Inherent Optical Properties Of Water Research Articles

Performance of two semi-analytical algorithms in deriving water inherent optical properties in the Southern Ocean

Remotely sensed inherent optical properties (IOPs) are key proxies for synoptic mapping of primary production and carbon export in the global ocean. However, the IOPs inversion algorithms are scarcely evaluated in the Southern Ocean (SO) because of limited field observations. In this study, the performance of two widely used semi-analytical algorithms (SAAs), i.e., the quasi-analytical algorithm (QAA) and the generalized IOP model (GIOP), were evaluated using a compiled in situ bio-optical dataset in SO, as well as measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS). Evaluations with in situ data show that QAA and GIOP have comparable performance in retrieving the total absorption coefficient (a(λ)), absorption coefficients of phytoplankton (aph(λ)), and that of detritus and colored dissolved organic matter (adg(λ)). Overall, it was found that remotely sensed a(λ) and aph(λ) by both SAAs agreed well with field measurements, with the mean absolute percentage difference (MAPD) of derived a(λ) and aph(λ) in the blue-green bands being ∼20% and ∼40%, respectively. However, derived adg(λ) by both SAAs were higher than the measured values at the lower end (adg(443) < ∼0.01 m-1), but lower at the higher end (adg(443) > ∼0.02 m-1), with MAPD of ∼60%. Results of this effort suggest confident products of a(λ) and aph(λ) from VIIRS in SO, but more dedicated efforts on the measurements and evaluation of adg(λ) in SO would be desired.

On the Influence of Spatial Fluctuations of the Water Inherent Optical Properties on the Energy of a Lidar Echo Signal Coming from a Water

Theoretical models of the statistical characteristics of the lidar echo signal have been developed to interpret the results of optical sounding of heavily eutrophicated water bodies. Formulas are obtained for calculating the statistically average value and coefficient of variation of the energy of the elastic backscattering signal coming from the near-surface layer of water with randomly inhomogeneous absorption and scattering coefficients. Examples of the dependence of the indicated signal characteristics on the coefficients of variation of the optical characteristics of water are given. It has been established that fluctuations in the absorption coefficient lead to an increase in the average energy of the received signal, and fluctuations in the scattering coefficient to its slight decrease. A significant decrease in the average echo signal energy can be observed with cross-correlated fluctuations in the absorption and scattering coefficients, i. e. in the case when the attenuation coefficient fluctuates at a constant single scattering albedo. Considerations are made on how algorithms for estimating the average values of the optical characteristics of water and the parameters of their inhomogeneities from the average value and the coefficient of variation of the echo signal energy can be constructed.

Direct measurement system of water-leaving albedo in the field by the skylight-blocked approach: Monte Carlo simulations.

Water-leaving albedo (αw(λ)) is an important component of the ocean surface albedo. Direct measurement of αw(λ) in the field is not yet available due to difficulties in removing the contribution of surface-reflected solar radiation. Following the concept of the skylight-blocked approach (SBA), a novel system, termed αwSBA, is proposed in this study to directly measure Ew(λ), where a wide-angle black cone is used to block the surface-reflected radiance. The shading errors associated with the cone and the measuring system are examined via Monte-Carlo (MC) simulations for a wide range of water inherent optical properties (IOPs), solar zenith angle, and different configurations of the αwSBA system (i.e., half cone angle, and the length of supporting arm). Based on sensitive analysis using MC simulations, an optimal configuration of αwSBA is recommended. We further propose a mathematical expression to parameterize the shading error (ɛ), along with an error correction scheme (αwOPT). It is found that, with the optimal configuration and αwOPT, the uncertainties of obtained αw(λ) by αwSBA are generally less than 7% for a wide range of waters with different IOPs and particulate scattering phase functions.

