Water-leaving Radiance Research Articles

Satellite retrieval of the linear polarization components of the water-leaving radiance in open oceans.

Atmospheric correction (AC) of polarized radiances acquired by polarization satellite sensors, remains a challenge due to the complex radiative transfer processes of the coupled ocean-atmosphere system. In this study, we proposed an innovative polarized AC algorithm built on the near-infrared band (PACNIR) with an emphasis on the retrieval of the linear polarization components of the water-leaving radiance in clear open oceans. This algorithm was based on the black ocean assumption in the near-infrared band and fitted polarized radiance measurements along multiple observation directions with nonlinear optimized processing. Our retrieval algorithm notably inverted the linearly polarized components of the water-leaving radiance and aerosol parameters. Compared with that of the simulated linear polarization components of the water-leaving radiance via the vector radiative transfer model for the studied sea regions, the mean absolute error of the PACNIR-retrieved linearly polarized components (nQw and nUw) exhibited a magnitude of 10-4, while the magnitude of that of the simulated nQw and nUw data was 10-3. Moreover, the PACNIR-retrieved aerosol optical thicknesses at 865 nm exhibited a mean absolute percentage error of approximately 30% relative to in situ values obtained from Aerosol Robotic Network-Ocean Color (AERONET-OC) sites. The PACNIR algorithm could facilitate AC of the polarized data provided by the next generation of multiangle polarization satellite ocean color sensors.

Enhanced Estimate of Chromophoric Dissolved Organic Matter Using Machine Learning Algorithms from Landsat-8 OLI Data in the Pearl River Estuary

Chromophoric Dissolved Organic Matter (CDOM) plays a critical role in the carbon and biogeochemical cycles within aquatic ecosystems. Satellite imagery can be employed to determine aquatic CDOM concentrations, highlighting the need for effective and precise algorithms for this task. In this study, a cruise survey dataset containing CDOM absorption coefficients and water-leaving radiances in the Pearl River estuary (PRE) was utilized to develop machine learning algorithms for CDOM retrieval from Landsat-8 Operational Land Imager (OLI) observations. Based on OLI wavelength bands, five bands and six band-ratios were chosen as input parameters for the machine learning models. Six machine learning models were trained to develop CDOM algorithms, including Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The results indicated that, among the six machine learning models, the XGBoost algorithm performed best, with the highest R2 value of 0.9 and the lowest CDOM root mean square error (RMSE) of 0.37 m−1, outperforming empirical algorithms. The XGBoost algorithm identified B4/B1 as the most critical input parameter, contributing 71%, followed by B3/B2 with a 16% contribution, where B1, B2, B3, and B4 are the wavelength bands of the OLI. These two band-ratios accounted for most of the contributions, suggesting their significant role in CDOM retrieval from Landsat OLI images. By employing the developed XGBoost algorithm, CDOM spatial patterns at six instances were derived from Landsat-8 OLI image reflectance, illustrating CDOM variations in the PRE influenced by various factors. Further analysis revealed that, in the PRE, tides and winds are the primary driving forces behind the spatial and temporal variability of CDOM. At present, the exploration of employing machine learning algorithms to infer CDOM concentrations in this region remains relatively limited; therefore, with a higher R2 value, the machine learning model we established unveils fresh and novel results.

A novel three-band macroalgae detection index (TMI) for aquatic environments

ABSTRACT Overgrowth of algae is a serious threat for aquatic ecosystems. This threat has occurred frequently in recent decades due to increased anthropogenic activities. Thus, understanding how the aquatic environment promotes algae growth by comprehensive monitoring is essential for protecting and sustaining aquatic ecosystems. We propose a novel Three band Macroalgae Index (TMI) using the green, red and near-infrared bands of Landsat-8 Operational Land Imager (OLI) as a robust remote sensing indicator for detecting and monitoring algae overgrowth in aquatic ecosystems. The proposed index has been tested in different water quality conditions in different aquatic environments. These conditions always vary between different images even if the study area is the same, e.g. atmospheric aerosols, refraction of water-leaving radiance according to sun and view angles, and sun glint on the surface which is influenced by waves and currents. Also, the six study areas were selected because of their water quality differences, including open ocean and inland lakes, e.g. Great Salt Lake being hypersaline waters, Lake St. Claire regarded as moderately eutrophic and open ocean waters in English Channel. A cross-comparison among the TMI and nine existing algal bloom indices indicated the superior performance of TMI (90%–100%), with reference to similarity to the OC2-based-TSI classifications. The performance of the existing indices was inconsistent across varying environmental and water quality conditions. This might be attributed to several factors including, uncertainty of shortwave infrared band retrievals used by some indices over turbid waters, and dependence of those indices on specific geographical location and/or sensor. The TMI overcame these issues and is therefore an alternative index for algal bloom detection in different water types, as it uses the wavebands which are commonly available on many remote sensing systems.

