Uniqueness in remote sensing of the inherent optical properties of ocean water

We examine the problem of uniqueness in the relationship between the remote-sensing reflectance (Rrs) and the inherent optical properties (IOPs) of ocean water. The results point to the fact that diffuse reflectance of plane irradiance from ocean water is inherently ambiguous. Furthermore, in the 400 < lambda < 750 nm region of the spectrum, Rrs(lambda) also suffers from ambiguity caused by the similarity in wavelength dependence of the coefficients of absorption by particulate matter and of absorption by colored dissolved organic matter. The absorption coefficients have overlapping exponential responses, which lead to the fact that more than one combination of IOPs can produce nearly the same Rrs spectrum. This ambiguity in absorption parameters demands that we identify the regions of the Rrs spectrum where we can isolate the effects that are due only to scattering by particulates and to absorption by pure water. The results indicate that the spectral shape of the absorption coefficient of phytoplankton, a(ph)(lambda), cannot be derived from a multiparameter fit to Rrs(lambda). However, the magnitude and the spectral dependence of the absorption coefficient can be estimated from the difference between the measured Rrs(lambda) and the best fit to Rrs(lambda) in terms of IOPs that exclude a(ph)(lambda).     

Bio-optical properties and ocean color algorithms for coastal waters influenced by the Mississippi River during a cold front

During the passage of a cold front in March 2002, bio-optical properties examined in coastal waters impacted by the Mississippi River indicated that westward advective flows and increasing river discharge containing high concentrations of nonalgal particles contributed significantly to surface optical variability. A comparison of seasonal data from three cruises indicated spectral models of absorption and scattering to be generally consistent with other coastal environments, while their parameterization in terms of chlorophyll (Chl) alpha concentration showed seasonal variability. The exponential slope of the colored dissolved organic matter (CDOM) averaged 0.0161+/-0.00054 nm(-1) and nonalgal absorption averaged 0.011 nm(-1) with deviations from general trends observed due to anomalous water properties. Although the phytoplankton specific absorption coefficients varied over a wide range [0.02 to 0.1 m2 (mg Chl)(-1) at 443 nm] being higher in offshore surface waters, values of phytoplankton absorption spectra at the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) wave bands were highly correlated to modeled values. Particulate scattering characteristics were similar to observations for other coastal waters, while backscattering ratios were on average lower in phytoplankton-dominated surface waters (0.011+/-0.003) and higher in low Chl near-bottom waters (0.0191+/-0.0045). Average percent differences in remote sensing reflectance Rrs derived from modeled and in-water radiometric measurements were highest in the blue wave bands (52%) and at locations with more stratified water columns. SeaWiFS estimates of Chl and CDOM absorption derived using regional empirical algorithms were highly correlated to in situ data.     

Modeling the optical properties of mineral particles suspended in seawater and their influence on ocean reflectance and chlorophyll estimation from remote sensing algorithms

The optical properties of mineral particles suspended in seawater were calculated from the Mie scattering theory for different size distributions and complex refractive indices of the particles. The ratio of the spectral backscattering coefficient to the sum of the spectral absorption and backscattering coefficients of seawater, b(b)(lambda)/[a(lambda) + b(b)(lambda)], was analyzed as a proxy for ocean reflectance for varying properties and concentrations of mineral particles. Given the plausible range of variability in the particle size distribution and the refractive index, the general parameterizations of the absorption and scattering properties of mineral particles and their effects on ocean reflectance in terms of particle mass concentration alone are inadequate. The variations in the particle size distribution and the refractive index must be taken into account. The errors in chlorophyll estimation obtained from the remote sensing algorithms that are due to the presence of mineral particles can be very large. For example, when the mineral concentration is 1 g m(-3) and the chlorophyll a concentration is low (0.05 mg m(-3)), current global algorithms based on a blue-to-green reflectance ratio can produce a chlorophyll overestimation ranging from approximately 50% to as much as 20-fold.     

