Editorial: Machine learning for advanced remote sensing: from theory to applications and societal impact

作者：Rui Li, Shaoqing Dai, Bin Jiang, C T Zhang, Haoyang Yang, Weidong Zhao · 发表于：Frontiers in Remote Sensing · 年份：2026 · DOI：10.3389/frsen.2026.1931394 · 研究领域：Computer science、Machine learning、Artificial intelligence、Data science、Remote sensing、Data mining

A central theme emerging from this collection is that remote-sensing machine learning remains most powerful when it is coupled with domain knowledge and spatial-temporal reasoning. In 10. 3389/frsen.2025.1696570, Huang et al. show that coffee mapping can be improved by integrating Sentinel-2 spectral information with vegetation indices, texture, terrain, administrative context, field samples, and phenological segmentation. The study illustrates that crop classification is not merely a pixel-level recognition task; it is a spatial-temporal inference problem shaped by crop calendars, topography, management geography, and regional ecological conditions.A related insight is developed in 10.3389/frsen.2025.1661528. Siddiqui et al. demonstrate that phenology-based irrigation detection is effective in semi-arid regions with a prolonged dry season, but less transferable to humid regions where non-irrigated vegetation does not senesce sufficiently. This contribution is important because it links classification validity to landscape process. It also shows how remote sensing can support food security, water planning, and energy infrastructure decisions by identifying where farmer-led dry-season cultivation is already occurring.Operational remote sensing also depends on models that are both accurate and deployable. In 10.3389/frsen.2025.1668978, Cui et al. address the challenge of extracting narrow, elongated, and often occluded roads from high-resolution satellite imagery. Their hierarc...