Ocean Color Inversion Research Articles

Sea surface Fresnel reflections difference driven de-glint algorithm for airborne optical images.

This paper presents a glint correction algorithm for high spatial resolution optical remote sensing imagery captured by the ER-2 Airborne Visual Infrared Imaging Spectrometer (AVIRIS). The algorithm employs linear and differential techniques to mitigate sun glint and sky glint effects, encompassing statistical glint reflections resulting from variations in imaging angles within strips and inter-strip variations due to Fresnel reflectance disparities. It aims to diminish Fresnel reflectance diversity on water surfaces and mitigate the distortions induced by glint reflectance during spectral and ocean color inversion. A comparative analysis of spectral and ocean color information in AVIRIS images before and after correction reveals enhanced accuracy following the glint correction. By systematically addressing multiple glint reflections and their ramifications, this method offers a valuable framework for correcting water surface glint in diverse high spatial resolution optical imagery.

Informing ocean color inversion products by seeding with ancillary observations.

Ocean reflectance inversion algorithms provide many products used in ecological and biogeochemical models. While a number of different inversion approaches exist, they all use only spectral remote-sensing reflectances (Rrs(λ)) as input to derive inherent optical properties (IOPs) in optically deep oceanic waters. However, information content in Rrs(λ) is limited, so spectral inversion algorithms may benefit from additional inputs. Here, we test the simplest possible case of ingesting optical data ('seeding') within an inversion scheme (the Generalized Inherent Optical Property algorithm framework default configuration (GIOP-DC)) with both simulated and satellite datasets of an independently known or estimated IOP, the particulate backscattering coefficient at 532 nm (bbp(532)). We find that the seeded-inversion absorption products are substantially different and more accurate than those generated by the standard implementation. On global scales, seasonal patterns in seeded-inversion absorption products vary by more than 50% compared to absorption from the GIOP-DC. This study proposes one framework in which to consider the next generation of ocean color inversion schemes by highlighting the possibility of adding information collected with an independent sensor.

Analysis of the Bidirectional Characteristic of Radiation of Flat and Rough Water–Air Interfaces Based on the Theory of Radiative Transfer

The Lambertian property of objects is one of the basic hypotheses in remote sensing research. However, the spectral radiance of natural objects is always anisotropic. On the sea surface, a large amount of sea foam is generated at the water–air interface, induced by wind speed and breaking gravity waves. Additionally, the scattering characteristic at the water–air interface significantly influences the accuracy of ocean color remote sensing and its output. The bidirectionality of the water light field is one of the sources of errors in ocean color inversion. Therefore, the knowledge of the bidirectional reflectance distribution of water surfaces is of great significance in quantitative remote sensing or for the evaluation of measurement errors in surface optical parameters. To clarify the bidirectional reflectance distribution, we used the coupled ocean–atmosphere radiative transfer (COART) model to simulate the bidirectional radiance of water bodies and explored the anisotropy of radiance at the water–air interface. The results indicate that the downward and upward irradiance just below the water surface and the water-leaving radiance changed with the sun-viewing geometry. The downward and upward radiance just below the water surface decreased as the zenith angle of the incident light increased. This effect can be mitigated using a function of the viewing angle. Additionally, the viewing azimuth angle and rough sea surface had no significant effect on the downward and upward radiance. The water-leaving radiance had an obvious bidirectional reflectance characteristic. Additionally, a backward hotspot was found in the simulated results. Then, the transmission coefficient was calculated, and the bidirectional distribution characteristic was found for flat and rough sea surfaces. This study can be used as a reference to correct bidirectional errors and to guide the spectral measurements of water and its error control for rough sea surfaces.

Atmospheric correction for HY-1C CZI images using neural network in western Pacific region

ABSTRACT With a spatial resolution of 50 m, a revisit time of three days, and a swath of 950 km, the coastal zone imager (CZI) offers great potential in monitoring coastal zone dynamics. Accurate atmospheric correction (AC) is needed to exploit the potential of quantitative ocean color inversion. However, due to the band setting of CZI, the AC over coastal waters in the western Pacific region with complex optical properties cannot be realized easily. This research introduces a novel neural network (NN) AC algorithm for CZI data over coastal waters. Total 100,000 match-ups of HY-1 C CZI-observed reflectance at the top-of-atmosphere and Operational Land Imager (OLI)-retrieved high-quality remote sensing reflectance (R rs) at the CZI bands are built to train the NN model. These reflectance data are obtained from the standard AC algorithm in the SeaDAS. Results indicate that the distributions of the CZI retrieved R rs were consistent with the quasi-synchronous OLI data, but the spatial information from the CZI is more detailed. Then, the accuracy of the CZI data for AC is evaluated using the multi-source in-situ data. Results further show that the NN-AC can successfully retrieve R rs for CZI and the coefficients of determination in the blue, green, red, and near-infrared bands were 0.70, 0.77, 0.76, and 0.67, respectively. The NN algorithm does not depend on shortwave-infrared bands and runs very fast once properly trained.

Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China

Total suspended matter (TSM) is a core parameter in the quantitative retrieval of ocean color remote sensing and an important indicator for evaluating the quality of the aquatic environment. This study selects part of Nansi Lake in North China as the study area. Researchers used Hyperion remote sensing data and field-measured TSM concentration as data sources. Firstly, the characteristic variables with high correlation were selected based on spectral analysis. Then, seven methods such as linear regression, BP neural network (BP), KNN, random forest (RF), and random forest based on genetic algorithm optimization (GA_RF) are used to construct the inversion model of TSM concentration. The retrieval accuracy of each model shows that the machine learning models are much more accurate than the linear model. Among them, the GA_RF model retrieves the suspended solids concentration with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.98, a root mean square error (RMSE) of 1.715 mg/L, and an average relative error (ARE) of 6.83%. Additionally, the spatial distribution of TSM concentration was inversed by Hyperion remote sensing image. The results showed that the concentration of TSM was lower in the northwest and higher in the southeast, and the concentration distribution was uneven, showing the characteristics of a typical shallow macrophytic lake. This study provides an effective method for monitoring TSM concentration and other water quality parameters in the shallow macrophytic lake and further proves the advantages of machine learning in ocean color inversion. All in all, this research provides some useful methods and suggestions for quantitative inversion of TSM concentration in shallow macrophytic lakes.

Application of Satellite Data for Retrieving the Light Absorption Characteristics in the Black Sea Waters

Purpose. he work is aimed at studying the effects of light absorption in the Black Sea waters with due regard for the variations of its individual components, and how they are manifested in the NASA archival results of calculating the chlorophyll a concentration obtained by processing satellite data using the universal operational method. Methods and Results. The NASA archival data of the MODIS and SeaWiFS satellite instruments, and the values of the light absorption components (determined by the method of Generalized ocean color inversion model for retrieving marine inherent optical properties (GIOP)) related to yellow substance and phytoplankton were analyzed. In order to avoid possible manifestations of various distortions in the results of determining the remote sensing reflectance of the sea and in the products resulted from application of the GIOP method, only the specially selected and sufficiently reliable test data from two areas located near the Crimea Southern Coast and south of the Danube estuary were used. Conclusions. In the considered examples with low content of chlorophyll a in the seawater, the yellow substance plays a predominant role in light absorption in the spectrum blue part, whereas if the chlorophyll a content is high, the phytoplankton contribution is dominant. The revealed relationship between the light absorption components related to yellow substance and phytoplankton significantly differs from that implicitly preset as a basis of the universal method (applied in NASA for the satellite data operational processing) for determining the chlorophyll a concentration. This, in its turn, is manifested in the fact that the data on the chlorophyll a concentration in the Black Sea stored in the NASA archive may be overestimated in case the chlorophyll a concentration is low, and underestimated – in case it is high.

Ispol'zovaniye sputnikovykh dannykh dlya opredeleniya kharakteristik pogloshcheniya sveta v vodakh Chernogo morya

Purpose. The work is aimed at studying the effects of light absorption in the Black Sea waters with due regard for the variations of its individual components, and how they are manifested in the NASA archival results of calculating the chlorophyll a concentration obtained by processing satellite data using the universal operational method. Methods and Results. The NASA archival data of the MODIS and SeaWiFS satellite instruments, and the values of the light absorption components (determined by the method of Generalized ocean color inversion model for retrieving marine inherent optical properties (GIOP)) related to yellow substance and phytoplankton were analyzed. In order to avoid possible manifestations of various distortions in the results of determining the remote sensing reflectances of the sea and in the products resulted from application of the GIOP method, only the specially selected and sufficiently reliable test data from two areas located near the Crimea Southern Coast and south of the Danube estuary were used. Conclusions. In the considered examples with low content of chlorophyll a in the seawater, the yellow substance plays a predominant role in light absorption in the spectrum blue part, whereas if the chlorophyll a content is high, the phytoplankton contribution is dominant. The revealed relationship between the light absorption components related to yellow substance and phytoplankton significantly differs from that implicitly preset as a basis of the universal method (applied in NASA for the satellite data operational processing) for determining the chlorophyll a concentration. This, in its turn, is manifested in the fact that the data on the chlorophyll a concentration in the Black Sea stored in the NASA archive may be overestimated in case the chlorophyll a concentration is low, and underestimated – in case it is high.

Information content of absorption spectra and implications for ocean color inversion.

The increasing use of hyperspectral optical data in oceanography, both in situ and via remote sensing, holds the potential to significantly advance characterization of marine ecology and biogeochemistry because, in principle, hyperspectral data can provide much more detailed inferences of ecosystem properties via inversion. Effective inferences, however, require careful consideration of the close similarity of different signals of interest, and how these interplay with measurement error and uncertainty to reduce the degrees of freedom (DoF) of hyperspectral measurements. Here we discuss complementary approaches to quantify the DoF in hyperspectral measurements in the case of in situ particulate absorption measurements, though these approaches can also be used on other such data, e.g., ocean color remote sensing. Analyses suggest intermediate (${\sim}5 $∼5) DoF for our dataset of global hyperspectral particulate absorption spectra from the Tara Oceans expedition, meaning that these data can yield coarse community structure information. Empirically, chlorophyll is an effective first-order predictor of absorption spectra, meaning that error characteristics and the mathematics of inversion need to be carefully considered for hyperspectral data to provide information beyond that which chlorophyll provides. We also discuss other useful analytical tools that can be applied to this problem and place our results in the context of hyperspectral remote sensing.

Atmospheric Correction Algorithm for HY-1C CZI over Turbid Waters

Tong, C.; Mu, B.; Liu, R.-J.; Ding, J.; Zhang, M.W.; Xiao, Y-.F.; Liang, X.-J., and Chen, X.-Y., 2019. Atmospheric correction algorithm for HY-1C CZI over turbid waters. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 156-163. Coconut Creek (Florida), ISSN 0749-0208.China's HY-1C ocean-observing satellite was launched successfully on September 7, 2018. It carries a four-channel wide-band coastal zone imager (CZI) that has 50 m spatial resolution and 950 km swath width. To exploit the potential of quantitative ocean color inversion, accurate atmospheric correction is needed. However, because of the CZI band settings, the realization of this goal is a challenge, especially for turbid water with complex optical properties. This study investigated the atmospheric correction algorithm for the CZI over turbid water. First, using the 6SV radiative transfer model, CZI Rayleigh lookup tables (LUTs) were built to correct for atmospheric molecular Rayleigh scattering, greatly shortening the time required for operational data processing. Second, CZI aerosol scattering was removed using the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol LUTs and quasi-synchronous MODIS aerosol products. The accuracy of the CZI atmospheric correction was validated using data from the highly turbid Bohai Sea. In comparison with synchronous in situ data, the results showed the average relative error of CZI remote sensing reflectance (Rrs) in the blue, green and red bands was 32.51 %, 25.38 % and 42.10 %, respectively. Comparison of CZI Rrs with quasi-synchronous MODIS data revealed similar spatial distributions, although the spatial information from the CZI was more detailed. The results proved the validity and accuracy of the CZI atmospheric correction algorithm over turbid water, which lays a foundation for quantitative ocean color inversion with high spatial resolution in the coastal zone.

Ocean Color Analytical Model Explicitly Dependent on the Volume Scattering Function

