Comparison of iCOR and Rayleigh atmospheric correction methods on Sentinel-3 OLCI images for a shallow eutrophic reservoir

Abstract

Remote sensing of inland waters is challenging, but also important, due to the need to monitor the ever-increasing harmful algal blooms (HABs), which have serious effects on water quality. The Ocean and Land Color Instrument (OLCI) of the Sentinel-3 satellites program is capable of providing images for the monitoring of such waters. Atmospheric correction is a necessary process in order to retrieve the desired surface-leaving radiance signal and several atmospheric correction methods have been developed through the years. However, many of these correction methods require programming language skills, or function as commercial software plugins, limiting their possibility of use by end users. Accordingly, in this study, the free SNAP software provided by the European Space Agency (ESA) was used to evaluate the possible differences between a partial atmospheric correction method accounting for Rayleigh scattering and a full atmospheric correction method (iCOR), applied on Sentinel-3 OLCI images of a shallow, highly eutrophic water reservoir. For the complete evaluation of the two methods, in addition to the comparison of the band reflectance values, chlorophyll (CHL) and cyanobacteria (CI) indices were also calculated and their values were intercompared. The results showed, that although the absolute values between the two correction methods did not coincide, there was a very good correlation between the two methods for both bands’ reflectance (r > 0.73) and the CHL and CI indices values (r > 0.95). Therefore, since iCOR correction image processing time is 25 times longer than Rayleigh correction, it is proposed that the Rayleigh partial correction method may be alternatively used for seasonal water monitoring, especially in cases of long time-series, enhancing time and resources use efficiency. Further comparisons of the two methods in other inland water bodies and evaluation with in situ chlorophyll and cyanobacteria measurements will enhance the applicability of the methodology.

Innovative Maritime Uncrewed Systems and Satellite Solutions for Shallow Water Bathymetric Assessment

“…The selection of these images was contingent on several criteria, including minimal cloud cover, alignment with the region of interest, and the type of Top-of-Atmosphere (TOA) product. Following the acquisition, the images underwent a series of preprocessing steps that encompassed the correction of atmospheric and aerosol effects, mitigation of sun glint, analysis of water turbidity, quantification of chlorophyll-a concentrations, assessment of the Normalized Difference Chlorophyll Index, and the evaluation of the Floating Algal Index [54]. For developing a general water depth estimation model, machine learning (ML) and deep learning algorithms were applied and tested.…”