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

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

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

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

Estimating phytoplankton biomass in coastal waters of Alaska using airborne remote sensing

Empirical airborne remote-sensing relationships were examined to estimate chlorophyll a concentration in the first optical depth (chl_FOD) of coastal waters of Afgonak/Kodiak Islands during July-August 2002. Band-ratio and spectral-curvature models were tested using satellite remote-sensing reflectance (R_rs(λ)) measurements. Additional shipboard and airborne R_rs(λ) data were also analysed to evaluate consistency of proposed chl_FOD-R_rs(λ) relationships. Validation of chlorophyll algorithms was performed using data collected in the northern-part of the Gulf of Alaska and Bering Sea during 1996, 2002, and 2003 cruises. Likewise, oceanographic conditions during the surveys were typified to interpret variability of chl_FOD fields. The SeaWiFS band-ratio algorithm OC2d was the most sensitive R_rs combination (R_rs(509)/R_rs(553)) to detect chl_FOD variability. Conversely, OC2a (R_rs(412)/R_rs(553)) had the lowest performance to derive chl_FOD values. No valid statistical regressions were established for spectral-curvature relationships in the blue spectrum (< 500 nm). Fertile waters (> 5 mg m^− 3) were preferentially located over shallow banks (∼50 m) and at the entrance of the bays. The approach used in this study to derive chl_FOD values could be universal for Alaskan coastal waters. However, chl_FOD-R_rs(λ) relationships must be calibrated locally for a given season.     

A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation

Accurate assessment of phytoplankton chlorophyll-a (chla) concentrations in turbid waters by means of remote sensing is challenging due to the optical complexity of case 2 waters. We have applied a recently developed model of the form [R_rs^? 1(λ₁) ? R_rs^? 1(λ₂)] × R_rs(λ₃) where R_rs(λ_i) is the remote-sensing reflectance at the wavelength λ_i, for the estimation of chla concentrations in turbid waters. The objectives of this paper are (a) to validate the three-band model as well as its special case, the two-band model R_rs^? 1(λ₁) × R_rs(λ₃), using datasets collected over a considerable range of optical properties, trophic status, and geographical locations in turbid lakes, reservoirs, estuaries, and coastal waters, and (b) to evaluate the extent to which the three-band model could be applied to the Medium Resolution Imaging Spectrometer (MERIS) and two-band model could be applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate chla in turbid waters.The three-band model was calibrated and validated using three MERIS spectral bands (660–670 nm, 703.75–713.75 nm, and 750?757.5 nm), and the 2-band model was tested using two MODIS spectral bands (λ₁ = 662–672, λ₃ = 743–753 nm). We assessed the accuracy of chla prediction in four independent datasets without re-parameterization (adjustment of the coefficients) after initial calibration elsewhere. Although the validation data set contained widely variable chla (1.2 to 236 mg m^? 3), Secchi disk depth (0.18 to 4.1 m), and turbidity (1.3 to 78 NTU), chla predicted by the three-band algorithm was strongly correlated with observed chla (r² > 0.96), with a precision of 32% and average bias across data sets of ? 4.9% to 11%. Chla predicted by the two-band algorithm was also closely correlated with observed chla (r² > 0.92); however, the precision declined to 57%, and average bias across the data sets was 18% to 50.3%. These findings imply that, provided that an atmospheric correction scheme for the red and NIR bands is available, the extensive database of MERIS and MODIS imagery could be used for quantitative monitoring of chla in turbid waters.     

Estimating chlorophyll a concentrations from remote-sensing reflectance in optically shallow waters

