HYDROPT: A fast and flexible method to retrieve chlorophyll-a from multispectral satellite observations of optically complex coastal waters

We present a generic innovative algorithm for remote sensing of coastal waters that can deal with a large range of concentrations of chlorophyll-a, SPM and CDOM and their inherent optical properties. The algorithm is based on the exact solutions of the HYDROLIGHT numerical radiative transfer model to support retrieval in optically complex waters with varying sensor wide swath viewing geometry. The algorithm estimates the concentrations by minimizing the difference between observed and modeled reflectance spectra. The use of a look-up table and polynomial interpolation greatly reduces computation time, allowing operational and near-real time processing of large sets of satellite imagery. Because the remote sensing reflectance was tabulated as a function of in-water light absorption and scattering, rather than actual constituents concentrations, the algorithm can be applied with any definition of the specific inherent optical properties of CHL, SPM and CDOM. A statistical measure for the goodness-of-fit and the formal standard errors in the fitted concentrations are provided, thus producing error maps with each thematic chlorophyll image, often lacking in most applications of innovative algorithms. The performance of the algorithm is demonstrated for multispectral observations of the North Sea, a shallow coastal sea with large concentration gradients in SPM (due to resuspension) and CDOM (from riverine influx). The standard errors of estimated chlorophyll-a concentrations ranged between 0.5 and 3 (mg m^− 3) for mean concentrations between 2 and 20 (mg m^− 3), quite acceptable results for these optically complex 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).     

Radiative transfer modelling of the relationship between seawater composition and remote sensing reflectance in sea lochs and fjords

Sea lochs (small Scottish fjords) are Case 2 waters whose distinctive features include high surface gelbstoff concentrations, strong pycnoclines, high phytoplankton densities and low concentrations of non-biogenic particles. Measurements of the remote sensing reflectance spectra of these waters were made using a floating spectroradiometer during the spring diatom bloom in 1998. Calculations of radiative transfer were then carried out using the Hydrolight software package. The water column was modelled using gelbstoff and chlorophyll a profiles derived from in situ measurements of salinity and fluorescence, calibrated by the analysis of water bottle samples. Solar-stimulated chlorophyll fluorescence was described by a Gaussian peak centered on 685 nm with an apparent quantum yield of 1-2%. Appropriate levels of particle backscattering could be generated by incorporating a Henyey-Greenstein phase function in the model provided that the asymmetry parameter g was allowed to vary systematically with chlorophyll concentration. Since optical depths at 550 nm (the wavelength of maximum penetration) were in the range of 2-8 m for all stations the reflectance spectra were mainly determined by the inherent optical properties of reduced-salinity surface layers.     

Non-isotropy of the upward radiance field in typical coastal (Case 2) waters

The in-water radiance field has been computed in typical Case 2 waters by using radiative transfer models and appropriate inherent optical properties (IOPs) combined with realistic boundary conditions. In particular, the bi-directional structure of the subsurface upward flux has been investigated in view of remote sensing applications related to ocean colour. In Case 2 waters, the IOPs are not controlled by the phytoplankton (or chlorophyll) concentration; rather they are essentially determined by the abundance of terrigenous optically active materials, either particulate or dissolved. Based on field data and related IOPs, two extreme situations were selected as representative instances of sedimentdominated and yellow-substance-dominated Case 2 waters. This study shows that even in very turbid natural waters, the upward radiance field is not isotropic and remains Sun-angle dependent. More than 100 successive events are needed to reach a quasi-isotropic, illumination independent, upward radiance field. In contrast, with a high yellow substance content resulting in high absorption (compared to scattering), single scattering prevails in such waters and this leads to strongly featured radiance fields that are heavily dependent on the Sun's position. It is necessary to account for these effects when interpreting waterleaving radiances as detected from space, and, perhaps more importantly, when carrying out at-sea radiometric measurements in support of calibration of remote ocean colour sensors. For this purpose, a practical approach and mean values of relevant coefficients are proposed to describe the bi-directional structure of the upward radiance field in the two extreme situations of strongly scattering or strongly absorbing waters.     

