The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, launched 8 February 2024, carries the Ocean Color Instrument (OCI), a hyperspectral scanning radiometer providing continuous spectral coverage from 315 to 895 nm at 2.5 nm sampling. This study presents a post-launch validation and requirements verification of the OCI-derived remote-sensing reflectance Rᵣₛ(λ) (Reprocessing Version 3.2, V3.2) using 932 quality-controlled satellite-to-in situ matchups drawn from three complementary sources: AERONET-OC fixed-platform radiometers (N = 743), SeaBASS hyperspectral field campaigns (N = 119), and SeaBASS multispectral campaigns (N = 70), spanning March 2024 to May 2026. The OCI water-leaving reflectance ρw(λ) [unitless] = π × Rᵣₛ(λ) is examined against the threshold and baseline requirements specified in the PACE Science Requirements Document (SRD) Table 2 in terms of the 50th-percentile absolute error |Δρw| over deep ocean (depth > 1000 m). We found that ρw(λ) at every assessed wavelength (350–710 nm) satisfies the absolute baseline criterion in deep water; bootstrap resampling confirms these assessments are statistically robust at all but the 443 and 490 nm bands, where the small deep-water sample (N ≈ 52) limits precision. Across all matchups, reduced-major-axis slopes within approximately ±3% of unity are achieved through 490–620 nm (Pearson r = 0.95–0.98), where OCI exhibits a small negative bias: the median signed difference Δₘ ranges from -2.1 to -0.5 × 10⁻⁴ sr⁻¹ and the median signed percent difference ψₘ from -3 to -8% across the green. Relative agreement in the violet-blue spectral range (412–443 nm; median absolute percentage difference |ψ|ₘ = 17–23% across all matchups including coastal, and 6.6–6.8% for deep waters) and red (665 nm; 29%) remains the principal gap, driven by residual atmospheric-correction and calibration uncertainty where the ocean signal is small; the negative sign of ψₘ throughout the visible indicates a systematic low bias rather than random scatter. Spectral-shape fidelity, assessed through the second derivative of Rᵣₛ, is high across the visible (Pearson r = 0.95–0.96 at 443–665 nm) and degrades only in the UV. Within the available deep-water matchup sample, OCI V3.2 Rᵣₛ is consistent with the SRD absolute baseline at all evaluated wavelengths.

Shaanxi Province is an important region for implementing the strategy of ecological conservation and high-quality development of the Yellow River Basin.Based on remote sensing data of vegetation growth,combined with meteorological raster data and digital elevation model data,we used trend analysis,partial correlation analysis,coefficient of variation,residual analysis,and relative impact analysis methods to examine the spatial-temporal varia-tion and driving factors of vegetation growth in the Yellow River Basin of Shaanxi Province during 2001-2020.The results showed that both the normalized difference vegetation index(NDVI)and gross primary productivity(GPP)exhibited a significant upward trend,with a growth rate of 0.066·(10 a)-1 and 133.610 g C·m-2·(10 a)-1,respectively.Spatially,78.0％and 92.1％of the areas showed significant increases in NDVI and GPP,respectively,with stable vegetation growth in most areas.NDVI and GPP initially decreased and then increased with increasing elevation,and peaking at slopes greater than 20°.Vegetation growth on the shady slope was slightly better than on the sunny slope.Both showed the highest growth rates at elevations of 750-1250 m and slopes of 2°-10°.The NDVI growth rate was greater on the west,southwest,and east slopes,while the GPP change trends were similar across different slope aspects.The areas where NDVI was positively correlated and negatively correlated with ave-rage temperature were approximately equal in size.About 17.0％of the area was significantly positively correlated with precipitation,and 5.6％was significantly negatively correlated with sunshine hours.The spatial distribution of GPP showed significantly positive correlation areas of 6.1％with average temperature and 12.3％with precipitation,with scattered significant correlation areas for sunshine hours.86.3％of the area showed an improvement in vegeta-tion growth driven by both climate change and human activities.In regions with enhancing vegetation condition,human activities had a relatively positive impact on vegetation growth,accounting for 84.5％,especially in the core areas of the project of returning farmland to forest and grassland.In regions with degradation of vegetation,areas where the relative impact of human activity exceeded 80％accounted for nearly 30％,primarily concentrated in the urban agglomeration of Guanzhong Plain.

