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Remote sensing of Chlorophyll-a is vital in monitoring climate change.Chlorphyll-a measurements give us an idea of the algae concentrations in theocean, which lets us monitor ocean health. However, a common problem is thatthe satellites used to gather the data are commonly obstructed by clouds andother artifacts. This means that time series data from satellites can sufferfrom spatial data loss.  There are a number of algorithms that are able to reconstruct the missingparts of these images to varying degrees of accuracy, with Data INterpolatingEmpirical 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 potentialreconstruction.  We propose a machine learning approach to reconstruction of Chlorophyll-adata using a Variational Autoencoder (VAE). Our accuracy results to date arecompetitive with but slightly less accurate than DINEOF. We show the benefitsof our method including vastly decreased computation time and ability togenerate multiple potential reconstructions. Lastly, we outline our plannedimprovements and future work.

VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder