The variability of the remote sensing reflectance, Rrs, now routinely retrieved from ocean color (OC) and high spatial resolution sensors, is often used to characterize water variability due to changes in inherent optical properties of the water body. At the same time, Rrs is partially variable because of uncertainties in its retrieval in the process of atmospheric correction. Using data from SNPP-VIIRS and Landsat-8 OLI sensors, the contribution of the main components to the variance of Rrs due to its spatial variability is determined based on a model in which variances were considered proportional to the mean values of the corresponding components. It is shown that there is practically no spatial variability in the open ocean waters and water variability is proportional to the spatial resolution of the sensor in coastal waters. Variances due to surface effects, inaccuracies of aerosol models, and sunglint can contribute significantly to Rrs variance, which characterizes Rrs spatial variability, with variances due to the water variability itself often being significantly smaller.
Uncertainties in the retrieval of remote sensing reflectance, Rrs, from Ocean Color (OC) satellite sensors have a strong impact on performance of algorithms for the estimation of chlorophyll-a concentrations and inherent optical properties (IOPs). Uncertainties are highest in the blue bands, especially in coastal waters with low blue-band Rrs values. We recently showed that the main uncertainty contributions when observing at sun glint-optimized geometries are due to two components: variability of in-water parameters and skylight reflected from the water surface. Sunlight propagates to the water and back to the top of the atmosphere (TOA), capturing the instantaneous state of in-water conditions and sky light reflected from the wind-roughened wave facets. Both processes are averaged with the spatial resolution of the sensor. This results in the satellite measured TOA radiance spectrum, which is typically different from vector radiative transfer simulations that are based on the mean values of sea surface reflectance coefficient. Preliminary analysis shows that these two uncertainty components are spatially highly variable. Using the recently released provisional Aquatic Reflectance product for Landsat 8, we analyzed spatial scales of these components for multiple scenes in the open ocean and coastal waters at spatial resolutions ranging from 30 m to several kilometers.
A novel polarization camera (Teledyne DALSA) based on the Sony first polarization imaging sensor provides a high resolution broadband image of the object in 400-900 nm spectral range, where each pixel contains four subpixels bearing built-in linear polarizers oriented at the 0, 45, 90 and -45 degrees. With an additional lens it has a field of view (FOV) of about 40° degrees. The camera was equipped with a filter wheel containing five band-pass filters, thus providing multispectral, multi-angular high quality polarimetric data with high spatial resolution. The camera has undergone radiometric calibration and was used in various illumination conditions and water environments in NYC area. Spatial and temporal distribution of water and sky Stokes vector components were characterized at various wind speeds. Polarization images were also used for the estimation of wave slope statistics from the ocean surface. Results are compared with concurrent measurements of total and polarized radiances by a state-of-the-art snapshot hyperspectral imager, which simultaneously acquires spectra with 4 nm spectral resolution in the wavelength range of 450- 750nm, also with a 40° FOV for 20 - 60° viewing angles. A computer-controlled filter wheel was installed in front of the imager, allowing division-of-time Stokes vector images from the ocean surface. The combination of these two instruments allows to observe spectral and polarization properties of the wind-roughened ocean at high spatial and temporal resolution, with the goal of advancing our understanding of the air-water interface and in-water light propagation.
Results are presented for measurements of the total and polarized radiances from the ocean surface by a state-of-the-art snapshot hyperspectral imager, which simultaneously acquires spectra with 4nm spectral resolution in the wavelength range of 450-950nm within a 40° field-of-view. The imager does not require any along track movement and allows the continuous collection of hyperspectral imagery from stationary structures or slow moving platforms such as ships or helicopters. In addition, a computer controlled filter wheel is installed in front of the imager allowing for division-oftime Stokes vector images from the ocean surface. Experiments are aimed at the application of the multi-angular polarimetric measurements for the retrieval of water parameters in addition to the ones retrieved from traditional unpolarized methods. Several sets of measurements used in the analysis were acquired from ocean platform in the NYC area, Duck, NC and from shipborne observations in the Gulf of Mexico and along the Florida coast. Measurements made by the imager are compared with simulations using a vector radiative transfer code showing good agreement. Analysis of pixel-to-pixel variability of the total and polarized above water radiance for the viewing angles of 20°-60° in different wind conditions enable the estimation of uncertainties in measurements of these radiances in un-polarized and polarized modes for the whole spectral range, thus setting requirements for the quality of polarized measurements. Impacts of aerosols on spectral variations of both the radiance and the polarized terms of the Stokes vector are studied.
