Comment on essd-2023-80

<strong class="journal-contentHeaderColor">Abstract.</strong> Radiative transfer (RT) simulations have long been used to study relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean from which the ocean apparent optical properties (AOPs) can be calculated. For example, inverse models to estimate IOPs from ocean color radiometric measurements have been developed or validated using results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range from the near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development was the generation of the synthetic dataset of seawater IOPs which served as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by probability distributions of IOPs that are consistent with global distributions representative of vast areas of open ocean pelagic environments and coastal regions covering a broad range of optical water types. The generation of the synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column and three simulation scenarios associated with sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll-a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0&deg;, 30, and 60&deg; assuming clear skies, standard atmosphere, and wind speed of 5 m s^&minus;1. Thus, overall 29880 RT simulations were performed. The output results of these simulations include the radiance distributions, plane and scalar irradiances, and the whole set of AOPs including the remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines where all optical variables are reported in the spectral range from 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving the IOPs and AOPs.

<strong class="journal-contentHeaderColor">Abstract.</strong> Radiative transfer (RT) simulations have long been used to study relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean from which the ocean apparent optical properties (AOPs) can be calculated. For example, inverse models to estimate IOPs from ocean color radiometric measurements have been developed or validated using results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range from the near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development was the generation of the synthetic dataset of seawater IOPs which served as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by probability distributions of IOPs that are consistent with global distributions representative of vast areas of open ocean pelagic environments and coastal regions covering a broad range of optical water types. The generation of the synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column and three simulation scenarios associated with sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll-a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0&deg;, 30, and 60&deg; assuming clear skies, standard atmosphere, and wind speed of 5 m s^&minus;1. Thus, overall 29880 RT simulations were performed. The output results of these simulations include the radiance distributions, plane and scalar irradiances, and the whole set of AOPs including the remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines where all optical variables are reported in the spectral range from 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving the IOPs and AOPs.

Compared with traditional single-view and radiometric-only observations, multiangle polarimetric observations of water-leaving radiation play a crucial role in enhancing the retrieval of ocean constituents and aerosol microphysical properties. In this study, the impacts of uncertainties in the degree of polarization (DOP) of water-leaving radiance (Lw) on the retrieval of oceanic constituents and inherent optical properties (IOPs) were investigated via global radiative transfer (RT) simulations and the fully connected U-Net (FCUN) model. The uncertainties in the retrieval of oceanic constituents and IOPs were further investigated with various sensor azimuth angles. The results indicated that the global mean absolute percentage errors (MAPEs) for differing oceanic constituents and IOPs significantly decreased as the number of observation angles increased. Taking the retrieval of Chla as an example, the global MAPEs between the FCUN predictions and RT simulation inputs for Chla concentrations under differing observation angles were 7.41%, 3.76%, 2.70%, 2.44%, 2.62%, and 1.82%. Moreover, the MAPEs at sensor azimuth angles of 0° and 30° were significantly lower than those at other azimuth angles for the single-view observations. As the number of observation angles increased, the variation in MAPEs with the sensor azimuth angle gradually weakened. Furthermore, the impact of errors in the Lw DOP on the retrieval uncertainties decreased as the number of observation angles increased, and the global MAPEs of Chla after adding the various random instrument noises were 46.56% (46.91%), 6.59% (7.21%), 5.21% (5.79%), 4.72% (4.98%), 3.99% (4.52%), and 3.64% (4.03%). Overall, the multiangle polarimetric observations can suppress or balance the impact of uncertainties in the Lw DOP on the retrieval of oceanic constituents and IOPs.

Let us begin by reminding ourselves just what we mean by “the inherent optical properties” and “the apparent optical properties” of surface waters. The inherent optical properties are those that belong to the aquatic medium itself: properties that belong to a small sample of the aquatic medium taken out of the water body just as much as they belong to a great mass of the medium existing within the water body itself. The properties of particular concern to us are the absorption coefficient, a, the scattering coefficient, b, and the volume scattering function, β(θ). The absorption coefficient at a given wavelength is a measure of the intensity with which the medium absorbs light from a parallel beam per unit pathlength of medium (see Eq. 1.18). The scattering coefficient at a given wavelength is a measure of the intensity with which the medium scatters light from a parallel beam per unit pathlength of medium (see Eq. 1.17). Both a and b have the units, m-1. The normalized volume scattering function specifies the angular (θ) distribution of single-event scattering around the direction of a parallel incident beam. It is often normalized to total scattering and referred to as the scattering phase function, P(θ) (see Eq. 1.21). Since these properties belong, as I have already said, to a small sample of the medium, just as much as they do to a great slab of ocean, they can be measured in the laboratory. The absorption coefficients at various wavelengths can be measured with a suitable spectrophotometer: the scattering coefficient and the volume scattering function can be measured with a light scattering photometer. The apparent optical properties are not properties of the aquatic medium as such although they are closely dependent on the nature of the aquatic medium. In reality they are properties of the light field that, under the incident solar radiation stream, is established within the water body.

