Bio-optical Data Research Articles

Spatial-temporal bio-optical classification of dynamic semi-estuarine waters in western North America

The use of standard ocean colour reflectance based algorithms to derive surface chlorophyll may have limited applicability for optically dynamic coastal waters due to the pre-defined coefficients based on global datasets. Reflectance based algorithms adjusted to regional optical water characteristics are a promising alternative. A class-based definition of optically diverse coastal waters was investigated as a first step towards the development of temporal and spatial constrained reflectance based algorithms for optically variable coastal waters.A large set of bio-optical data were collected as part of five research cruises and bi-weekly trips aboard a ship of opportunity in the west coast of Canada, to assess the spatial and temporal variability of above-water reflectance in this contrasted coastal environment. To accomplish this, in situ biophysical and optical measurements were collected in conjunction with above-water hyperspectral remote sensing reflectance (Rrs) at 145 stations. The concentrations of measured biophysical data varied considerably; chlorophyll a (Chla) (mean = 1.64, range: 0.10–7.20 μg l−1), total suspended matter (TSM) (3.09, 0.82–20.69 mg l−1), and absorption by chromophoric dissolved organic matter (CDOM) (acdom(443nm)) (0.525, 0.007–3.072 m-1), thus representing the spatio-temporal variability of the Salish Sea. Optically, a similar large range was also found; particulate scattering (bp(650nm)) (1.316, 0.250–7.450 m-1), particulate backscattering (bbp(650nm)) (0.022, 0.005–0.097 m-1), total beam attenuation coefficient (ct(650)) (1.675, 0.371–9.537 m-1) and particulate absorption coefficient (ap(650nm)) (0.345, 0.048–2.020 m-1). An empirical orthogonal function (EOF) analysis revealed that Rrs variability was highly correlated to bp (r = 0.90), bbp (r = 0.82) and concentration of TSM (r = 0.80), which highlighted the dominant role of water turbidity in this region. Hierarchical clustering analysis was applied to the normalized Rrs spectra to define optical water classes. Class 1 was defined by the highest Rrs values, particularly above 570 nm, indicating more turbid waters; Class 2 was dominated by high Chla and TSM concentrations, which is shown by high Rrs at 570 nm as well as fluorescence and absorption peaks; Class 3 shows strong fluorescence signatures accompanied by low TSM influence; and Class 4 is most representative of clear waters with a less defined absorption peak around 440 nm. By understanding the bio-optical factors which control the variability of the Rrs spectra this study aims to develop a sub-regional characterization of this coastal region aiming to improve bio-optical algorithms in this complex coastal area.

Optical discrimination of harmful Cochlodinium polykrikoides blooms in Korean coastal waters.

We investigated the possibility of optically discriminating harmful algal blooms (HABs), focusing on Cochlodinium polykrikoides, the major HAB causative dinoflagellate species in Korean waters. We produced a large data set of simulated remote sensing reflectance (Rrs) spectra in a wide range of bio-optical conditions using Hydrolight software and bio-optical data provided by the International Ocean-Color Coordinating Group. The two Rrs band ratios (Rrs(555)/Rrs(531) and Rrs(488)/Rrs(443)) were determined to be effective in discriminating high-density C. polykrikoides blooms. The results were consistent with in situ observations and seem applicable to diverse coastal environments. Our findings provide theoretical and quantitative criteria upon which in-water HAB detecting algorithms can be developed.

A new insight into black blooms: Synergies between optical and chemical factors

Black blooms have been associated with fish-kills and the loss of benthic fauna as well as closure of potable water supplies. Their frequency and duration has increased in recent decades in rivers, inland lakes and reservoirs, and has often been associated with the decay and release of organic matter (planktonic algae, aquatic macrophytes, sediment release, etc.). However, the interactions between microbial, chemical, hydrodynamic and optical conditions necessary for black blooms are poorly understood. The present study combines field investigations and laboratory mesocosm studies to show that black blooms are caused by a combination of high CDOM (chromophoric dissolved organic matter) absorption, the formation of CDOM-Fe complexes and low backscattering. Mesocosm experiments showed that black bloom conditions occur after 4 days, with a significant increase in the concentrations of Fe2+ and ∑S2−. Total absorption (excluding absorption due to water) at 440 nm increased by 30% over this time to 7.3 m−1. In addition, the relative contribution of CDOM absorption to the non-water total absorption increased from 18% to 50%. Regression analyses between chemical and bio-optical data in both field and mesocosm experiments indicated that the concentrations of Fe2+ co-varied positively with CDOM absorption ag(440) (R2 > 0.70), and the specific CDOM absorption (ag(440)/DOC). Conditions that favored the development of black blooms were elevated algal or macrophyte biomass and limited water column mixing.

