A General Convolutional Neural Network to Reconstruct Remotely Sensed Chlorophyll-a Concentration

Abstract

Satellite-observed chlorophyll-a (Chl-a) concentrations are key to studies of phytoplankton dynamics. However, there are gaps in remotely sensed images mainly due to cloud coverage which requires reconstruction. This study proposed a method to build a general convolutional neural network (CNN) model that can reconstruct images in unfamiliar areas. Although several CNN models to reconstruct Chl-a in a specific area have already been proposed, the model in this research has the advantage of generality. The model uses a more flexible U-net architecture so that it can accept input of different shapes. Images from three areas of different shapes were used in model training to improve the generality of the model. Six models, with different auxiliary input schemes and architectures, were trained and evaluated. Results show that the model with bathymetry input and coarse-to-fine architecture has the best performance and can give reasonable reconstruction for the unfamiliar area. The best model shows better results than traditional interpolation methods when reconstructing for an unfamiliar area, especially in regions outside the data coverage.