The color of natural waters – oceanic, coastal, and inland – is determined by the spectral absorption and scattering properties of dissolved and particulate water constituents. Remote sensing of aquatic ecosystems requires a comprehensive understanding of these inherent optical properties (IOPs), their interdependencies, and their impact on ocean (water) color, i.e., remote-sensing reflectance. We introduce a bio-geo-optical model for natural waters that includes revised spectral absorption and scattering parameterizations, based on a comprehensive analysis of precisely measured IOPs and water constituents. In addition, specific IOPs of the most significant phytoplankton groups are modeled and a system is proposed to represent the optical variability of phytoplankton diversity and community structures. The model provides a more accurate representation of the relationship between bio-geo-optical properties and can better capture optical variability across different water types. Based on the evaluation both using the training and independent testing data, our model demonstrates an accuracy of within ±5% for most component IOPs throughout the visible spectrum. We also discuss the potential of this model for radiative transfer simulations and building a comprehensive synthetic dataset especially for optically complex waters. Such datasets are the crucial basis for the development of satellite-based ocean (water) color algorithms and atmospheric correction methods. Our model reduces uncertainties in ocean color remote sensing by enhancing the distinction of optically active water constituents and provides a valuable tool for predicting the optical properties of natural waters across different water types.

In-situ measurements of the bio-optical properties of the seawater are important to develop algorithms for seawater constituent estimation using satellite remote sensing. A data collection campaign was conducted for bio-optical characterization of the open and coastal waters of the Arabian Sea during April 15-29, 2006. Bio-optical measurements were made using the Satlantic hyper-spectral underwater radiometer (Hyperpro-II) for 13 sampling stations include oligotrophic, <i>Trichodesmium</i> bloom dominated and coastal waters in 400-800 nm spectral range. For open oceans stations 1% light was available at 50 to 70 meter depth, whereas, for coastal waters it varied from 18 to 35 meter. The deep chlorophyll maxima (DCM) was observed at 30 to 42 meter depth during the bloom conditions with surface chlorophyll-a concentration ranging between 0.1 to 0.85 mg m^-3 whereas, for open ocean and non-bloom conditions the DCM depth varied from 35 to 60 m with surface chlorophyll ranging between 0.05 to 0.12 mgm^-3. Particulate back scattering coefficient at 700-nm vary from 0.0011 to 0.0031 for bloom waters and 0.00046 to 0.0012 for open ocean waters. The normalized water leaving radiance computed from these spectra in the spectral bands of IRS-P4, OCM bands were examined. The global ocean chlorophyll-2 (OC2), and 4 (OC4) algorithms performed reasonably well for open ocean waters, however both the algorithms overestimated chlorophyll concentration for bloom dominated waters.

Abstract. Because of the large diversity of case 2 waters ranging from extremely absorbing to extremely scattering waters and the complexity of light transfer due to external terrestrial inputs, retrieving main biogeochemical parameters such as chlorophyll-a or suspended particulate matter concentration in these waters is still challenging. By providing optical and biogeochemical parameters for 180 sampling stations with turbidity and chlorophyll-a concentration ranging from 1 to 700 FNU and from 0.9 to 180 mg m−3 respectively, the HYPERMAQ dataset will contribute to a better description of marine optics in optically complex water bodies and can help the scientific community to develop algorithms. The HYPERMAQ dataset provides biogeochemical parameters (i.e. turbidity, pigment and chlorophyll-a concentration, suspended particulate matter), apparent optical properties (i.e. water reflectance from above water measurements) and inherent optical properties (i.e. absorption and attenuation coefficients) from six different study areas. These study areas include large estuaries (i.e. the Rio de la Plata in Argentina, the Yangtze estuary in China, and the Gironde estuary in France), inland (i.e. the Spuikom in Belgium and Chascomùs lake in Argentina), and coastal waters (Belgium). The dataset is available from Lavigne et al. (2022) at https://doi.org/10.1594/PANGAEA.944313.