Cluster analysis of hyperspectral optical data for discriminating phytoplankton pigment assemblages in the open ocean

Optical measurements including remote sensing provide a potential tool for the identification of dominant phytoplankton groups and for monitoring spatial and temporal changes in biodiversity in the upper ocean. We examine the application of an unsupervised hierarchical cluster analysis to phytoplankton pigment data and spectra of the absorption coefficient and remote-sensing reflectance with the aim of discriminating different phytoplankton assemblages in open ocean environments under non-bloom conditions. This technique is applied to an optical and phytoplankton pigment data set collected at several stations within the eastern Atlantic Ocean, where the surface total chlorophyll-a concentration (TChla) ranged from 0.11 to 0.62 mg m^− 3. Stations were selected on the basis of significant differences in the ratios of the two most dominant accessory pigments relative to TChla, as derived from High Performance Liquid Chromatography (HPLC) analysis. The performance of cluster analysis applied to absorption and remote-sensing spectra is evaluated by comparisons with the cluster partitioning of the corresponding HPLC pigment data, in which the pigment-based clusters serve as a reference for identifying different phytoplankton assemblages. Two indices, cophenetic and Rand, are utilized in these comparisons to quantify the degree of similarity between pigment-based and optical-based clusters. The use of spectral derivative analysis for the optical data was also evaluated, and sensitivity tests were conducted to determine the influence of parameters used in these calculations (spectral range, smoothing filter size, and band separation). The results of our analyses indicate that the second derivative calculated from hyperspectral (1 nm resolution) data of the phytoplankton absorption coefficient, a_ph(λ), and remote-sensing reflectance, R_rs(λ), provide better discrimination of phytoplankton pigment assemblages than traditional multispectral band-ratios or ordinary (non-differentiated) hyperspectral data of absorption and remote-sensing reflectance. The most useful spectral region for this discrimination extends generally from wavelengths of about 425-435 nm to wavelengths within the 495-540 nm range, although in the case of phytoplankton absorption data a broader spectral region can also provide satisfactory results.     

Remote estimation of chlorophyll a in optically complex waters based on optical classification

Accurate assessment of phytoplankton chlorophyll a (Chla) concentration in turbid waters by means of remote sensing is challenging due to optically complexity and significant variability of case 2 waters, especially in inland waters with multiple optical types. In this study, a water optical classification algorithm is developed, and two semi-analytical algorithms (three- and four-band algorithm) for estimating Chla are calibrated and validated using four independent datasets collected from Taihu Lake, Chaohu Lake, and Three Gorges Reservoir. The optical classification algorithm is developed using the dataset collected in Taihu Lake from 2006 to 2009. This dataset is also used to calibrate the three- and four-band Chla estimation algorithms. The optical classification technique uses remote sensing reflectance at three bands: Rrs(G), Rrs(650), and Rrs(NIR), where G indicates the location of reflectance peak in the green region (around 560 nm), and NIR is the location of reflectance peak in the near-infrared region (around 700 nm). Optimal reference wavelengths of the three- and four-band algorithm are located through model tuning and accuracy optimization. The three- and four-band algorithm accuracy is further evaluated using other three independent datasets. The improvement of optical classification in Chla estimation is revealed by comparing the performance of the two algorithms for non-classified and classified waters.Using the slopes of the three reflectance bands, the 138 reflectance spectra samples in the calibration dataset are classified into three classes, each with a specific spectral shape character. The three- and four-band algorithm performs well for both non-classified and classified waters in estimating Chla. For non-classified waters, strong relationships are yielded between measured and predicted Chla, but the performance of the two algorithms is not satisfactory in low Chla conditions, especially for samples with Chla below 30 mg m^− 3. For classified waters, the class-specific algorithms perform better than for non-classified waters. Class-specific algorithms reduce considerable mean relative error from algorithms for non-classified waters in Chla predicting. Optical classification makes that there is no need to adjust the optimal position to estimate Chla for other waters using the class-specific algorithms. The findings in this study demonstrate that optical classification can greatly improve the accuracy of Chla estimation in optically complex waters.     