The choice of aerosol model in the atmospheric correction is critical in the process of the derivation of the water leaving radiances from the Ocean Color (OC) imagery for ocean monitoring. For the current sensors like MODIS, VIIRS and now OLCI atmospheric correction procedures include assumptions about the characteristics of atmospheric aerosols based on relative humidity and particle size distributions. At the sea level, SeaPRISM radiometric instruments which are part of the Aerosol Robotic Network (AERONET) make direct measurements of the water leaving radiances from the ocean, as well as observations of sky radiances from which aerosol parameters such as aerosol optical depth (AOD), fraction of fine and coarse aerosols and others are determined. The discrepancies between satellite and AERONET data are usually significant in coastal areas which are primarily due to the more complex atmospheres near the coast than in the open ocean. Using NASA SeaDAS software, characteristics of aerosols in atmospheric correction models for VIIRS and MODIS sensors are retrieved and compared with the ones from AERONET-OC data in terms of AOD, and remote sensing reflectance (Rrs) at the several AERONET-OC sites. The impact of the solar angles, scattering angles determined by the Sun-sensor geometry and wind speed on the differences in aerosols parameters are evaluated and correlated with the accuracies in the retrieval of the remote sensing reflectance spectra from ocean waters. Significant dependence of AOD on the wind speed is demonstrated which is most likely related to the modeling of the state of the ocean surface.
Results of measurements by a novel snapshot hyperspectral polarimetric imager are presented using several data sets acquired from ocean platforms. Based on the unique availability of the pixel-to-pixel total, sky and water leaving radiances at multiple wavelengths, variations of these parameters for wind-roughened surface are assessed and possible errors in measurements of these parameters are estimated. Measurements made by the imager are compared with coincident ones from the green-band SALSA Stokes vector imaging camera, a push-broom hyperspectral polarimetric imager operated by Naval Research Laboratory (NRL), and with simulations using a vector radiative transfer code, all demonstrating excellent agreement.
Imaging of underwater targets is challenging because of the significant attenuation of the propagating light field due to the absorption and scattering by water and suspended/dissolved matter. Some living and manmade objects in water have surfaces which partially polarize the light, whose properties can be used to camouflage or, conversely, to detect such objects. The attenuation of light by the intervening water (so-called veiling light) changes both the intensity and polarization characteristics at each pixel of the image, but does not contain any information about the target and contributes to image degradation and blurring. Its properties need to be understood in order to isolate the true optical signature of the target. The main goal of this study is to retrieve the polarization characteristics of the target from the image in different water environmental and illumination conditions by taking into account coincidentally measured inherent water optical properties (IOPs) during recent field campaigns outside the Chesapeake Bay and in New York Bight. Data, in the form of images and videos, were acquired using a green-band full-Stokes polarimetric video camera. Analysis of the acquired images show reasonable agreement in Stokes vector components with the measurements by the underwater polarimeter and modeled polarized signals. In addition, Stokes vector components of the veiling light were also estimated and compared with the models. Finally, retrieval of the attenuation coefficient for the light from the target is attempted from the measurements and compared with the results of the independent measurements of IOPs.
Data quality of the satellite sensors for ocean monitoring (Ocean Color –OC) like MODIS, VIIRS, MERIS, and now OLCI sensor on Sentinel-3a are often validated through matchups between normalized water leaving radiances nLw (or remote sensing reflectance Rrs) from satellite data and data from radiometric systems (SeaPRISMs) installed on ocean platforms and which are part of the NASA Aerosol Robotic Network (AERONET) and AERONET-OC networks. While matchups are usually good in open ocean waters, significant discrepancies are observed in coastal areas which are primarily due to the more complex atmospheres near the coast and therefore less accurate atmospheric correction. Satellite-derived water leaving radiances are determined by applying atmospheric correction procedures which include assumptions about the characteristics of atmospheric aerosols. At sea level, SeaPRISM makes direct measurements of nLw from the ocean, as well as observations of sky from which aerosol parameters such as aerosol optical thickness, single scattering albedo, fraction of fine and coarse aerosols, and others are determined. Using NASA SeaDAS software for OC satellite data processing, characteristics of aerosols in atmospheric correction models for VIIRS sensor are explicitly retrieved and compared with the ones from AERONET-OC data, primarily in terms of aerosol optical depth (AOD), thus characterizing the validity of the aerosol models and evaluating possible errors and reasons for discrepancies. Comparisons are presented for the coastal site at CCNY’s Long Island Sound Coastal Observatory (LISCO) and a less coastal WaveCIS Gulf of Mexico’ AERONET-OC site with variable water and atmospheric conditions.
