VConstruct: a computationally efficient method for reconstructing satellite derived Chlorophyll-a data




Ehrler, Matthew

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The annual phytoplankton bloom is an important marine event. Its annual variability can be easily recognized by ocean-color satellite sensors through the increase in surface Chlorophyll-a concentration, a key indicator to quantitatively characterize all phytoplankton groups. However, a common problem is that the satellites used to gather the data are 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 most popular. However, DINEOF has a high computational cost, taking both significant time and memory to generate reconstructions. We propose a machine learning approach to reconstruction of Chlorophyll-a data using a Variational Autoencoder (VAE). Our method is 3-5x times faster (50-200x if the method has already been run once in the area). Our method uses less memory and increasing the size of the data being reconstructed causes computational cost to grow at a significantly better rate than DINEOF. We show that our method's accuracy is within a margin of error but slightly less accurate than DINEOF, as found by our own experiments and similar experiments from other studies. Lastly, we discuss other potential benefits of our method that could be investigated in future work, including generating data under certain conditions or anomaly detection.



VAE, deep learning, chl-a, reconstruction, computational efficency, cloud removal, generative modelling