An analytical radiative transfer (RT) model for remote sensing reflectance that includes the bidirectional reflectance distribution function (BRDF) is described. The model, called ZTT (Zaneveld-Twardowski-Tonizzo), is based on the restatement of the RT equation by Zaneveld (1995) in terms of light field shape factors. Besides remote sensing geometry considerations (solar zenith angle, viewing angle, and relative azimuth), the inputs are Inherent Optical Properties (IOPs) absorption a and backscattering bb coefficients, the shape of the particulate volume scattering function (VSF) in the backward direction, and the particulate backscattering ratio. Model performance (absolute error) is equivalent to full RT simulations for available high quality validation data sets, indicating almost all residual errors are inherent to the data sets themselves, i.e., from the measurements of IOPs and radiometry used as model input and in match up assessments, respectively. Best performance was observed when a constant backward phase function shape based on the findings of Sullivan and Twardowski (2009) was assumed in the model. Critically, using a constant phase function in the backward direction eliminates a key unknown, providing a path toward inversion to solve for a and bb. Performance degraded when using other phase function shapes. With available data sets, the model shows stronger performance than current state-of-the-art look-up table (LUT) based BRDF models used to normalize reflectance data, formulated on simpler first order RT approximations between rrs and bb/a or bb/(a + bb) (Morel et al., 2002; Lee et al., 2011). Stronger performance of ZTT relative to LUT-based models is attributed to using a more representative phase function shape, as well as the additional degrees of freedom achieved with several physically meaningful terms in the model. Since the model is fully described with analytical expressions, errors for terms can be individually assessed, and refinements can be readily made without carrying out the gamut of full RT computations required for LUT-based models. The ZTT model is invertible to solve for a and bb from remote sensing reflectance, and inversion approaches are being pursued in ongoing work. The focus here is with development and testing of the in-water forward model, but current ocean color remote sensing approaches to cope with an air-sea interface and atmospheric effects would appear to be transferable. In summary, this new analytical model shows good potential for future ocean color inversion with low bias, well-constrained uncertainties (including the VSF), and explicit terms that can be readily tuned. Emphasis is put on application to the future NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission.

中国近海水色遥感研究进展

海洋水色遥感是海洋环境监测的重要手段，具有观测频率高、空间覆盖广以及受海况影响小的优点，近年来逐渐受到海洋科研工作者和海洋监测部门的重视。本文概述了水色传感器的发展历程，对水色反演算法进行了总结分类，并以中国近海为研究区域综述了中国近海遥感研究成果，展示近年来海洋水色研究的现状、取得的进展以及应用前景。 Ocean color remote sensing is an important means of monitoring the marine environment; it has the advantages of high observation frequency, wide spatial coverage and small influence by sea condition. In recent years, marine scientific researchers and marine monitoring branches have been paid more and more attention. This paper reviews the development process of ocean color sensor, summarizes and classifies the ocean color inversion algorithms, and further takes remote sensing of ocean color in Chinese coastal regions as an example, to show the present status, progress and application prospect of ocean color in recent years.

MEETC2: Ocean color atmospheric corrections in coastal complex waters using a Bayesian latent class model and potential for the incoming sentinel 3 — OLCI mission

From top-of-atmosphere (TOA) observations, atmospheric correction for ocean color inversion aims at distinguishing atmosphere and water contributions. From a methodological point of view, our approach relies on a Bayesian inference using Gaussian Mixture Model prior distributions on reference spectra of aerosol and water reflectance. A reference spectrum for the aerosol characterizes the specific signature of the aerosols on the observed aerosol reflectance. A reference spectrum for the water characterizes the specific signature of chlorophyll-a, suspended particulate matters and colored dissolved organic matters on the observed sea surface reflectance.In our Bayesian inversion scheme, prior distributions of the marine and aerosol variables are set conditionally to the observed values of covariates, typically acquisition geometry acquisition conditions and pre-estimates of the aerosol and water reflectance in the near-infrared part of the spectrum. The numerical inversion exploits a gradient-based optimization from quasi-randomized initializations. We evaluate our estimates of the sea surface reflectance from the MERIS TOA observations. Using the MERMAID radiometric in-situ dataset, we obtain significant improvements in the estimation of the sea surface reflectance, especially for the 412, 442, 490 and 510 nm bands, compared with the standard ESA MEGS algorithm and the a state-of-the-art neural network approach (C2R). The mean gain value on the relative error for the 13 bands between 412 and 885 nm is of 57% compared with MEGS algorithm and 10% compared with the C2R. The water leaving reflectances are used in Ocean Color for the estimation of the chl-a concentration, the colored dissolved organic matters absorption and the suspended particulate matters concentration underlying the potential of such approach to improve the standard level 2 products in coastal areas. We further discuss the potential of MEETC2 for the incoming OLCI/Sentinel 3 mission that will be launched in December 2015.