A multi-spectral classification and quantification technique is developed for estimating chlorophyll a concentrations, Chl, in shallow oceanic waters where light reflected by the bottom can contribute significantly to the above-water remote-sensing reflectance spectra, R_rs(λ). Classification criteria for determining bottom reflectance contributions for shipboard R_rs(λ) data from the west Florida shelf and Bahamian waters (1998-2001; n = 451) were established using the relationship between R_rs(412)/R_rs(670) and the spectral curvature about 555 nm, [R_rs(412) ? R_rs(670)]/R_rs(555)². Chlorophyll concentrations for data classified as “optically deep” and “optically shallow” were derived separately using best-fit cubic polynomial functions developed from the band-ratios R_rs(490)/R_rs(555) and R_rs(412)/R_rs(670), respectively. Concentrations for transitional data were calculated from weighted averages of the two derived values. The root-mean-square error (RMSE_log10) calculated for the entire data set using the new technique was 14% lower than the lowest error derived using the best individual band-ratio. The standard blue-to-green, band-ratio algorithm yields a 26% higher RMSE_log10 than that calculated using the new method. This study demonstrates the potential of quantifying chlorophyll a concentrations more accurately from multi-spectral satellite ocean color data in oceanic regions containing optically shallow waters.     

Optical closure for remote-sensing reflectance based on accurate radiative transfer approximations: the case of the Changjiang (Yangtze) River Estuary and its adjacent coastal area,China

Optical closure exercises are pivotal for evaluating the accuracy of water quality remote-sensing techniques. The agreement between radiometrically derived and inherent optical property (IOP)-derived above-water spectral remote-sensing reflectance R_rs(λ) is necessary for resolving IOPs, the diffuse attenuation coefficient, and biogeochemical parameters from space. We combined spectral radiometric and IOP measurements to perform an optical closure exercise for two optically contrasting Chinese waters – the Changjiang (Yangtze) River Estuary and its adjacent coastal area in the East China Sea. The final aim of our investigation was to compare two derivations of R_rs(λ): R_rs(λ), derived from radiometric measurements; and R_rs(λ), derived from simultaneous IOP measurements. Five subsequent steps have been taken to achieve this goal, including (1) estimation of the R_rs(λ) from radiometric measurements; (2) scattering correction for the non-water spectral absorption coefficient a_pd(λ); (3) estimation of the below-water spectral remote-sensing reflectance r_rs(λ) from IOPs measurements; (4) the estimation of the R_rs(λ) from the r_rs(λ) values; and (5) the comparison between the R_rs(λ) derived from radiometric and IOP measurements. All steps were realized by using both direct measurements and different models based on radiative transfer theory. Results demonstrated that the impact of the errors caused by the scattering correction procedure and conversion of radiometric quantities into R_rs(λ) may be rather significant, especially in the long-wavelength spectrum range. Nevertheless, spectral features were similar between these R_rs(λ) sets for all waters – from relatively clear to very turbid. Exploiting this fact allows use of the spectral reflectance ratios for remote sensing of the estuarine and coastal Chinese waters.     

Bio-optical relationships for the northern Adriatic Sea

The first three years of a time-series of marine bio-optical measurements performed from an oceanographic tower in the northern Adriatic Sea are used to derive empirical relationships for ocean colour applications in coastal waters. The site presents bio-optical characteristics pertaining to Case 1 and to Case 2 waters. Log linear relationships allow the diffuse attenuation coefficient for downwelling irradiance, K_{E d}, at different wavelengths to be derived from its value at 490?nm. A local empirical algorithm making use of the remote sensing reflectance ratio R _rs(490)/R _rs(555) is shown to provide lower surface chlorophyll-a values (by a factor of 2 to 4) in the range 0.1–1.0?mg?m^?3 than the SeaWiFS OC2v4 algorithm.     

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

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

Remote chlorophyll-a retrieval in turbid, productive estuaries: Chesapeake Bay case study

