Extension of atmospheric correction Polymer to hyperspectral sensors: application to HICO and first results for DESIS data

Abstract

Satellite sensor configurations enabling high spatial and high spectral resolution could be far more suited for monitoring inland and coastal water ecosystems’ water quality than common ocean color sensors. It is expected that even the composition of phytoplankton, the primary producer in these ecosystems, could be determined. Up to now, atmospheric correction is limiting the exploitation of these sensors’ data. Here, we evaluate the atmospheric correction method Polymer applied to hyper- and multispectral satellite data (HICO, DESIS and OLCI) over coastal and inland waters. We assess the quality of retrieved water reflectance and biomass for all and specific phytoplankton groups by comparison to in-situ matchup data and results from other retrieval methods.