Irrigation Channel Mapping from Very High-Resolution Satellite Imagery and LiDAR-Derived Digital Elevation Models

Comprehensive spatial inventories of irrigation channels inform water-resource management, hydrological modeling, and infrastructure planning, yet remain unavailable for many irrigated regions due to the high cost of manual mapping. Here we present a novel approach for irrigation channel mapping that integrates very high-resolution (0.3–0.5 m) WorldView satellite imagery, 1 m LiDAR-derived digital elevation model data, and ancillary land cover and irrigation datasets. The approach employs a U-Net convolutional neural network and a Hessian filter to identify potential surface-water features from optical imagery, while geomorphon classification identifies potential channel locations from digital elevation model data. A constrained dilation algorithm expands high-confidence channel detections along topographically favorable paths, and an XGBoost classifier distinguishes irrigation channels from natural surface-water features and false positives. Applied to a 569 km2 study area in the San Luis Valley, Colorado, the approach mapped 552 linear kilometers of irrigation channels—18% more than the U.S. Geological Survey National Hydrography Dataset Plus High Resolution and 35% more than the Colorado Division of Water Resources inventory. Object-based accuracy assessment indicated that 92.3% of mapped channel segments were definite or possible irrigation channels, with only 7.7% representing false positives. Among channel segments not appearing in existing datasets, 76% were judged to be definite (43%) or possible (33%) irrigation channels either missed by previous mapping efforts or constructed after the creation of those datasets. While recall (the proportion of reference channel pixels that were correctly mapped) remained limited (0.28), the approach demonstrates potential for rapidly generating spatially explicit channel inventories that provide a starting point for manual refinement: false positives can be removed quickly, whereas adding missed channels requires more substantial effort.