Over the past several decades, satellite ocean color remote sensing has greatly improved our understanding of the biogeochemical properties of the global ocean. Particulate organic nitrogen (PON) refers to the nitrogen contained in biological or other debris particles, and it plays important roles in the ecosystem functions and biogeochemical processes of the marine biology. However, few studies have focused on the satellite retrieval of oceanic PON concentrations. With an attempt to fill the gap, this study aimed to explore the feasibility of retrieving oceanic PON concentrations from remote sensing data, determine the bio-optical proxies for satellite PON retrievals, and develop satellite oceanic PON retrieval models for the global ocean. In situ PON data collected over the global ocean and Moderate-resolution Imaging Spectroradiometer (MODIS) Level-3 products were used. Three different types of models were tested: (1) apparent optical property (AOP)-based models, 2) inherent optical property (IOP)-based models, and 3) biological property-based models. Results showed that ocean color remote sensing could be used for oceanic PON concentration retrieval in the global ocean, and AOP-based models calibrated produced better fitting performance than the other two types; models based on blue-to-green band ratio (BG) and normalized difference nitrogen index (NDNI) produced comparable and better fitting and validation performance; and IOP-based and biological property-based models produced lower but also acceptable performance. With the PON models developed, the monthly variations of PON concentrations in the global ocean were also explored. In further studies, PON models will be used to explore oceanic PON spatiotemporal variations and the underlying driving forces.

The total absorption coefficient a(λ) and total backscattering coefficient b b (λ) are the basic inherent optical properties (IOPs) in ocean color remote sensing, which is the key to connect appearance optical properties (AOPs) from the remote sensor and the concentration of water component. In this paper, based on the stable and strong spectral relationship of the absorption coefficient (excluding the contribution of pure water) and particle backscattering coefficient at 510nm and 555nm, a new quasi-analytical remote sensing IOPs algorithm with two reference wavelengths is developed. The absorption coefficient and particle backscattering coefficient at 510nm and 555nm is calculated firstly through algebraic equations, and then based on the spectral model of particle backscattering coefficient, the a(λ) and b b (λ) at the other wavelength are extrapolated. Three optical data sets are used to validate the algorithm, including the in situ data set of China Yellow Sea and East China Sea in the spring of 2003, the synthesis data set and the global in situ data set from IOCCG. Meanwhile, the performance of this new algorithm is also compared with the results of GSM01 and Lee-QAA algorithms with the ECS data set. The results show that, in the coastal area with high influence of terrestrial material, the two reference wavelength algorithm has better flexibility for the special optical water.

This study started on 1 June 1989 and ended 14 November 1991. Two moored in situ natural fluorometers were deployed in January 1990 to collect bio-optical data for one year, making ground truth measurements around the mooring during 4 cruises. This one-year time series would investigate how the short-term physical forcing aliases the long-term primary production record such that the apparent, larger interannual variability in the record is in reality ``noise`` due to short-term fluctuations in the rate of nutrient input to the euphotic zone. These continuous measurements from moored bio-optical instruments would also allow better estimates of the mean and variance in primary production in these waters than has previously been available from shipboard measurements, as well as, phytoplankton response to short-term physical events. Ancillary measurements that were made were: (1) characterization of the apparent and inherent optical properties, (2) photosynthetic pigment distributions using both HPLC and standard fluorometric methods, (3) carbon, hydrogen and nitrogen content of suspended particulate matter, (4) primary production using conventional {sup 14}C methods from simulated in situ experiments.

Abstract. In this work, we demonstrate the utilization of a compact, consumer-grade 360° camera for measuring the in-ice spectral angular radiance distribution. This novel technique allows for the instantaneous acquisition of all radiometric quantities at a given depth with a non-intrusive probe. This gives the opportunity to monitor the light field structure (mean cosines) from the atmosphere to the underlying ocean beneath ice. In this study, we report vertical profiles of the light field geometric distribution measured at two sites representative of distinct ice types: High Arctic multi-year ice and Chaleur Bay (Quebec, Canada) landfast first-year ice. We also propose a technique to empirically retrieve the depth-resolved inherent optical properties by matching simulated profiles of spectral irradiances calculated with the HydroLight radiative transfer model to the observed ones. As reported in other studies, the derived reduced scattering coefficients were high (641.57 m−1; 72.85 m−1) at the top (2 cm; 5 cm) for both sites (High Arctic; Chaleur Bay) and lower in the interior part of the ice (0.48–4.10 m−1; 0.021–7.79 m−1). Due to the inherent underdetermined nature of the inversion problem, we emphasize the importance of using a similarity parameter that considers both the absorption and the reduced scattering coefficients. Finally, we believe that this radiometric device, combined with the proposed inversion technique, will allow the research community to scale up the measurements of the inherent optical properties of different kinds of sea ice, making it possible to take better account of terrain variability in radiative transfer models.