AERONET-OC LWN Uncertainties: Revisited

Abstract The Ocean Color Component of the Aerosol Robotic Network (AERONET-OC) aims at supporting the assessment of satellite ocean color radiometric products with in situ reference data derived from automated above-water measurements. This study, applying metrology principles and taking advantage of recent technology and science advances, revisits the uncertainty estimates formerly provided for AERONET-OC normalized water-leaving radiances LWN. The new uncertainty values are quantified for a number of AERONET-OC sites located in marine regions representative of chlorophyll-a-dominated waters (i.e., Case 1) and a variety of optically complex waters. Results show uncertainties typically increasing with the optical complexity of water and wind speed. Relative and absolute uncertainty values are provided for the various sites together with contributions from each source of uncertainty affecting measurements. In view of supporting AERONET-OC data users, the study also suggests practical solutions to quantify uncertainties for LWN from its spectral values. Additionally, results from an evaluation of the temporal variability characterizing LWN at various AERONET-OC sites are presented to address the impact of temporal mismatches between in situ and satellite data in matchup analysis.

Automated Atmospheric Correction of Nanosatellites Using Coincident Ocean Color Radiometer Data

Here we present a machine-learning-based method for utilizing traditional ocean-viewing satellites to perform automated atmospheric correction of nanosatellite data. These sensor convolution techniques are required because nanosatellites do not usually possess the wavelength combinations required to atmospherically correct upwelling radiance data for oceanographic applications; however, nanosatellites do provide superior ground-viewing spatial resolution (~3 m). Coincident multispectral data from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (Suomi NPP VIIRS; referred to herein as “VIIRS”) were used to remove atmospheric contamination at each of the nanosatellite’s visible wavelengths to yield an estimate of spectral water-leaving radiance [Lw(l)], which is the basis for surface ocean optical products. Machine learning (ML) algorithms (KNN, decision tree regressors) were applied to determine relationships between Lw and top-of-atmosphere (Lt)/Rayleigh (Lr) radiances within VIIRS training data, and then applied to test cases for (1) the Marine Optical Buoy (MOBY) in Hawaii and (2) the AErosol RObotic Network Ocean Color (AERONET-OC), Venice, Italy. For the test cases examined, ML-based methods appeared to improve statistical results when compared to alternative dark spectrum fitting (DSF) methods. The results suggest that ML-based sensor convolution techniques offer a viable path forward for the oceanographic application of nanosatellite data streams.

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.

Aerosol Optical Properties above Productive Waters of Gorky Reservoir for Atmospheric Correction of Sentinel-3/OLCI Images