We have examined, in earlier work, the relationship between naturally induced chlorophyll-a fluorescence and the underwater polarized oceanic light field. This shows the un-polarized fluorescence causes a reduction in the degree of polarization over the fluorescence spectral range. Theory shows that the peak of the reduction in polarization occurs at or near the fluorescence peak. Furthermore, it also shows that the magnitude of this reduction in degree of polarization can be related to both the magnitude of the fluorescence as well as the intensity of the underwater light field over the fluorescence spectral range. To examine this relationship in detail, a vector radiative transfer code (VRTE) for the coupled atmosphere-ocean system was employed for a variety of oligotrophic and eutrophic water conditions. The VRTE used measured inherent optical properties (IOPs) for these water conditions as inputs to simulate the complete elastic and inelastic components of the underwater light field, as well as the degree of linear polarization (DoLP) associated with it. These theoretical predictions were then compared with the results of DoLP measurements carried out using by our multiangular hyperspectral polarimeter. A comparison of the measured reduction in degree polarization of the underwater light field over the fluorescence spectral range, and the magnitude of the fluorescence causing it, confirmed the validity of our theoretical relationship, and the feasibility of determining the natural fluorescence existing in an underwater light field from polarization measurements.
Polarimetric characteristics of light from ocean water in combination with standard remote sensing reflectance provide important information about water constituents; they are useful in retrieval of additional water parameters like attenuation-to-absorption ratio and attenuation coefficients and/or establishing additional constraints for retrieval algorithms. The Stokes vectors of light above and below the water surface, which fully represent polarimetric characteristics of water leaving radiance, strongly depend on the particle size distribution and related Mueller matrices of water particulates. In this work we investigate the effect of various hydrosol mixtures of chlorophyllous particles on the polarized light field. The Stokes vectors of scattered light and the degree of polarization (DOP) are generated as outputs of vector radiative transfer simulations for various water compositions. Mie theory as well as T-matrix approaches are used for the generation of scattering matrices. The impact of their variability on the Stokes vectors of polarized light is analyzed.
Knowledge of the underwater light field is fundamental to determining the health of the world's oceans and coastal regions. For decades, traditional remote sensing retrieval methods that rely solely on the spectral intensity of the water-leaving light have provided indicators of marine ecosystem health. As the demand for retrieval accuracy rises, use of the polarized nature of light as an additional remote sensing tool is becoming necessary. For two weeks in December 2015, the NOAA NPP-VIIRS Calibration/Validation cruise continuously observed the polarized radiance of the ocean and the sky using a HyperSAS-POL system. Additionally, a full Stokes imaging polarimetric camera was used to acquire images and videos of the sea surface and sky during stations at coincident angles with HyperSAS-POL. Polarized remote sensing reflectance is computed for all viewing elevations present in the polarization images, and the results are compared to vector radiative transfer calculations.
Standard blue-green ratio algorithms do not usually work well in turbid productive waters because of the contamination of the blue and green bands by CDOM absorption and scattering by non-algal particles. One of the alternative approaches is based on the two- or three band ratio algorithms in the red/NIR part of the spectrum, which require 665, 708, 753 nm bands (or similar) and which work well in various waters all over the world. The critical 708 nm band for these algorithms is not available on MODIS and VIIRS sensors, which limits applications of this approach. We report on another approach where a combination of the 745nm band with blue-green-red bands was the basis for the new algorithms. A multi-band algorithm which includes ratios Rrs(488)/Rrs(551)and Rrs(671)/Rrs(745) and two band algorithm based on Rrs671/Rrs745 ratio were developed with the main focus on the Chesapeake Bay (USA) waters. These algorithms were tested on the specially developed synthetic datasets, well representing the main relationships between water parameters in the Bay taken from the NASA NOMAD database and available literature, on the field data collected by our group during a 2013 campaign in the Bay, as well as NASA SeaBASS data from the other group and on matchups between satellite imagery and water parameters measured by the Chesapeake Bay program. Our results demonstrate that the coefficient of determination can be as high as R2 > 0.90 for the new algorithms in comparison with R2 = 0.6 for the standard OC3V algorithm on the same field dataset. Substantial improvement was also achieved by applying a similar approach (inclusion of Rrs(667)/Rrs(753) ratio) for MODIS matchups. Results for VIIRS are not yet conclusive.