We previously established a derivative-based approach to generate a pixel-level spectral error covariance matrix in satellite-retrieved remote sensing reflectance, ∑Rrs. However, one practical issue is the delivery of the products without increasing the file size by an order of magnitude or more, considering that for N sensor spectral bands, there are N × (N+1)/2 covariance matrix elements to be specified at each pixel. The issue becomes more pertinent for hyperspectral imaging spectroradiometers such as the Ocean Color Instrument (OCI) on NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem mission (PACE), which has 286 bands, resulting in ∼40,000 unique elements in ∑Rrs per pixel that would lead to a ∼60 GB Level-2 file for one 5-min granule. As a first step to tackle the issue, we took OCI and Moderate Resolution Imaging Spectroradiometer (MODIS) data to explore the possibility of approximating ∑Rrs using a third-degree polynomial, thereby decreasing the memory overhead to 4×N numbers. We found that ∑Rrs derived from the polynomial fitting matches well with the original value, with the difference smaller than 5%. We then compared the relative uncertainty in two derived ocean color data products (chla and Kd(490)) calculated using the original fully computed ∑Rrs and then using the polynomial model approximation for ∑Rrs, finding the absolute difference between the two approaches to be smaller than 0.5%. These evaluations suggest the polynomial approximation of ∑Rrs is suitable without degrading the scientific quality. By including the coefficients derived from polynomial fitting instead of the full error covariance matrix, a typical 5-min Level-2 file for OCI decreases from ∼60 GB to a more practical ∼1.7 GB.

Spectral remote sensing reflectance, R rs ( λ ) (sr -1 ), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved R rs . Furthermore, since the associated algorithms require R rs at multiple spectral bands, the spectral (i.e., band-to-band) error covariance in R rs is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into R rs , as retrieved using NASA’s multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in R rs . The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in R rs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chl a , mg/m 3 ) and diffuse attenuation coefficient of downwelling irradiance at 490 nm ( K d (490), m -1 ). Accounting for the error covariance in R rs generally reduces the estimated relative uncertainty in chl a by ∼1-2% (absolute value) in waters with chl a < 0.25 mg/m 3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chl a > 0.35 mg/m 3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For K d (490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in R rs is further verified through forward-calculating chl a from MODIS-retrieved and in situ R rs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chl a can be achieved over deep ocean waters (chl a ≤ 0.1 mg/m 3 ). While the derivative-based approach generates reasonable error covariance in R rs , some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.

Ocean color (OC) remote sensing requires compensation for atmospheric scattering and absorption (aerosol, Rayleigh, and trace gases), referred to as atmospheric correction (AC). AC allows inference of parameters such as spectrally resolved remote sensing reflectance (Rrs(λ);sr-1) at the ocean surface from the top-of-atmosphere reflectance. Often the uncertainty of this process is not fully explored. Bayesian inference techniques provide a simultaneous AC and uncertainty assessment via a full posterior distribution of the relevant variables, given the prior distribution of those variables and the radiative transfer (RT) likelihood function. Given uncertainties in the algorithm inputs, the Bayesian framework enables better constraints on the AC process by using the complete spectral information compared to traditional approaches that use only a subset of bands for AC. This paper investigates a Bayesian inference research method (optimal estimation [OE]) for OC AC by simultaneously retrieving atmospheric and ocean properties using all visible and near-infrared spectral bands. The OE algorithm analytically approximates the posterior distribution of parameters based on normality assumptions and provides a potentially viable operational algorithm with a reduced computational expense. We developed a neural network RT forward model look-up table-based emulator to increase algorithm efficiency further and thus speed up the likelihood computations. We then applied the OE algorithm to synthetic data and observations from the moderate resolution imaging spectroradiometer (MODIS) on NASA's Aqua spacecraft. We compared the Rrs(λ) retrieval and its uncertainty estimates from the OE method with in-situ validation data from the SeaWiFS bio-optical archive and storage system (SeaBASS) and aerosol robotic network for ocean color (AERONET-OC) datasets. The OE algorithm improved Rrs(λ) estimates relative to the NASA standard operational algorithm by improving all statistical metrics at 443, 555, and 667 nm. Unphysical negative Rrs(λ), which often appears in complex water conditions, was reduced by a factor of 3. The OE-derived pixel-level Rrs(λ) uncertainty estimates were also assessed relative to in-situ data and were shown to have skill.