The main challenge that one has to face during the atmospheric correction (AC) of productive inland waters is the inability to correctly separate aerosol radiance from water-leaving radiance in the near-infrared range (NIR) bands. This leads both to incorrect estimates of the aerosol parameters and the remote-sensing reflectance (Rrs). For the Gorky Reservoir, where we are developing regional bio-optical models, the situation is complicated by the lack of field measurements of aerosol optical properties due to the significant remoteness of AERONET stations. The standard AC algorithms, as shown earlier, greatly overestimated the aerosol radiance in all spectral bands up to red bands during the period of intense cyanobacteria blooms, while the algorithm with a fixed aerosol optical depth (AOD) obtained in a clean water area gave encouraging results. Therefore, it was important to investigate the characteristics of the atmosphere above the reservoir and validate the proposed approach for regular use of Sentinel-3 imagery of the Gorky Reservoir. To solve these issues, regular in situ aerosol measurements using the handheld sun photometer SPM were performed. The measured AOD and the Angstrom exponent were compared with the estimates of these parameters from two Sentinel-3/OLCI Level-2 products, namely, Synergy (SYN) and Water Full Resolution products (OL_2_WFR). It was found that AOD and the Angstrom exponent from these standard products were overestimated by 2–3 times and almost 2 times in all cases. Atmospheric correction with fixed AOD, defined by measurements, allows us to completely get rid of negative Rrs, and its shapes and values became typical for the Gorky Reservoir. Despite the overestimation of AOD in traditional AC and its large variations in general, it was found that the minimum AOD spectrum is close to the measured spectrum. Therefore, the AOD spectra, which correspond to the two percentiles of the distribution, can be used for preliminary AC with a fixed AOD of the Sentinel-3/OLCI imaginary. The relative errors of the Rrs retrievals using the two percentile AOD compared to the measured AOD were 3–35% in the green and red bands of Sentinel-3/OLCI.

Automated Quality Control of AERONET-OC LWN Data

Abstract Quality control (QC) practices are a fundamental requirement for any measurement program targeting the delivery of high-quality data. In agreement with such a need, the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) includes a number of QC steps ensuring the delivery of normalized water-leaving radiance LWN spectra at incremental accuracy levels identified as level 1.0, level 1.5, and level 2.0. Currently, the final QC step allowing for rising level 1.5 LWN spectra to level 2.0 implies the execution of an expert-based procedure, which is extremely time consuming and naturally undergoes subjective decisions on dubious cases. These limitations solicited the development of an automated procedure, so-called , mimicking the steps supporting an expert analyst during the final QC of AERONET-OC LWN spectra. applies hierarchical tests to check (i) the relative consistency of level 1.5 LWN spectra (called candidates) with respect to LWN reference spectra (called prototypes) constructed using LWN spectra formerly and independently quality controlled; (ii) the absence of any pronounced spectral feature in portions of each LWN candidate spectrum expected to exhibit a regular shape; and additionally, when applicable, (iii) the temporal consistency of the LWN candidate spectrum with respect to close-in-time spectra as a criterion to further strengthen the quality of data. performance has been verified using LWN spectra from AERONET-OC measurement sites representative of various water types embracing oligotrophic/mesotrophic waters dominated by chlorophyll-a concentration and coastal waters exhibiting increasing levels of optical complexity. has shown an acceptance rate of AERONET-OC level 1.5 LWN candidate spectra varying between approximately 89% and 93% with agreement in the range of 88%–93% with respect to the LWN spectra independently quality controlled through the expert-based procedure. The additional capability of to rank the fully quality-controlled LWN spectra combining weights depending on the various tests, anticipates the possibility to best support applications with diverse accuracy needs. Finally, acceptance rates of for LWN prototype spectra built using level 1.5 data, an alternative to fully quality-controlled level 2.0, have shown values generally increased by less than 1%. This indicates the possibility to lessen the constraint implying the existence of reference level 2.0 LWN data for the relative-consistency test at the expense of a fairly low reduction in accuracy.

Developing and Using Empirical Bio-Optical Algorithms in the Western Part of the Bering Sea in the Late Summer Season

