in situ Chl a data, and the results reveal a systematic overestimation of Chl a concentration by National Aeronautics and Space Administration (NASA) global algorithms (OC2v4, OC4v4, and OC3M). The RMSEs of the selected algorithms are larger than 0.35 except OC2_D’Ortenzio (one regional algorithm for the Mediterranean Sea). The overestimation seems to correlate with numerous (≈77%) low Chl a concentration (< 0.1 mg m -3 ) due to the oligotrophic characteristics of the South China Sea (SCS) in summer, and to correlate with the error in atmosphere correction introduced by aerosols. Therefore, the OC2 and OC4 algorithms for SeaWiFS and OC3M algorithm for MODIS are adapted to NSCS by fitting the satellite data set to in situ Chl a data in NSCS. With the new coefficients based on our field data, the regional version of the three algorithms (TP series) showed good performance with RMSE values of 0.245, 0.245, and 0.288 respectively, which were slightly higher than the algorithm “noise” (0.222 in RMSE). Those TP series algorithms may be considered preliminary due to the relatively small number of available in situ data, and they are suitable in summer season in NSCS.

Comparison of shipboard and satellite measurements of surface water temperature and chlorophyll a in Lake Ontario

The binational Lake Ontario Lower Aquatic Foodweb Assessment program (LOLA) intensively sampled Lake Ontario in the Spring (April 28-May 3), Summer (August 10–11 and August 19–21) and Fall (September 21–25) of 2003. However, the timing of shipboard surveys often misses critical periods in biological productivity. We directly compared surface water temperature and chlorophyll a (chl a) measurements made during these cruises to Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images for May 3, August 18, and September 21, 2003. Satellite measurements were strongly correlated to shipboard measurements for surface water temperature (r2 = 0.98) and chl a (r2 = 0.62, offshore sites only). The OC4 algorithm for chl a greatly overestimated nearshore sites because of the probable presence of color producing agents other than chl a. However, its relative reliability for offshore sites adds confidence in using the imagery to fill the gaps between sampling cruises.

Reply to comment by Jinchun Yuan et al. on “Reduction of primary production and changing of nutrient ratio in the East China Sea: Effect of the Three Gorges Dam?”

[1] A brief discussion is presented here in response to Yuan et al.’s [2007] comment on our paper entitled ‘‘Reduction of primary production and changing of nutrient ratio in the East China Sea: Effect of the Three-Gorges Dam?’’ [Gong et al., 2006]. [2] First, we would like to point out that our research provides sufficient seasonal variations using accurate chlorophyll a (Chl a) data in the traditionally nutrient-rich (TNR) region of the East China Sea, as determined in several previous high-frequency sea-based investigations [Gong et al., 1996, 2003, 2006]. In contrast, Yuan et al. [2007] used questionable Chl a data (derived from the SeaWiFS OC4 algorithm: see second point) to study the inter-annual variations in Chl a in the entire East China Sea. This is clearly apparent in Figure 1 which illustrates the comparison between our average sea-based Chl a concentrations in the entire East China Sea (solid brown stars in Figure 1) and the SeaWiFS-derived Chl a data reported in Figure 2 of Yuan et al.’s comment. The results show that the SeaWiFS-derived Chl a data were substantially higher than our sea-based Chl a data. There can be no question that Yuan et al.’s [2007] data were invalid. [3] Second, we further question the accuracy of the SeaWiFS-derived Chl a data for the East China Sea which were used in Yuan et al.’s [2007] comments. Our reasons are as follows: [4] (1) It is widely believed that the standard SeaWiFSOC4 chlorophyll algorithm is only valid for ‘‘Case 1 waters’’ [O’Reilly et al., 1998; Carder et al., 1999]. But this type of global algorithm is flawed when it comes to ‘‘Case 2 waters’’ [Mueller and Austin, 1995; International Ocean-Colour Coordinating Group, 2000; Liew et al., 2001; Gong, 2004]. The key reason for the algorithmic failure in the case of ‘‘Case 2 waters’’ is that there is interference from the strong absorption of colored dissolved organic matter (CDOM) in the blue band that overlaps with phytoplankton chlorophyll absorption. When the absorption coefficient due to colored dissolved organic matter at 380 nm (acdom(l = 380 nm)) is greater than 0.1 m 1 [Mueller and Austin, 1995], the ocean waters are regarded as ‘‘Case 2 waters’’, and the use of the global satellite ocean-color OC4 algorithm is inappropriate. But unfortunately, Yuan et al. [2007] used the OC4 algorithm to process their Chl a data for the East China Sea, a region with ‘‘Case 2 waters.’’ [5] (2) O’Reilly et al. [1998] collected a large data set including coincident in-situ chlorophyll and remote sensing information to evaluate the performance of a wide variety of ocean color chlorophyll algorithms for SeaWiFS data. One of their conclusions is that, while the performance of the OC4 was superior to that of the OC2, it is not as suitable as the initial operational algorithm for SeaWiFS-derived Chl a data because its use requires accurate atmospheric correction and on-orbit calibrations in four bands (instead of two). They explain that this can only be assessed after the collection of sufficient data to validate and fine-tune sensor calibrations. It is currently widely known that atmospheric pollution in the shelf area of the East China Sea is very serious because of rapid industrial development in the coastal cities of China. Additionally, the high frequency of Asian dust storms has been observed in the East China Sea. As a result, the SeaWiFS-derived Chl a data are not likely very accurate without proper atmospheric correction. A second conclusion by O’Reilly et al. [1998] is that ‘‘Case 2 waters’’ present additional complications and challenges as their properties change depending on whether they are dominated by CDOM, nochlorophyllous particles, or a variable mix of both [Carder et al., 1989]. In short, specific algorithms or different parameterizations are required to process data from different regions. [6] (3) Gong [2004] presented CDOM data for four seasons from surface waters of the entire East China Sea. These data showed that the values of acdom(l = 325 nm) were higher than 0.2 m 1 (equivalent to acdom(l = 380 nm) = 0.1 m ) for most of the shelf waters year-round (Figure S1 of the auxiliary material). Figure S1 clearly shows that the values of acdom(l = 380 nm) in the TNR region are always higher than 0.1 m . These results support the optical classification of the TNR region as ‘‘Case 2 waters.’’ Additionally, a significant overestimation of the SeaWiFS-derived Chl a concentrations (2.3 mg m ) at a station located about 200 km east off the Changjiang River mouth (see star-symbol in Figure S1a) was found in