The spectral distribution of marine remote sensing reflectance, Rrs, is the fundamental measurement of ocean color science, from which a host of bio-optical and biogeochemical properties of the water column can be derived. Estimation of uncertainty in these derived properties is thus dependent on knowledge of the uncertainty in satellite-retrieved Rrs (uc(Rrs)) at each pixel. Uncertainty in Rrs, in turn, is dependent on the propagation of various uncertainty sources through the Rrs retrieval process, namely the atmospheric correction (AC). A derivative-based method for uncertainty propagation is established here to calculate the pixel-level uncertainty in Rrs, as retrieved using NASA's multiple-scattering epsilon (MSEPS) AC algorithm and verified using Monte Carlo (MC) analysis. The approach is then applied to measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, with uncertainty sources including instrument random noise, instrument systematic uncertainty, and forward model uncertainty. The uc(Rrs) is verified by comparison with statistical analysis of coincident retrievals from MODIS and in situ Rrs measurements, and our approach performs well in most cases. Based on analysis of an example 8-day global products, we also show that relative uncertainty in Rrs at blue bands has a similar spatial pattern to the derived concentration of the phytoplankton pigment chlorophyll-a (chl-a), and around 7.3%, 17.0%, and 35.2% of all clear water pixels (chl-a ≤ 0.1 mg/m3) with valid uc(Rrs) have a relative uncertainty ≤ 5% at bands 412 nm, 443 nm, and 488 nm respectively, which is a common goal of ocean color retrievals for clear waters. While the analysis shows that uc(Rrs) calculated from our derivative-based method is reasonable, some issues need further investigation, including improved knowledge of forward model uncertainty and systematic uncertainty in instrument calibration.

Water in oil (WO) and oil in water (OW) emulsions from marine oil spills have different physical properties, volume concentrations, and spectral characteristics. Identification and quantification of these different types of oil emulsions are important for oil spill response and post-spill assessment. While the spectral characteristics of WO and OW emulsions have been presented in previous studies including Part I of this series, their application to airborne and satellite imagery is further demonstrated here. Using AVIRIS and Landsat observations, we firstly show that false color Red-Green-Blue composite images from Landsat-like sensors (R: 1677 nm, G: 839 nm, B: 660 nm) are effective in differentiating WO and OW emulsions as they show reddish and greenish colors, respectively, in such composite images. This is a consequence of the relative difference in the reflectance of WO and OW emulsions at 1677 and 839 nm, which is not impacted by the presence of medium-strength sunglint or the surface heterogeneity within medium-resolution pixels (e.g., 30 m). Based on image statistics, a decision tree method is proposed to classify oil type, and oil quantification is further attempted, with results partially validated through spectral analysis and spatial coherence test. The numerical mixing experiments using AVIRIS pixels further indicate that the SWIR bands might be used to develop linear unmixing models in the future once the coarse-resolution oiled pixels are first classified to WO and OW types, and 1295 nm is the optimal wavelength to perform spectral unmixing of mixed coarse-resolution pixels.

