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The sea-surface reflectance factor ρ required for the determination of the water- leaving radiance from above-water radiometric measurements is derived from radiative transfer simulations relying on models of the sky-radiance distribution and sea-surface statistics. This work primarily investigates the impact on ρ of various sky-radiance and sea-surface models. A specific replicability analysis, restricted to the 550 nm wavelength, has been performed with the Monte Carlo code for Ocean Color Simulations (so-called MOX) accounting for the measurement geometry recommended in protocols for the validation of satellite ocean color data and commonly applied for operational measurements. Results indicate that the variability of ρ increases with wind speed and reaches the largest values for sun elevations close to the zenith or approaching the horizon. In particular, a variability up to about 2% is observed for wind speeds below 4 ms−1 and sun zenith angles larger than 20°. Finally, the benchmark of the ρ values from this study with those formally determined with the Hydrolight code and widely utilized by the ocean color community, exhibits systematic differences. The source of these differences is discussed and the implications for field measurements are addressed.

The adjacency radiance field surrounding a small island (i.e., the Lampedusa Island in the Central Mediterranean Sea) was theoretically analyzed to address implications on a hypothetical nearby system vicarious calibration (SVC) infrastructure for satellite ocean color sensors. Simulations, performed in the visible and near-infrared regions for the Ocean Land Color Instrument (OLCI) operated onboard Sentinel-3 satellites, show different patterns of adjacency effects (AE) around the island. In the direction of the reflected sunbeam (i.e., in the north-western region), AE mainly originate by missing glint contributions from the sea surface masked by the island. These AE are mainly negative, decrease with wavelength, and strongly depend on sea surface anisotropy (i.e., sea state) and illumination conditions; this hinders the capability to provide a general unique description of their features. In the remaining marine regions, AE are positive and do not exceed the radiometric sensitivity of OLCI data beyond approximately 14 km from the coast. At shorter distances, uncertainties in satellite radiance due to AE would hence not allow fulfilling requirements for SVC.

The detectability of adjacency effects (AE) in ocean color remote sensing by SeaWiFS, MODIS-A, MERIS, OLCI, OLI and MSI is theoretically assessed for typical observation conditions up to 36 km offshore (20 km for MSI). The methodology detailed in Bulgarelli et al. (2014) is applied to expand previous investigations to the wide range of terrestrial land covers and water types usually encountered in mid-latitude coastal environments. Simulations fully account for multiple scattering within a stratified atmosphere bounded by a non-uniform reflecting surface, sea surface roughness, sun position and off-nadir sensor view. A harmonized comparison of AE is ensured by adjusting the radiometric sensitivity of each sensor to the same input radiance. Results show that average AE in data from MODIS-A, and from MERIS and OLCI in reduced spatial resolution, are still above the sensor noise level (NL) at 36 km offshore, except for AE caused by green vegetation at the red wavelengths. Conversely, in data from the less sensitive SeaWiFS, OLI and MSI sensors, as well as from MERIS and OLCI in full spatial resolution, sole AE caused by highly reflecting land covers (such as snow, dry vegetation, white sand and concrete) are above the sensor NL throughout the transect, while AE originated from green vegetation and bare soil at visible wavelengths may become lower than NL at close distance from the coast. Such a distance increases with the radiometric resolution of the sensor. It is finally observed that AE are slightly sensitive to the water type only at the blue wavelengths. Notably, for an atmospheric correction scheme inferring the aerosol properties from NIR data, perturbations induced by AE at NIR and visible wavelengths might compensate each other. As a consequence, biases induced by AE on radiometric products (e.g., the water-leaving radiance) are not strictly correlated to the intensity of the reflectance of the nearby land., Highlights • Quantification of adjacency effects for a wide range of coastal environments • Comparison of adjacency effects for data from various ocean color sensors • Relevance of adjacency effects as a function of the SNR of satellite sensors

The minimization of adjacency effects (AE) in SeaWiFS primary products at the Aqua Alta Oceanographic Tower (AAOT) was investigated using sample images concurrent with in situ measurements. The validation exercise was performed with the NASA SeaDAS processing scheme ingesting original SeaWiFS data and alternatively SeaWiFS top-of-atmosphere data corrected for AE, and additionally including and excluding the default turbid water (TW) correction algorithm. Results show overestimates of the TW contributions partially compensating for AE. The analysis also suggests that intra-annual biases observed in SeaWiFS radiometric products at the AAOT may result from a misinterpretation of the NIR atmospheric signal as water contribution in data acquired in winter, and from uncompensated AE in data acquired in summer.