Relationships between suspended mineral concentrations and red-waveband reflectances in moderately turbid shelf seas

This paper considers the uncertainties that arise in estimating the concentration of suspended minerals by optical remote sensing in waters which contain unknown concentrations of other optically significant constituents. Relationships between suspended mineral concentrations and remote sensing reflectance were calculated by radiative transfer modelling using representative specific inherent optical properties (SIOPs) for phytoplankton (CHL), suspended mineral particles of terrigenous origin (MSS_ter) and coloured dissolved organic matter (CDOM) that were derived from measurements at 173 stations in UK shelf seas. When only suspended minerals were present, remote sensing reflectance (R_rs) was related to MSS_ter by a family of saturation curves whose shape depended strongly on wavelength. However the addition of CHL and CDOM made this relationship considerably more complex. Polynomial expressions were therefore derived for the maximum and minimum values of MMS_ter consistent with a given R_rs667 in the presence of independently varying concentrations of CHL and CDOM. For CHL ranging from 0 to 10 mg m⁻³ and CDOM from 0 to 1 m⁻¹, for example, an R_rs667 observation 0.01 sr⁻¹ could corresponded to MSS_ter values between 7 and 12 g m⁻³. The presence of biogenic minerals in the form of diatom frustules, MSS_dia had little influence on the accuracy of MSS_ter retrieval. The degree of variability in the relationship between MSS_ter and Rrs667 predicted by the model was confirmed by measurements of radiometric profiles and mineral concentrations at 110 Irish Sea stations. Uncertainties in the remote sensing of MSS_ter in coastal waters are more appropriately indicated by upper and lower limits set according to the likely ranges of other optically significant constituents than by percentage errors. Moreover, the influence of these constituents should be eliminated before variations in the relationship between MSS_ter and Rrs are attributed to qualitative changes in mineral particle characteristics.     

Impact of sea ice on the retrieval of water-leaving reflectance, chlorophyll a concentration and inherent optical properties from satellite ocean color data

Two physical phenomena by which satellite remotely sensed ocean color data are contaminated by sea ice at high latitudes are described through simulations and observations: (1) the adjacency effect that occurs along sea ice margins and (2) the sub-pixel contamination by a small amount of sea ice within an ocean pixel. The signal at the top of the atmosphere (TOA) was simulated using the 6S radiative transfer code that allows modeling of the adjacency effect for various types of sea ice surrounding an open water area. In situ sea ice reflectance spectra used in the simulations were measured prior to and during the melt period as part of the 2004 Canadian Arctic Shelf Exchange Study (CASES). For sub-pixel contamination, the TOA signal was simulated for various surface reflectances obtained by linear mixture of both sea ice and water-leaving reflectances (ρ_w). The standard atmospheric correction algorithm was then applied to the simulated TOA spectra to retrieve ρ_w spectra from which chlorophyll a concentrations (CHL) and inherent optical properties (IOPs) were derived. The adjacency effect was associated with large errors (> 0.002) in the retrieval of ρ_w as far as 24 km from an ice edge in the blue part of the spectrum (443 nm). Therefore, for moderate to high CHL (> 0.5 mg m^− 3), any pixel located within a distance of ∼ 10-20 km from the ice edge were unreliable. It was also found necessary to consider the adjacency effect when the total absorption coefficient (a_t) was to be retrieved using a semi-analytical algorithm. a_t(443) was underestimated by more than 35% at a distance of 20 km from an ice edge for CHL > 0.5 mg m^− 3. The effect on the retrieval of the particle backscattering coefficient (b_bp) was important only for clear waters (CHL ∼ 0.05 mg m^− 3). In contrast, sub-pixel contamination by a small amount of sea ice produced systematic underestimation of ρ_w in the blue because of incorrect interpretation of enhanced reflectance in the near infrared that is attributed to higher concentrations of atmospheric aerosols. In general, sub-pixel contamination was found to result in overestimations of CHL and a_t, and underestimations of b_bp. A simple method was proposed to flag pixels contaminated by adjacency effect.     

Sampling biases in MODIS and SeaWiFS ocean chlorophyll data

Although modern ocean color sensors, such as MODIS and SeaWiFS, are often considered global missions, in reality it takes many days, even months, to sample the ocean surface enough to provide complete global coverage. The irregular temporal sampling of ocean color sensors can produce biases in monthly and annual mean chlorophyll estimates. We quantified the biases due to sampling using data assimilation to create a “truth field”, which we then sub-sampled using the observational patterns of MODIS and SeaWiFS. Monthly and annual mean chlorophyll estimates from these sub-sampled, incomplete daily fields were constructed and compared to monthly and annual means from the complete daily fields of the assimilation model, at a spatial resolution of 1.25° longitude by 0.67° latitude.The results showed that global annual mean biases were positive, reaching nearly 8% (MODIS) and > 5% (SeaWiFS). For perspective the maximum interannual variability in the SeaWiFS chlorophyll record was about 3%. Annual mean sampling biases were low (< 3%) in the mid-latitudes (between − 40° and 40°). Low interannual variability in the global annual mean sampling biases suggested that global scale trend analyses were valid.High latitude biases were much higher than the global annual means, up to 20% as a basin annual mean, and over 80% in some months. This was the result of the high solar zenith angle exclusion in the processing algorithms. Only data where the solar angle is < 75° are permitted, in contrast to the assimilation which samples regularly over the entire area and month. High solar zenith angles do not facilitate phytoplankton photosynthesis and low chlorophyll concentrations occurring here are missed by the data sets. Ocean color sensors selectively sample in locations and times of favorable phytoplankton growth, producing overestimates of chlorophyll.The biases derived from lack of sampling in the high latitudes varied monthly, leading to artifacts in the apparent seasonal cycle from ocean color sensors. A false secondary peak in chlorophyll occurred in May-August, which resulted from lack of sampling in the Antarctic.Persistent clouds, characteristic in the North Pacific, also produced overestimates, again by selectively sampling only the high growth periods. In contrast, areas characterized by thick aerosols showed chlorophyll underestimates to nearly − 30% in basin monthly means. This was the result of selective sampling in lower aerosol thickness periods, which corresponded with lower phytoplankton growth periods.A combination of MODIS and SeaWiFS sampling was most effective at reducing mid-latitude biases due to inter-orbit gaps, sun glint, and sensor tilt changes. But these biases were low using a single sensor, suggesting multiple sensors had little effect in reducing global and regional monthly and annual mean biases.Ocean color data are an invaluable source of information about global biological processes. However, these results suggest that sampling errors need to be considered in applications involving global and regional mean chlorophyll biomasses as well as seasonal variability and regional trend analysis.     

Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters

Near real-time data from the MODIS satellite sensor was used to detect and trace a harmful algal bloom (HAB), or red tide, in SW Florida coastal waters from October to December 2004. MODIS fluorescence line height (FLH in W m^− 2 μm^− 1 sr^− 1) data showed the highest correlation with near-concurrent in situ chlorophyll-a concentration (Chl in mg m^− 3). For Chl ranging between 0.4 to 4 mg m^− 3 the ratio between MODIS FLH and in situ Chl is about 0.1 W m^− 2 μm^− 1 sr^− 1 per mg m^− 3 chlorophyll (Chl = 1.255 (FLH × 10)^0.86, r = 0.92, n = 77). In contrast, the band-ratio chlorophyll product of either MODIS or SeaWiFS in this complex coastal environment provided false information. Errors in the satellite Chl data can be both negative and positive (3-15 times higher than in situ Chl) and these data are often inconsistent either spatially or temporally, due to interferences of other water constituents. The red tide that formed from November to December 2004 off SW Florida was revealed by MODIS FLH imagery, and was confirmed by field sampling to contain medium (10⁴ to 10⁵ cells L^− 1) to high (> 10⁵ cells L^− 1) concentrations of the toxic dinoflagellate Karenia brevis. The FLH imagery also showed that the bloom started in mid-October south of Charlotte Harbor, and that it developed and moved to the south and southwest in the subsequent weeks. Despite some artifacts in the data and uncertainty caused by factors such as unknown fluorescence efficiency, our results show that the MODIS FLH data provide an unprecedented tool for research and managers to study and monitor algal blooms in coastal environments.     

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

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

Spatio-temporal dynamics of phytoplankton and primary production in Lake Tanganyika using a MODIS based bio-optical time series

Lake Tanganyika, the second largest freshwater ecosystem in Africa, is characterised by a significant heterogeneity in phytoplankton concentration linked to its particular hydrodynamics. To gather a proper understanding of primary production, it is necessary to consider spatial and temporal dynamics throughout the lake. In the present work, daily MODIS-AQUA satellite measurements were used to estimate chlorophyll-a concentrations and the diffuse attenuation coefficient (K490) for surface waters. The spatial regionalisation of Lake Tanganyika, based on Empirical Orthogonal Functions of the chlorophyll-a dataset (July 2002-November 2005), allowed for the separation of the lake in 11 spatially coherent and co-varying regions, with 2 delocalised coastal regions. Temporal patterns of chlorophyll-a showed significant differences between regions. Estimation of the daily primary production in each region indicates that the dry season is more productive than the wet season in all regions with few exceptions. Whole-lake daily primary productivity calculated on an annual basis (2003) was 646 ± 142 mg C m^− 2 day^− 1. Comparing our estimation to previous studies, photosynthetic production in Lake Tanganyika appears to be presently lower (about 15%), which is consistent with other studies which used phytoplankton biovolume and changes of δ¹³C in the lake sediments. The decrease in lake productivity in recent decades may be associated to changes in climate conditions.     

Spectral discrimination of phytoplankton colour groups: The effect of suspended particulate matter and sensor spectral resolution

Remote sensing has been used extensively to provide quantitative information on the distribution of phytoplankton in inland waters through the surrogate mapping of chlorophyll a, but as chlorophyll a is common to almost all species of phytoplankton it cannot provide any information on the taxonomic composition of phytoplankton communities. However, the varied optical properties of phytoplankton taxa may present a means to their discrimination via remote sensing data. This paper presents the results of an experimental study in which the spectral dissimilarities of brown, green, blue-green and red algae were examined with a view to establishing a basis upon which broad changes in phytoplankton communities might be monitored through remote sensing. Pseudo phytoplankton communities were simulated in a series of mesocosm experiments from which spectral reflectance measurements were acquired. The results demonstrated that the phytoplankton colour groups examined were indeed spectrally dissimilar. The spectral distinction between colour groups was noted to be greatest at high concentrations of chlorophyll a and between pseudo-communities dominated by a single species; spectral differences were lower in mixed pseudo-communities with co-dominant species. Moreover, it proved possible to quantify the concentration of two potential biomarker pigments, fucoxanthin and C-phycocyanin, through the derivation of simple spectral indices. The coincidental presence of varying concentrations of SPM (SPIM and SPOM) caused significant attenuation of the spectral response of the pseudo-communities and affected the accuracy of biomarker pigment estimation. It is considered that the realisation of a remote sensing technique for the discrimination of phytoplankton taxa in inland waters would be an extremely useful tool for limnological research and water resource management and thus the future application of this research to inland waters is also discussed.     