Spatio-temporal variations in the water quality of the Doorndraai Dam, South Africa: An assessment of sustainable water resource management

The problem of water scarcity and clean water in sub-Saharan Africa is a growing concern. This study aims to quantify the water quality on a temporal scale in the Doorndraai dam site in sub-Saharan Africa to design possible management options. Here, an integrated approach using both in-situ measurements of water quality parameters and remote sensing data was used to derive the water quality index (WQI) and inherent optical properties of water to deduce the factors governing seasonal and annual variability. The results show that all the water quality parameters analyzed fall under the permissible limit of the World Health Organization (WHO) for drinking water, except turbidity. The average value of turbidity for the dry and wet periods was 12.52 and 3.39 NTU, respectively. WQI value ranges from good to excellent during the wet season, and poor in the dry season owing to the high values of turbidity in the water samples. Both in-situ and remote sensing-based analysis shows that during the last five years, the value of suspended particulate matter (SPM) based on Landsat-8 increased gradually in the study area. The Sentinel-2 derived modified normalized difference water index (MNDWI) shows a decreasing trend in the water area due to encroachment. The strong correlation between in-situ and remote sensing data supports the usefulness of remote sensing techniques for water resource management, especially in data-scarce regions. Looking at the spatio-temporal trend of water quality evolution, the findings of this study will help local decision-makers design sustainable plans for water resource management of Doorndraai dam.

Chlorophyll-a concentration inversion based on the modified Quasi-Analytical Algorithm and Sentinel-3 OLCI in Daihai Lake, China

Abstract The Quasi-Analytical Algorithm (QAA) is effective in retrieving water inherent optical properties (IOPs) from remote sensing spectral reflectance and has wider applications in studies of the open ocean and coastal waters than of inland waters. This research aimed to modify the QAA model based on measured field spectral reflectance and absorption coefficients to render it applicable to studies of Daihai Lake, China. The improvements mainly included the reference wavelength selection, the power index of the particle backscattering coefficient and the exponential slope of the absorption coefficient of the colored detrital matter estimation. The average relative error between the inverse and measured absorption coefficients was less than 20%. A linear model was established between the phytoplankton absorption coefficient at a wavelength of 674 nm (aph(674)) and the chlorophyll-a (Chl-a) concentration, with a determination coefficient of 0.88. Additionally, the modified Quasi-Analytical Algorithm (MQAA) model was applied to the Ocean and Land Color Instrument (OLCI) data aboard the Sentinel-3 satellite. Finally, a spatial distribution map for the Chl-a concentrations in Daihai Lake on August 10, 2017, was drawn and the mid-eutrophication area was found to occur in the north and border.

The Imprint of Primary Production on High-Frequency Profiles of Lake Optical Properties.

Water inherent optical properties (IOPs) contain integrative information on the optical constituents of surface waters. In lakes, IOP measurements have not been traditionally collected. This study describes how high-frequency IOP profiles can be used to document short-term physical and biogeochemical processes that ultimately influence the long-term trajectory of lake ecosystems. Between October 2018 and May 2020, we collected 1373 high-resolution hyperspectral IOP profiles in the uppermost 50 m of the large mesotrophic Lake Geneva (Switzerland-France), using an autonomous profiler. A data set of this size and content does not exist for any other lake. Results showed seasonal variations in the IOPs, following the expected dynamic of phytoplankton. We found systematic diel patterns in the IOPs. Phases of these diel cycles were consistent year-round, and amplitudes correlated to the diurnal variations of dissolved oxygen, clarifying the link between IOPs and phytoplankton metabolism. Diel amplitudes were largest in spring and summer under low wind condition. Wind-driven changes in thermal stratification impacted the dynamic of the IOPs, illustrating the potential of high-frequency profiles of water optical properties to increase our understanding of carbon cycling in lake ecosystems.

Interpretation of Spectral LiDAR Backscattering off the Florida Coast

A multispectral backscattering LiDAR (Light detection and range) system (hereafter Oculus) was integrated into a wave glider and used to estimate the scattering order (i.e., single vs multiple collisions) of LIDAR backscattering, the water inherent optical properties (IOPs), the biogeo-chemical characteristics of particulate scatterers (i.e., relative size, composition) and their motion) on shelf waters of South East Florida. Oculus has a dual-wavelength configuration (473 and 532 nm) and two detection geometries (off- and on-axis). Characteristics of scatterers were investigated based on two complementary LiDAR-derived proxies (the Structural Dissimilarity Index and the spectral slope of LiDAR backscattering). In March 2017, field measurements showed a covariation between direct and diffuse backscattering contributions during morning hours and away from shore. LiDAR attenuation coefficients explained up to 57% of IOPs variability. The analysis of LiDAR-derived proxies suggested higher turbidity and larger particulates near the coast

Hybrid Sequence Networks for Unsupervised Water Properties Estimation From Hyperspectral Imagery

Generating accurate estimation of water inherent optical properties (IOPs) from hyperspectral images plays a significant role in marine exploration. Traditional methods mainly adopt bathymetric models and numerical optimization algorithms to deal with this problem. However, these methods usually tend to simplify the bathymetric models and lack the capability of describing the discrepancy between reference spectrum and estimation spectrum, resulting in a limited estimation performance. To get a more precise result, in this work, we propose a novel network based on deep learning to retrieve the IOPs. The proposed network, named as IOPs estimation network (IOPE-Net), explores a hybrid sequence structure to establish IOPs estimation module that acquires high-dimensional nonlinear features of water body spectrums for water IOPs estimation. Moreover, considering the insufficiency of labeled training samples, we design a spectrum reconstruction module combined with classical bathymetric model to train the proposed network in an unsupervised manner. Then, aiming at further promoting the estimation performance, a multicriterion loss is developed as the objective function of IOPE-Net. In particular, we construct a hierarchical multiscale sequence loss as the key component to retain the details of original spectral information. Thus, the discrepancy between different spectrums can be better described by the obtained learning model. Experimental results on both simulated and real datasets demonstrate the effectiveness and efficiency of our method in comparison with the state-of-the-art water IOPs retrieving methods.

Impact of Transmission Scheme of Visible Solar Radiation on Temperature and Mixing in the Upper Water Column With Inputs for Transmission Derived From Ocean Color Remote Sensing

AbstractThe penetration of visible solar radiation (VSR) in the upper ocean contributes to heating in the upper water column, and this process is modulated by constituents in water such as phytoplankton. Various schemes have been developed to propagate surface VSR to deeper depths, which are incorporated in ocean circulation models to study basin‐scale impacts on ocean and atmosphere by phytoplankton. However, none of these studies evaluated sensitivity of the schemes of VSR transmission to upper‐water dynamics, especially when the required input is derived from ocean color remote sensing. We used an idealized one‐dimensional (1‐D) Regional Ocean Modeling System (ROMS) to simulate profiles of upper water column temperature at six locations with different ocean color characteristics (containing different water constituents). We incorporated and tested four different schemes of VSR transmission: the default water‐type scheme within the ROMS, two schemes based on concentrations of chlorophyll (Chl), and one scheme based on water's inherent optical properties (IOPs). The results showed that, although using the same ocean color information and the same circulation model, the IOPs‐ and Chl‐based schemes resulted in very different vertical temperature and upper‐water mixing compared to those using the default scheme, with differences in sea surface temperature up to 1.5–2°C. These results highlight the importance of developing and incorporating more appropriate VSR transmission schemes into large‐scale ocean circulation simulations, where the inputs for the transmission are inherently estimates from ocean color—the actual measurement from satellite remote sensing.

Sensitivity of a 3D Shelf Sea Ecosystem Model to Parameterizations of the Underwater Light Field

The inherent optical properties of water over the North Sea vary widely in space and time. Their impact on the performance of 3D-ecosystem-model of the North Sea needs to be critically evaluated, which is the major research issue in the present paper. We have performed a sensitivity analysis to a modification of a common approach of light treatment that is both valid for the North Sea, as well as computationally efficient to implement and run within a 3D-ecosystem-model. Using a coupled hydrodynamical model (Regional Ocean Modelling System, ROMS) and biological model (Carbon Silicate and Nitrogen Ecosystem model, CoSiNE), we found that simple changes to the original parameterization can yield significant improvements. ROMS is shown to be suitable for application in the North Sea. The model accurately reproduces the seasonal cycle of primary production in terms of timing and magnitude, while still being more affordable in comparison to full hyperspectral treatment or solving the radiative transfer equation. The modification introduces vertically increasing attenuation that is stronger in shallow domains, in a way that is similar to attenuation due to sediment. The resulting reduction of light availability leads to strongly reduced phytoplankton growth in shallow areas with high turbidity. Areas of depths between $50m$ and $100m$ show greatest relative change with respect to their total ranges, while the deepest areas remain largely unchanged. We found that the consideration of spacial varibility of light attenuation is necessary when modelling a heterogeneous domain, such as the North Sea.

Analysis of the Optimal Wavelength for Oceanographic Lidar at the Global Scale Based on the Inherent Optical Properties of Water

Understanding the optimal wavelength for detecting the water column profile from a light detection and ranging (lidar) system is important in the design of oceanographic lidar systems. In this research, the optimal wavelength for detecting the water column profile using a lidar system at the global scale was analyzed based on the inherent optical properties of water. In addition, assuming that the lidar system had a premium detection characteristic in its hardware design, the maximum detectable depth using the established optimal wavelength was analyzed and compared with the mixed layer depth measured by Argo data at the global scale. The conclusions drawn are as follows: first, the optimal wavelengths for the lidar system are between the blue and green bands. For the open ocean, the optimal wavelengths are between 420 and 510 nm, and for coastal waters, the optimal wavelengths are between 520 and 580 nm. To obtain the best detection ability using a lidar system, the best configuration is to use a lidar system with multiple bands. In addition, a 490 nm wavelength is recommended when an oceanographic lidar system is used at the global scale with a single wavelength. Second, for the recommended 490 nm band, a lidar system with the 4 attenuating length detection ability can penetrate the mixed layer for 80% of global waters.

BIO OPTICAL MODELLING OF LAGUNA LAKE USING BOMBER TOOL AND WASI-DERIVED INVERTED PARAMETERS

Abstract. Laguna Lake is significant to its surrounding cities and municipalities as it serves multiple purposes: flood basin, aquaculture, water source for irrigation and domestic use, among others. Monitoring the lake’s water quality is an integral part ensuring that the lake would continue to serve its purposes. Bio-optical modelling is a type of empirical model that relates the inherent optical properties of water to different biological properties like chlorophyll-a. The BOMBER (Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images) tool makes use of the different IOPs apparent optical properties (AOPs) of satellite images to be able to produce water quality maps. To localize the parameters used by the BOMBER tool, the use of WASI (The Water Color Simulator) tool was introduced. Inverting in situ spectral measurements of the lake, WASI tool was able to produce parameters localized for the lake. This research used 2018 Landsat 8 Images to produce images and used a water profiler to validate results. Results show the bio-optical model provided a R-squared value of 0.6912 and an RMSE of 2.43 μg/l which shows good correlation between the in-situ and the bio-optical model results.

Empirical Formulas for Estimating Backscattering and Absorption Coefficients in Complex Waters from Remote-Sensing Reflectance Spectra and Examples of Their Application.

Many standard methods used for the remote sensing of ocean colour have been developed, though mainly for clean, open ocean waters. This means that they may not always be effective in complex waters potentially containing high concentrations of optically significant constituents. This paper presents new empirical formulas for estimating selected inherent optical properties of water from remote-sensing reflectance spectra Rrs(λ), derived, among other things, for waters with high concentrations of dissolved and suspended substances. These formulas include one for estimating the backscattering coefficient bb(620) directly from the magnitude of Rrs in the red part of the spectrum, and another for estimating the absorption coefficient a(440) from the hue angle α. The latter quantity represents the water’s colour as it might be perceived by the human eye (trichromatic colour vision); it is easily calculated from the shape of the Rrs spectrum. These new formulas are based on a combined dataset. Most of the data were obtained in the specific, optically complex environment of the Baltic Sea. Additional data, taken from the NASA bio-Optical Marine Algorithm Dataset (NOMAD) and representing various regions of the global oceans, were used to widen the potential applicability of the new formulas. We indicate the reasons why these simple empirical relationships can be derived and compare them with the results of straightforward modelling; possible applications are also described. We present, among other things, an example of a simple semi-analytical algorithm using both new empirical formulas. This algorithm is a modified version of the well-known quasi-analytical algorithm (QAA), and it can improve the results obtained in optically complex waters. This algorithm allows one to estimate the full spectra of the backscattering and absorption coefficients, without the need for any additional a priori assumptions regarding the spectral shape of absorption by dissolved and suspended seawater constituents.