Retrieval of chlorophyll from remote-sensing reflectance in the china seas

The East China Sea is a typical case 2 water environment, where concentrations of phytoplankton pigments, suspended matter, and chromophoric dissolved organic matter (CDOM) are all higher than those in the open oceans, because of the discharge from the Yangtze River and the Yellow River. By using a hyperspectral semianalytical model, we simulated a set of remote-sensing reflectance for a variety of chlorophyll, suspended matter, and CDOM concentrations. From this simulated data set, a new algorithm for the retrieval of chlorophyll concentration from remote-sensing reflectance is proposed. For this method, we took into account the 682-nm spectral channel in addition to the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) channels. When this algorithm was applied to a field data set, the chlorophyll concentrations retrieved through the new algorithm were consistent with field measurements to within a small error of 18%, in contrast with that of 147% between the SeaWiFS ocean chlorophyll 2 algorithm and the in situ observation.     

Spectral parameters of inherent optical property models: method for satellite retrieval by matrix inversion of an oceanic radiance model

Inherent optical property (IOP) spectral models for the phytoplankton absorption coefficient, chromophoric dissolved organic matter (CDOM) absorption coefficient, and total constituent backscattering (TCB) coefficient are linear in the reference wavelength IOP and nonlinear in the spectral parameters. For example, the CDOM absorption coefficient IOP a(CDOM)(lambda(i)) = a(CDOM)(lambda(ref))exp[-S(lambda(i)- lambda(ref))] is linear in a(CDOM)(lambda(ref)) and nonlinear in S. Upon linearization by Taylor's series expansion, it is shown that spectral model parameters, such as S, can be concurrently accommodated within the same conventional linear matrix formalism used to retrieve the reference wavelength IOP's. Iteration is used to adjust for errors caused by truncation of the Taylor's series expansion. Employing an iterative linear matrix inversion of a water-leaving radiance model, computer simulations using synthetic data suggest that (a) no instabilities or singularities are introduced by the linearization and subsequent matrix inversion procedures, (b) convergence to the correct value can be expected only if starting values for a model parameter are within certain specific ranges, (c) accurate retrievals of the CDOM slope S (or the phytoplankton Gaussian width g) are generally reached in 3-20 iterations, (d) iterative retrieval of the exponent n of the TCB wavelength ratio spectral model is not recommended because the starting values must be within approximately +/-5% of the correct value to achieve accurate convergence, and (e) concurrent retrieval of S and g (simultaneously with the phytoplankton, CDOM, and TCB coefficient IOP's) can be accomplished in a 5 x 5 iterative matrix inversion if the starting values for S and g are carefully chosen to be slightly higher than the expected final retrieved values.     

Apparent and inherent optical properties of turbid estuarine waters: measurements, empirical quantification relationships, and modeling

Spectral measurements of remote-sensing reflectance (Rrs) and absorption coefficients carried out in three European estuaries (Gironde and Loire in France, Tamar in the UK) are presented and analyzed. Typical Rrs and absorption spectra are compared with typical values measured in coastal waters. The respective contributions of the water constituents, i.e., suspended sediments, colored dissolved organic matter, and phytoplankton (characterized by chlorophyll-a), are determined. The Rrs spectra are then reproduced with an optical model from the measured absorption coefficients and fitted backscattering coefficients. From Rrs ratios, empirical quantification relationships are established, reproduced, and explained from theoretical calculations. These quantification relationships were established from numerous field measurements and a reflectance model integrating the mean values of the water constituents' inherent optical properties. The model's sensitivity to the biogeochemical constituents and to their nature and composition is assessed.     

Determination of the fluorescence quantum yield by oceanic phytoplankton in their natural habitat

Sun-stimulated chlorophyll a fluorescence has been measured in situ, within the upward and downward light fields, in oceanic waters with chlorophyll concentrations of 0.04-3 mg m(-3). We combined these signals with phytoplankton absorption spectra to derive the fluorescence quantum yield, phi (number of photons emitted by fluorescence/number of absorbed photons). phi was derived separately from hyperspectral (upward and downward) irradiance measurements (with a LI-COR Instruments spectroradiometer) and from nadir radiance near 683 nm (with a Biospherical Instruments profiler). The contribution of inelastic Raman scattering to the signal in the red band was assessed and subtracted. Raman-corrected phi values derived from the two instruments compared well. Vertical phi profiles were strongly structured, with maximal (5-6%) values at depth, whereas phi was approximately =1% in near-surface waters (measurements made approximately at solar noon). These near-surface values are needed for interpretation of remotely sensed fluorescence signals. This optical study shows that the fluorescence yield of algae in their natural environment can be accurately derived in a nonintrusive way with available instrumentation and adequate protocols.     