Spectral variations in the near-infrared ocean reflectance

The optical properties of natural waters beyond the visible range, in the near-infrared (NIR, 700-900 nm), have received little attention because they are often assumed to be mostly determined by the large absorption coefficient of pure water, and because of methodological difficulties. It is now growingly admitted that the NIR represents a potential optical source of unambiguous information about suspended sediments in turbid waters, thence the need for better understanding the NIR optical behaviour of such waters. It has recently been proposed (Ruddick et al., Limnology and Oceanography. 51, 1167-1179, 2006) that the variability in the shape of the surface ocean reflectance spectrum in the NIR is negligible in turbid waters. In the present study, we show, based on both in situ and remote sensing data, that the shape of the ocean reflectance spectrum in the NIR does vary in turbid to extremely turbid waters. Space-borne ocean reflectance data were collected using 3 different sensors (SeaWiFS, MODIS/Aqua and MERIS) over the Amazon, Mackenzie and Rio de la Plata turbid river plumes during extremely clear atmospheric conditions so that reliable removal of gas and aerosol effects on reflectance could be achieved. In situ NIR reflectance data were collected in different European estuaries where extremely turbid waters were found. In both data sets, a flattening of the NIR reflectance spectrum with increasing turbidity was observed. The ratio of reflectances at 765 nm and 865 nm, for instance, varied from ca. 2 down to 1 in our in situ data set, while a constant value of 1.61 had been proposed based on theory in a previous study. Radiative transfer calculations were performed using a range of realistic values for the seawater inherent optical properties, to determine the possible causes of variations in the shape of the NIR reflectance spectrum. Based on these simulations, we found that the most significant one was the gradual increase in the contribution of suspended sediments to the color of surface waters, which often leads to the flattening of the reflectance spectrum. Changes in the scattering and absorption properties of particles also contributed to variations in the shape of the NIR surface ocean reflectance spectrum. The impact of such variations on the interpretation of ocean color data is discussed.     

Bio-optical properties of east coast Malaysia waters in relation to remote sensing of chlorophyll

Surface water samples collected during the monsoon and inter-monsoon seasons of 2009 off the east coast of Peninsular Malaysia have been analysed for concentrations of total chlorophyll, suspended particulates and coloured dissolved organic matter (CDOM). Spectral absorption coefficients of dissolved and particulate materials have also been measured. Significant seasonal variabilities in concentrations and optical properties were reported with high concentrations of all parameters during the northeast monsoon (NEM) season and low during the southwest monsoon (SWM) and inter-monsoon seasons. Contrary to previous reports on the oligotrophic nature of the waters during the inter-monsoon season, relatively high concentrations of chlorophyll (>3 mg m^?3) were observed at offshore stations in the study area in the spring and fall inter-monsoon months. The chlorophyll-specific absorption spectrum changes with the seasons with the greatest absorption per unit chlorophyll during the SWM and the least during the inter-monsoon seasons, probably in response to seasonal changes in phytoplankton community and cell size structure. The water is classified as optical case 2. At the blue end of the spectrum (440 nm), light absorption by non-phytoplankton materials (CDOM and detritus) accounts for nearly 70% of the total non-water absorption regardless of the season. At the wavelength (676 nm) of the secondary chlorophyll absorption peak in the red part of the spectrum, light absorption by chlorophyll contributes 80–90% to total non-water absorption at most stations and this may provide the basis for remote sensing of phytoplankton chlorophyll in these waters.     