Characterization of phytoplankton variability in the Cariaco Basin using spectral absorption, taxonomic and pigment data

The spectral absorption coefficient of marine phytoplankton provides information on phytoplankton community structure, biomass, and general physiological conditions. These variables are necessary for understanding and predicting ocean productivity, carbon fluxes, underwater light propagation, water quality, and for assessing marine photochemical processes. The Cariaco Basin, located on the continental shelf of Venezuela in the southeastern Caribbean Sea, is the site of the CARIACO Ocean Time-Series project. Since 1995, CARIACO has collected bio-optical (hyperspectral inherent and apparent optical properties — IOPs and AOPs, respectively), biogeochemical and ecological observations to characterize local ecosystem variations in response to regional and global changes in climate. We examine phytoplankton taxonomic and pigment time series data collected by this program between 2006 and 2012 to understand how seasonal changes in these parameters relate to bio-optical data (i.e., absorption spectra). TChla and accessory pigments varied seasonally in response to changes in the phytoplankton community composition, with higher concentrations of microphytoplankton (>20μm; 45%) during upwelling (December–April) than during the rainy season (16%; May/June–October/November). Picophytoplankton (<2μm) dominated during the rainy season (66%). The absorption properties also exhibited seasonal variations. Diagnostic pigments could not be identified in a quantitative way using derivative analysis of phytoplankton absorption, likely because of overlapping of absorption spectra among the pigments present. The POC:TChla ratio at CARIACO was variable and dependent on bulk carbon (not necessarily related to phytoplankton) and the functional groups present at any given time, underscoring the fact that using a fixed ratio of POC:Chla in biogeochemical models can lead to large uncertainties in carbon budgets from coastal zones. Low POC:TChla was associated with microphytoplankton size class (diatoms), while picophytoplankton (cyanobacteria) exhibited higher ratios. These results contribute to furthering our understanding of coastal phytoplankton dynamics and how they relate to optical signatures.

Bio-optical data integration based on a 4 D database system approach

Abstract. Bio-optical characterization of water bodies requires spatio-temporal data about Inherent Optical Properties and Apparent Optical Properties which allow the comprehension of underwater light field aiming at the development of models for monitoring water quality. Measurements are taken to represent optical properties along a column of water, and then the spectral data must be related to depth. However, the spatial positions of measurement may differ since collecting instruments vary. In addition, the records should not refer to the same wavelengths. Additional difficulty is that distinct instruments store data in different formats. A data integration approach is needed to make these large and multi source data sets suitable for analysis. Thus, it becomes possible, even automatically, semi-empirical models evaluation, preceded by preliminary tasks of quality control. In this work it is presented a solution, in the stated scenario, based on spatial – geographic – database approach with the adoption of an object relational Database Management System – DBMS – due to the possibilities to represent all data collected in the field, in conjunction with data obtained by laboratory analysis and Remote Sensing images that have been taken at the time of field data collection. This data integration approach leads to a 4D representation since that its coordinate system includes 3D spatial coordinates – planimetric and depth – and the time when each data was taken. It was adopted PostgreSQL DBMS extended by PostGIS module to provide abilities to manage spatial/geospatial data. It was developed a prototype which has the mainly tools an analyst needs to prepare the data sets for analysis.