Effect of suspended particulate and dissolved organic matter on remote sensing of coastal and riverine waters

We use remote sensing reflectance (RSR) together with the inherent optical properties of suspended particulates to determine the backscattering ratio b(b)/b for coastal waters. We examine the wavelength dependence of b(b)(lambda) and f(lambda)/Q(lambda) and establish the conditions when C(lambda) in RSR(lambda) approximately or = C(lambda)b(b)(lambda)/a(lambda) can be treated as a constant. We found that for case 2 waters, RSR was insensitive to the natural fluctuations in particle-size distributions. The cross-sectional area of the suspended particulate per unit volume, x(g), showed an excellent correlation with the volume scattering coefficient.     

Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization

In earlier studies of passive remote sensing of shallow-water bathymetry, bottom depths were usually derived by empirical regression. This approach provides rapid data processing, but it requires knowledge of a few true depths for the regression parameters to be determined, and it cannot reveal in-water constituents. In this study a newly developed hyperspectral, remote-sensing reflectance model for shallow water is applied to data from computer simulations and field measurements. In the process, a remote-sensing reflectance spectrum is modeled by a set of values of absorption, backscattering, bottom albedo, and bottom depth; then it is compared with the spectrum from measurements. The difference between the two spectral curves is minimized by adjusting the model values in a predictor-corrector scheme. No information in addition to the measured reflectance is required. When the difference reaches a minimum, or the set of variables is optimized, absorption coefficients and bottom depths along with other properties are derived simultaneously. For computer-simulated data at a wind speed of 5 m/s the retrieval error was 5.3% for depths ranging from 2.0 to 20.0 m and 7.0% for total absorption coefficients at 440 nm ranging from 0.04 to 0.24 m(-1). At a wind speed of 10 m/s the errors were 5.1% for depth and 6.3% for total absorption at 440 nm. For field data with depths ranging from 0.8 to 25.0 m the difference was 10.9% (R(2) = 0.96, N = 37) between inversion-derived and field-measured depth values and just 8.1% (N = 33) for depths greater than 2.0 m. These results suggest that the model and the method used in this study, which do not require in situ calibration measurements, perform very well in retrieving in-water optical properties and bottom depths from above-surface hyperspectral measurements.     

Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters

For open ocean and coastal waters, a multiband quasi-analytical algorithm is developed to retrieve absorption and backscattering coefficients, as well as absorption coefficients of phytoplankton pigments and gelbstoff. This algorithm is based on remote-sensing reflectance models derived from the radiative transfer equation, and values of total absorption and backscattering coefficients are analytically calculated from values of remote-sensing reflectance. In the calculation of total absorption coefficient, no spectral models for pigment and gelbstoff absorption coefficients are used. Actually those absorption coefficients are spectrally decomposed from the derived total absorption coefficient in a separate calculation. The algorithm is easy to understand and simple to implement. It can be applied to data from past and current satellite sensors, as well as to data from hyperspectral sensors. There are only limited empirical relationships involved in the algorithm, and they are for less important properties, which implies that the concept and details of the algorithm could be applied to many data for oceanic observations. The algorithm is applied to simulated data and field data, both non-case1, to test its performance, and the results are quite promising. More independent tests with field-measured data are desired to validate and improve this algorithm.     

Method to derive ocean absorption coefficients from remote-sensing reflectance

A method to derive in-water absorption coefficients from total remote-sensing reflectance (ratio of the upwelling radiance to the downwelling irradiance above the surface) analytically is presented. For measurements made in the Gulf of Mexico and Monterey Bay, with concentrations of chlorophyll-a ranging from 0.07 to 50 mg/m(3), comparisons are made for the total absorption coefficients derived with the suggested method and those derived with diffuse attenuation coefficients. For these coastal to open-ocean waters, including regions of upwelling and the Loop Current, the results are as follows: at 440 nm the difference between the two methods is 13.0% (r(2) = 0.96) for total absorption coefficients ranging from 0.02 to 2.0 m(-1); at 488 nm the difference is 14.5% (r(2) = 0.97); and at 550 nm the difference is 13.6% (r(2) = 0.96). The results indicate that the method presented works very well for retrieval of in-water absorption coefficients exclusively from remotely measured signals, and that this method has a wide range of potential applications in oceanic remote sensing.     