During two cruises in 2014, the polarized radiance of the ocean and the sky were continuously acquired using a HyperSAS-POL system. The system consists of seven hyperspectral radiometric sensors, three of which (one unpolarized and two polarized) look at the water and similarly three at the sky. The system autonomously tracks the Sun position and the heading of the research vessel to which it is attached in order to maintain a fixed relative azimuth angle with respect to the Sun (i.e. 90°) and therefore avoid the specular reflection of the sunlight. For the duration of both cruises, (NASA Ship Aircraft Bio-Optical Research (SABOR), and NOAA VIIRS Validation/Calibration), in situ inherent optical properties (IOPs) were continuously acquired using a set of instrument packages modified for underway measurement, and hyperspectral radiometric measurements were taken manually at all stations. During SABOR, an underwater polarimeter was deployed when conditions permitted. All measurements were combined in an effort to first develop a glint (sky + Sun) correction scheme for the upwelling polarized signal from a wind driven ocean surface and compare with one assuming that the ocean surface is flat.
Remote estimations of oceanic constituents from optical reflectance spectra in coastal waters are challenging because of the complexity of the water composition as well as difficulties in estimation of water leaving radiance in several bands possibly due to inadequacy of current atmospheric correction schemes. This work focuses on development of a multiband inversion algorithm that combines remote sensing reflectance measurements at several wavelengths in the blue, green and red for retrievals of the absorption coefficients of phytoplankton, color dissolved organic matter and nonalgal particulates at 443nm as well as the particulate backscatter coefficient at 443nm. The algorithm was developed, using neural networks (NN), and was designed to use as input measurements on ocean color bands matching those of the Visible Infrared Imaging Radiometer Suite (VIIRS). The NN is trained on a simulated data set generated through a biooptical model for a broad range of typical coastal water parameters. The NN was evaluated using several statistical indicators, initially on the simulated data-set, as well as on field data from the NASA bio-Optical Marine Algorithm Data set, NOMAD, and data from our own field campaigns in the Chesapeake Bay which represent well the range of water optical properties as well as chlorophyll concentrations in coastal regions. The algorithm was also finally applied on a satellite - in situ databases that were assembled for the Chesapeake Bay region using MODIS and VIIRS satellite data. These databases were created using in-situ chlorophyll concentrations routinely measured in different locations throughout Chesapeake Bay and satellite reflectance overpass data that coexist in time with these in-situ measurements. NN application on this data-sets suggests that the blue (412 and 443nm) satellite bands are erroneous. The NN which was assessed for retrievals from VIIRS using only the 486, 551 and 671 bands showed that retrievals that omitted the 671 nm band was the most effective, possibly indicating an inaccuracy in the VIIRS 671 band that needs to be further investigated.
The relationship between the degree of linear polarization (DoLP) and attenuation-to-absorption coefficients ratio (c/a) of the water from which the scattering coefficient is readily computed (b = c-a) for two main types of oceanic waters (Case I and II) was investigated using the vector radiative transfer simulation. It is found the for Case I waters that only the green channels of the spectrum can be used to retrieve the scattering coefficient of the water whereas blue and red channels are dominated by the pure water effects of either Rayleigh scattering or high water absorption showing no variability between DoLP and c/a. On the other hand, Case II waters showed a strong relationship between DoLP and c/a for all wavelength of light under study (440, 550, 665 nm). Those relationships have been parameterized for all possible viewing geometries (sensor zenith and azimuth relative to the Sun’s principle plane) and for varying Sun zenith angles. That relationship has been tested and validated against a dataset of in-situ measurements using a custom developed underwater polarimeter that measures the DoLP and an in-water package of instruments (WetLabs ac-s) that measure the absorption and the attenuation coefficients. Another polarimeter fixed on a platform in Long Island Sound at the LISCO station measures the DoLP of the light above water while a moored instrument package (WQM and C-star) that measures in-water optical properties have been used for a time serious validation.
The analysis of images of several underwater targets that exhibits different polarization properties measured using an underwater camera in various water conditions is presented. The measurements are compared with an imaging model which combines vector radiative transfer simulations by the RayXP program for the propagation of light in the atmosphere-interface-ocean system and the Monte Carlo simulations for the near horizontal imaging in the water. Modeling includes analysis of the vector point spread function (PSF) from the target and the contribution of the veiling light between the target and the camera.