Estimating phycocyanin pigment concentration in productive inland waters using Landsat measurements: A case study in Lake Dianchi

Using remote sensing reflectance (R(rs)(λ), sr(-1)) and phycocyanin (PC, mg m(-3)) pigment data as well as other bio-optical data collected from two cruises in September and December 2009 in Lake Dianchi (a typical plateau lake of China), we developed a practical approach to estimate PC concentrations that could be applied directly to Landsat measurements. The visible and near-IR bands as well as their band ratios of simulated Landsat data were used as inputs to the algorithms, where the algorithm coefficients for each Landsat sensor were determined through multivariate regressions. The coefficients of determination (R(2)) between the R(rs)-modeled and measured PC were all > 0.97 for the spectral bands corresponding to Landsat 8 OLI, Landsat 7 ETM + , Landsat 5 TM, and Landsat 4 TM, with mean absolute percentage errors (MAPE) < 10% for PC ranging between ~80 and 700 mg m(-3) (n = 14). The algorithms were further evaluated using an independent data set (n = 14), yielding larger but still acceptable MAPE (~30%) for PC ranging between ~80 and 500 mg m(-3). Application of the approach to Landsat 8 measurements over Lake Dianchi suggests potential use of the approach for periodical assessment of the lake's bloom conditions, yet its empirical nature together with the lack of specific narrow bands on Landsat sensors to explicitly account for the PC absorption around 625 nm calls for extra caution when applied to other eutrophic lakes.

In situ phytoplankton distributions in the Amundsen Sea Polynya measured by autonomous gliders

Abstract The Amundsen Sea Polynya is characterized by large phytoplankton blooms, which makes this region disproportionately important relative to its size for the biogeochemistry of the Southern Ocean. In situ data on phytoplankton are limited, which is problematic given recent reports of sustained change in the Amundsen Sea. During two field expeditions to the Amundsen Sea during austral summer 2010–2011 and 2014, we collected physical and bio-optical data from ships and autonomous underwater gliders. Gliders documented large phytoplankton blooms associated with Antarctic Surface Waters with low salinity surface water and shallow upper mixed layers (&amp;lt; 50 m). High biomass was not always associated with a specific water mass, suggesting the importance of upper mixed depth and light in influencing phytoplankton biomass. Spectral optical backscatter and ship pigment data suggested that the composition of phytoplankton was spatially heterogeneous, with the large blooms dominated by Phaeocystis and non-bloom waters dominated by diatoms. Phytoplankton growth rates estimated from field data (≤ 0.10 day−1) were at the lower end of the range measured during ship-based incubations, reflecting both in situ nutrient and light limitations. In the bloom waters, phytoplankton biomass was high throughout the 50-m thick upper mixed layer. Those biomass levels, along with the presence of colored dissolved organic matter and detritus, resulted in a euphotic zone that was often &amp;lt; 10 m deep. The net result was that the majority of phytoplankton were light-limited, suggesting that mixing rates within the upper mixed layer were critical to determining the overall productivity; however, regional productivity will ultimately be controlled by water column stability and the depth of the upper mixed layer, which may be enhanced with continued ice melt in the Amundsen Sea Polynya.

On the consistency of MODIS chlorophyll $a$ products in the northern South China Sea

Abstract. Chlorophyll a (Chl) concentrations derived from satellite measurements have been used in oceanographic research, for example to interpret eco-responses to environmental changes on global and regional scales. However, it is unclear how existing Chl products compare with each other in terms of accuracy and consistency in revealing temporal and spatial patterns, especially in the optically complex marginal seas. In this study, we examined three MODIS (Moderate Resolution Imaging Spectroradiometer) Chl data products that have been made available to the community by the US National Aeronautics and Space Administration (NASA) using community-accepted algorithms and default parameterization. These included the products derived from the OC3M (ocean chlorophyll three-band algorithm for MODIS), GSM (Garver–Siegel–Maritorena model) and GIOP (generalized inherent optical properties) algorithms. We compared their temporal variations and spatial distributions in the northern South China Sea. We found that the three products appeared to capture general features such as unique winter peaks at the Southeast Asian Time-series Study station (SEATS, 18° N, 116° E) and the Pearl River plume associated blooms in summer. Their absolute magnitudes, however, may be questionable in the coastal zones. Additional error statistics using field measured Chl as the truth demonstrated that the three MODIS Chl products may contain high degree of uncertainties in the study region. Root mean square error (RMSE) of the products from OC3M and GSM (on a log scale) was about 0.4 and average percentage error (ε) was ~ 115% (Chl between 0.05–10.41 mg m−3, n = 114). GIOP with default parameterization led to higher errors (ε = 329%). An attempt to tune the algorithms based on a local coastal-water bio-optical data set led to reduced errors for Chl retrievals, indicating the importance of local tuning of globally-optimized algorithms. Overall, this study points to the need of continuous improvements for algorithm development and parameterization for the coastal zones of the study region, where quantitative interpretation of the current Chl products requires extra caution.

