Toward a structured workflow to harmonize soil water content products: A comparative study across different methods

This study introduces a structured, multi-step workflow for harmonizing diverse soil water content (SWC) datasets to improve their meaningful comparability while pre-serving their intrinsic characteristics. The workflow addresses four components: unit, temporal, vertical, and lateral spatial harmonization, and is applied to point-scale (PS) sensors, cosmic-ray neutron sensing (CRNS), and remote sensing (RS) data from seven European sites. Statistical metrics and physically based plausibility checks using pedotransfer functions define realistic SWC ranges. A literature review of 60 studies identified five methodological classes: general statistical, spatial statistical, machine learning (ML), triple collocation (TC), and process-based models. For unit harmonization, total water capacity conversion outperformed mean-variance matching, improving the proportion of values within the field capacity (FC)–wilting point (WP) range. Temporal harmonization showed negligible differences between daily averaging and satellite overpass selection (<1% for CRNS, ∼16% for PS for both methods). Depth harmonization using weighted averaging and exponential filtering enhanced dataset alignment (ΔR = 0.15, ΔubRMSD [unbiased root mean square deviation = −0.03]). For lateral spatial, while simpler methods like areal mean are easy to implement with a single PS sensor, they often fail to preserve the distinct signals. The TC method provides a more robust, error-aware approach for data-scarce sites. ML methods like random forest can capture nonlinearity but risk overfitting and are more applicable to data fusion. More spatially faithful method, ordinary kriging, provided the highest quantitative improvements (FC–WP plausibility +16% for CRNS, +30% for RS). Optimal harmonization is context-dependent, balancing data availability, interpretability, and physical consistency. The proposed framework enhances comparability and traceability across multi-sensor SWC studies.