The ability to detect oil spills remotely is important in marine environmental monitoring. The optical polarization remote sensing has the unique advantage of inversion of refractive index of spilled oils which is the key parameter for calculation of sunglint reflectance. Compared to nonpolarization optical image, the degree of linear polarization (DOLP) of spilled oil’s sunglint depends on the refractive index and viewing angles but not on the surface roughness. Accurate correction of sunglint reflectance can promote optical estimation of spilled oils. In this article, a polarized optical model was used to calculate equivalent refractive index of Deepwater Horizon (DWH) spilled oils using space-borne Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) images covering Gulf of Mexico (GOM) in 2010. When the angle ( $\theta _{m}$ ) between the direction of the flat surface specular reflection and that of observation is less than 20°, the PARASOL-derived and modeled DOLPs agree well, and the atmospheric polarization effects can be neglected. The equivalent refractive index of the spilled oil area, which implies the relative proportions of seawater and spilled oil in each pixel, could be estimated using polarized remote sensing under sunglint. Furthermore, if the relationship between the equivalent refractive index and remote sensing reflectance ( $R_{rs}$ ) of spilled oils in the remote sensing images can be given, it might be used to correct the sunglint effect on various spilled oils, thereby leading to an improvement for optical quantifying spilled oil volume.

With a five-day revisit frequency over coastal regions and a spatial resolution of 10-60 m, the Sentinel-2 multispectral instrument (MSI) has shown its capacity to provide a reasonably accurate remote sensing reflectance (R-rs) data product over water when the standard "black pixel" (BP) atmospheric correction algorithm was applied to the top-of-atmospheric (TOA) reflectance data. Alternative atmospheric correction approaches, such as the POLYnomial-based algorithm applied to Medium Resolution Imaging Spectrometer (MERIS) (POLYMER), may show advantages under nonoptimal observation conditions (e.g., in the presence of strong sun glint). Here, POLYMER is implemented to process the data collected by both MSI and the Moderate Resolution Imaging Spectroradiometer (MODIS) with the resulting R-rs evaluated with concurrent and colocated in situ R-rs data collected from the AERONET-OC platforms. The results indicate less uncertainties in the MSI R-rs than those in the MODIS R-rs, and also less uncertainties in the MSI R-rs than those reported earlier. This is possibly attributed to the spatial heterogeneity of coastal waters where MODIS coarse-resolution data may suffer, and to the high-quality AERONET-OC data. In addition, for the evaluation data set, MSI R-rs does not appear to suffer from adjacency effects from the AERONET-OC platform and clouds, leading to more coverage than MODIS in nearshore waters. However, MSI R-rs is noisy in relatively clear waters, possibly due to the noisy TOA reflectance in the atmospheric correction bands over clear waters.

The atmospheric correction approach currently being used operationally by NASA [termed as NASA standard atmospheric correction (NSAC) approach] to process ocean color data relies on traditional “black pixel” approach, with additional modifications to account for nonnegligible water-leaving radiance in the near-infrared (NIR) bands. The NSAC approach underestimates remote-sensing reflectance (R rs , sr -1 ) in blue wavelengths in the presence of absorbing aerosols. Addressing this issue requires realistic absorbing-aerosol model and knowledge of the vertical distribution of aerosols, which are currently difficult to achieve. An alternative atmospheric correction approach has been evaluated in this paper for Moderate Resolution Imaging Spectroradiometer (MODIS) data. The approach is based on a previously developed spectra-matching optimization [POLYnomial-based approach established for the atmospheric correction of MERIS data (POLYMER)], where polynomial functions are used to express atmospheric contribution to the measured radiance and where a bio-optical model is used to estimate the water contribution. Evaluation against in situ data measured over the regions frequently affected by absorbing aerosols indicates that, compared with the NSAC approach, the POLYMER approach improves the R rs retrievals in blue wavelengths while having a slightly worse performance in other wavelengths. Evaluation using NSAC-retrieved Rrs in adjacent days free of absorbing aerosols suggests that the POLYMER approach could improve the spectral shape and increase valid spatial coverage. When applied to time-series MODIS data, the POLYMER approach could generate more temporary coherent daily and monthly R rs patterns than the NSAC approach. These results suggest that the POLYMER approach could be an alternative approach to partly correct for absorbing aerosols in the absence of explicit information on the aerosol type and the vertical distribution.