Hydrography-Driven Optical Domains in the Vaigat-Disko Bay and Godthabsfjord: Effects of Glacial Meltwater Discharge

We present here an assessment of in situ hyperspectral bio-optical variability in the Vaigat-Disko Bay and Godthabsfjord along the southwest coast of Greenland. The dataset consists of state-of-the-art profiler measurements of hyperspectral apparent and inherent optical properties of water complemented by traditional observations of Secchi disk and Forel Ule scale in the context of ocean color. Water samples were collected and analyzed for concentration of optically active constituents (OACs). Near-surface observations of hydrographic parameters revealed three different water masses in the Bay: meltwater plume, frontal zone, and Atlantic water mass. Underwater spectral light availability reveals three different spectral types. Low salinity, increased temperature, deep euphotic depths, and case-1 water type remote sensing reflectance spectra with tabletop peaks in the 400–500 nm wavelength range characterize the glacial meltwater plume in the Vaigat-Disko Bay. The conservative relationship between salinity and chromophoric dissolved organic matter (CDOM) commonly observed in estuarine and shelf seas is weaker in the Godthabsfjord and reverses in the Vaigat-Disko Bay. Efficiency of machine learning techniques such as cluster analysis is tested in delineating water masses in the bay w.r.t. hydrographic and bio-optical parameters. Tests of optical closure yield low root mean square error at longer wavelengths. The study provides strong evidence that despite similar geographic setting, fjord ecosystems exhibit contrasting bio-optical properties which necessitate fjord-specific investigations.

Semi-analytical algorithms of ocean color remote sensing under high solar zenith angles.

With the increasing interest in ocean color remote sensing in polar oceans and geostationary ocean color satellite with diurnal observations, it is unavoidable to encounter ocean color retrievals under high solar zenith angles. Under these scenarios, the capability of current remote sensing algorithms is poorly known. In this study, the performance of the two widely used semi-analytical algorithms for the water inherent optical properties (QAA and GSM01) under high solar zenith angle conditions were firstly evaluated based on global in situ data set (SeaBASS-NOMAD). The results showed that the performances of both QAA and GSM01 degraded significantly with the increasing in solar zenith angle (SZA), and the biases increased about 1.3-fold when SZA varied from 30° to 80°. The high uncertainties at high SZA was mainly induced by the systematic overestimation of the key parameter u (ratio of backscattering coefficient to the sum of absorption and backscattering coefficients) at high solar zenith angles. Based on the Hydrolight-simulated data set, a new model (NN-algorithm) for retrieving u from remote sensing reflectance was developed for high solar zenith angle conditions using the neural network method. The validation results revealed that the NN-algorithm could improve the estimation of parameter u and further ocean color products. In addition, our results indicate that a more accurate atmosphere correction is needed to deal with ocean color remote sensing data acquired under large solar zenith angle conditions.

Deriving inherent optical properties from classical water color measurements: Forel-Ule index and Secchi disk depth.