This study aimed to assess the applicability of global bio-optical algorithms for the estimation of chlorophyll-a (chl-a) concentration (C) and develop regional empirical bio-optical algorithms for estimating C and colored dissolved organic matter (CDOM) content (D) from ocean remote sensing reflectance spectra in the western part of the Bering Sea in the late summer period. The analysis took into account possible problems with the different relative contributions of phytoplankton and CDOM to water-leaving radiance and possible errors associated with the atmosphere correction procedure for ocean color satellite data. Shipborne remote sensing measurements obtained using an above-water hyperspectral ASD HandHeld spectroradiometer, satellite measurements collected via MODIS and VIIRS radiometers, and in situ measurements of C and D in seawater were used. The simulated values of the different multispectral satellite radiometers with daily or 2-day global coverage, obtained by applying the corresponding spectral response functions to ship hyperspectral data, were also analyzed. In this paper, a list of recommended regional bio-optical algorithms is presented. Recommendations are given depending on the possible quality of atmospheric correction and the purpose of use. To obtain more precise estimations of C, OC3/OC4-like algorithms should be used. If the atmosphere correction is poor, then use OC2-like algorithms in which spectral bands in the 476–539 nm range should be used to estimate C and bands near 443 nm to estimate D; however, in the last case, this will provide only the order of magnitude. To estimate more independent fields of C and D, it is necessary to use a spectral range of 501–539 nm for chl-a and bands near 412 nm in the case of modern satellite radiometers (e.g., OLCI or SGLI), for which this band is not the first. Additionally, we showed that global bio-optical algorithms can be applied with acceptable accuracy and similar recommendations.

A revision of NASA SeaDAS atmospheric correction algorithm over turbid waters with artificial Neural Networks estimated remote-sensing reflectance in the near-infrared

For atmospheric correction over turbid waters, due to non-negligible water-leaving radiance (Lw) in the near-infrared (NIR), measurements in the short-wave infrared (SWIR) are usually required to achieve reliable remote-sensing reflectance (Rrs). But several ocean color satellite sensors, such as the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and other small satellites, have no bands in the SWIR domain. We here present an atmospheric correction algorithm (termed as ACANIR-NN) based on NASA SeaDAS (version 7.5.3), which can achieve atmospheric correction seamlessly over clear and turbid waters, even for sensors having no spectral bands in SWIR. Specifically, ACANIR-NN uses estimated Rrs(NIR) from available Rrs in the visible bands with a specifically designed artificial Neural Networks to carry out atmospheric correction, and the performance of ACANIR-NN is evaluated over eight coastal locations having ground measurements by the Aerosol Robotic Network-Ocean Color (AERONET-OC) system. It is found that the Mean Absolute Percent Difference (MAPD) of Rrs retrievals by ACANIR-NN for this dataset is smaller by a factor of two or more than that by the standard SeaDAS algorithm (termed as ACANIR-bio) for each band, especially for Rrs(412) and Rrs(443), which is 7.5% and 7.7%, respectively, from ACANIR-NN, but they are 44.0% and 27.5% from ACANIR-bio. We further demonstrated the applicability of ACANIR-NN to SeaWiFS measurements over turbid waters, where consistent Rrs products were also obtained compared to that generated from the same-day MODerate resolution Imaging Spectrometer (MODIS) measurements using SWIR bands. These results indicate that ACANIR-NN can generate reliable Rrs over turbid coastal areas, as well as clear ocean waters, for sensors having no SWIR bands.

Evaluation of seven atmospheric correction algorithms for OLCI images over the coastal waters of Qinhuangdao in Bohai Sea

Atmospheric correction (AC) over coastal waters is always critical in ocean color remote sensing. The performance of the AC algorithms determines the reliability of the retrieval of the water-leaving radiance and subsequent bio-optical parameters. In this study, the seven AC algorithms for the Ocean and Land Color Instrument (OLCI) were evaluated with an in situ dataset over the coastal waters of Qinhuangdao in the Bohai Sea, China: the Baseline Atmospheric Correction (BAC), the Case 2 Regional Coast Color Atmospheric Correction (C2RCC) and its alternative version (C2RCC-AltNets), the Management Unit Mathematical Models (MUMM), the polynomial-based algorithm applied to MERIS (POLYMER), the image correction for atmospheric effects (iCOR), and ACOLITE for Dark Spectrum Fitting (ACOLITE-DSF). For OLCI-derived remote sensing reflectance, Rrs(λ), where λ is light wavelength, on the respective match-ups, all algorithms except ACOLITE-DSF had higher accuracy in the wavelength range from 490 nm to 560 nm, with the absolute percentage difference (APD) < 43%. On the common match-ups of all algorithms, the performance of seven AC algorithms was better than that of the respective match-ups. POLYMER obtained the best performance in visible bands, with APD less than 24.04% and 17.13% for respective and common match-ups, respectively. In terms of blue/green band ratios, Rrs(490)/Rrs(560) obtained by all algorithms except C2RCC performed better than Rrs(442)/Rrs(560). POLYMER performed best in blue/green band ratios, with the APD of 10.38% and 5.51% for Rrs(442)/Rrs(560) and Rrs(490)/Rrs(560), respectively. Using a scoring scheme based on all statistical parameters, POLYMER scored the highest. ACOLITE-DSF represented the worst accuracy for all evaluations over our in situ dataset.