Seasonal variability of SeaWiFS chlorophyll a in the Malacca Straits in relation to Asian monsoon

The influences of Asian monsoon on chlorophyll a (chl a) in Malacca Straits (MS) are investigated using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data (November 1997–January 2003). Results show that chl a in MS experienced seasonal variability. At northern MS, SeaWiFS chl a increased as much as twice during northeast monsoon (NEM) compared to southwest monsoon (SWM). At the southern MS, SeaWiFS OC4 algorithm was found over-estimating chl a at the areas of high normalized water leaving radiance at 555 nm value (nLw 555). Strong relationship between the nLw 555 and total suspended sediment concentration suggested that high turbidity could contribute to the over-estimation of SeaWiFS chl a. Monsoon wind strongly influenced the spatial distribution and seasonal variation of the chl a at northern MS. During NEM, bloom of phytoplankton started from eastern MS, and it was advected towards Andaman Sea by northeasterlies. NEM wind created positive wind stress curl along 4.5–6.5°N in the Straits, which caused positive Ekman pumping resulting upwelling. Consequently, upwelling helped replenish the surface water nutrients, thus, maintaining the phytoplankton bloom over the continental shelf. Low chl a was observed during SWM, and this was attributed to negative wind stress curl and downwelling. The unique topology of MS, where the mountain ranges at Malay Peninsular and Sumatra Island acted as the monsoon windshield, enable both upwelling and downwelling at northern MS during different monsoons.

SeaWiFS data analysis and match-ups with in situ chlorophyll concentrations in Danish waters

For the year 1999 all Sea viewing Wide Field of view Sensor (SeaWiFS) scenes of the Danish waters from the North Sea to the Baltic Sea were browsed, and a total of 47 SeaWiFS scenes with reasonably low cloud cover and, therefore, potential in situ match-ups were found and processed. The in situ data used as match-ups were collected on routine monitoring cruises by Danish and Swedish environmental authorities. A few stations in the North Sea, Skagerak and the western Baltic Sea were sampled, while most stations were located in Kattegat and the inner Danish waters. A turbid water SeaWiFS atmospheric correction algorithm was applied, since the standard SeaWiFS algorithm for chlorophyll-a (CHL) has been shown to be fairly inaccurate in turbid coastal waters. This is due to both inaccurate atmospheric and to relatively high and variable abundance of yellow substance. The application of the turbid atmospheric correction substantially improved the SeaWiFS CHL estimates. Regressions between SeaWiFS estimates using the OC2 and OC4 algorithms used in the SeaDAS software (versions 3.3 and 4.0, respectively) and in situ CHL values were made as well, and regression with a number of other possible reflectance ratios with SeaWiFS channels. The best correlation was found to be R2=0.54 using a double-ratio algorithm using both R510/R555 and R443/R670, while the OC4v4 had the second best correlation of R2=0.39. Among other single ratios, the R510/R555 had the highest correlation with CHL, which was expected since this is also the ratio that OC4v4 most often switches to in the waters investigated here. The range of CHL concentrations in this study was rather limited (all but three points from 0.5–3 mg m−3) so there is a need for inclusion of more data to expand the concentration range. This should be possible using also data from 2000, 2001 and onwards and, hereafter, a more ‘stable’ empirical algorithm can be derived for the Danish waters.

