Evaluation of ocean color and sea surface temperature sensors algorithms using in situ data: A case study of temporal and spatial variability on two northeast Atlantic seamounts

Abstract

Main objectives of this paper are to evaluate SeaWiFS, MODIS, and AVHRR satellite imagery performances against in situ data around two Northeast Atlantic seamounts, Sedlo and Seine. The temporal and spatial variability of satellite-derived near-surface chlorophyll a (Chl a) and sea surface temperature (SST) is also analysed. SeaWiFS tends to show good accuracy with the in situ data for Sedlo seamount, while for Seine it tends to slightly overestimate the values. Oppositely, MODIS tends to underestimate Chl a for both seamounts. Match-up SST analyses show that MODIS underestimates the in situ values on Seine seamount. The best correlation was attained with AVHRR on Sedlo. Seasonal variations are clearly pronounced on Sedlo with typical spring and autumn Chl a blooms, while further to the south, on Seine, less intense blooms are registered, as expected. Higher/lower SST values are observed during summer/winter respectively, showing clear seasonal patterns. A time lag of about one month for the maximum SST heating/cooling from Sedlo to Seine is noted.