China's HY-1C ocean-observing satellite was launched successfully on September 7, 2018. It carries a four-channel wide-band coastal zone imager (CZI) that has 50 m spatial resolution and 950 km swath width. To exploit the potential of quantitative ocean color inversion, accurate atmospheric correction is needed. However, because of the CZI band settings, the realization of this goal is a challenge, especially for turbid water with complex optical properties. This study investigated the atmospheric correction algorithm for the CZI over turbid water. First, using the 6SV radiative transfer model, CZI Rayleigh lookup tables (LUTs) were built to correct for atmospheric molecular Rayleigh scattering, greatly shortening the time required for operational data processing. Second, CZI aerosol scattering was removed using the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol LUTs and quasi-synchronous MODIS aerosol products. The accuracy of the CZI atmospheric correction was validated using data from the highly turbid Bohai Sea. In comparison with synchronous in situ data, the results showed the average relative error of CZI remote sensing reflectance (Rrs) in the blue, green and red bands was 32.51 %, 25.38 % and 42.10 %, respectively. Comparison of CZI Rrs with quasi-synchronous MODIS data revealed similar spatial distributions, although the spatial information from the CZI was more detailed. The results proved the validity and accuracy of the CZI atmospheric correction algorithm over turbid water, which lays a foundation for quantitative ocean color inversion with high spatial resolution in the coastal zone.

A laboratory experiment was conducted to obtain a floating algae index (FAI) of the floating macroalgae (Ulva prolifera), corresponding to various values of biomass per unit area (BPA). A piecewise empirical model was used to fit the statistical relationships between BPA and FAI, corresponding to FAI ≤ 0.2 (BPA ≤ 1.81kg/m2) and FAI ˃ 0.2 (BPA ˃ 1.81 kg/m2). Spectral mixing derived results show that a linear relationship between FAI and BPA is maintained when the BPA of endmembers is less than 1.81 kg/m2. However, when the BPA of the endmembers exceeds 1.81 kg/m2, there is substantial uncertainty in the optical remote estimation of biomass. Although the MODIS-derived FAI of Ulva prolifera is often less than 0.2, it is very difficult to determine whether the FAI results from low BPA (≤ 1.81kg/m2) of the endmembers, or from a low area ratio including high BPA (˃ 1.81 kg/m2), due to pixel mixing. If it is assumed that the unit biomass distribution of pure endmembers is a standard Gaussian distribution, then the uncertainty in the biomass estimation of Ulva prolifera from MODIS data can be expressed. This results in the uncertainty of ~36% in total biomass estimation, ~43% of which was contributed by a few pixels (10% of total pixels) with high FAI (˃ 0.05). The uncertainty in BPA caused by high FAI (˃ 0.05) pixels is about 7.2 times that for low FAI (≤ 0.05) pixels. In future research, the spatial distribution characteristics of the FAI of pure endmembers need to be considered in order to improve the accuracy of optical remote estimation of floating Ulva prolifera.

Optical identification and quantification of various marine-spilled oils play an important role in oil spill monitoring, assessment, and response. Through weathering processes, oil may become emulsified in two forms of oil water mixture: water in oil (WO) and oil in water (OW). These two forms of oil emulsion are significantly different in their volume concentration (oil/water ratio), physical properties (viscosity, density, thickness), and optical properties (spectral reflectance (R-u(lambda), sr(-1)), and spectral absorption (a(lambda), m(-1))). In this study, the optical properties of both types of oil emulsion, with different volumetric concentrations, are determined from carefully prepared oil emulsion samples, with the aim of helping to interpret optical remote sensing imagery. The concentrations of stable WO and OW emulsions range from 45% to 95% and from 0.025% to 3%, respectively. They exhibit different R-u spectral shapes in the near-infrared and shortwave-infrared wavelengths, with five "-CH" molecular bonds evident in the WO emulsion spectra. R-u (600-1400 nm) of OW emulsions increases with volume concentrations from 0% to 3.0%, but R-u (600-2400 nm) of the WO emulsions decreases with volume concentrations from 45% to 100%. On the other hand, for a fixed concentration (80%), R-u (600-2400 nm) of WO emulsions increases monotonically with thicknesses of up to similar to 0.4 mm, beyond which R-u (600-2400 nm) no longer increases with oil thickness. The difference between the R-u spectral shapes of OW and WO emulsions, as well as the statistical relationships between volume concentrations and R-u (NIR-SWIR) and between oil thickness and Ru (NIR-SWIR), provide the basis for developing optical models to classify oil emulsion types and for quantifying oil volume from remote sensing imagery. The potential of such an application is demonstrated using hyperspectral AVIRIS imagery collected over the Deepwater Horizon (DWH) oil spill in the Gulf of Mexico (GoM).