Validation of ocean color remote sensing reflectance data: Analysis of results at European coastal sites

From its initial measurements 20 years ago, the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) has produced large validation data sets to assess the ocean color satellite data records. This study, applied to the standard atmospheric correction algorithm l2gen of the National Aeronautics and Space Administration, analyses the populations of residuals (differences between matching satellite and field data) of remote sensing reflectance RRS and, secondarily, aerosol optical thickness τa, and their validation statistics associated with data collected at seven AERONET-OC sites located in European coastal regions for six satellite missions. Validation statistics do not appear particularly sensitive to observation conditions, represented by viewing and solar geometry, aerosol optical thickness τa, Ångström exponent or water properties indexed by RRS, the most obvious exceptions being a systematic increase of the residuals for high solar zenith angles for all missions and sites. Residuals of RRS and τa tend to be inversely correlated as deviations in aerosol radiance and water-leaving radiance compensate each other. The spectra of a RRS uncertainty estimate σ decrease in a monotonous manner from 0.7 to 1.5 10−3 sr−1 for wavelengths around 412nm to 0.1–0.35 10−3 sr−1 around 667nm, with σ usually relatively high for waters associated with high amplitudes of RRS. At 412nm, σ for τa is mostly in the interval 0.03–0.04. For RRS, variations across missions of σ or of the relative differences between satellite and field data are overall smaller than variations across sites, which suggests that the products derived from various missions are rather consistent at a given location when processed with the considered algorithm. For a given mission, RRS residuals are well correlated between bands (inter-band correlation coefficient mostly larger than 0.5), which has implications on the propagation of uncertainties through bio-optical algorithms. For a given wavelength, residuals are correlated between missions to various degrees (most often with inter-mission correlation coefficients larger than 0.5). Besides implications as far as uncertainties of multi-mission merged products are concerned, this inter-mission correlation among residuals suggest that the residuals are not random and could be reduced.

Spatial Variability of In Situ Above-Water Reflectance in Coastal Dynamic Waters: Implications for Satellite Match-Up Analysis

The validation of ocean color satellite retrievals generally relies on analyzing match-ups between in situ measurements and satellite retrievals. These analyses focus on the quality of the satellite data, however, of the same importance is the quality of the in situ data. Here, we present the spatial variability of in situ above-water reflectance (Rrs(0+)) within the spatial resolution of different ocean color satellites—300, 900, 1500, and 3000 m spatial resolutions, mimicking Sentinel 3 OLCI and MODIS-Aqua satellites, and possible 3 × 3 and 5 × 5 windows. Radiometric data was acquired with autonomous radiometric sensors installed in the British Columbia Ferry Services Inc. vessel “Queen of Alberni” from May to September 2019, crossing the optically dynamic waters of the Strait of Georgia, Canada. The dataset followed optimal geometry of acquisition and processing, including corrections for skylight radiance signals, ship superstructure, the non-isotropic distribution of the water-leaving radiances, and quality control. A total of 33,073 spectra at full resolution, corresponding to 10 days, were considered for the analysis presented here. The results showed that, overall, the subpixel variability increased as the spatial resolution of the sensor or the window size increased, mainly in a linear fashion. Specifically, spatial variability of Rrs(0+) was the largest (∼18% and 68% for 900 and 3000 m pixel resolution, respectively) in Near Field Plume Interface waters, followed by in the Ocean Water Interface (∼28% and 35%, respectively), thus indicating spatial heterogeneity of interface waters. Further, we found that the estuarine waters showed higher subpixel Rrs(0+) variability (∼8% and 16% for 900 and 3000 m, respectively) compared with plume and oceanic waters. We showed that the high spatial variability in Rrs(0+) was primarily associated with the spatial dynamics of the optical water constituents, thus limiting the use of these datasets as Fiducial Reference Measurements and for validation of satellite-derived atmospherically corrected reflectance. We suggest that spatial variability of the in situ Rrs(0+) should also be considered in the selection criteria for good match-up data, especially for data acquired in coastal dynamic systems. As a result, it will advocate for the exclusion of interface or transition water pixel grids in order to avoid compromising the statistical result of satellite validation.

