Adjacency Effects Research Articles

Genetic Algorithm for Atmospheric Correction (GAAC) of water bodies impacted by adjacency effects

Adjacency effect (AE) corrections over inland water surfaces has been a known issue in space-borne optical remote sensing over more than four decades. Here we present a novel algorithm able to simultaneously retrieve the aerosol optical depth, sun glint, AE, water reflectance, and water inherent optical properties (IOPs). The method was evaluated against an in situ data set of remote sensing reflectance (Rrs) collected in ∼100 lakes across Canada. The new algorithm is based on a genetic optimization scheme (GAAC: Genetic Algorithm for Atmospheric Correction), and was here compared to the most popular atmospheric correction algorithms available (ACOLITE, iCOR+SIMEC). The statistical metrics of the Rrs retrieval were improved by a factor of almost 2 in all wavelengths, and for all metrics (Bias, Error, Similarity Angle) relative to other algorithms. Demonstrations of GAAC on scenes of Lansdat-8 OLI, and Sentinel-2 MSI sensors demonstrate the algorithm’s robustness when applied to spatially complex small lake (∼10 km of width) surfaces.

Adjacency radiance around a small island: implications for system vicarious calibrations.

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.

Blind Hyperspectral Unmixing Considering the Adjacency Effect

This paper focuses on the blind unmixing technique for analyzing hyperspectral images (HSIs). A joint deconvolution and blind hyperspectral unmixing (DBHU) algorithm is proposed, which is aimed at eliminating the impact of the adjacency effect (AE) on unmixing. In remote sensing imagery, the AE occurs in the presence of atmospheric scattering over a heterogeneous surface. The AE leads to blurring and additional mixing of HSIs and makes it difficult to estimate endmembers and abundances accurately. In this paper, we first model the blurred HSIs by the use of a bilinear mixing model (BMM), where a blurring kernel is used to model the mixing caused by the AE. Based on the BMM, the DBHU problem is formulated as a constrained and biconvex optimization problem. Specifically, the minimum-volume simplex (MVS) is incorporated to deal with the additional mixing caused by the AE, and 3-D total variation (TV) priors are adopted to model the spectral–spatial correlation of the data. In DBHU, the biconvex problem is efficiently solved by a nonstandard application of the alternating direction method of multipliers (ADMM) algorithm, where a block coordinate descent scheme is applied by splitting the original problem into two saddle point subproblems, and then minimizing the subproblems alternately via the ADMM until convergence. The experimental results obtained with both simulated and real data confirm the viability of the proposed algorithm, and DBHU works well, even where both blurring and noise are present in the scene.

On the minimization of adjacency effects in SeaWiFS primary data products from coastal areas.

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.

Seasonal Impact of Adjacency Effects on Ocean Color Radiometry at the AAOT Validation Site

The seasonal impact of adjacency effects (AE) on satellite ocean color data at visible and near-infrared (NIR) wavelengths by the Sea-Viewing Wide Field-of-View Sensor, the Moderate Resolution Imaging Spectroradiometer onboard the Aqua platform (MODISA), the Medium Resolution Imaging Spectrometer, the Ocean and Land Color Instrument, the Operational Land Imager (OLI), and the MultiSpectral Imagery (MSI) was theoretically evaluated at a validation site in the northern Adriatic Sea. The analysis made use of comprehensive simulations accounting for multiple scattering, sea surface roughness, sensor viewing geometry, actual coastline, typical and extreme atmospheric conditions, and the seasonal variability of solar illumination and, land and water optical properties. Results, obtained by relying on the normalization of the radiometric sensitivity of each sensor to the same input radiance, show that the spectral and seasonal impacts of AE considerably vary among sensors. AE significantly exceed the radiometric sensitivity of MSI at its sole blue band in winter, whereas they significantly outdo the noise threshold of OLI and MODISA high-resolution data exclusively in the NIR in summer. Conversely, for all other sensors and for MODISA low-resolution data, AE are particularly significant at NIR bands between March and October and at the blue-green bands in winter.

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

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 36km offshore (20km 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 36km 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.

Cloud adjacency effects on top-of-atmosphere radiance and ocean color data products: A statistical assessment

Abstract Ocean color measurements taken near cloud boundaries suffer from cloud adjacency effects (AEs). As a result, ~ 50% of the cloud-free ocean data are flagged as low quality. Quantitative assessment of such effects, as well as the methodology required to minimize, or correct for them, is rarely available. The goal of this study is to quantify such effects on top-of-atmosphere (TOA) radiance and ocean color data products for MODIS/Terra, MODIS/Aqua, and SeaWiFS measurements. The AEs estimation was based on statistics and an objective method applied to carefully selected clear-water scenes (the number of cloud patches was > 100,000 for each instrument) where ocean properties are relatively homogeneous, over both the North Atlantic and South Pacific. The AEs were quantified as the relative difference between the near-cloud pixels and pixels at least 20 km away from any cloud. Results show that the AEs on TOA radiance share similar patterns among the three missions. Specifically, the AEs decrease sharply as distance increases from cloud edges, and the AEs increase monotonically with increasing wavelengths because they were evaluated in relative rather than absolute terms. However, while discernable memory effects (MEs) are observed on cloud-adjacency pixels of both MODIS missions, they are insignificant on the SeaWiFS mission, and are found in measurements along the scan direction downstream of the clouds, representing > 15% of the total AEs in TOA radiance. The AEs on the retrieved remote sensing reflectance (Rrs) data products are different among the three missions possibly due to their differences in vicarious calibration and uncertainties in atmospheric correction, leading to different patterns in the chlorophyll-a (Chl-a) and normalized Florescence Line Height (nFLH) data products. Large AEs (> 50%) are observed in nFLH of both MODIS/Terra and MODIS/Aqua, likely due to the opposite AEs on Rrs between 667 and 678 nm. Finally, when the OCI Chl-a algorithm is used, the current MODIS stray-light masking window (7 × 5) used to mask the AE-contaminated pixels may be relaxed to 3 × 3 without sacrificing data quality, leading to > 40% of the previously masked low-quality data being recovered for clear waters.

Error Analysis of Retrieved Aerosol Optical Depth due to Adjacency Effect for ROCSAT-2 RSI Bands

The adjacency effect (AE), caused by the scattering of reflected radiance from an inhomogeneous surface, is known to introduce a blurring effect on remotely sensed data, especially for high spatial resolution data. The retrieval of aerosol optical depth (AOD) by the dark target (DT) method usually neglects the AE. Hence, significant errors may be induced in aerosol retrieval. In this study, the errors of retrieved AOD due to AE by the DT method for very high spatial resolution data of 8 m in the multi-spectral bands of ROCSAT-2 RSI are investigated. A fast, accurate simple atmospheric correction model is applied. Target size is considered the same as the spatial resolution. The errors are mainly studied for both low and medium contrasts, in both clear and hazy skies for both continental and urban aerosol models. The results indicate that for the continental model, AE may cause significant retrieval errors for AOD over DTs for medium contrast in both skies in blue and red bands. Error becomes negligible if the inhomogeneous surface is for low contrast in clear sky. It is larger for the urban model where there is more absorption than for the continental model, due to the less sensitivity of top-of-atmosphere reflectance to AOD for DTs in inhomogeneous surfaces. This suggests that AE be considered for medium contrast in both skies, especially for urban-industrial regions for ROCSAT2 RSI.