Forecasting Lakes' Chlorophyll Concentrations Using Satellite Images and Generative Adversarial Networks

Abstract

Satellite data are extensively used for water quality monitoring purposes, offering a significantly reduced cost compared to in situ data sampling. Using past measurements to predict future conditions remains a challenging task, because of the complexity of the natural phenomena that are involved, with great potential in terms of water resources management. This paper proposes a model that can be used to forecast Chlorophyll-$\alpha$ (Chl-$\alpha$) values in water bodies, which are a common water quality indicator. The operation of the model lays on the fact that typically Chl-$\alpha$ increases and decreases periodically. First, we apply C2RCC, which is a common atmospheric correction algorithm, to Sentinel-2 images to get Chl-$\alpha$ maps for 15 lakes for 12 consecutive months around Europe. Then, we use this data set ($\sim$1,000 Sentinel-2 images) to train a Generative Adversarial Network (GAN) to recognize spatiotemporal patterns. To accomplish this task, pix2pix algorithm is employed, matching consecutive past and current Chl-$\alpha$ maps to future Chl-$\alpha$ maps. This model has been applied to 3 water bodies around Europe that are not included in the 15-lakes training data set and has been found to perform accurately, achieving high Pearson and Spearman correlations and low RMSE values. Overall, the model can be used to make Chl-$\alpha$ maps' predictions with low computational cost and without using any in situ data and without the requirement of training for every water body.