Secchi disk depth (ZSD) and Forel-Ule index (FUI) are the two oldest and easiest measurements of water optical properties based on visual determination. With an overarching objective to obtain water inherent optical properties (IOPs) using these historical measurements, this study presents a model for associating remote-sensing reflectance (Rrs) with FUI and ZSD. Based upon this, a scheme (FZ2ab) for converting FUI and ZSD to absorption (a) and backscattering coefficients (bb) is developed and evaluated. For a data set from HydroLight simulations, the difference is <11% between FZ2ab-derived a and known a, and <28% between FZ2ab-derived bb and known bb. Further, for a data set from field measurements, the difference is < 30% between FZ2ab-derived a and measured a. These results indicate that FZ2ab can bridge the gap between historical measurements and the focus of IOP measurements in modern marine optics, and potentially extend our knowledge on the bio-optical properties of global seas to the past century through the historical measurements of FUI and ZSD.

Underwater image restoration algorithm for free-ascending deep-sea tripods

A Free-Ascending Tripod (FAT) was deployed at a water depth of 2100 m to measure the currents and sediment movement at the seafloor. FAT is used to better understand how and where deep-seafloor sediment moves and accumulates. We also use FATs to study deep-sea biology. In the images obtained by the camera, biological animals can hardly be distinguished. In this paper, we use image processing technology to uncover the real deep-sea scene. We propose four methods for improving the underwater image quality. First, we use the deep-sea optical imaging model to determine the properties of water in different sea areas and then remove the haze from underwater images using the underwater dual dark channel model. Next, we remove the footprint of artificial light through halo-estimation devignetting. Then, we obtain the real deep-sea scene color based on the color temperature of the camera and the inherent optical properties of water. Finally, we propose a semi-self-similarity-based super resolution for super-resolving the low-quality images. The experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art methods.

Atmospheric correction over coastal waters using multilayer neural networks

Standard atmospheric correction (AC) algorithms work well in open ocean areas where the water inherent optical properties (IOPs) are correlated with pigmented particles. However, the IOPs of turbid coastal waters may independently vary with pigmented particles, suspended inorganic particles, and colored dissolved organic matter (CDOM). In turbid coastal waters standard AC algorithms often exhibit large inaccuracies that may lead to negative water-leaving radiances (Lw) or remote sensing reflectance (Rrs). We introduce a new atmospheric correction algorithm for coastal waters based on a multilayer neural network (MLNN) method. We use a coupled atmosphere-ocean radiative transfer model to simulate the Rayleigh-corrected radiance (Lrc) at the top of the atmosphere (TOA) and the Rrs just above the surface simultaneously, and train a MLNN to derive the aerosol optical depth (AOD) and Rrs directly from the TOA Lrc. The method is validated using both a synthetic dataset and Aerosol Robotic Network – Ocean Color (AERONET–OC) measurements. The SeaDAS NIR algorithm, the SeaDAS NIR/SWIR algorithm, and the MODIS version of the Case 2 regional water - CoastColour (C2RCC) algorithm are also included in the comparison with AERONET–OC measurements. The performance of the AC algorithms is evaluated with four statistical metrics: the Pearson correlation coefficient (R), the average percentage difference (APD), the mean percentage bias, and the root mean square difference (RMSD). The comparison with AERONET–OC measurements shows that the MLNN algorithm significantly improves retrieval of normalized Lw in blue bands (412nm and 443nm) and yields minor improvements in green and red bands compared with the other three algorithms. On a global scale, the MLNN algorithm reduces APD in normalized Lw by up to 13% in blue bands and by 2–7% in green and red bands when compared with the standard SeaDAS NIR algorithm. In highly absorbing coastal waters, such as the Baltic Sea, the MLNN algorithm reduces APD in normalized Lw by more than 60% in blue bands compared to the standard SeaDAS NIR algorithm, while in highly scattering coastal waters, such as the Black Sea, the MLNN algorithm reduces APD by more than 25%. These results indicate that the MLNN algorithm is suitable for application in turbid coastal waters. Application of the MLNN algorithm to MODIS Aqua images in several coastal areas also shows that it is robust and resilient to contamination due to sunglint or adjacency effects of land and cloud edges. The MLNN algorithm is very fast once the neural network has been properly trained and is therefore suitable for operational use. A significant advantage of the MLNN algorithm is that it does not need SWIR bands.

Water-leaving contribution to polarized radiation field over ocean.