Discrimination of diatoms from other phytoplankton using ocean-colour data

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 272:59-68 (2004) - doi:10.3354/meps272059 Discrimination of diatoms from other phytoplankton using ocean-colour data Shubha Sathyendranath1,2, Louisa Watts3, Emmanuel Devred1,2,*, Trevor Platt2, Carla Caverhill2, Heidi Maass2 1Department of Oceanography, Dalhousie University, Halifax, Nova Scotia B3H 4J1, Canada 2Biological Oceanography Division, Bedford Institute of Oceanography, Box 1006, Dartmouth, Nova Scotia B2Y 4A2, Canada 3Atmospheric Science Team, Natural Environment Research Council, Polaris House, North Star Avenue, Swindon SN2 1EU, UK *Corresponding author. Email: devrede@mar.dfo-mpo.gc.ca ABSTRACT: Recent papers have highlighted the differences between the absorption characteristics of phytoplankton populations dominated by diatoms and those of other types of phytoplankton populations from the North West Atlantic. It has been suggested that these differences could introduce a bias in satellite-derived concentrations of the phytoplankton pigment, chl a. In this paper, these differences in optical properties of diatoms are exploited to develop a bio-optical algorithm to distinguish diatom populations from other types of phytoplankton populations in the region. The algorithm is applied to SeaWiFS data on ocean colour, and the results are compared with in situ data on phytoplankton population types based on HPLC data. The comparison shows that the algorithm successfully distinguishes between diatoms and non-diatom populations in the majority of cases studied. A branching algorithm is then applied to the satellite data to estimate chl a concentration in the region: a diatom-specific algorithm is used when diatoms are identified in a pixel, and another algorithm for mixed populations when this is not the case. The estimated chl a concentrations are compared with in situ estimates when matching observations exist. The results show that the branching bio-optical algorithm often performs better than the OC4 algorithm used in standard processing of SeaWiFS data. However, the results may be poor when the initial identification of population types is wrong. Finally, the new algorithm is used to map the distribution of diatoms in the region in spring and summer: the patterns that emerge are consistent with the known features of diatom distributions in the region. KEY WORDS: Phytoplankton community structure · Ocean colour · Diatoms · Remote sensing · SeaWiFS · North West Atlantic Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 272. Online publication date: May 19, 2004 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2004 Inter-Research.

Optical properties of waters in the Australasian sector of the Southern Ocean

During March 1998 we studied in situ bio‐optical parameters along a north‐south transect (142?°E) between 42° and 55?°S. Surface chorophyll a (chla) reflected mixed layer chlaconcentrations and showed a general decrease with increasing latitude. Changes in chlaconcentration often coincided with physical boundaries, and differences in fluorescence yield and photoadaption by the phytoplankton were observed north and south of the Subantarctic Front. In this region chromophoric dissolved organic matter (CDOM) absorption, generally exceeded phytoplankton pigment absorption at 443 nm. Satellite‐derived chla, using the Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) OC4 algorithm, generally underestimated the in situ chlaconcentration except in areas of low chla(&lt;0.15 mg m−3) where the SeaWiFS algorithm was found to overestimate in situ chla.

Multispectral in situ measurements of organic matter and chlorophyll fluorescence in seawater: Documenting the intrusion of the Mississippi River plume in the West Florida Shelf

We performed multispectral, in situ fluorescence measurements of detrital colored organic matter (COM) and chlorophyll a (Chl a) in surface waters of the West Florida Shelf using the Wet Labs spectral absorption and fluorescence instrument (SAFIre). Continuous underway measurements allowed simultaneous mapping of the dispersal pattern of riverine organic material and Chl a on the shelf. We used two fluorescence emission ratios to differentiate between riverine and marine COM. The data showed unusually high concentrations of COM offshore. These were attributed to an offshore extension of the Mississippi River plume. Comparisons between in situ Chl a concentrations measured with the SAFIre and Chl a values obtained from the sea‐viewing wide field‐of‐view sensor (SeaWiFS) satellite data using OC4 and MODIS algorithms showed that, although both algorithms overestimated Chl a, MODIS performed better than OC4, particularly in areas with high COM concentrations. Analysis of the relationship between Chl a and COM concentrations within the study area showed regional variability probably caused by differences in river source.