Assessment of VIIRS on the Identification of Harmful Algal Bloom Types in the Coasts of the East China Sea

Visible Infrared Imaging Radiometer Suite (VIIRS) data were systematically evaluated and used to detect harmful algal bloom (HAB) and classify algal bloom types in coasts of the East China Sea covered by optically complex and sediment-rich waters. First, the accuracy and spectral characteristics of VIIRS retrieved normalized water-leaving radiance or the equivalent remote sensing reflectance from September 2019 to October 2020 that were validated by the long-term observation data acquired from an offshore platform and underway measurements from a cruise in the Changjiang Estuary and adjacent East China Sea. These data were evaluated by comparing them with data from the Moderate-Resolution Imaging Spectroradiometer. The bands of 486, 551, and 671 nm provided much higher quality than those of 410 and 443 nm and were more suitable for HAB detection. Secondly, the performance of four HAB detection algorithms were compared. The Ratio of Algal Bloom (RAB) algorithm is probably more suitable for HAB detection in the study area. Importantly, although RAB was also verified to be applicable for the detection of different kinds of HAB (Prorocentrum donghaiense, diatoms, Ceratium furca, and Akashiwo sanguinea), the capability of VIIRS in the classification of those algal species was limited by the lack of the critical band near 531 nm.

Evaluating Atmospheric Correction Algorithms Applied to OLCI Sentinel-3 Data of Chesapeake Bay Waters

Satellite remote sensing permits large-scale monitoring of coastal waters through synoptic measurements of water-leaving radiance that can be scaled to relevant water quality metrics and in turn help inform local and regional responses to a variety of stressors. As both the incident and water-leaving radiance are affected by interactions with the intervening atmosphere, the efficacy of atmospheric correction algorithms is essential to derive accurate water-leaving radiometry. Modern ocean color satellite sensors such as the Ocean and Land Colour Instrument (OLCI) onboard the Copernicus Sentinel-3A and -3B satellites are providing unprecedented operational data at the higher spatial, spectral, and temporal resolution that is necessary to resolve optically complex coastal water quality. Validating these satellite-based radiance measurements with vicarious in situ radiometry, especially in optically complex coastal waters, is a critical step in not only evaluating atmospheric correction algorithm performance but ultimately providing accurate water quality metrics for stakeholders. In this study, a regional in situ dataset from the Chesapeake Bay was used to evaluate the performance of four atmospheric correction algorithms applied to OLCI Level-1 data. Images of the Chesapeake Bay are processed through a neural-net based algorithm (C2RCC), a spectral optimization-based algorithm (POLYMER), an iterative two-band bio-optical-based algorithm (L2gen), and compared to the standard Level-2 OLCI data (BAC). Performance was evaluated through a matchup analysis to in situ remote sensing reflectance data. Statistical metrics demonstrated that C2RCC had the best performance, particularly in the longer wavelengths (&gt;560 nm) and POLYMER contained the most clear day coverage (fewest flagged data). This study provides a framework with associated uncertainties and recommendations to utilize OLCI ocean color data to monitor the water quality and biogeochemical dynamics in Chesapeake Bay.