Subsatellite polygon for studying hydrophysical processes in the Black Sea shelf-slope zone

The first data on the creation of the subsatellite polygon on the Black Sea shelf and continental slope in the Gelendzhik area (designed in order to permanently monitor the state of the aquatic environment and biota) and the plans for maintaining and developing this polygon are presented. The autonomous measuring systems of the polygon in the composition of bottom stations with acoustic Doppler current profilers (ADCP), Aqualog robotic profilers, and thermo-chains on moored buoy stations should make it possible to regularly obtain hydrophysical, hydrochemical, and bio-optical data with a high spatial-time resolution and transmit these data to the coastal center on a real-time basis. These field data should be used to study the characteristics and formation mechanisms of the marine environment and biota variability, as well as the water-exchange processes in the shelf-deep basin system, ocean-atmosphere coupling, and many other processes. These data are used to calibrate the satellite measurements and verify the water circulation numerical simulation. It is assumed to use these data in order to warn about the hazardous natural phenomena and control the marine environment state and its variation under the action of anthropogenic and natural factors, including climatic trends. It is planned to use the polygon subsatellite monitoring methods and equipment in other coastal areas, including other Black Sea sectors, in order to create a unified system for monitoring the Black Sea shelf-slope zone.

Assessing algal biomass and bio-optical distributions in perennially ice-covered polar ocean ecosystems

Under-ice observations of algal biomass and seasonality are critical for understanding better how climate-driven changes affect polar ocean ecosystems. However, seasonal and interannual variability in algal biomass has been studied sparsely in perennially ice-covered polar ocean regions. To address this gap in polar ocean observing, bio-optical sensors for measuring chlorophyll fluorescence, optical scattering, dissolved organic matter fluorescence, and incident solar radiation were integrated into Ice-Tethered Profilers (ITPs). Eight such systems have been deployed in the Arctic Ocean, with five profilers completing their deployments to date including two that observed an entire annual cycle in the central Arctic Ocean and Beaufort Sea respectively. These time series revealed basic seasonal differences in the vertical distributions of algal biomass and related bio-optical properties in these two regions of the Arctic Ocean. Because they conduct profiles on daily or sub-daily scales, ITP bio-optical data allow more accurate assessments of the timing of changes in under-ice algal biomass such as the onset of the growing season in the water column, the subsequent export of particulate organic matter at the end, and the frequency of intermittent perturbations, which in the central Arctic Ocean were observed to have time scales of between one and two weeks.

Contribution to a bio-optical model for remote sensing of Lena River water

Abstract. Bio-optical measurements and sampling were carried out in the delta of the Lena River (northern Siberia, Russia) between 26 June and 4 July 2011. The aim of this study was to determine the inherent optical properties of the Lena water, i.e., absorption, attenuation, and scattering coefficients, during the period of maximum runoff. This aimed to contribute to the development of a bio-optical model for use as the basis for optical remote sensing of coastal water of the Arctic. In this context the absorption by CDOM (colored dissolved organic matter) and particles, and the concentrations of total suspended matter, phytoplankton-pigments, and carbon were measured. CDOM was found to be the most dominant parameter affecting the optical properties of the river, with an absorption coefficient of 4.5–5 m−1 at 442 nm, which was almost four times higher than total particle absorption values at visible wavelength range. The wavelenght-dependence of absorption of the different water constituents was chracterized by determining the semi logarithmic spectral slope. Mean CDOM, and detritus slopes were 0.0149 nm−1(standard deviation (stdev) = 0.0003, n = 18), and 0.0057 nm−1 (stdev = 0.0017, n = 19), respectively, values which are typical for water bodies with high concentrations of dissolved and particulate carbon. Mean chlorophyll a and total suspended matter were 1.8 mg m−3 (stdev = 0.734 n = 18) and 31.9 g m−3 (stdev = 19.94, n = 27), respectively. DOC (dissolved organic carbon) was in the range 8–10 g m−3 and the total particulate carbon (PC) in the range 0.25–1.5 g m−3. The light penetration depth (Secchi disc depth) was in the range 30–90 cm and was highly correlated with the suspended matter concentration. The period of maximum river runoff in June was chosen to obtain bio-optical data when maximum water constituents are transported into the Laptev Sea. However, we are aware that more data from other seasons and other years need to be collected to establish a general bio-optical model of the Lena water and conclusively characterize the light climate with respect to primary production.

