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.     

A three component classification of phytoplankton absorption spectra: Application to ocean-color data

A method is presented to identify absorption characteristics of three optically-distinct phytoplankton classes from a suite of measurements of total phytoplankton absorption coefficient and chlorophyll-a concentration by successive application of the two-population absorption model of Sathyendranath et al. (2001) and Devred et al. (2006a). The total phytoplankton absorption coefficient at multiple wavelengths is expressed as the weighted sum of the absorption coefficients of each class at those wavelengths. The resultant system of equations is solved under some constraints to derive the fraction of each class present in any given sample of seawater, given the spectrum of total phytoplankton absorption coefficient. When applied to a large database, the results compare well with phytoplankton size-classes derived from pigment composition, so that we can assume that the three phytoplankton classes derived from absorption coefficients are representative of the pico-, nano- and microphytoplankton size classes. A modification is proposed to the pigment-based phytoplankton size classification of Uitz et al. (2006) to account for the effect of fucoxanthin associated with nanophytoplankton. Comparison between satellite and in situ data demonstrates the potential of satellite ocean-color data to yield the distribution of phytoplankton size classes from space. The algorithm is applied to phytoplankton absorption coefficients derived from remotely-sensed reflectance values collected by SeaWiFS over the Northwest Atlantic in 2007. Monthly composites for April, August and November, representative of Spring, Summer and Fall, give synoptic views of the phytoplankton community structure: a Spring bloom dominated by microphytoplankton is followed by a second, less intense, bloom in the Fall dominated by nanophytoplankton. Picophytoplankton are dominant in the study area in Summer.     

Retrieval of oceanic chlorophyll concentration with relevance vector machines

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

Consistent merging of satellite ocean color data sets using a bio-optical model

While many (and more on the way) ocean color satellite sensors presently provide routine observations of ocean biological processes, limited concrete effort has taken place to demonstrate how these data can be used together in any systematic way. One obvious way is to merge these data streams together to provide robust merged climate data records with measurable uncertainty bounds. Here, we present and implement a formalism for merging global satellite ocean color data streams to produce uniform data products. Normalized water-leaving radiances (L_wN(λ)) from SeaWiFS and MODIS are used together in a semianalytical ocean color merging model to produce global retrievals of 3 biogeochemically relevant variables (chlorophyll, combined dissolved and detrital absorption coefficient, particulate backscattering coefficient). The model-based merging approach has various benefits over techniques that blend end products, such as chlorophyll concentrations; (1) merging at the level of water-leaving radiance ensures simultaneity and consistency of the retrievals, (2) it works with single or multiple data sources regardless of their specific bands, (3) it exploits band redundancies and band differences, (4) it can account for the uncertainties of the incoming L_wN(λ) data streams and, (5) it provides confidence intervals for the derived products. These features are illustrated through several examples of ocean color data merging using SeaWiFS and MODIS Terra and Aqua L_wN(λ) imagery. Compared to each of the original data source, the products derived from the merging procedure show enhanced global daily coverage and lower uncertainties in the retrieved variables.     

Diurnal variability of ocean optical properties during a coastal algal bloom: implications for ocean colour remote sensing

Understanding the diurnal variability of ocean optical properties is critical for better interpretation of satellite ocean colour data and characterizing biogeochemical processes. The daytime variability of ocean optical properties throughout an algal bloom event is analysed in this article based on in situ observations from dawn to dusk at a fixed coastal site in the South China Sea. Diurnal variability during the sunlit period of the ocean optical properties is found to be significant. During the 6 hours around noon, the temporal variability (defined by the coefficient of variation) of phytoplankton absorption, coloured dissolved organic matter and non-algal particle absorption, and particle backscattering at 443 nm can reach 21% ± 15%, 12% ± 9%, and 17% ± 9%, respectively. The diurnal variability during the bloom is much more pronounced than that of the non-bloom phase. With atmospheric radiative transfer modelling, it is further demonstrated that the geostationary satellite detection of within-day optical variability in algae-dominated waters depends on the reliability of the aerosol retrieval. The implications of the diurnal bio-optical variability for the retrieval, validation, and interpretation of satellite ocean colour products are also discussed.     