Assessment of OLCI-A and OLCI-B radiometric data products across European seas

The Ocean and Land Color Instruments (OLCI) operated onboard the Copernicus Sentinel-3 satellites are providing globally distributed Ocean Color Radiometry (OCR) data products of relevance for environmental and climate applications. This work summarizes results on the assessment of fundamental OCR data from the Operational Baseline 3 Collection OL_L2M.003.01 of OLCI-A and OLCI-B onboard Sentinel-3A and Sentinel-3B, respectively. Evaluated products are the satellite derived normalized water-leaving radiance LWN(λ), aerosol optical depth at 865 nm τa(865) and Ångström exponent α determined in the near-infrared spectral region. The analyses were performed relying on in situ reference data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) from sites representative of diverse water types. The comparison of OLCI-A and OLCI-B with AERONET-OC LWN(λ) for oligotrophic/mesotrophic waters shows cross-mission consistent spectral median percent differences (i.e., biases) varying within ±6% at the blue-green center-wavelengths. The analysis of data from regions characterized by optically complex waters, however, displays systematic negative biases for both OLCI-A and OLCI-B further increasing for waters dominated by chromophoric dissolved organic matter, thus suggesting a dependence of the atmospheric correction on water type. The direct inter-comparison of OLCI-B and OLCI-A LWN(λ) from the Tandem Phase characterized by Sentinel-3B and Sentinel-3A flying 30 s apart on the same orbit, shows spectral median percent differences lower than ±1% in the 412–560 nm interval, of approximately +5% at 620 and 665 nm, and −7% at 400 nm. However, outside the Tandem Phase, the inter-comparison of OLCI-B and OLCI-A data products indicates large and systematic differences explained by a notable dependence on the viewing angle. The evaluation of τa(865) and α across different geographic regions exhibits overestimated values between +48 and + 79% for the former and underestimated values between −28% and − 41% for the latter. A complementary evaluation of OCR data products from the Visible Infrared Imager Radiometer Suite on board the Suomi National Polar-orbiting Partnership (VIIRS-S), proposed as a further indirect term of reference for OLCI-A and OLCI-B data, shows large underestimates of LWN(λ) with respect to the in situ reference data in the various water types at 410 nm. Nevertheless, opposite to OLCI-A and OLCI-B data products, absolute differences between VIIRS-S and in situ reference data do not reveal any large or systematic dependence on water type and satellite viewing angle. Overall results suggest the need for further developing the OLCI-A and OLCI-B atmospheric correction, possibly improving the capability to identify aerosol types and to model scattering processes.

Semi-Empirical Models for the Bidirectional Water-Leaving Radiance: An Analysis of a Turbid Inland Lake

When a precise quantitative analysis of satellite measurements over bodies of water is required, the bidirectional effects of water-leaving radiance must be considered. The bidirectional reflectance distribution function (BRDF) is used to estimate the directional dependency of the radiance. Previous research on BRDF has focused on oceanic waters; few studies on turbid inland waters have been conducted. In this article, using multi-angle MISR measurements, five semi-empirical BRDF models (MAG2002, Lee2004, Park-Ruddick 2005, Woerd-Pasterkamp2008, and Lee2011) were quantitatively compared, and an adaptive algorithm was proposed over a typical turbid inland lake, Taihu Lake, China. Our results reveal the following: 1) the Woerd-Pasterkamp2008 and Lee2011 models provide the best fits with correlation coefficients greater than 0.8; 2) when prior modeling parameters were used, the Lee2011 model was still the most accurate with RMSEs less than 1.1%, while the accuracy of the Woerd-Pasterkamp2008 model varied; and 3) the use of an adaptive algorithm including an empirical rule based on the ratio ofbb/aimproved the accuracy. The results provide a theoretical basis for BRDF models and BRDF effects over inland Case II waters. They also providea prioriknowledge for future studies on water constituents and the quantitative inversion of atmospheric parameters.

Effect of the Vertical Distribution of Absorbing Aerosols on the Atmospheric Correction for Satellite Ocean Color Remote Sensing

