Surface water mapping in rice paddies and wetlands using SENTINEL-1 data, random forests, and texture features

We aimed to provide an efficient and user-friendly methodology for mapping surface water in rice paddies and wetlands by utilizing a novel combination of high-resolution satellite images from Sentinel-1, the random forests (RF) machine learning algorithm using Sentinel Application Platform software provided by the European Space Agency and texture surface features. We focused on the Thessaloniki plain, with dominant surface water bodies being the rice fields in the broader Chalastra region and Kalochori lagoon. The optimal set of seven features yielding the best results regarding the highest classification accuracy included vertical − vertical (VV) and vertical − horizontal) (VH) polarizations, a combination of these polarizations VV + VH and VV − VH, and texture features based on entropy on both VV and VH polarizations and dissimilarity on VV polarization. Various values for the optimal number of trees in the RF algorithm were tested, including 70, 100, 128, 400, and 500 trees. The most suitable one was found to be the value 128, achieving accuracy levels ranging from 85.3% to 98.3%. Despite potential constraints, such as the occurrence of floating algae on the water’s surface, the proposed methodology can be effectively utilized as an enhanced approach to integrating machine learning algorithms, including texture features into surface water mapping techniques achieving high accuracy.