Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes

Abstract

Satellite data provide the only viable means for extensive monitoring of remote and large freshwater systems, such as the Amazon floodplain lakes. However, an accurate atmospheric correction is required to retrieve water constituents based on surface water reflectance (R W ). In this paper, we assessed three atmospheric correction methods (Second Simulation of a Satellite Signal in the Solar Spectrum (6SV), ACOLITE and Sen2Cor) applied to an image acquired by the MultiSpectral Instrument (MSI) on-board of the European Space Agency's Sentinel-2A platform using concurrent in-situ measurements over four Amazon floodplain lakes in Brazil. In addition, we evaluated the correction of forest adjacency effects based on the linear spectral unmixing model, and performed a temporal evaluation of atmospheric constituents from Multi-Angle Implementation of Atmospheric Correction (MAIAC) products. The validation of MAIAC aerosol optical depth (AOD) indicated satisfactory retrievals over the Amazon region, with a correlation coefficient (R) of~0.7 and 0.85 for Terra and Aqua products, respectively. The seasonal distribution of the cloud cover and AOD revealed a contrast between the first and second half of the year in the study area. Furthermore, simulation of top-of-atmosphere (TOA) reflectance showed a critical contribution of atmospheric effects (>50%) to all spectral bands, especially the deep blue (92%-96%) and blue (84%-92%) bands. The atmospheric correction results of the visible bands illustrate the limitation of the methods over dark lakes (R W < 1%), and better match of the R W shape compared with in-situ measurements over turbid lakes, although the accuracy varied depending on the spectral bands and methods. Particularly above 705 nm, R W was highly affected by Amazon forest adjacency, and the proposed adjacency effect correction minimized the spectral distortions in R W (RMSE < 0.006). Finally, an extensive validation of the methods is required for distinct inland water types and atmospheric conditions.

Selecting the Best Band Ratio to Estimate Chlorophyll-a Concentration in a Tropical Freshwater Lake Using Sentinel 2A Images from a Case Study of Lake Ba Be (Northern Vietnam)

“…The Sen2cor atmospheric correction procedure in the Sentinel-2 toolbox was not designed for water bodies [20] and was proven inappropriate for estimating reflectances within the NIR region, particularly for reflectance at 705 nm (S2A band 5). Although ACOLITE, an atmospheric correction method for water bodies [19], provided a better estimation for the NIR region, the estimation was not always stable and accurate for visible region [30]. Therefore, the traditional empirical line method (ELM), which was recognized as the most precise method for atmospheric correction, particularly for atmospheric correction over water areas [31][32][33], was used in this study.…”

Temporal and Spatial Variations of Secchi Depth and Diffuse Attenuation Coefficient from Sentinel-2 MSI over a Large Reservoir

“…In this study, the atmospheric correction is performed using two models: Sen2Cor and 6SV. Sen2Cor was developed by ESA for applications over land surfaces but it is also applied over water surfaces, with several studies presenting good estimates of surface reflectances used for monitoring water quality parameters in lakes and reservoirs, such as colored dissolved organic matter, chlorophyll a, turbidity, or SD [23,27,32]. The 6SV is an advanced radiative transfer code capable of accounting for radiation polarization in a mixed molecular-aerosol atmosphere being a vector version of the 6S [33].…”

“…The 6SV is an advanced radiative transfer code capable of accounting for radiation polarization in a mixed molecular-aerosol atmosphere being a vector version of the 6S [33]. The 6S atmospheric correction has already been successfully used in lakes and reservoirs [21,32,[34][35][36][37].…”

Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity

“…López-Serrano et al [46] evaluated the performance of the COST, ATCOR2, FLAASH, 6S, and TOA algorithms for the estimation of forest above-ground biomass. Martins et al [47] compared the 6S, ACOLITE, and Sen2Cor methods applied to the new platform Sentinel 2-MSI. However, these studies lack the multitemporal component and only a few studies [48][49][50] have been developed to compare the methods of radiometric correction on different dates.…”