On the detectability of adjacency effects in ocean color remote sensing of mid-latitude coastal environments by SeaWiFS, MODIS-A, MERIS, OLCI, OLI and MSI

Abstract

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.

Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

“…We deemed it necessary to apply a previous water type classification based on the trophic status of the lakes, according to the Chl-a and Z sd values measured in situ: Type 1 ultra-to-oligotrophic, Type 2 mesotrophic-to-eutrophic and Type 3 hypertrophic [18]. This water type classification would help to understand the performance of the different AC processor, because this factor can affect the results in several ways: for instance, the AC algorithm may have not been trained for a certain range or another IOPs, or it might influence on the magnitude of the adjacency effect (AE) in the visible bands [49].…”

“…Both ACOLITE and Sen2Cor had in 740 and 783 nm higher values than the in situ measurements. Once established this fact, the adjacency correction may not be so necessary in some cases, that is, the perturbations caused by the adjacency effect show a great dependence on the position of the sun with respect to land, with the Fresnel value increasing if the sun is over the land portion of the image, which is not the case in many of the scenes of our study area [49].…”

“…Other issues should be taken into consideration. As Bulgarelli and Zibordi [49] pointed out, the different geometry of the sensor observations, sun angles and water and land use type influence the adjacency effect contributions. Ruescas et al [52] evaluated Sen2Cor over the Albufera lagoon, an extremely eutrophic lake with reflectance values that could approximate those of land and they had good response from the processor.…”

Correlation of Remotely Sensed Surface Reflectance With Forcing Variables in Six Different Estuaries

“…An accurate AC scheme is important for reliable applications of satellite remote sensing in coastal and estuarine areas. In those areas, applications of satellite ocean color data may be affected by sun glint, surface wave effects (Cox & Munk, 1954;Harmel et al, 2018), and adjacency effects, such as contributions from the surrounding land, ice, cloud, and object shadows (Bulgarelli & Zibordi, 2018;Zheng & DiGiacomo, 2017). The top-of-the-atmosphere radiance recorded by the satellite radiometer is separated into the signal from the atmospheric gases, aerosols, and the surface water column (Novoa et al, 2017).…”

“…Another important issue regarding ocean color in coastal and estuarine areas is atmospheric correction (AC), the goal of which is to remove the effects of scattering by aerosols, which can be different over land and over water. In addition to the AC in coastal zones, the effects of surface wave glint and adjacency of surfaces of differing reflectance (i.e., land and water) also complicate the retrieval of ocean color in coastal zones (Bulgarelli & Zibordi, ; Zheng & DiGiacomo, ). With appropriate AC methods, various empirical algorithms have been derived for the retrieval of water constituents (e.g., SPM, chlorophyll‐a concentration) based on field data and remote sensing reflectance ( ) or reflectance band ratios in coastal regions (Doxaran et al, , ; Hu et al, ; Miller & McKee, ).…”