Particle Backscattering Variability in the Coastal Waters of Bay of Bengal: A Case Study Along Off Kakinada and Yanam Regions

The distribution and variability of particulate back scattering coefficient (bbp) in the Godavari basin are reported for the first time using in situ measurements. A coastal cruise was conducted in the coastal waters Off Kakinada and Yanam along the East coast of India, from 2-9 March 2012. Hyperspectral underwater radiometer was used to collect the in situ bio-optical data as function of wavelength in 21 sampling stations from the coast ( ~ 10 m) to toward the open ocean ( ~ 150 m). Data from these sites exhibits two orders of magnitude in bbp and covered the typical coastal and estuarine waters. The bbp at a single wavelength, i.e., 700 nm was measured with the embedded echo-VSF (Wetlabs) series sensor. In this letter, an attempt was made to: 1) describe the variability of particle bbp of estuarine coastal system using in situ measurements; 2) model the bbp from Quasi Analytical Algorithm (QAA) and verify its accuracy by comparing with in-situ measurements of bbp; and 3) examine the relationship of bbp and chlorophyll- a concentration in the coastal system. A significant relationship was observed between the in-situ and model derived bbp values. The in-situ measured bbp values vary from 0.01 to 0.065 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> in this region. The relationship between chlorophyll- a concentration and the bbp has been studied. The large spread in the scatter in the chlorophyll Vs. in-situ bbp infers that chlorophyll- a is a poor predictor of bbp. The relationship between chlorophyll-a concentration and the bbp shows an inverse relationship for the Godavari Estuarine waters.

Bio-optical characteristics of the Patagonia Shelf break waters: Implications for ocean color algorithms

Bio-optical data for spring and summer phytoplankton blooms at 176 stations along the Patagonia Shelf break and adjacent areas were collected from October 2006 to January 2009 during six cruises. Data included chlorophyll-a concentration (Chla), coefficients of particulate absorption, ap(λ), phytoplankton absorption, aph(λ), beam attenuation, cp(660), diffuse attenuation, Kd(λ), and hyperspectral remote sensing reflectance, Rrs(λ). Surface Chla varied within a wide range from 0.10 to 18.87mgm−3 (mean of 2.82±3.35), reflecting differences in both the timing of the blooms and the geographical sampling area. Considerable dispersion was verified for ap(λ), cp(660), and Kbio(440) (Kd(440) minus diffuse attenuation for pure water) as a function of Chla. A hierarchical cluster analysis (HCA) applied to the Rrs(λ) spectra (N=116) resulted in three classes ordered by spectral features that varied according to Chla. Class 1 (N=22), Class 2 (N=52), and Class 3 (N=42) were grouped according to the Rrs(λ) associated with Chla average of 0.86 (±0.51), 2.42 (±1.62), and 8.40mgm−3 (±3.96), respectively. The estimation of Chla and aph(λ) by empirical and semi-analytical algorithms was evaluated using satellite data. Errors in the Chla estimates from the empirical algorithm OC4v6 using in situ Rrs(λ) showed significant statistical relationships with Chla, aph(440)/Chla, and bp(660)/Chla (where bp(660)=cp(660)−ap(660)). Although reasonable agreements were found between the measured and satellite-estimated values, the dependence of the OC4v6 errors on the Chla-specific phytoplankton absorption and particulate scattering coefficients reinforces the need to regionally refine both empirical and semi-analytical algorithms to improve satellite estimates of the bio-optical variables for the Patagonia region.

Correction of hyperspectral reflectance measurements for surface objects and direct sun reflection on surface waters

Satellite, airborne, or platform-based remote sensing reflectance measurements of aquatic targets are frequently compromised by water-surface effects such as specular sun reflection (glint) or transient objects like buoys or boats. For temporal or spatial data series where sub-surface reflectance is of interest, the elimination of affected data may require time-consuming manual selection of spectra and substantial data loss. Here, we present a method for the automated elimination of data points containing surface objects or strong sun reflection, which is based on the spectral slope in the ultra-violet to blue (350 nm to 450 nm). To minimize data loss, an automated sun glint correction combining two previously published methods is also presented. The method operates by subtracting a glint spectrum by means of a regression curve characterized from low to medium glint data points and is further automated by selecting these low glint data on the basis of the oxygen absorption depth in the near infrared (NIR). The elimination and correction algorithms facilitate rapid automated processing of large bio-optical data sets for both spatial and temporally resolved remote-sensing reflectance data sets. Here we demonstrate their efficacy on a three-month data set of hourly light field measurements from a fixed platform in the northwest Mediterranean.

Impact of bio‐optical data assimilation on short‐term coupled physical, bio‐optical model predictions

Data assimilation experiments with the coupled physical, bio‐optical model of Monterey Bay are presented. The objective of this study is to investigate whether the assimilation of satellite‐derived bio‐optical properties can improve the model predictions (phytoplankton population, chlorophyll) in a coastal ocean on time scales of 1–5 days. The Monterey Bay model consists of a physical model based on the Navy Coastal Ocean Model and a biochemical model which includes three nutrients, two phytoplankton groups (diatoms and small phytoplankton), two groups of zooplankton grazers, and two detrital pools. The Navy Coupled Ocean Data Assimilation system is used for the assimilation of physical observations. For the assimilation of bio‐optical observations, we used reduced‐order Kalman filter with a stationary forecast error covariance. The forecast error covariance is specified in the subspace of the multivariate (bio‐optical, physical) empirical orthogonal functions estimated from a monthlong model run. With the assimilation of satellite‐derived bio‐optical properties (chlorophyll a or absorption due to phytoplankton), the model was able to reproduce intensity and tendencies in subsurface chlorophyll distributions observed at water sample locations in the Monterey Bay, CA. Data assimilation also improved agreement between the observed and model‐predicted ratios between diatoms and small phytoplankton populations. Model runs with or without assimilation of satellite‐derived bio‐optical observations show underestimated values of nitrate as compared to the water sample observations. We found that an instantaneous update of nitrate based on statistical relations between temperature and nitrate corrected the model underestimation of the nitrate fields during the multivariate update.

A Reflectance Model for Relatively Clear and Turbid Waters

Accurate modeling of spectral remote sensing reflectance (Rrs) is of great interest for ocean colour studies in highly turbid and relatively clear waters. In this work a semianalytical model that simulates the spectral curves of remote sensing reflectance of these waters is developed based on the inherent optical properties (IOPs) and f and Q factors. For accommodating differences in the optical properties of the water and accounting for their directional variations, IOPs and f and Q factors are derived as a function of phytoplankton pigments, suspended sediments and solar zenith angle. Results of this model are compared with in-situ bio-optical data collected at 83 stations encompassing highly turbid/relatively cleared waters of the South Sea of Korea. Measured and modeled remote sensing reflectances agree favorably in both magnitude and spectral shape, with considerably low errors (mean relative error MRE -0.0327; root mean square error RMSE 0.205, bias -0.0727 and slope 1.15 and correlation coefficient R2 0.74). These results suggest that the new model has the ability to reproduce measured reflectance values and has potentially profound implications for remote sensing of complex waters in this region.

Influence of the parametrization of water optical properties on the modelled sea surface temperature in the Baltic Sea

Treatment of light propagation in the water column requires further improvements in the biogeochemical models of the Baltic Sea. Regional models of the Baltic Sea usually assume a simple exponential vertical distribution of the total downward irradiance in the visible spectral range (PAR, photosynthetically available radiation). This is in spite of the fact that modelling studies for open ocean regions have stressed the importance of more detailed optical parameterization for the quality prediction of sea surface temperature and thermal structure of surface waters. In recent years extensive regional in situ bio-optical data sets have become available for the Baltic Sea, which can be used to develop a better understanding of the feedbacks between optics and other processes simulated by the models. In this paper we compare four optical parameterizations used in numerical ocean models and their effects on modelled SSTs. This has been achieved using a one-dimensional ocean model coupled with the bio-optical models. Our results indicate that the differences between the various modelled SSTs using three optical parameterization schemes designed specifically for the Baltic Sea can give differences of up to 4°C in the modelled SSTs. This result warrants further research into the subject.