A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product

Global chlorophyll products derived from NASA's ocean color satellite programs have a nominal uncertainty of ± 35%. This metric has been hard to assess, in part because the data sets for evaluating performance do not reflect the true distribution of chlorophyll in the global ocean. A new technique is introduced that characterizes the chlorophyll uncertainty associated with distinct optical water types, and shows that for much of the open ocean the relative error is under 35%. This technique is based on a fuzzy classification of remote sensing reflectance into eight optical water types for which error statistics have been calculated. The error statistics are based on a data set of coincident MODIS Aqua satellite radiances and in situ chlorophyll measurements. The chlorophyll uncertainty is then mapped dynamically based on fuzzy memberships to the optical water types. The uncertainty maps are thus a separate, companion product to the standard MODIS chlorophyll product.     

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.     

Detection of Rossby waves in multi-parameters in multi-mission satellite observations and HYCOM simulations in the Indian Ocean

Rossby waves are difficult to detect with in situ methods. However, as we show in this paper, they can be clearly identified in multi-parameters in multi-mission satellite observations of sea surface height (SSH), sea surface temperature (SST) and ocean color observations of chlorophyll-a (chl-a), as well as 1/12° global HYbrid Coordinate Ocean Model (HYCOM) simulations of SSH, SST and sea surface salinity (SSS) in the Indian Ocean. While the surface structure of Rossby waves can be elucidated from comparisons of the signal in different sea surface parameters, models are needed to gain direct information about how these waves affect the ocean at depth. The first three baroclinic modes of the Rossby waves are inferred from the Fast Fourier Transform (FFT), and two-dimensional Radon Transform (2D RT). At many latitudes the first and second baroclinic mode Rossby wave phase speeds from satellite observations and model parameters are identified. Wavelet transforms of these multi-parameters from satellite observations and model simulations help to discriminate between the annual and semi-annual signal of these Rossby waves. This comprehensive study reveals that the surface signature of Rossby waves in SSS anomalies is likely to be between 0.05 and 0.3 psu in the South Indian Ocean.     

Estimating constituent concentrations in case II waters from MERIS satellite data by semi-analytical model optimizing and look-up tables

Remote estimation of water constituent concentrations in case II waters has been a great challenge, primarily due to the complex interactions among the phytoplankton, tripton, colored dissolved organic matter (CDOM) and pure water. Semi-analytical algorithms for estimating constituent concentrations are effective and easy to implement, but two challenges remain. First, a dataset without a sampling bias is needed to calibrate estimation models; and second, the semi-analytical indices were developed based on several specific assumptions that may not be universally applicable. In this study, a semi-analytical model-optimizing and look-up-table (SAMO-LUT) method was proposed to address these two challenges. The SAMO-LUT method is based on three previous semi-analytical models to estimate chlorophyll a, tripton and CDOM. Look-up tables and an iterative searching strategy were used to obtain the most appropriate parameters in the models. Three datasets (i.e., noise-free simulation data, in situ data and Medium Resolution Imaging Spectrometer (MERIS) satellite data) were collected to validate the performance of the proposed method. The results show that the SAMO-LUT method yields error-free results for the ideal simulation dataset; and is able also to accurately estimate the water constituent concentrations with an average bias (mean normalized bias, MNB) lower than 9% and relative random uncertainty (normalized root mean square error, NRMS) lower than 34% even for in situ and MERIS data. These results demonstrate the potential of the proposed algorithm to accurately monitor inland and coastal waters based on satellite observations.     

