Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes
Image Processing Division, Brazilian Institute for Space Research, São José dos Campos 12227-010, Brazil
Remote Sensing Division, Brazilian Institute for Space Research, São José dos Campos 12227-010, Brazil
Author to whom correspondence should be addressed.
Academic Editors: Yunlin Zhang, Claudia Giardino, Linhai Li, Xiaofeng Li and Prasad S. Thenkabail
Received: 16 January 2017 / Revised: 18 March 2017 / Accepted: 24 March 2017 / Published: 29 March 2017
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 (
). 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 (
< 1%), and better match of the
shape compared with in-situ measurements over turbid lakes, although the accuracy varied depending on the spectral bands and methods. Particularly above 705 nm,
was highly affected by Amazon forest adjacency, and the proposed adjacency effect correction minimized the spectral distortions in
(RMSE < 0.006). Finally, an extensive validation of the methods is required for distinct inland water types and atmospheric conditions.
This is an open access article distributed under the Creative Commons Attribution License
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MDPI and ACS Style
Martins, V.S.; Barbosa, C.C.F.; de Carvalho, L.A.S.; Jorge, D.S.F.; Lobo, F.L.; Novo, E.M.L.M. Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes. Remote Sens. 2017, 9, 322.
Martins VS, Barbosa CCF, de Carvalho LAS, Jorge DSF, Lobo FL, Novo EMLM. Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes. Remote Sensing. 2017; 9(4):322.
Martins, Vitor S.; Barbosa, Claudio C.F.; de Carvalho, Lino A.S.; Jorge, Daniel S.F.; Lobo, Felipe L.; Novo, Evlyn M.L.M. 2017. "Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes." Remote Sens. 9, no. 4: 322.
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