Time-series surface water gap filling based on spatiotemporal neighbourhood similarity

Abstract

Optical satellite-derived surface water monitoring is challenging because of the spatial gaps in images caused by clouds, cloud shadows, voids, etc. Here, an efficient method for filling gaps in time-series surface water images is proposed, based on the spatiotemporal characteristics of water. This method utilises the accurately classified historical ternary (gap, water, non-water) or binary (water, non-water) water image time-series and the clear part of the ternary gap water image. Pixels with values of 0 and 1 in the same period water occurrence image are first used to correct the gap water image. The spatial neighbourhood similarity is then calculated as a quality control band for mosaicking the accurately classified historical water images. The final result is generated by replacing the gap pixels with a mosaic image. The proposed method was implemented on the Google Earth Engine, and 93 Landsat 8 top-of-atmosphere (TOA) images were used to verify its validity. Quantitative evaluations were adequate, with a mean accuracy, recall, and precision of 0.98, 0.90, and 0.85, respectively. The proposed method could improve the utilisation of optical remote sensing data and would be applicable to the production of large-area homogeneous surface water time-series and water resource monitoring.

Keywords

• Surface water
• Google Earth Engine
• Gap filling
• Spatiotemporal neighbourhood