Benchmarking methods for identifying phenological regions

Delineating contiguous phenological regions (phenoregions) from time-series data requires methods that balance spatial contiguity, temporal homogeneity, and computational scalability, yet guidance on method selection under realistic spatial conditions remains limited. We systematically evaluate six spatially explicit regionalization methods—Automatic Regionalization with Initial Seed Location (Arisel), Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (Redcap), Spatial “K”luster Analysis by Tree Edge Removal (Skater), Extended Simple Linear Iterative Clustering (ESLIC), Spatial K-Means (SKM), and Spatially Constrained Spectral Clustering (SCSC)—using (i) a controlled synthetic landscape and (ii) real-world remote-sensing datasets comprising NDVI and EVI time series from different sensors in Mt. Kenya National Park and the Argentine Chaco. Methods are assessed with respect to cluster homogeneity, spatial contiguity, agreement with reference solutions, and runtime, enabling analysis of performance trade-offs across spatial scales. Results show that Arisel, Redcap, Skater, and ESLIC consistently emerged as the strongest methods, although their relative performance varied by landscape context and by whether runtime was considered. Arisel performed best on quality-focused evaluations in the synthetic and Mt. Kenya cases, but its high computational cost reduced its suitability as dataset size increased. Redcap and Skater were robust high-performing alternatives, while ESLIC provided the best overall balance between delineation quality and efficiency as computational cost is given greater weight. Beyond identifying high-performing methods, the framework offers an objective, transparent, and reusable basis for method selection, supporting informed algorithm choice in phenological analysis and in a broader class of geospatial and remote-sensing regionalization applications where contiguity, homogeneity, and scalability must be jointly considered.