Using in situ data of spectral remote sensing reflectance (R-rs, sr(-1)) collected over North American oceanic, coastal and estuarine waters between 2002 and 2016 (N = 942), we evaluate two atmospheric correction approaches applied to MODIS measurements. One is the POLYnomial based approach originally designed for MERIS (POLYMER) but adopted and implemented for MODIS, and the other is the traditional Gordon and Wang (1994b) near-infrared (NIR) approach with iteration to account for non-negligible NIR water-leaving radiance, which is currently embedded in the SeaWiFS Data Analysis System (SeaDAS) software package and used operationally by NASA for processing MODIS data (termed as NASA standard atmospheric correction or NSAC). The approaches are evaluated for both quality and quantity of their retrieved R-rs in the visible domain. The quality is gauged through three statistical measures between in situ and MODIS-retrieved R-rs: root mean square error (RMSE, sr(-1)), unbiased root mean square (uRMS), and mean bias (delta, sr(-1)). For common points where both approaches yield valid R-rs retrievals, POLYMER shows worse performance than NSAC for blue bands (< 488 nm) and comparable performance for green and red bands. However, POLYMER shows the ability to retrieve more valid R-rs data points (2-3 folds) than NSAC for this evaluation dataset primarily because the latter fails over strong sun glint regions where the MODIS NIR bands saturate but the former is designed to work over sun glint regions using non-saturation MODIS bands. For those data points where only POLYMER yield valid R-rs retrievals, data quality is slightly worse than from the common data points. Although these results may vary slightly among individual subregions, it is generally true that POLYMER may be used as a surrogate of NSAC for atmospheric correction of MODIS when data quantity is significantly limited due to perturbations of sun glint and thin clouds that are typical for subtropical and tropical regions.

During the weathering of marine-spilled oils, various types of oil pollution are formed that can harm marine and coastal environments. Thus, the remote detection, classification and quantification of spilled oils is important in marine environmental monitoring. Although multispectral images can be used to observe various spilled oils, due to confusion between the multispectral backscattered signals, distinguishing spilled oils from floating algae in the same image is challenging. The spectral features of carbon-hydrogen (-C-H) and oxygen-hydrogen (-O-H) groups, and pigments, are diagnostic absorption features and are different from the backscattering signal, they have not been used to improve detection independently. In this study, all the spectral features of the groups were clearly interpreted using reflectance spectra collected from an airborne visible infrared imaging spectrometer (AVIRIS). A reflectance peak-trough detection method to characterize the different spectral groups was used to determine the spectral features of Deepwater Horizon (DWH) oil emulsions and floating Sargassum in the Gulf of Mexico (GOM). The results show that the spilled oils and floating Sargassum can be clearly identified, and the various spilled oils (i.e., different oil emulsions and oil slicks) could also be determined from the differences in the spectral features of the above groups. Finally, we discuss the spectral requirements for the identification of these groups and we conclude that optical remote sensing, including imaging spectrometers, will play an increasingly important role in assessing marine oil spills.

The critical angle is the angle at which the contrast of oil slicks reverse their contrasts against the surrounding oil-free seawater under sunglint. Accurate determination of the critical angle can help estimate surface roughness and refractive index of the oil slicks. Although it's difficult to determine a certain critical angle, the potential critical angle range help to improve the estimation accuracy. In this study, the angle between the viewing direction and the direction of mirror reflection is used as an indicator for quantifying the critical angle and could be calculated from the solar/viewing geometry from observations of the Moderate Resolution Imaging Spectroradiometer (MODIS). The natural seep oil slicks in the Gulf of Mexico were first delineated using a customized segmentation approach to remove noise and apply a morphological filter. On the basis of the histograms of the brightness values of the delineated oil slicks, the potential range of the critical angle was determined, and then an optimal critical angle between oil slicks and seawater was then determined from statistical and regression analyses in this range. This critical angle corresponds to the best fitting between the modeled and observed surface roughness of seep oil slicks and seawater.

