2022 · Remote Sensing · Sentinel-3

Toward Atmospheric Correction Algorithms for Sentinel-3/OLCI Images of Productive Waters

Molkov, A., Fedorov, S., Pelevin, V.

Atmospheric correction of remote-sensing imagery over optically complex waters is still a challenging task, and algorithms that work for turbid waters need testing over eutrophic inland basins. Such a test was carried out on a Sentinel-3/OLCI image of the productive Gorky Reservoir during an intense blue-green algal bloom, using chlorophyll a and remote-sensing reflectance measured from a motorboat. Four common atmospheric-correction (AC) algorithms all showed unsatisfactory accuracy: aerosol optical depth varied widely (AOD(865) = 0.005–0.692) even over a 10 × 10 km area and correlated with chlorophyll a, so part of the water-leaving signal from the bloom was mistaken for atmosphere. A proposed algorithm with a fixed AOD — set from relatively clean water as the 5th percentile of AOD across water pixels — recovered remote-sensing reflectance with high accuracy (regression slopes near 1, intercepts near 0, R² > 0.9) and notably lower error than the other AC algorithms.

Cite as · BibTeX

@article{2022olciatmosphericcorrection, title = {Toward Atmospheric Correction Algorithms for Sentinel-3/OLCI Images of Productive Waters}, author = {Molkov, A. and Fedorov, S. and Pelevin, V.}, year = {2022}, journal = {Remote Sensing}, doi = {10.3390/rs14153663}, url = {https://doi.org/10.3390/rs14153663}, }