Despite its considerable potential for agricultural monitoring and precision agriculture, the operational adoption of remote sensing remains limited by several challenges, including the robustness of measurements under varying acquisition conditions and the transferability of models across different environments and datasets. This session aims to address the methodological challenges associated with scaling remote sensing approaches from experimental settings to reliable operational applications in agriculture. We welcome contributions focusing on methods for improving the consistency and comparability of remote sensing measurements under varying acquisition conditions, including atmospheric and weather effects, illumination and viewing geometry, and bidirectional reflectance distribution function corrections. We also encourage studies developing and evaluating robust and transferable models, including AI-based approaches, with particular emphasis on validation using independent datasets and assessment of model generalization across different sites, seasons, crops, sensors, and environmental conditions. The session is open to all remote sensing platforms, including UAS, ground-based and tractor-mounted systems, and satellites, as well as a wide range of sensors, such as RGB, multispectral, hyperspectral, and thermal. Contributions may address diverse agricultural applications, including pest and disease detection, weed mapping, nutrient and chlorophyll assessment, crop monitoring, and yield estimation. By advancing reliable and transferable remote sensing approaches, this session can contribute to evidence-based agricultural and environmental policies by enabling consistent large-scale monitoring of crop conditions, resource use, and climate-related impacts.