Accurate remote assessment of phytoplankton chlorophyll a (chla) concentration is particularly challenging in turbid, productive waters. Recently a conceptual model containing reflectance in three spectral bands in the red and near infra-red range of the spectrum was suggested for retrieving chla concentrations in turbid productive waters; it was calibrated and validated in lakes and reservoirs in Nebraska and Iowa. The objective of this paper is to evaluate the performance of this three band model as well as its special case, the two-band model to estimate chla concentration in Chesapeake Bay, as representative of estuarine Case II waters, and to assess the accuracy of chla retrieval. To evaluate the model performance, dual spectroradiometers were used to measure subsurface spectral radiance reflectance in the visible and near infra-red range of the spectrum. Water samples were collected concurrently and contained widely variable chla (9 to 77.4 mg/m³) and total suspended solids (7-65 mg/L dry wt). Colored dissolved organic matter (CDOM) absorption at 440 nm was 0.20 to 2.50 m^− 1; Secchi disk transparency ranged from 0.28 to 1.5 m. The two- and three-band models were spectrally tuned to select the spectral bands for most accurate chla estimation. Strong linear relationships were established between analytically measured chla and both the three-band model [R^− 1(675)-R^− 1(695)] × R(730) and the two-band model R(720)/R(670), where R(λ) is reflectance at wavelength λ. The three-band model accounted for 81% of variation in chla and allowed estimation of chla with a root mean square error (RMSE) of less than 7.9 mg/m³, whereas the two-band model accounted for 79% of chla variability and RMSE of chla estimation was below 8.4 mg/m³. The three-band model with MERIS spectral bands allows accurate chla estimation with RMSE below 9.1 mg/m³. Two-band model with SeaWiFS bands and MODIS 667 nm and 748 nm bands can estimate chla with RMSE below 11 mg/m³. The findings underlined the rationale behind the conceptual model and demonstrated the robustness of this algorithm for chla retrieval in turbid, productive estuarine waters.     

A four-band semi-analytical model for estimating chlorophyll a in highly turbid lakes: The case of Taihu Lake, China

Accurate estimation of phytoplankton chlorophyll a (Chla) concentration from remotely sensed data is particularly challenging in turbid, productive waters. The objectives of this study are to validate the applicability of a semi-analytical three-band algorithm in estimating Chla concentration in the highly turbid, widely variable waters of Taihu Lake, China, and to improve the algorithm using a proposed four-band algorithm. The improved algorithm is expressed as [Rrs(λ₁)^− 1 − Rrs(λ₂)^− 1][Rrs(λ₄)^− 1 − Rrs(λ₃)^− 1]^− 1. The two semi-analytical algorithms are calibrated and evaluated against two independent datasets collected from 2007 and 2005 in Taihu Lake. Strong linear relationships were established between measured Chla concentration and that derived from the three-band algorithm of [Rrs^− 1(660) − Rrs^− 1(692)]Rrs(740) and the four-band algorithm of [Rrs^− 1(662) − Rrs^− 1(693)][Rrs^− 1(740) − Rrs^− 1(705)]^− 1. The first algorithm accounts for 87% and 80% variation in Chla concentration in the 2007 and 2005 datasets, respectively. The second algorithm accounts for 97% of variability in Chla concentration for the 2007 dataset and 87% of variation in the 2005 dataset. The three-band algorithm has a mean relative error (MRE) of 43.9% and 34.7% for the 2007 and 2005 datasets. The corresponding figures for the four-band algorithm are 26.7% and 28.4%. This study demonstrates the potential of the four-band model in estimating Chla even in highly turbid case 2 waters.     

Spatial coherence between remotely sensed ocean color data and vertical distribution of lidar backscattering in coastal stratified waters

Detection of sub-surface optical layers in marine waters has important applications in fisheries management, climate modeling, and decision-based systems related to military operations. Concurrent changes in the magnitude and spatial variability of remote sensing reflectance (R_rs) ratios and submerged scattering layers were investigated in coastal waters of the northern Gulf of Alaska during summer of 2002 based on high resolution and simultaneous passive (MicroSAS) and active (Fish Lidar Oceanic Experimental, FLOE) optical measurements. Principal Component Analysis revealed that the spatial variability of total lidar backscattering signal (S) between 2.1 and 20 m depth was weakly associated with changes in the inherent optical properties (IOPs) of surface waters. Also based on a 250-m footprint, the vertical attenuation of S was inversely related to the IOPs (Spearman Rank Correlation up to −0.43). Low (arithmetic average and standard deviation) and high (skewness and kurtosis) moments of R_rs(443)/R_rs(490) and R_rs(508)/R_rs(555) ratios were correlated with vertical changes in total lidar backscattering signal (S) at different locations. This suggests the use of sub-pixel ocean color statistics to infer the spatial distribution of sub-surface scattering layers in coastal waters characterized by stratified conditions, well defined S layers (i.e., magnitude of S maximum comparable to near surface values), and relatively high vertically integrated phytoplankton pigments in the euphotic zone (chlorophyll a concentration > 150 mg m^− 2).     

