Wetland classification based on the synergy of multi-source spatio-temporal spectral data — An example from Kenya

This study based on the Google Earth Engine (GEE) platform proposes a wetland classification method tailored to the diverse wetland types within Kenya, Africa, by leveraging multi-source feature extraction and integration. Firstly, a large-scale wetland sample collection strategy is developed by integrating existing land cover products and wetland-related datasets. The study places particular emphasis on the use of time-series Sentinel-2 imagery and Shuttle Radar Topography Mission (SRTM) data to design high-resolution texture feature extraction and spatiotemporal spectral information reconstruction techniques. The process yields four categories of multi-source feature sets, including spectral bands, spectral indices, topographic attributes, and texture features. Finally, a random forest algorithm is employed to perform remote sensing-based classification of wetland types across the study area. The experimental results obtained demonstrate that the integration of multi-source features has a significant effect on the enhancement of classification accuracy. In comparison with single-feature inputs, the optimal feature combination attains an overall accuracy of 82.65% and a Kappa coefficient of 77.51%. This study provides a reliable foundation for the scientific management and sustainable development of wetland ecosystems, as well as an efficient technical framework for large-scale wetland classification and mapping.