Model for the interpretation of hyperspectral remote-sensing reflectance

Remote-sensing reflectance is easier to interpret for the open ocean than for coastal regions because the optical signals are highly coupled to the phytoplankton (e.g., chlorophyll) concentrations. For estuarine or coastal waters, variable terrigenous colored dissolved organic matter (CDOM), suspended sediments, and bottom reflectance, all factors that do not covary with the pigment concentration, confound data interpretation. In this research, remote-sensing reflectance models are suggested for coastal waters, to which contributions that are due to bottom reflectance, CDOM fluorescence, and water Raman scattering are included. Through the use of two parameters to model the combination of the backscattering coefficient and the Q factor, excellent agreement was achieved between the measured and modeled remote-sensing reflectance for waters from the West Florida Shelf to the Mississippi River plume. These waters cover a range of chlorophyll of 0.2-40 mg/m(3) and gelbstoff absorption at 440 nm from 0.02-0.4 m(-1). Data with a spectral resolution of 10 nm or better, which is consistent with that provided by the airborne visible and infrared imaging spectrometer (AVIRIS) and spacecraft spectrometers, were used in the model evaluation.     

Chlorophyll biomass in the global oceans: satellite retrieval using inherent optical properties

In the upper layer of the global ocean, 2082 in situ chlorophyll biomass values (Chl) are retrieved by concurrent satellite-derived inherent optical properties (IOP). It is found that (1) the phytoplankton absorption coefficient IOP alone does not provide satisfactory (Chl) retrieval; (2) the chromophoric dissolved organic matter (CDOM) absorption coefficient IOP must also be used to obtain satisfactory retrieval through (Chl) alpha a ph + pa CDOM where p is a constant and a ph and aCDOM are, respectively, the phytoplankton and CDOM absorption coefficients; (3) the IOP-based (Chl) retrieval performance is comparable to standard satellite reflectance ratio retrievals (that have CDOM absorption intrinsically embedded within them); (4) inclusion of the total backscattering coefficient IOP does not contribute significantly to (Chl) retrieval; and (5) the new IOP-based algorithm may provide the possibility for future research to establish the actual role of extracellular CDOM from all sources in the intracellular production of chlorophyll biomass.     

Inversion of irradiance and remote sensing reflectance in shallow water between 400 and 800 nm for calculations of water and bottom properties

What we believe to be a new inversion procedure for multi- and hyperspectral data in shallow water, represented by the subsurface irradiance and remote sensing reflectance spectra, was developed based on analytical equations by using the method of nonlinear curve fitting. The iteration starts using an automatic determination of the initial values of the fit parameters: concentration of phytoplankton and suspended matter, absorption of gelbstoff, bottom depth, and the fractions of up to six bottom types. Initial values of the bottom depth and suspended matter concentration are estimated analytically. Phytoplankton concentration and gelbstoff absorption are initially calculated by the method of nested intervals. A sensitivity analysis was made to estimate the accuracy of the entire inversion procedure including model error, error propagation, and influence of instrument characteristics such as noise, and radiometric and spectral resolution. The entire inversion technique is included in a public-domain software (WASI) to provide a fast and user-friendly tool of forward and inverse modeling.     

Toward closure of upwelling radiance in coastal waters

We present three methods for deriving water-leaving radiance L(w)(lambda) and remote-sensing reflectance using a hyperspectral tethered spectral radiometer buoy (HyperTSRB), profiled spectroradiometers, and Hydrolight simulations. Average agreement for 53 comparisons between HyperTSRB and spectroradiometric determinations of L(w)(lambda) was 26%, 13%, and 17% at blue, green, and red wavelengths, respectively. Comparisons of HyperTSRB (and spectroradiometric) L(w)(lambda) with Hydrolight simulations yielded percent differences of 17% (18%), 17% (18%), and 13% (20%) for blue, green, and red wavelengths, respectively. The differences can be accounted for by uncertainties in model assumptions and model input data (chlorophyll fluorescence quantum efficiency and the spectral chlorophyll-specific absorption coefficient for the red wavelengths, and scattering corrections for input ac-9 absorption data and volume scattering function measurements for blue wavelengths) as well as radiance measurement inaccuracies [largely differences in the depth of the L(u)(lambda, z) sensor on the HyperTSRB].     