Scaling of coastal phytoplankton features by optical remote sensors: comparison with a regional ecosystem model

Different scales of hydrological and biological patterns of the Bay of Biscay are assessed using space‐borne and airborne optical remote sensing data, field measurements and a 3‐dimensional biophysical model. If field measurements provide accurate values on the vertical dimension, ocean colour data offer frequent observations of surface biological patterns at various scales of major importance for the validation of ecosystem modelling. Although the hydro‐biological model of the continental margin reproduces the main seasonal variability of surface biomass, the optical remote sensing data have helped to identify low grid resolution, input inaccuracies and neglect of swell‐induced erosion mechanism as model limitations in shallow waters. Airborne remote sensing is used to show that satellite data and field measurements are unsuitable for comparison in the extreme case of phytoplankton blooms in patches of a few hundred metres. Vertically, the satellite observation is consistent with near surface in situ measurements as the sub‐surface chlorophyll maximum usually encountered in summer is not detected by optical remote sensing. A mean error (δC/C) of 50.5% of the chlorophyll‐a estimate in turbid waters using the SeaWiFS‐OC5 algorithm allows the quantitative use of ocean colour data by the coastal oceanographic community.     

SeaWiFS sensing of hazardous algal blooms and their underlying mechanisms in shelf-slope waters of the Northwest Pacific during summer

RCA-chlorophyll (red tide index chlorophyll algorithm — RCA) estimates from the SeaWiFS, sea surface height (SSH) variations/geostrophic currents from the multi-satellite altimeters, sea surface temperature (SST) from the NOAA-AVHRR, and wind speed/direction from the QuikSCAT are used in conjugation with field observation data to first describe comprehensively the occurrences of various hazardous algal blooms (HABs) and their underlying mechanisms and link to nutrient enrichment during the summer (June-September) in shelf-slope waters off the Northwest Pacific (NWP) covering China, Korea, Japan and Russia (perhaps this is the first satellite-based study in Russia). These datasets provide a coherent view of the summertime evolution of HABs and related physical processes in the above regional segments with four common dynamic regions: coastal cold/estuary water zones, upwelling zones next to the coast, repeated meanders/eddies, and frontal regimes induced by the Kuroshio and its tributaries. Summer HABs numerically dominated by dinoflagellates and diatoms (only in few cases) were initiated in these hydrodynamically active coastal regions and subsequently transported throughout their coastal and oceanic ranges by major currents and eddy systems. As a consequence, dense and colossal blooms displayed mean RCA of > 7 mg m^− 3 and TBCA (total bloom covered area) of > 20 × 10³ km², which limits the research vessels to observe concomitantly their spatially explicit phases together with physical oceanographic features in such large regions. Less dense and spatially disbanded blooms were characterized by mean RCA of < 3 mg m^− 3 and TBCA of < 8 × 10³ km². Besides those of the nutrient-abundant zones, high blooms coincided with the coastal upwelling and cyclonic eddy regimes that followed SST minimum and large negative SSH along with favorable phase of winds. By contrast, relatively low mean RCA were consistent with the fronts and anticyclonic meanders revealing moderate-high SSH fields along with variable winds blown off the NWP coast. These anticyclonic meanders, on some occasions, when nutrient-containing coastal water setoff higher chlorophyll biomass and major currents gained force in August, straddled the continental margin, entraining high chlorophyll water from the coast and from the adjacent cyclonic eddies (and upwelling) located nearby into their outer rings that formed a conveyer-belt system of transport to inject coastal blooms into the deep-sea (e.g., East Sea) region of the NWP. The above findings based on satellite data combined with field hydrographic/bloom observation data evidently illustrated richness of the response of summer HABs to the surface circulation and nutrient enrichment processes in shelf-slope waters off the NWP coast.     

Variability in measured and modelled remote sensing reflectance for coastal waters at LEO-15

A large database of in situ bio-optical measurements were collected at the LEO-15 (Long-term Ecosystem Observatory) off the southern coast of New Jersey, USA. The data were used to quantify the impact of coastal upwelling on near-shore bulk apparent (AOP) and inherent (IOP) optical properties. There was good qualitative agreement between the AOPs and IOPs in space and time. The measured IOPs were used as inputs to the Hydrolight radiative transfer model (RTE). Estimated spectral AOPs from the RTE were strongly correlated (generally R²>0.80) to measured AOPs. If optical closure between in-water measurements was achieved then the RTE was used to construct the spectral remote sensing reflectance. The modelled remote sensing reflectances were compared to satellite-derived reflectance estimates from four different algorithms. Quantitative agreement between the satellite-measured and in-water modelled remote sensing reflectance was good but results were variable between the different models. The strength of the correlation and spectral consistency was variable with space and time. Correlations were strongest in clear offshore waters and lowest in the near-shore turbid waters. In the near-shore waters, the correlation was strongest for blue wavelengths (400–555?nm) but lower for the red wavelengths of light.     

