  Remote sensing of Chlorophyll-a is vital in monitoring climate change.
Chlorphyll-a measurements give us an idea of the algae concentrations in the
ocean, which lets us monitor ocean health. However, a common problem is that
the satellites used to gather the data are commonly obstructed by clouds and
other artifacts. This means that time series data from satellites can suffer
from spatial data loss.
  There are a number of algorithms that are able to reconstruct the missing
parts of these images to varying degrees of accuracy, with Data INterpolating
Empirical Orthogonal Functions (DINEOF) being the current standard. However,
DINEOF is slow, suffers from accuracy loss in temporally homogenous waters,
reliant on temporal data, and only able to generate a single potential
reconstruction.
  We propose a machine learning approach to reconstruction of Chlorophyll-a
data using a Variational Autoencoder (VAE). Our accuracy results to date are
competitive with but slightly less accurate than DINEOF. We show the benefits
of our method including vastly decreased computation time and ability to
generate multiple potential reconstructions. Lastly, we outline our planned
improvements and future work.