Covariance of the absorption of phytoplankton, colored dissolved organic matter, and detritus in case I waters, as deduced from the Coastal Zone Color Scanner bio-optical algorithm

The universal bio-optical algorithm of the Coastal Zone Color Scanner (CZCS) for case I waters implicitly contains an average covariance of the absorption by phytoplankton and colored dissolved organic matter (CDOM) and detritus. We made that covariance explicit by combining the CZCS algorithm with an expression for reflectance. The spectral variation of absorption by CDOM plus detritus for case I waters may be estimated by the expression a(gd(λ)) = 2a(ph)(443)*chl{exp[-0.013(λ - 443)].     

Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables

A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance (Rrs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured Rrs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.     

Neural network approach to retrieve the inherent optical properties of the ocean from observations of MODIS

Retrieving the inherent optical properties of water from remote sensing multispectral reflectance measurements is difficult due to both the complex nature of the forward modeling and the inherent nonlinearity of the inverse problem. In such cases, neural network (NN) techniques have a long history in inverting complex nonlinear systems. The process we adopt utilizes two NNs in parallel. The first NN is used to relate the remote sensing reflectance at available MODIS-visible wavelengths (except the 678 nm fluorescence channel) to the absorption and backscatter coefficients at 442 nm (peak of chlorophyll absorption). The second NN separates algal and nonalgal absorption components, outputting the ratio of algal-to-nonalgal absorption. The resulting synthetically trained algorithm is tested using both the NASA Bio-Optical Marine Algorithm Data Set (NOMAD), as well as our own field datasets from the Chesapeake Bay and Long Island Sound, New York. Very good agreement is obtained, with R2 values of 93.75%, 90.67%, and 86.43% for the total, algal, and nonalgal absorption, respectively, for the NOMAD. For our field data, which cover absorbing waters up to about 6 m?1, R2 is 91.87% for the total measured absorption.     

Spectral calibration of hyperspectral imagery using atmospheric absorption features

One of the initial steps in the preprocessing of remote sensing data is the atmospheric correction of the at-sensor radiance images, i.e., radiances recorded at the sensor aperture. Apart from the accuracy in the estimation of the concentrations of the main atmospheric species, the retrieved surface reflectance is also influenced by the spectral calibration of the sensor, especially in those wavelengths mostly affected by gaseous absorptions. In particular, errors in the surface reflectance appear when a systematic shift in the nominal channel positions occurs. A method to assess the spectral calibration of hyperspectral imaging spectrometers from the acquired imagery is presented in this paper. The fundamental basis of the method is the calculation of the value of the spectral shift that minimizes the error in the estimates of surface reflectance. This is performed by an optimization procedure that minimizes the deviation between a surface reflectance spectrum and a smoothed one resulting from the application of a low-pass filter. A sensitivity analysis was performed using synthetic data generated with the MODTRAN4 radiative transfer code for several values of the spectral shift and the water vapor column content. The error detected in the retrieval is less than +/- 0.2 nm for spectral shifts smaller than 2 nm, and less than +/- 1.0 nm for extreme spectral shifts of 5 nm. A low sensitivity to uncertainties in the estimation of water vapor content was found, which reinforces the robustness of the algorithm. The method was successfully applied to data acquired by different hyperspectral sensors.     

Remote-sensing reflectance in the Beaufort and Chukchi seas: observations and models

Two semianalytical remote-sensing reflectance models were evaluated and validated by use of bio-optical data collected in the Beaufort and Chukchi seas. Both models were efficient at retrieving chlorophyll concentration, phytoplankton absorption coefficients,and particulate backscattering coefficients. In contrast, they were not accurate in predicting an absorption coefficient for colored dissolved organic matter plus nonpigmented particulates. The poor model performance is attributed to the high variability in the concentrations of these colored materials. A chlorophyll-dependent reflectance model was also assessed, and it proved to be highly successful in reproducing measured reflectance spetra. A four-component, case 2 model with mean absorption spectra for phytoplankton, soluble materials, and nonpigmented particulates was employed in Hydrolight radiative-transfer model simulations. The remote sensing reflectance spectra simulated inthe radiative-transfer model were in excellent agreement with field data. The similarity between the model and the measurement confirms the accuracy of the underlying bio-optical relationships and underscores the utility of modeling for better understanding of the variability of ocean color observations. The latest SeaWiFS algorithm (OC4V4) overestimated chlorophyll by approximately 1.5 fold across most of the observed range of biomass (0.07-9 mg chlorophyll m(-3)). Regionally tuned algorithms explained > 93% of the variability in the surface chlorophyll concentration.