Abstract

Fresh water is a vital resource for all aspects of agricultural and industrial production. Monitoring the variation of surface water level allows resource managers to detect perturbations, predict long-term trends in water availability, and set consumption guidelines accordingly. Satellite imaging data has been increasingly used to map surface water at global scales. Improving the performance of current water mapping strategies requires high-resolution image data with low revisit times. However, imaging devices on board of existing satellites face a trade-off between their spatial resolution and revisit period, which limits the applicability of those methods. In this work, a multimodal image fusion methodology is developed for water mapping. By combining data from multiple instruments, high-resolution image sequences with low revisit times are generated, leading to improved water mapping results. The proposed methodology was based on Bayesian filtering and smoothing theory, and is able to combine each observed images recursively for a reduced computation complexity. Experiments with real data acquired by Sentinel and Landsat instruments showed that the proposed strategy can lead to significant improvements in water mapping results with compared to competing methodologies.