The presented adjacency correction algorithm is based on the use of the point spread function (PSF) which allows calculating the contribution of reflections from the nearby pixels to the apparent radiance of the target. The analytical expression of the PSF for an arbitrary stratified atmosphere is obtained in the approximation of primary scattering, whereas the full equation of radiative transfer is used for the estimation of the radiance reflected from the surface. The algorithm is sensor independent and can be applied for processing images of water basins with arbitrary shape of the shore line and under different geometries of observation. The program using this algorithm is included in Modular Inversion Program — MIP (Heege et al., 2014) for processing of satellite images on a routine basis. Examples of processing results are presented in the paper.

Biases induced by land perturbations in satellite-derived water-leaving radiance are theoretically estimated for typical observation conditions in a coastal area of the northern Adriatic Sea hosting the Aqua Alta Oceanographic Tower (AAOT) validation site. Two different correction procedures are considered: not deriving (AC-1) or alternatively deriving (AC-2) the atmospheric properties from the remote sensing data. In both cases, biases due to adjacency effects largely increase by approaching the coast and with the satellite viewing angle. Conversely, the seasonal and spectral dependence of biases significantly differ between AC-1 and AC-2 schemes. For AC-1 schemes average biases are within ±5% throughout the transect at yellow-green wavelengths, but at the coast they can reach -21% and 34% at 412 and 670 nm, respectively, and exceed 100% at 865 nm. For AC-2 schemes, adjacency effects at those wavelengths from which atmospheric properties are inferred add significant perturbations. For the specific case of a correction scheme determining the atmospheric properties from the near-infrared region and by adopting a power-law spectral extrapolation of adjacency perturbations on the derived atmospheric radiance, average biases become all negative with values up to -60% and -74% at 412 and 670 nm at the coast, respectively. The seasonal trend of estimated biases at the AAOT is consistent with intra-annual variation of biases from match-ups between in situ and satellite products derived with SeaDAS from SeaWiFS and MODIS data. Nevertheless, estimated biases at blue wavelengths exceed systematic differences determined from match-up analysis. This may be explained by uncertainties and approximations in the simulation procedure, and by mechanisms of compensation introduced by the turbid water correction algorithm implemented in SeaDAS.

A methodology has been developed and applied to accurately quantify and analyze adjacency effects in satellite ocean color data for a set of realistic and representative observation conditions in the northern Adriatic Sea. The study, properly accounting for sea surface reflectance anisotropy, off-nadir views, coastal morphology and atmospheric multiple scattering, include a sensitivity analysis on commonly applied approximations. Results indicate that, within the accuracy limits defined by the radiometric resolution of the ocean color sensors, adjacency effects in coastal waters might be significant at both visible and NIR wavelengths up to several km off the coast. These results additionally highlight a significant dependence on the angle of observation, on the directional reflectance properties of the sea surface, and on the atmospheric multiple scattering., JRC.H.1-Water Resources

The collection of high-quality in situ measurements is a key component for the calibration and validation of space-borne radiometric data and the development of inversion schemes for retrieving higher level product maps underpinning climate-change studies. In situ radiometric measurements to compute quantities such as the remote sensing reflectance are, however, subject to different perturbing factors. Scope of this chapter is to discuss perturbations due to large deployment structures, such as ships and oceanographic towers, as well as instrument self-shading and sea-surface light focusing. The radiance distribution induced by these perturbing elements cannot be evaluated analytically by directly solving the radiative transfer equation. Among numerical simulations, the use of deterministic approaches is partially limited by the absence of translational invariance in the perturbed light field. On the other hand, results show that Monte Carlo simulations of radiometric perturbations represent an effective mean for enhancing the quality of field measurements by offering the possibility to define correction schemes and refine measurement protocols.

A methodology is presented to quantify the adjacency effects induced in satellite ocean color radiometric data by the presence of land in coastal regions. Specifically, the adjacency radiance, defined as the difference in top-ofatmosphere (TOA) signal when accounting for and when neglecting nearby mainland, is parameterized to decouple the dependence on the optical properties of land and water from those of atmosphere and sea surface, and from measurement geometry. The methodology is applied for a set of realistic satellite observation conditions, along a transect in the Northern Adriatic Sea crossing the Acqua Alta Oceanographic Tower (AAOT, 45.31N, 12.51E) ocean color validation site. The newly developed Novel Adjacency pert Urbation Simulator for Coast Al Areas (NAUSICAA) 3D backward Monte Carlo code and the well established highly accurate plane-parallel FEM radiative transfer code, are used to simulate signal contributions at TOA. Results at relevant ocean color center-wavelengths for the AAOT site i...

The confidence level of oil spill detections in satellite Synthetic Aperture Radar (SAR) imagery requires the analysis of many different factors. Unfortunately, oil slicks are not the only phenomena which can appear as a dark feature in a SAR image. These include a number of parameters like wind speed, currents, internal waves, upwelling sea areas, algae bloom, mixing water areas, et cetera. These phenomena are called look-alikes. The largest challenge in detecting oil spills in SAR images remains in the accurate discrimination between oil spills and look-alikes. This study introduces the vantages of using geospatial analysis of various metocean data (e.g. wind speed and direction, sea surface temperature, wave direction, ocean colour data) and environmental ancillary data (e.g. vessel traffic, port locations) as a supplementary information source for the oil spill probability assessment in SAR imagery. The analysed data exists in different formats with different value scales. In addition, the parameters of the metocean data analysis are not equally important for a reliability of oil spill detection. The weight of metocean parameters depends on the impact of natural phenomena on SAR systems (e.g. wind and currents have pro rata more influence on the probability than sea surface temperature and chlorophyll-a) and the area of interest (e.g. chlorophyll-a is a more important value for the Baltic Sea than for the Mediterranean Sea). The derived oil spill probability categorisation based on the weighted analysis of metocean environmental ancillary data could be a useful tool for authorities for an efficient planning of cost-intensive verification flights.

The cosine error of in situ seven-channel radiometers designed to measure the in-air downward irradiance for ocean color applications was investigated in the 412-683 nm spectral range with a sample of three instruments. The interchannel variability of cosine errors showed values generally lower than +/-3% below 50 degrees incidence angle with extreme values of approximately 4-20% (absolute) at 50-80 degrees for the channels at 412 and 443 nm. The intrachannel variability, estimated from the standard deviation of the cosine errors of different sensors for each center wavelength, displayed values generally lower than 2% for incidence angles up to 50 degrees and occasionally increasing up to 6% at 80 degrees. Simulations of total downward irradiance measurements, accounting for average angular responses of the investigated radiometers, were made with an accurate radiative transfer code. The estimated errors showed a significant dependence on wavelength, sun zenith, and aerosol optical thickness. For a clear sky maritime atmosphere, these errors displayed values spectrally varying and generally within +/-3%, with extreme values of approximately 4-10% (absolute) at 40-80 degrees sun zenith for the channels at 412 and 443 nm. Schemes for minimizing the cosine errors have also been proposed and discussed.

The presence of an aerosol layer over a nonuniform surface causes the blurring effect in remotely sensed images. In principle from the study of this effect it seems to be possible to evaluate aerosol properties. In this paper an aerosol layer superimposed on a surface with 1-D periodical variation in reflectivity and with half plane reflectivity variation is considered, in the single scattering approximation.

Scattering from atmospheric aerosols causes blurring of satellite images. A study of this effect suggests that it might be possible to evaluate aerosol properties from these images. We consider an aerosol layer superimposed on a surface with a one-dimensional sinusoidal variation in reflectivity and with a half-plane reflectivity variation. A single scattering approximation is used to show that information about the aerosol layer height and optical depth can be extracted provided the variation in ground reflectivity is known.

The accuracy analysis of an approximate atmospheric correction algorithm for the processing of SeaWiFS data has been investigated for the Baltic Sea. The analysis made use of theoretical radiances produced with the FEM radiative transfer code for representative atmosphere-water test cases. The study showed uncertainties in the determination of the aerosol optical thickness at 865 nm and of the Ångström exponent lower than ± 5% and ± 10%, respectively. These results were confirmed by the analysis of 59 match-ups between satellite-derived and in situ measurements for a site located in the central Baltic. Because of the relatively high yellow substance absorption, often combined with the slanted solar illumination, the retrieval of the water-leaving radiance in the blue part of the spectrum appeared to be highly degraded, to the extent that almost no correlation was found between retrieved and simulated values. Better results were obtained at the other wavelengths. The accuracy in the estimation of the remote sensing reflectance ratio R35 decreased with diminishing chlorophyll a concentration and increasing yellow substance absorption, ranging between ± 7% and ± 47%. The propagation of R35 uncertainties on chlorophyll a estimation was quantified. Keeping the same atmosphere-water conditions, the atmospheric correction scheme appeared sensitive to seasonal changes in the Sun zenith.