The vertical distribution of absorbing aerosols has nonnegligible impact on the atmospheric correction of satellite ocean color remote sensing, especially for the water-leaving radiance retrieval at blue and ultraviolet bands. In this study, we investigated the impact of the vertical distribution of absorbing aerosols on the satellite-measured radiance at the top of the atmosphere (TOA) and the retrieved water-leaving radiance. First, the global occurrence frequency of absorbing aerosols was mapped, and it was found that the annual averaged occurrence frequencies of absorbing aerosols were >30% over the coasts of the Sahara and Arabian Desert, China, South-Central Africa, and the Indian Peninsula. Second, a new aerosol classification algorithm was developed to establish absorbing aerosol optical models based on AERosol RObotic NETwork (AERONET) site observations. Finally, the effects of the vertical distribution of absorbing aerosols on the upward radiance at the TOA at 412 nm and the retrieved water-leaving radiance were evaluated. The results showed that the influence of the vertical distribution on the TOA radiance could be up to 8% for dust and 10% for fine-dominated absorbing aerosols (FDAs), which was comparable with the influence of aerosol optical depth. Not considering absorbing aerosols during atmospheric correction might produce <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 4$ </tex-math></inline-formula> %–10% errors in the water-leaving radiance retrieval at 412 nm over turbid waters under the assumption of a Gaussian distribution. Simplified exponential and two-layer atmospheric vertical distribution models can lead to errors of water-leaving radiance retrieval up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 12$ </tex-math></inline-formula> %–15% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 30$ </tex-math></inline-formula> %–40%, respectively. Overall, the imperfect vertical distribution assumption and aerosol model selection might induce uncertainty in water-leaving radiance retrieval from 10% to 80% under absorbing aerosol conditions.

River Bathymetry Retrieval From Landsat-9 Images Based on Neural Networks and Comparison to SuperDove and Sentinel-2

The Landsat mission has kept an eye on our planet, including water bodies, for 50 years. With the launch of Landsat-9 and its onboard Operational Land Imager 2 (OLI-2) in September 2021, more subtle variations in brightness (14-bit dynamic range) can be captured than previous sensors in the Landsat series (e.g., 12-bit Landsat-8). The enhanced radiometric resolution of OLI-2 appeals to the aquatic remote sensing community because the instrument might be capable of resolving smaller differences in water-leaving radiance. This study evaluates the potential to map river bathymetry from Landsat-9 imagery. We employ a neural network (NN)-based regression model for bathymetry retrieval and compare the results with optimal band ratio analysis (OBRA). The effect of Landsat-9 pan-sharpening on depth retrieval is also examined. In addition, we perform an intersensor comparison with Sentinel-2 and newly available 8-band SuperDoves from the PlanetScope constellation. Depth retrieval results from the Colorado and Potomac Rivers imply that Landsat-9 provided more accurate bathymetry across a range of depths up to 20 m, particularly when pan-sharpened. Downsampling the SuperDove data improved bathymetry retrieval due to enhanced signal-to-noise ratio, most notably in deep waters (maximum detectable depth increased from &#x223C;15 to &#x223C;20 m). Similarly, the enhanced spectral resolution of 8-band SuperDoves improved depth retrieval relative to 4-band Doves. The NN-based model outperformed OBRA by incorporating more spectral information.

Ocean Color Hyperspectral Remote Sensing With High Resolution and Low Latency—The HYPSO-1 CubeSat Mission

Sporadic ocean color events with characteristic spectra, in particular algal blooms, call for quick delivery of high-resolution remote sensing data for further analysis. Motivated by this, we present the mission design for HYPerspectral Smallsat for Ocean observation (HYPSO-1), a 6U CubeSat at 500 km orbital altitude hosting a custom-built pushbroom hyperspectral imager with wavelengths 387–801 nm at 3.33 nm bandpass and a swath width of 70 km. The imager’s expected signal-to-noise ratio is characterized for typical open ocean water-leaving radiance which can be flexibly increased by binning pixels. Using geometric principles, the satellite shall execute a slew maneuver during a scan to induce greater overlap in the pixels with a goal to enable better than 100 m spatial resolution. Since high-dimensional hyperspectral data need to be transmitted over limited space-to-ground communications, we have designed a modular FPGA-based onboard image processing architecture that significantly reduces the data size without losing important spatial-spectral information. We justify the concept with a simulated scenario where HYPSO-1 first collects numerous hyperspectral images of a 40 km by 40 km coastal area in Norway and aims to immediately transfer these to nearby ground stations. Using CCSDS123 lossless compression, it takes about one orbital revolution to obtain the complete data product when considering overhead in satellite bus communications and less than 10 min without the overhead. It is shown that even better latency can be achieved with more advanced onboard processing algorithms.