Autumn leaf phenology: discrepancies between in situ observations and satellite data at urban and rural sites

ABSTRACT

Autumn phenophases, such as leaf colouration (LC) and leaf fall (LF), have received considerably less attention than their spring counterparts (budburst and leaf unfolding) but are equally important determinants of the duration of the growing season and thus have a controlling in?uence on the carbon-uptake period. Here, we examined THE trends (1968–2016) in in situ observations of the timing of LC and LF from a suite of deciduous trees at three rural sites and one urban site in Ireland. Satellite-derived autumn phenological metrics including mid-senescence (MS) and end of senescence (ES) based on two-band enhanced vegetation index (EVI2) from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) from 1982 to 2016 at a spatial resolution of 5km² were also examined. The aim of this study was to assess the effectiveness of satellite remote sensing in capturing autumn phenology as determined by in situ observations . Analysis of in situ data (1968–2016) revealed the urban site to be significantly different from the rural sites as LC and LF occurred later in the season and the duration of the autumn season (LF-LC) became shorter over time. These trends may be partly driven by the presence of artificial light in the city. On average (1982–2016), there was a 6-day delay in the timing of MS compared to LC and a much larger difference (21 days) between ES and LF. This resulted in a 31-day autumn duration as defined by satellite data compared to 16 days from in situ observations. Furthermore, there was little overlap in timing between LC and MS, and LF and ES at the rural sites only. Discrepancies between in situ and satellite data may be attributed to the satellite data integrating a much broader vegetation signal across a heterogeneous landscape than in situ observations of individual trees. Therefore, at present, satellite-derived autumn phenology may be more successful in capturing in situ observations across large homogeneous landscapes of similar vegetation types (e.g. forested areas) than in heterogeneous landscapes (e.g. small mixed farms, urban areas, etc.) as is the case in Ireland where the in situ observations of trees may not be reflective of the overall vegetation. Matching the scale of satellite data with in situ observations remains a challenging task but may, at least in part, be overcome by increasing the extent of observations to include a wider range of species and in future as satellite data become available at higher spatial and temporal resolutions.     

Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data

Using the NASA maintained ocean optical and biological in situ data that were collected during 2002-2005, we have evaluated the performance of atmospheric correction algorithms for the ocean color products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua. Specifically, algorithms using the MODIS shortwave infrared (SWIR) bands and an approach using the near-infrared (NIR) and SWIR combined method are evaluated, compared to the match-up results from the NASA standard algorithm (using the NIR bands). The in situ data for the match-up analyses were collected mostly from non-turbid ocean waters. It is critical to assess and understand the algorithm performance for deriving MODIS ocean color products, providing science and user communities with the important data quality information. Results show that, although the SWIR method for data processing has generally reduced the bias errors, the noise errors are increased due mainly to significantly lower sensor signal-noise ratio (SNR) values for the MODIS SWIR bands, as well as the increased uncertainties using the SWIR method for the atmospheric correction. This has further demonstrated that future ocean color satellite sensors will require significantly improved sensor SNR performance for the SWIR bands. The NIR-SWIR combined method, for which the non-turbid and turbid ocean waters are processed using the NIR and SWIR method, respectively, has been shown to produce improved ocean color products.     

Extending the MODIS 1 km ocean colour atmospheric correction to the MODIS 500 m bands and 500 m chlorophyll-a estimation towards coastal and estuarine monitoring

National and regional obligations to control and maintain water quality have led to an increase in coastal and estuarine monitoring. A potentially valuable tool is high temporal and spatial resolution satellite ocean colour data. NASA's MODIS-Terra and -Aqua can capture data at both 250 m and 500 m spatial resolutions and the existence of two sensors provides the possibility for multiple daily passes over a scene. However, no robust atmospheric correction method currently exists for these data, rendering them unusable for quantitative monitoring applications. Therefore, this paper presents an automatic and dynamic atmospheric correction approach allowing the determination of ocean colour. The algorithm is based around the standard MODIS 1 km atmospheric correction, includes cloud masking and is applicable to all of the visible 500 m bands. Comparison of the 500 m ocean colour data with the standard 1 km data shows good agreement and these results are further supported by in situ data comparisons. In addition, a novel method to produce 500 m chlorophyll-a estimates is presented. Comparisons of the 500 m estimates with the standard MODIS OC3M algorithm and to in situ data from a near-coast validation site are given.     