Remote estimation of chlorophyll-a concentration [Chl-a] in the Chesapeake Bay from reflectance spectra is challenging because of the optical complexity and variability of the water composition as well as atmospheric corrections for this area. This work is focused on algorithms for near surface measurements. The performance and tuning of several well established global inversion algorithms that use the NIR and Blue-Green parts of the spectrum are analyzed together with recently proposed algorithm that use the Red-Green part of the spectrum. These algorithms are evaluated and tuned on our field data collected during summer 2013 field campaign in the in the Chesapeake Bay region . These data consist of a full range of water optical properties as well as chlorophyll concentrations and specific absorption spectra from in water samples.
We then compare these algorithms with a multiband retrieval algorithm that was developed using neural networks (NN) and which was trained on simulated data generated through bio-optical modeling typical for a broad range of coastal water parameters, including those known for the Chesapeake Bay. This NN algorithm was then applied to our field measurements and used to retrieve the phytoplankton absorption at 443nm which was then related to [Chl-a]. In this process, special attention was paid to field data consistency in terms of both measured reflectance and [Chl-a] values, to avoid undesirable biases and trends. All algorithm retrievals were finally evaluated by several statistical indicators to arrive at their relative merits and potential for further improvements and application to satellite data.
We present a method for the separation of the non-algal absorption coefficient into its independent components of dissolved species and non-algal particulate absorptions from remote sensing reflectance (Rrs) measurements in the visible part of the spectrum. This separation is problematic due to the similar absorption spectra of these substances. Due to this complication, we approach the problem by constructing a neural network which relates the remote sensing reflectance at the available MODIS visible wavelengths (412, 443, 488, 531, 547 and 667nm) with the ratio of the absorption coefficient of non-algal particulates to the absorption coefficient of dissolved species, thereby permitting analytical separation of the total non-algal absorption into particulate and dissolved components. The resulting synthetically trained algorithm is tested on simulated data as well as independently on the NASA Bio-Optical Marine Algorithm Data set (NOMAD). Very good agreement is obtained, with R2 values of 87% and 78% for the non-algal particulate and dissolved absorption components, respectively for the NOMAD. Finally, we apply the algorithm to MODIS data and present global distributions for these parameters.
Underwater imaging is challenging because of the significant attenuation of light due to absorption and scattering of light in water. Using polarization properties of light is one of the options for improving image quality. We present results of imaging of a polarized target in open ocean (Curacao) and coastal (NY Bight) waters. The target in the shape of a square is divided into several smaller squares, each of which is covered with a polarizing film with different polarization orientations or transmission coefficients was placed on a mirror and imaged under water by a green-band full-Stokes polarimetric video camera at the full range of azimuth angles against the Sun. The values of the Stokes vector components from the images are compared with the modeled image of the target using radiative transfer code for the atmosphere-ocean system combined with the simple imaging model. It is shown that even in clear water the impact of the water body on the polarized underwater image is very significant and retrieval of target polarization characteristics from the image is extremely challenging.
Using a dataset consisting of 9000 reflectance spectra simulated using HYDROLIGHT 5 for a broad range of observable natural water conditions, we have developed three neural networks (NNs) working in parallel to model the inverse problem for both oceanic and coastal waters. These NNs are used to relate the water leaving remote sensing reflectance (Rrs) at available MODIS visible wavelengths (412, 443, 488, 531, 547 and 667nm) to the phytoplankton (aph), non-phytoplankton particulate (adm), dissolved (ag) absorption and particulate backscattering (bbp) coefficients at 443nm. These reflectance derived parameters (aph(443), adm(443), ag(443), bbp(443)) are then combined with the measured reflectance values and used as input to a fourth NN, (IOP NN [Chl]), to derive chlorophyll concentration ([Chl]). Unlike NNs previously developed by us that were trained on a synthetic dataset and then tested on the NASA Bio-Optical Marine Algorithm Dataset (NOMAD), the (IOP NN [Chl]) network was both trained and tested solely on NOMAD. Although the inherent optical properties (IOP) can be derived from the optical signal through their direct relation to the Rrs, the relationship of [Chl] to IOP varies with location and season, and is therefore difficult to model globally. In order to demonstrate that the inclusion of derived IOP estimates along with radiance measurements can improve the retrieval of [Chl], we construct a neural network that is trained to derive [Chl] from reflectance measurements only We also compare our [Chl] product to that obtained from the current OC3 algorithm implemented by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). Finally, we apply our algorithm to MODIS data and present and analyze the global seasonal variability for all three parameters.
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