A semianalytical ocean color inversion algorithm with explicit water column depth and substrate reflectance parameterization

AbstractA semianalytical ocean color inversion algorithm was developed for improving retrievals of inherent optical properties (IOPs) in optically shallow waters. In clear, geometrically shallow waters, light reflected off the seafloor can contribute to the water‐leaving radiance signal. This can have a confounding effect on ocean color algorithms developed for optically deep waters, leading to an overestimation of IOPs. The algorithm described here, the Shallow Water Inversion Model (SWIM), uses pre‐existing knowledge of bathymetry and benthic substrate brightness to account for optically shallow effects. SWIM was incorporated into the NASA Ocean Biology Processing Group's L2GEN code and tested in waters of the Great Barrier Reef, Australia, using the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua time series (2002–2013). SWIM‐derived values of the total non‐water absorption coefficient at 443 nm, at(443), the particulate backscattering coefficient at 443 nm, bbp(443), and the diffuse attenuation coefficient at 488 nm, Kd(488), were compared with values derived using the Generalized Inherent Optical Properties algorithm (GIOP) and the Quasi‐Analytical Algorithm (QAA). The results indicated that in clear, optically shallow waters SWIM‐derived values of at(443), bbp(443), and Kd(443) were realistically lower than values derived using GIOP and QAA, in agreement with radiative transfer modeling. This signified that the benthic reflectance correction was performing as expected. However, in more optically complex waters, SWIM had difficulty converging to a solution, a likely consequence of internal IOP parameterizations. Whilst a comprehensive study of the SWIM algorithm's behavior was conducted, further work is needed to validate the algorithm using in situ data.

Development and evaluation of a genetic algorithm-based ocean color inversion model for simultaneously retrieving optical properties and bottom types in coral reef regions

This work presents a novel approach that integrates a shallow water semi-analytical (SSA) model and a genetic algorithm (GA) to retrieve water column inherent optical properties (IOPs) and identify bottom types simultaneously from measurement of subsurface remote sensing reflectance. This GA-SSA approach is designed based on the assumption that each pixel is homogeneous with regard to the bottom type when viewed at small (centimeter) scales, and it is validated against a synthetic data set (N=11,250) that consists of five types of bottom, three levels of bottom depth, 15 concentrations of chlorophyll-a (Chl-a), and a wide range of modeled IOP variations in clear and optically complex waters representing the coral reef environment. The results indicate that the GA-SSA approach is accurate and robust in the retrieval of IOPs and its success rate in identifying the real bottom type is limited by the level of Chl-a and bottom depth. When a pixel is homogeneous at a small scale, the maximum allowable concentrations for GA-SSA to perfectly identify all the five bottom types are 0.7 mg/m3 at 5 m bottom depth, 0.2 mg/m3 at 10 m, and 0.07 mg/m3 at 15 m. A promising 80% recognition rate of the benthic community is possible with GA-SSA when an underwater hyperspectral imager is deployed to examine the health status of coral reefs in a clean (Chl-a<1 mg/m3) and shallow (bottom depth<10 m) environment. Further study that collects field data for direct validation is required to ensure that the GA-SSA approach is also applicable in real coral reef regions.

Comparison between MERIS and regional high-level products in European seas

Standard ocean color data products from the Medium Resolution Imaging Spectrometer (MERIS) are compared with equivalent regional products in European seas exhibiting different bio-optical properties: the northern Adriatic Sea, the Baltic Sea and the Western Black Sea (ADRS, BLTS and BLKS, respectively). Investigated quantities are: 1) the algal-2 pigment index, alg2; 2) the composite absorption coefficient of yellow substance and non-pigmented particles at 442nm, adg; and 3) the concentration of the total suspended matter, TSM. Regional data products are created using ocean color inversion schemes based on MultiLayer Perceptron (MLP) neural nets trained with field measurements from the Coastal Atmosphere and Sea Time Series (CoASTS) and Bio-Optical mapping of Marine Properties (BiOMaP) programs. MLP input is the remote sensing reflectance RRS at MERIS center-wavelengths specifically selected for different water types in view of minimizing the perturbing effects of inaccurate atmospheric correction on the retrieval of regional data products. A new method is also proposed to define the applicability of regional MLPs to input RRS. Results indicate that MERIS alg2 values tend to overestimate the equivalent quantity computed with MLP regional algorithms. The agreement between MERIS and regional TSM data products is significantly better than that reported for alg2 and adg, especially for BLKS. Findings highlight the relevance of using regional inversion schemes to evaluate standard products over extended oceanographic regions as a complement to the analysis of match-ups between marine products measured in situ and derived from space-born data.