An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation

Global satellite ocean color instruments provide the scientific community a high-resolution means of studying the marine biosphere. Satellite data product validation and algorithm development activities both require the substantial accumulation of high-quality in-situ observations. The NASA Ocean Biology Processing Group maintains a local repository of in-situ marine bio-optical data, the SeaWiFS Bio-optical Archive and Storage System (SeaBASS), to facilitate their ocean color satellite validation analyses. Data were acquired from SeaBASS and used to compile a large set of coincident radiometric observations and phytoplankton pigment concentrations for use in bio-optical algorithm development. This new data set, the NASA bio-Optical Marine Algorithm Data set (NOMAD), includes over 3400 stations of spectral water-leaving radiances, surface irradiances, and diffuse downwelling attenuation coefficients, encompassing chlorophyll a concentrations ranging from 0.012 to 72.12 mg m^− 3. Metadata, such as the date, time, and location of data collection, and ancillary data, including sea surface temperatures and water depths, accompany each record. This paper describes the assembly and evaluation of NOMAD, and further illustrates the broad geophysical range of stations incorporated into NOMAD.     

Validation of MERIS ocean-color products in the Bohai Sea: A case study for turbid coastal waters

An extensive in situ data set in the Bohai Sea of China was collected to assess radiometric properties and concentrations of ocean constituents derived from Medium Resolution Imaging Spectrometer (MERIS). The data collected include spectral normalized water-leaving radiance L_wn(λ) and concentrations of suspended particulate matter (SPM) and chlorophyll a (Chl-a). A strict spatio-temporal match-up method was adopted in view of the complexity and variability of the turbid coastal area, resulting in 13, 48 and 18 match-ups for MERIS L_wn(λ), SPM and Chl-a estimates, respectively. For MERIS L_wn(λ), the match-ups showed mean absolute percentage differences (APD) of 17%-20% in the 412, 443, 620 and 665 nm bands, whereas L_wn(λ) at bands from 490 and 560 nm had better APD of 15-16%. The band ratio of L_wn(490) to L_wn(560) of the satellite data was in good agreement with in situ observations with an APD of 4%. MERIS SPM and Chl-a products overestimated the in situ values, with the APD of approximately 50% and 60%, respectively. When match-up criteria were relaxed, the assessment results degraded systematically. Hence, in turbid coastal areas where temporal variability and spatial heterogeneity of bio-optical properties may be pronounced as the result of terrestrial influences and local dynamics, the strict spatio-temporal match-up is recommended.     

Remote sensing chlorophyll a of optically complex waters (rias Baixas, NW Spain): Application of a regionally specific chlorophyll a algorithm for MERIS full resolution data during an upwelling cycle

This study takes advantage of a regionally specific algorithm and the characteristics of Medium Resolution Imaging Spectrometer (MERIS) in order to deliver more accurate, detailed chlorophyll a (chla) maps of optically complex coastal waters during an upwelling cycle. MERIS full resolution chla concentrations and in situ data were obtained on the Galician (NW Spain) shelf and in three adjacent rias (embayments), sites of extensive mussel culture that experience frequent harmful algal events. Regionally focused algorithms (Regional neural network for rias Baixas or NNRB) for the retrieval of chla in the Galician rias optically complex waters were tested in comparison to sea-truth data. The one that showed the best performance was applied to a series of six MERIS (FR) images during a summer upwelling cycle to test its performance. The best performance parameters were given for the NN trained with high-quality data using the most abundant cluster found in the rias after the application of fuzzy c-mean clustering techniques (FCM). July 2008 was characterized by three periods of different meteorological and oceanographic states. The main changes in chla concentration and distribution were clearly captured in the images. After a period of strong upwelling favorable winds a high biomass algal event was recorded in the study area. However, MERIS missed the high chlorophyll upwelled water that was detected below surface in the ria de Vigo by the chla profiles, proving the necessity of in situ observations. Relatively high biomass “patches” were mapped in detail inside the rias. There was a significant variation in the timing and the extent of the maximum chla areas. The maps confirmed that the complex spatial structure of the phytoplankton distribution in the rias Baixas is affected by the surface currents and winds on the adjacent continental shelf. This study showed that a regionally specific algorithm for an ocean color sensor with the characteristics of MERIS in combination with in situ data can be of great help in chla monitoring, detection and study of high biomass algal events in an area affected by coastal upwelling such as the rias Baixas.     

