  Deep learning provide successful applications in many fields. Recently,
machines learning are involved for oceans remote sensing applications. In this
study, we use and compare about eight (8) deep learning estimators for
retrieval of a mainly pigment of phytoplankton. Depending on the water case and
the multiple instruments simultaneouslyobserving the earth on a variety of
platforms, several algorithm are used to estimate the chlolophyll-a from marine
reflectance. By using a long-term multi-sensor time-series of satellite
ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc, we make a unique deep
network model able to establish a relationship between sea surface reflectance
and chlorophyll-a from any measurement satellite sensor over West Africa. These
data fusion take into account the bias between case water and instruments. We
construct several chlorophyll-a concentration prediction deep learning based
models, compare them and therefore use the best for our study. Results obtained
for accuracy training and test are quite good. The mean absolute error are very
low and vary between 0,07 to 0,13 mg/m3.
