Assessment of Adjacency Correction over Inland Waters Using Sentinel-2 MSI Images

Abstract

Satellite remote sensing data have been used for water quality mapping, but accurate water reflectance retrieval is dependent on multiple procedures, such as atmospheric and adjacency corrections. For the latter, physical-based methods are used to minimize the adjacency effects caused by neighboring land targets close to water pixels, and implementation requires atmospheric and environmental parameters, such as aerosol optical depth and horizontal range (i.e., distance in meters) of the adjacency effect (HAdj). Generally, the HAdj is empirically defined by users and can lead to substantial errors in water reflectance when incorrectly used. In this research, a physical-based approach with three empirical methods to determine the HAdj (fixed, SIMilarity Environment Correction—SIMEC, and Adaptative Window by Proportion—AWP-Inland Water) were used to correct and characterize the adjacency effects in Sentinel-2 images over Brazilian inland waters. An interactive inversion method of the deep blue waveband estimated the aerosol loading for the atmospheric correction procedure. The results of atmospheric and adjacency corrections were validated against in-situ reflectance data. The inverted aerosol loading achieved a good agreement with in-situ measurements, especially at visible wavelengths (Mean Absolute Percentage Error—MAPE for eutrophic (~56%), bright (~80%), and dark (~288%) waters). The adjacency correction performance was near similar between the SIMEC and AWP-Inland Water methods in eutrophic and bright waters (MAPE difference < 3%). However, only the AWP-Inland Water method provided a smaller error (MAPE ~53%) for dark waters compared to the fixed (~108%) and SIMEC (~289%) methods, which shows how critical HAdj parametrization is for low water reflectance values. Simulations of different atmospheric and adjacency effects were performed, and they highlighted the importance of adjacency correction under aerosol loading higher 0.1, which is a typical aerosol loading in a dry climate season, and over extremely dark, low-reflectance waters. This paper contributes to further understanding adjacency effects in medium spatial resolution imagery of inland waters using a physical-based approach including the uncertainties in HAdj determination.

Optimizing Optical Coastal Remote-Sensing Products: Recommendations for Regional Algorithm Calibration

“…In contrast, the underestimation of turbidity and SPM by POLYMER may reflect the restriction imposed by its bio-optical model [22]. In the present work, turbidity and SPM estimates using the NIR band (865 nm) were not systematically overestimated, as would be expected in the presence of adjacency effects [62]. Hence, one may consider that adjacency did not significantly affect the estimates.…”

“…Adjacency effects are associated with surfaces with contrasting reflectance, where photons from the higher reflectance surface (usually the land) are scattered into the instantaneous field of view (IFOV) of the sensor, mixing with the signal from the lower reflectance surface (usually water) [60]. This effect depends upon the characteristics of the atmosphere (aerosol optical depth), the target (water reflectance), and the background (land cover's albedo) [60][61][62]. Paulino et al [62] evaluated adjacency effects on Brazilian inland waters, showing that this effect is relevant for distances between 0.1 and 2 km from land.…”

“…This effect depends upon the characteristics of the atmosphere (aerosol optical depth), the target (water reflectance), and the background (land cover's albedo) [60][61][62]. Paulino et al [62] evaluated adjacency effects on Brazilian inland waters, showing that this effect is relevant for distances between 0.1 and 2 km from land. On the other hand, Bulgarelli and Zibordi [61] observed adjacency effects up to a few tens of kilometers from the coast.…”

Advances in Remote Sensing of the Inland and Coastal Water Zones

“…Quantifying water quality using medium-resolution optical satellite sensors for small inland lakes is challenging due to distortions of the water signal by radiation reflected from the surrounding land surface and scattered in the atmosphere towards the radiometer. In [3], the authors used Sentinel-2/MSI images and in situ optical measurements to assess the uncertainty of reflectance derived from satellite data due to adjacency effects for lakes surrounded by both natural and urbanized environments and characterized by different optical characteristics of water. They assessed the importance of factors such as aerosol load, water body shape and size, differences in the reflectance of water and land cover on the strength of this effect, and the accuracy of its correction.…”