A global analysis of SAR altimetry signals over different landcover types

Satellite radar altimeters were originally designed for water applications, but their echoes over land surfaces remain less well understood. In this study we analyze how Sentinel-3 (S3) synthetic aperture radar (SAR) altimetry waveforms respond to different surface types and what physical characteristics are encoded in the signal. To probe this, we conduct classification experiments with a feature-enhanced one-dimensional convolutional neural network (1D-CNN) and analyze its performance. Since surface type information is relevant for climate, hydrology, and biodiversity applications, understanding these signal responses shows to what extent altimetric waveforms may provide consistent class-specific information despite their large elliptical footprint and heterogeneous landscapes. This study investigates the response of Sentinel-3 altimetry waveforms to different land cover types by employing a 1D-CNN to extract land cover information, complemented by a visual analysis of waveform patterns in relation to surface structures. Our results show that information about the underlying surface is embedded in the signals and can be extracted. They further reveal the sensitivity of Sentinel-3 altimetry to variations in land cover. By enhancing our 1D-CNN model with shape-based and contextual features, it effectively captures surface characteristics despite the large altimeter footprint. An ablation study highlights the complementary role of these features, as their removal negatively impacts performance. The best-performing 1D-CNN achieves a macro-averaged F1 (Macro-F1) score of 0.57 and an overall accuracy of 0.67, outperforming both a random forest and a dummy baseline. The classification includes six surface types: Tree, Shrub, Grass, Crop, Bare/Sparse Vegetation, and Water. Although some misclassification occurs, particularly in transition zones and among classes with similar vegetation structures and soil properties, the model provides valuable insights into systematic waveform behavior, highlighting the potential of SAR altimetry signals to capture broad surface characteristics. • A deep learning model classifies global land cover using satellite radar altimetry. • New features enhance land cover classification from Sentinel-3 waveform data. • The model outperforms traditional classifiers, achieving 67% overall accuracy. • Misclassifications reveal challenges in differentiating similar land cover types.