Quantitative assessment of the UV effects on aquatic ecosystems requires an estimate of the in-water hyperspectral radiation field. Solar UV radiation in ocean waters is estimated on a global scale by combining extraterrestrial solar irradiance from the Total and Spectral Solar Irradiance Sensor (TSIS-1), satellite estimates of cloud/surface reflectivity, ozone from the Ozone Monitoring Instrument (OMI) and in-water chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) with radiative transfer computations in the ocean-atmosphere system. A comparison of the estimates of collocated OMI-derived surface irradiance with Marine Optical Buoy (MOBY) measurements shows a good agreement within 5% for different seasons. To estimate scalar irradiance at the ocean surface and in water, we propose scaling the planar irradiance, calculated from satellite observation, on the basis of Hydrolight computations. Hydrolight calculations show that the diffuse attenuation coefficients of scalar and planar irradiance with depth are quite close to each other. That is why the differences between the planar penetration and scalar penetration depths are small and do not exceed a couple of meters. A dominant factor defining the UV penetration depths is chlorophyll concentration. There are other constituents in water that absorb in addition to chlorophyll; the absorption from these constituents can be related to that of chlorophyll in Case I waters using an inherent optical properties (IOP) model. Other input parameters are less significant. The DNA damage penetration depths vary from a few meters in areas of productive waters to about 30–35 m in the clearest waters. A machine learning approach (an artificial neural network, NN) was developed based on the full physical algorithm for computational efficiency. The NN shows a very good performance in predicting the penetration depths (within 2%).

Abstract. This data paper outlines the development and the structure of a new synthetic dataset within an extended optical domain, encompassing inherent and apparent optical properties (IOPs and AOPs) alongside associated optically active constituents (OACs). Bio-optical modeling benefited from knowledge and data accumulated over the past 3 decades, enabling the imposition of rigorous quality standards and the definition of novel bio-optical relationships that are significant contributions on their own. Employing the HydroLight scalar radiative transfer equation solver, above-surface and submarine light fields between 350 and 800 nm at 1 nm steps were generated, facilitating algorithm development and assessment for present and forthcoming hyperspectral satellite missions. A smaller version of the dataset, delivered at 12 Sentinel-3 Ocean and Land Colour Instrument (OLCI) bands (400 to 753 nm), was also produced, targeting multispectral sensor algorithm research. Derived AOPs encompass an array of above- and below-surface reflectances, diffuse attenuation coefficients, average cosines, and Q factor. The dataset is distributed in 5000 files, each encapsulating a specific IOP scenario, ensuring sufficient data volume for each represented water type. AOPs are resolved across the complete range of solar and viewing zenith and azimuthal angles as per the HydroLight default quadrants, amounting to 1300 angular combinations. This comprehensive directional coverage caters to studies investigating signal directionality, which previously lacked sufficient reference data. The dataset is publicly available for anonymous retrieval via the FAIR repository Zenodo at https://doi.org/10.5281/zenodo.11637178 (Pitarch and Brando, 2024).

: The overall goal of this work is to understand in detail how various types of microbial and mineral particles and dissolved substances determine the inherent and apparent optical properties (IOPs and AOPs) of oceanic waters. In particular, we wish to quantify how variability in the detailed composition of oceanic water determines the variability in the IOPs (such as absorption and scattering coefficients) and AOPs (such as spectral reflectance and diffuse attenuation functions). This work is fundamental to the development of bio-geo-optical models for Case II waters, for which the presently available bio-optical models are known to fail.

The inherent and apparent optical properties (IOPs and AOPs) of seawater limit the performance of free-space optical (FSO), underwater wireless optical communication (UWOC), and imaging systems. Absorption, scattering, and downwelling irradiance are three such properties that influence system performance and often evolve independently. In situ measurements of multiple IOPs and AOPs would provide environmental sensing for fielded optical systems, but such comprehensive measurements are typically expensive or impractical. This effort analyzed existing oceanographic data sets to uncover wavelength-dependent correlations between IOPs, AOPs, test depths, and ocean depths. We then employed machine learning (ML) methods to predict the optical properties of diffuse attenuation (Kd) and backscatter (bb) using beam attenuation (c) and compared these results to ground-truth values. Predicted values of Kd and bb were well matched to their ground-truth data. Furthermore, we demonstrate ML-based Jerlov optical water type classification using beam attenuation as the optical data input. With our methods validated, we collected new optical data sets and processed them using our ML models. Results are promising and indicate future in situ classification and prediction capability. To highlight one practical application, we present a preliminary ML-enabled performance estimator for a generic FSO or UWOC system.