Delineation of seasonal changes of chlorophyll frontal boundaries in Mediterranean Coastal waters with Nimbus-7 Coastal Zone Color Scanner data

The seasonal changes in chlorophyll-like pigment distribution in the Northwestern Mediterranean Sea (Gulf of Lions) were monitored during the year 1979. Data were collected from the Coastal Zone Color Scanner (CZCS), carried on the Nimbus-7 satellite. A series of cloud-free images covering the whole year was selected and processed to evaluate chlorophyll-like pigment concentrations. The upwelling subsurface radiance of the ocean (L_ss) was extracted from the apparent upwelling signal reaching the satellite sensor (L_O) using an algorithm removing atmospheric effects. Chlorophyll-like pigment concentration (C) was then derived from L_ss in different wavelengths. Characteristic boundaries between water masses with different phytoplankton content were obtained. The results demonstrate that phytoplankton distribution is a good indicator of seasonal variations of oceanic fronts. Features such as coastal upwellings, cyclonic eddies, or plume of the Rhône river could be monitored.     

Automatic neural classification of ocean colour reflectance spectra at the top of the atmosphere with introduction of expert knowledge

We propose an automatic neural classification method for ocean colour (OC) reflectance measurements taken at the top of the atmosphere (TOA) by satellite-borne sensors. The goal is to identify aerosol types and cloud contaminated pixels. This information is of importance when selecting appropriate atmospheric correction algorithms for retrieving ocean parameters such as phytoplankton concentrations. The methodology is based on the use of Topological Neural network Algorithms (TNA, so-called Kohonen maps). The pixels of the remotely sensed image are characterised by a vector whose components are the spectral TOA measurement and the standard deviation of a small spatial structure. The method is a three-step method. The first step is an unsupervised classification built from a learning data set; it clusters pixel vectors which are similar into a certain number of groups. Each group is characterised by a specific vector, the so-called reference vector (rv), which summarises the information contained in all the pixels belonging to that group. The second step of the method consists of labeling the reference vectors with the help of an expert in ocean optics. The groups are then clustered into classes corresponding to physical characteristics provided by the expert. The third step consists of analyzing full images and classifying them by using the classifier which has been determined during the first two steps. The method was applied to the Cape Verde region, which exhibits important seasonal variability in terms of aerosols, cloud coverage and ocean chlorophyll-a concentration. We processed POLDER data to test the algorithm. We considered four classes: pixels contaminated by clouds; two types of pixels containing mineral dusts; and pixels containing maritime aerosols only. The method was able to take into account the information given by the expert and apply it to unlabeled pixels. This methodology could easily be extended to a larger number of classes, the major problem being to find adequate expertise to label the classes.     

Ecological indicators for the pelagic zone of the ocean from remote sensing

It is generally accepted that responsible stewardship of the ocean implies ecosystem-based management. A requirement then arises for ecosystem indicators that can be applied in serial fashion with a view to detection of ecosystem change in response to environmental perturbations such as climate change or overfishing. The status of ecological indicators for the pelagic ecosystem is reviewed. The desirable properties of such indicators are listed and it is pointed out that remote sensing (ocean colour, supplemented by sea-surface temperature) is an important aid to achieving them. Some ecological indicators that can be developed from remotely-sensed data on ocean colour are tabulated. They deal with the seasonal cycle of phytoplankton biomass, production and loss terms, annual production, new production, ratio of production to respiration, spatial variances in phytoplankton biomass and production, spatial distribution of phytoplankton functional types, delineation of ecological provinces and phytoplankton size structure.     

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

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