Remote estimation of chl-a concentration in turbid productive waters — Return to a simple two-band NIR-red model?

Today the water quality of many inland and coastal waters is compromised by cultural eutrophication in consequence of increased human agricultural and industrial activities. Remote sensing is widely applied to monitor the trophic state of these waters. This study investigates the performance of near infrared-red models for the remote estimation of chlorophyll-a concentrations in turbid productive waters and evaluates several near infrared-red models developed within the last 34 years. Three models were calibrated for a dataset with chlorophyll-a concentrations from 0 to 100 mg m⁻³ and validated for independent and statistically different datasets with chlorophyll-a concentrations from 0 to 100 mg m⁻³ and 0 to 25 mg m⁻³ for the spectral bands of the MEdium Resolution Imaging Spectrometer (MERIS) and MODerate resolution Imaging Spectroradiometer (MODIS). The MERIS two-band model estimated chlorophyll-a concentrations slightly more accurately than the more complex models, with mean absolute errors of 2.3 mg m⁻³ for chlorophyll-a concentrations from 0 to 100 mg m⁻³ and 1.2 mg m⁻³ for chlorophyll-a concentrations from 0 to 25 mg m⁻³. Comparable results from several near infrared-red models with different levels of complexity, calibrated for inland and coastal waters around the world, indicate a high potential for the development of a simple universally applicable near infrared-red algorithm.     

Validation of SeaWiFS chlorophyll a concentrations in the Southern Ocean: A revisit

Surface chlorophyll a concentrations (C_a, mg m^− 3) in the Southern Ocean estimated from SeaWiFS satellite data have been reported in the literature to be significantly lower than those measured from in situ water samples using fluorometric methods. However, we found that high-resolution (∼ 1 km²/pixel) daily SeaWiFS C_a (C_a^SWF) data (SeaDAS4.8, OC4v4 algorithm) was an accurate measure of in situ C_a during January-February of 1998-2002 if concurrent in situ data measured by HPLC (C_a^HPLC) instead of fluorometric (C_a^Fluor) measurements were used as ground truth. Our analyses indicate that C_a^Fluor is 2.48 ± 2.23 (n = 647) times greater than C_a^HPLC between 0.05 and 1.5 mg m^− 3 and that the percentage overestimation of in situ C_a by fluorometric measurements increases with decreasing concentrations. The ratio of C_a^SWF/C_a^HPLC is 1.12 ± 0.91 (n = 96), whereas the ratio of C_a^SWF/C_a^Fluor is 0.55 ± 0.63 (n = 307). Furthermore, there is no significant bias in C_a^SWF (12% and − 0.07 in linear and log-transformed C_a, respectively) when C_a^HPLC is used as ground truth instead of C_a^Fluor. The high C_a^Fluor/C_a^HPLC ratio may be attributed to the relatively low concentrations of chlorophyll b (C_b/C_a = 0.023 ± 0.034, n = 482) and relatively high concentrations of chlorophyll c (C_c/C_a = 0.25 ± 0.59, n = 482) in the phytoplankton pigment composition when compared to values from other regions. Because more than 90% of the waters in the study area, as well as in the entire Southern Ocean (south of 60° S), have C_a^SWF between 0.05 and 1.5 mg m^− 3, we consider that the SeaWiFS performance of C_a retrieval is satisfactory and for this C_a range there is no need to further develop a “regional” bio-optical algorithm to account for the previous SeaWiFS “underestimation”.