An absorption model to determine phytoplankton size classes from satellite ocean colour

We have developed a model linking phytoplankton absorption to phytoplankton size classes (PSCs) that uses a single variable, the optical absorption by phytoplankton at 443 nm, a_ph(443), which can be derived from the inversion of ocean colour data. The model is based on the observation that the absolute value of a_ph(443) co-varies with the spectral slope of phytoplankton absorption in the range of 443-510 nm, which is also a characteristic of phytoplankton size classes. The model when used for analysis of SeaWiFS global data, showed that picoplankton dominated ~ 79.1% of surface waters, nanoplankton ~ 18.5% and microplankton the remainder (2.3%). The N. and S. Atlantic and the N. and S. Pacific Oceans showed seasonal cycles with both micro and nanoplankton increasing in spring and summer in each hemisphere, while picoplankton, dominant in the oligotrophic gyres, decreased in the summer. The PSCs derived from SeaWiFS data were verified by comparing contemporary 8-day composites with PSCs derived from in situ pigment data from quasiconcurrent Atlantic Meridional Transect cruises.     

Mapping lake CDOM by satellite remote sensing

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

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.     

Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands

Bio-optical algorithms for remote estimation of chlorophyll-a concentration (Chl) in case-1 waters exploit the upwelling radiation in the blue and green spectral regions. In turbid productive waters other constituents, that vary independently of Chl, absorb and scatter light in these spectral regions. As a consequence, the accurate estimation of Chl in turbid productive waters has so far not been feasible from satellite sensors. The main purpose of this study was to evaluate the extent to which near-infrared (NIR) to red reflectance ratios could be applied to the Sea Wide Field-of-View Sensor (SeaWiFS) and the Moderate Imaging Spectrometer (MODIS) to estimate Chl in productive turbid waters. To achieve this objective, remote-sensing reflectance spectra and relevant water constituents were collected in 251 stations over lakes and reservoirs with a wide variability in optical parameters (i.e. 4 ≤ Chl ≤ 240 mg m^− 3; 18 ≤ Secchi disk depth ≤ 308 cm). SeaWiFS and MODIS NIR and red reflectances were simulated by using the in-situ hyperspectral data. The proposed algorithms predicted Chl with a relative random uncertainty of approximately 28% (average bias between − 1% and − 4%). The effects of reflectance uncertainties on the predicted Chl were also analyzed. It was found that, for realistic ranges of R_rs uncertainties, Chl could be estimated with a precision better than 40% and an accuracy better than ± 35%. These findings imply that, provided that an atmospheric correction scheme specific for the red-NIR spectral region is available, the extensive database of SeaWiFS and MODIS images could be used to quantitatively monitor Chl in turbid productive waters.     

Mapping aerosol optical thickness and water-leaving signals using an improved NIR/SWIR switching atmospheric correction model: A case study in the Bohai Sea

In this study, the performance of the near-infrared & short wave infrared switching atmospheric correction (NSSAC) model in estimating remote sensing reflectance (R_rs(λ)) and aerosol optical thickness at 869 nm (τ_a(869)) were assessed by field measurements taken in the Bohai Sea. It was found that the NSSAC model had approximately 30% uncertainty for retrievals of R_rs(λ) in the green regions but provided approximately 50% uncertainty for estimations of τ_a(869) and R_rs(λ) at all other moderate resolution imaging spectroradiometer (MODIS) visible wavelengths. Therefore, an optimised method is proposed for optimizing the retrieval results of the NSSAC model; it was validated using the field measurements collected from the Oujiang River estuary. The results show that the performance of the NSSAC model for τ_a(869) and R_rs(λ) at the blue, red, and near-infrared bands was greatly improved by using the optimised NSSAC model. Moreover, the study also finds that the τ_a(869) shows a large variation in the Bohai Sea, decreasing from coastal to offshore regions. The monthly average τ_a(869) has a maximum at February and August. Due to the imperfect atmospheric correction procedure, the NSSAC model-derived R_rs(λ) is always larger than those of the field measurements. Future work is needed to minimise the detected water-leaving signals in the short wave infrared (SWIR) images.     