Under the hood of satellite empirical chlorophyll a algorithms: revealing the dependencies of maximum band ratio algorithms on inherent optical properties

Empirically-based satellite estimates of chlorophyll a [Chl] (e.g. OC3) are an important indicator of phytoplankton biomass. To correctly interpret [Chl] variability, estimates must be accurate and sources of algorithm errors known. While the underlying assumptions of band ratio algorithms such as OC3 have been tacitly hypothesized (i.e. CDOM and phytoplankton absorption covary), the influence of component absorption and scattering on the shape of the algorithm and estimated [Chl] error has yet to be explicitly revealed. We utilized the NOMAD bio-optical data set to examine variations between satellite estimated [Chl] and in situ values. We partitioned the variability into (a) signal contamination and (b) natural phytoplankton variability (variability in chlorophyll-specific phytoplankton absorption). Not surprisingly, the OC3 best-fit curve resulted from a balance between these two different sources of variation confirming the bias by detrital absorption on global scale. Unlike previous descriptions of empirical [Chl] algorithms, our study (a) quantified the mean detrital:phytoplankton absorption as ~1:1in the global NOMAD data set, and (b) removed detrital (CDOM + non-algal particle) absorption in radiative transfer models directly showing that the scale of the remaining variability in the band ratio algorithm was dominated by phytoplankton absorption cross section.

An improved algorithm for retrieving chlorophyll-a from the Yellow River Estuary using MODIS imagery

In this study, an improved Moderate-Resolution Imaging Spectroradiometer (MODIS) ocean chlorophyll-a (chla) 3 model (IOC3M) algorithm was developed as a substitute for the MODIS global chla concentration estimation algorithm, OC3M, to estimate chla concentrations in waters with high suspended sediment concentrations, such as the Yellow River Estuary, China. The IOC3M algorithm uses [Formula: see text] to substitute for switching the two-band ratio of max [R (rs) (443nm), R (rs) (488nm)]/R (rs) (551nm) of the OC3M algorithm. In the IOC3M algorithm, the absorption coefficient of chla can be isolated as long as reasonable bands are selected. The performance of IOC3M and OC3M was calibrated and validated using a bio-optical data set composed of spectral upwelling radiance measurements and chla concentrations collected during three independent cruises in the Yellow River Estuary in September of 2009. It was found that the optimal bands of the IOC3M algorithm were λ(1) = 443nm, λ(2) = 748nm, λ(3) = 551nm, and λ(4) = 870nm. By comparison, the IOC3M algorithm produces superior performance to the OC3M algorithm. Using the IOC3M algorithm in estimating chla concentrations from the Yellow River Estuary decreases 1.03mg/m(3) uncertainty from the OC3M algorithm. Additionally, the chla concentration estimated from MODIS data reveals that more than 90% of the water in the Yellow River Estuary has a chla concentration lower than 5.0mg/m(3). The averaged chla concentration is close to the in situ measurements. Although the case study presented herein is unique, the modeling procedures employed by the IOC3M algorithm can be useful in remote sensing to estimate the chla concentrations of similar aquatic environments.

Simulation of Future Geostationary Ocean Color Images

The objective of this work is to simulate global images that would be provided by a theoretical ocean color sensor on a geostationary orbit at longitude 0, in order to assess the range of radiance value data reaching the sensor throughout the day for 20 spectral bands similar to those of the Ocean and Land Color Imager (OLCI). The secondary objective is to assess the illumination and viewing geometries that result in sunglint. For this purpose, we combined a radiative transfer model for ocean waters (Hydrolight) and a radiative transfer model for atmosphere (MODTRAN) to construct the simulated radiance images at the sea surface and at the Top-Of-Atmosphere (TOA). Bio-optical data from GlobColour level 3 products are used as input maps in the ocean radiance model. The first result of this study is the radiance dynamic range of the scene during the day. The second result indicates the angular limit to avoid the sun glint phenomenon ( and ), where the viewing zenith angle, the solar zenith angle and the relative azimuth angle. We have also shown that a significant signal from water is measured when the ratio is higher than 3%, i.e., when is lower than 90, with a limit of 60 for the two angles.