The Geostationary Coastal and Air Pollution Events Airborne Simulator (GCAS) instrument has been used as a precursor for a hyperspectral instrument on the future geostationary satellite, yet its ability to "measure" ocean reflectance needs to be evaluated. Here, we demonstrate its capacity through vicarious calibration and atmospheric correction of data collected during flight campaigns over the Louisiana shelf in September 2013 and over the North Atlantic Ocean in November 2015. GCAS-measured at-sensor radiance was first vicariously calibrated using concurrent measurements by the Moderate Resolution Imaging Spectrometer (MODIS) and radiative transfer simulations with the MODerate resolution atmospheric TRANsmission (MODTRAN). Then, atmospheric correction has been implemented using MODTRAN-developed lookup tables and the traditional Gordon and Wang "black pixel" approach but with nonzero water-leaving radiance in the near-infrared accounted for through iteration. The atmospheric correction algorithm was applied to the vicariously calibrated GCAS imagery, with resulting R-rs compared with concurrent MODIS R-rs and in situ R-rs. The comparison shows a mean relative difference of about 25% (N = 11) between GCAS and in situ R-rs in the blue-green bands for clear to moderately turbid waters.

The critical angle at which the brightness of oil slicks and oil-free seawater is reversed occurs under sunglint and is often shown as an area of uncertainty due to different roughness and surface Fresnel reflection parameters. Consequently, differentiating oil slicks from the seawater in these areas using optical sensors is a challenge. Polarized optical remote sensing techniques provide complementary information for intensity imagery with different physical properties and, thus, possess the ability to resolve this difficult problem. In the polarized reflectance model, the degree of linear polarization (DOLP) of sunglint depends on accurately knowing the Stokes parameter for the reflected light, and varies with the refractive index of the surface layer and viewing angles. For the polarized detection of oil slicks, the highest sensitivity of the DOLP to the refractive index is located within the specular reflection direction where the sum of the solar and sensor zenith angles is 82.6 degrees. The modeled results clearly indicate that the DOLP of oil slicks is weaker in comparison with oil-free seawater under sunglint. Using measurements from the space-borne Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) over the Deepwater Horizon oil spill in the Gulf of Mexico, we illustrate that the PARASOL-derived DOLP difference between the oil spill and seawater is obvious and is in accordance with the modeled results. These preliminary results suggest that the potential of multiangle measurement and feasibility of deriving refractive index of ocean surface from space-borne sensors need further researches.

Using hyperspectral data collected by the Airborne Compact Atmospheric Mapper (ACAM) and a ship borne radiometer in Chesapeake Bay in July August 2011, this study investigates diurnal changes of surface remote sensing reflectance (R-rs). Atmospheric correction of ACAM data is performed using the traditional "black pixel" approach through radiative transfer based look-up-tables (LUTs) with non-zero R-rs in the near-infrared (NIR) accounted for by iterations. The ACAM-derived R-rs was firstly evaluated through comparison with Rrs derived from the Moderate Resolution Imaging Spectroradiometer satellite measurements, and then validated against in situ R-rs using a time window of +/- 1 h or +/- 3 h. Results suggest that the uncertainties in ACAM-derived R-rs are generally comparable to those from MODIS satellite measurements over coastal waters, and therefore may be used to assess whether R-rs diurnal changes observed by ACAM are realistic (i.e., with changes > 2 x uncertainties). Diurnal changes observed by repeated ACAM measurements reaches up to 66.8% depending on wavelength and location and are consistent with those from the repeated in situ R-rs measurements. These findings suggest that once airborne data are processed using proper algorithms and validated using in situ data, they are suitable for assessing diurnal changes in moderately turbid estuaries such as Chesapeake Bay. The findings also support future geostationary satellite missions that are particularly useful to assess shortterm changes. (C) 2017 Elsevier Ltd. All rights reserved.