Absorption properties of shoal-dominated waters in the Atchafalaya Shelf,Louisiana, USA

Spectral absorption coefficients of coloured dissolved organic matter (a _CDOM(λ)) and particulate matter (a _p(λ)) (phytoplankton (a _PHY(λ)) plus non-algal particles (a _NAP(λ)), measured on the shoal-dominated region off the Atchafalaya River (AR) Shelf, Louisiana, USA, are analysed, and their effect on chlorophyll-a retrievals from ocean-colour sensors examined. Compared to a _CDOM(λ) and a _NAP(λ), a _PHY(λ) is relatively constant, with a _CDOM(λ) and a _NAP(λ) varying by approximately 1.2 and 1.8 times as much as a _PHY(λ) at 443 nm, respectively. The specific a _PHY(λ) (a*_PHY(λ)) ranges from 0.006 to 0.0612 m^?2(mg chla)^?1 at 443 nm, which indicates a pigment-packaging effect or a variation in pigment composition. The a _NAP(λ) accounts for approximately 3–93% of a _p(λ) at 443 nm, with a higher contribution to a _p(λ) during an October 2007 cruise (62–93%) as compared to an August 2007 cruise (31–89%). Our results indicate that a _CDOM(λ) and a _NAP(λ) collectively dominate light absorption, even at higher wavelengths where their effect is expected to be minimal. In situ and satellite data match-up of chlorophyll-a yield root-mean square errors of 2.17 and 2.62 for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the Medium Resolution Imaging Spectrometer (MERIS), respectively. The non-covarying a _CDOM(λ) and a _NAP(λ), along with variable a*_PHY(λ), greatly influenced the remote retrieval of biogeochemical variables using satellite ocean-colour algorithms in this region.     

Seasonal and spatial distribution of chlorophyll-a concentrations and water conditions in the Gulf of Tonkin, South China Sea

The Gulf of Tonkin is a semi-closed gulf northwest of the South China Sea, experiencing reversal seasonal monsoon. Previous studies of water conditions have been conducted in the western waters of the gulf, but very few studies of the Chlorophyll-a (Chl-a) distribution have been carried out for the entire gulf. The present study investigates seasonal and spatial distributions of Chl-a and water conditions in the Gulf of Tonkin by analyzing Sea-viewing Wide Field-of-View Scanner (SeaWiFS) derived Chlorophyll-a (Chl-a), in situ measurements, sea surface temperatures (SST), and other oceanographic data obtained in 1999 and 2000. The results show seasonality of Chl-a and SST variations in the Gulf of Tonkin, and reveal phytoplankton blooming events in the center part of the gulf during the northeast monsoon season. In summer, Chl-a concentrations were relatively low (<0.3 mg m⁻³) and distributed uniformly throughout most of the area, with a belt of higher Chl-a concentrations along the coast, particularly the coast of Qiongzhou Peninsula; in winter, Chl-a concentration increased (0.5 mg m⁻³) in the entire gulf, and phytoplankton blooms offshore-ward from the northeast coast to the center of the gulf, while Chl-a concentrations reached high levels (0.8-1 mg m⁻³) in the center of the blooms. One peak of Chl-a concentrations was observed during the northeast monsoon season in the year. SST were high (27-29 °C) and distributed uniformly in summer, but lower with a large gradient from northeast (17 °C) to southwest (25 °C) in winter, while strong northeast winds (8-10 m/s) were parallel to the east coast of the gulf. Comparison of Chl-a values shows that SeaWiFS derived Chl-a concentrations match well with in situ measurements in most parts of the gulf in May 1999, but SeaWiFS derived Chl-a are higher than in situ data in river mouth waters. The seasonal variation of Chl-a concentrations and SST distribution were associated with the seasonally reversing monsoon; the winter phytoplankton blooms were related to vertical mixing and upwelling nutrients drawn by the northeast wind.     

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

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