Influence of Raman scattering on ocean color inversion models

Raman scattering can be a significant contributor to the emergent radiance spectrum from the surface ocean. Here, we present an analytical approach to directly estimate the Raman contribution to remote sensing reflectance, and evaluate its effects on optical properties estimated from two common semianalytical inversion models. For application of the method to ocean color remote sensing, spectral irradiance products in the ultraviolet from the OMI instrument are merged with MODerate-resolution Imaging Spectroradiometer (MODIS) data in the visible. The resulting global fields of Raman-corrected optical properties show significant differences from standard retrievals, particularly for the particulate backscattering coefficient, b(bp), where average errors in clear ocean waters are ~50%. Given the interest in transforming b(bp) into biogeochemical quantities, Raman scattering must be accounted for in semianalytical inversion schemes.

Generalized ocean color inversion model for retrieving marine inherent optical properties

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

The optimal retrieval of ocean color constituent concentrations based on the variational method

In this paper, a new approach to the optimal retrieval of the ocean color based on the variational method is developed by setting up a rational target functional combining with the Broydor Fletcher, Goldfarb, Shanno (BFGS) optimal algorithm. The numerical tests and the exemplary retrievals are carried out and compared with the statistical retrievals and the optimal retrievals based on the genetic algorithm. The results show that this approach enjoys a higher accuracy as compared to the statistical method and a higher efficiency as compared to the genetic algorithm. The optimal retrieval method presented in this paper provides a new idea for the ocean color inversion and could also be used as a reference for the direct assimilation of the satellite data into the ecological models.

Adaptive semianalytical inversion of ocean color radiometry in optically complex waters

To address the challenges of the parameterization of ocean color inversion algorithms in optically complex waters, we present an adaptive implementation of the linear matrix inversion method (LMI) [J. Geophys. Res.101, 16631 (1996)], which iterates over a limited number of model parameter sets to account for naturally occurring spatial or temporal variability in inherent optical properties (IOPs) and concentration specific IOPs (SIOPs). LMI was applied to a simulated reflectance dataset for spectral bands representing measured water properties of a macrotidal embayment characterized by a large variability in the shape and amplitude factors controlling the IOP spectra. We compare the inversion results for the single-model parameter implementation to the adaptive parameterization of LMI for the retrieval of bulk IOPs, the IOPs apportioned to the optically active constituents, and the concentrations of the optically active constituents. We found that ocean color inversion with LMI is significantly sensitive to the a priori selection of the empirical parameters g0 and g1 of the equations relating the above-surface remote-sensing reflectance to the IOPs in the water column [J. Geophys. Res.93, 10909 (1988)]. When assuming the values proposed for open-ocean applications for g0 and g1 [J. Geophys. Res.93, 10909 (1988)], the accuracy of the retrieved IOPs, and concentrations was substantially lower than that retrieved with the parameterization developed for coastal waters [Appl. Opt.38, 3831 (1999)] because the optically complex waters analyzed in this study were dominated by particulate and dissolved matter. The adaptive parameterization of LMI yielded consistently more accurate inversion results than the single fixed SIOP model parameterizations of LMI. The adaptive implementation of LMI led to an improvement in the accuracy of apportioned IOPs and concentrations, particularly for the phytoplankton-related quantities. The adaptive parameterization encompassing wider IOP ranges were more accurate for the retrieval of bulk IOPs, apportioned IOPs, and concentration of optically active constituents.

Uncertainties of optical parameters and their propagations in an analytical ocean color inversion algorithm

Following the theory of error propagation, we developed analytical functions to illustrate and evaluate the uncertainties of inherent optical properties (IOPs) derived by the quasi-analytical algorithm (QAA). In particular, we evaluated the effects of uncertainties of these optical parameters on the inverted IOPs: the absorption coefficient at the reference wavelength, the extrapolation of particle backscattering coefficient, and the spectral ratios of absorption coefficients of phytoplankton and detritus/gelbstoff, respectively. With a systematically simulated data set (46,200 points), we found that the relative uncertainty of QAA-derived total absorption coefficients in the blue-green wavelengths is generally within +/-10% for oceanic waters. The results of this study not only establish theoretical bases to evaluate and understand the effects of the various variables on IOPs derived from remote-sensing reflectance, but also lay the groundwork to analytically estimate uncertainties of these IOPs for each pixel. These are required and important steps for the generation of quality maps of IOP products derived from satellite ocean color remote sensing.