Perspectives and Challenges of Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing

Recent advances in Earth observation and machine learning have significantly enhanced the capacity to monitor agricultural systems and terrestrial vegetation across spatial and temporal scales. This editorial synthesizes the contributions of eleven studies published in the Special Issue ‘Machine Learning for Applications in Agriculture and Vegetation Using Remote Sensing’. These contributions highlight emerging methodological trends, as well as persistent challenges in remote sensing for agriculture and vegetation measurement and monitoring, and reflect the growing dominance of deep learning in high-resolution mapping and segmentation. An increasing importance of multi-sensor data fusion, integrating multi- and hyperspectral satellites, UAV, and environmental data, is found. Deep learning is emerging as an effective approach for retrieving key biophysical parameters such as biomass, crop height, and yield. The collected studies also emphasize the critical role of sensor characteristics and scale, particularly the trade-offs between spectral, spatial, and temporal resolution in vegetation analysis. Despite notable progress, several limitations remain. Model transferability across regions and sensors is still constrained and multi-source data integration often lacks standardized frameworks. Empirical approaches still dominate the retrieval of biophysical variables, limiting robustness and physical interpretability. The contributions also reveal a persistent gap between high-resolution, small-scale analyses and their scalability to regional or global applications. Therefore, this editorial argues for a transition towards hybrid modeling approaches that combine physical knowledge with data-driven machine learning methods, the adoption of formal data assimilation frameworks for multi-source integration, and the development of scalable and uncertainty-aware workflows. The broader scientific context of the contributions is given by providing a critical perspective on the current state of the field and outlining the key research directions necessary to advance remote sensing in agriculture and ecosystem monitoring.