The top-of-atmosphere (TOA) radiation field from a coupled atmosphere-ocean system (CAOS) includes contributions from the atmosphere, surface, and water body. Atmospheric correction of ocean color imagery is to retrieve water-leaving radiance from the TOA measurement, from which ocean bio-optical properties can be obtained. Knowledge of the absolute and relative magnitudes of water-leaving signal in the TOA radiation field is important for designing new atmospheric correction algorithms and developing retrieval algorithms for new ocean biogeochemical parameters. In this paper we present a systematic sensitivity study of water-leaving contribution to the TOA radiation field, from 340 nm to 865 nm, with polarization included. Ocean water inherent optical properties are derived from bio-optical models for two kinds of waters, one dominated by phytoplankton (PDW) and the other by non-algae particles (NDW). In addition to elastic scattering, Raman scattering and fluorescence from dissolved organic matter in ocean waters are included. Our sensitivity study shows that the polarized reflectance is minimized for both CAOS and ocean signals in the backscattering half plane, which leads to numerical instability when calculating water leaving relative contribution, the ratio between polarized water leaving and CAOS signals. If the backscattering plane is excluded, the water-leaving polarized signal contributes less than 9% to the TOA polarized reflectance for PDW in the whole spectra. For NDW, the polarized water leaving contribution can be as much as 20% in the wavelength range from 470 to 670 nm. For wavelengths shorter than 452 nm or longer than 865 nm, the water leaving contribution to the TOA polarized reflectance is in general smaller than 5% for NDW. For the TOA total reflectance, the water-leaving contribution has maximum values ranging from 7% to 16% at variable wavelengths from 400 nm to 550 nm from PDW. The water leaving contribution to the TOA total reflectance can be as large as 35% for NDW, which is in general peaked at 550 nm. Both the total and polarized reflectances from water-leaving contributions approach zero in the ultraviolet and near infrared bands. These facts can be used as constraints or guidelines when estimating the water leaving contribution to the TOA reflectance for new atmospheric correction algorithms for ocean color imagery.

In Situ Observations of Biological and Environmental Parameters by Means of Optics—Development of Next-Generation Ocean Sensors With Special Focus on an Integrating Cavity Approach

To meet the requirements of increasing environmental awareness in aquatic ecosystems, optical techniques offer a fast and reliable opportunity for a wide range of applications providing high-resolution measurements. In this respect, important parameters which have to be addressed include phytoplankton biomass and taxonomic composition, total suspended matter, dissolved organic matter, as well as hazardous substances, e.g., polycyclic aromatic hydrocarbons (PAHs). Requiring comparable low effort, optical methods are a convenient and noninvasive way to derive information on the optical active substances in different water bodies. Various approaches and devices are available, either aiming on the determination of the water's inherent optical properties or on measuring the fluorescence properties of different constituents. This contribution presents the objectives and measurement principles of two new optical sensor developments in this respect. A special focus lies on an integrating cavity approach for hyperspectral absorption measurement. This approach overcomes two common problems in classical absorption measurement of seawater: 1) usually low concentration of absorbing material in the water negatively affecting accurate measurements of untreated samples; and 2) errors introduced by light scattering of particles requiring empirical corrections to obtain good accuracy. To combine these advantages with the possibilities of automated, long-term high-frequency measurements, an integrating cavity was adapted for flow-through operation. First field results obtained by the resulting Hyperspectral Absorption Sensor (HyAbS) in the North Sea and off the Norwegian coast are evaluated and compared with discrete measurements. The second development is a matrix-fluorescence sensor with flexible wavelength configuration for the detection and characterization of dissolved substances, such as fluorescent dissolved organic matter (FDOM) and PAHs. Here, the measurement principle of the sensor and first field results from a related, laboratory-based method will be presented as a preliminary work necessary for the development of the final in situ sensor. Furthermore, future plans for both instruments as well as a possible combination will be discussed. In summary, both approaches have the potential to be multiparameter instruments for high-resolution measurements of environmental parameters.