Sustainable agriculture and forestry face the challenges of lacking scalable solutions and sufficient data for monitoring vegetation structural and physiological traits, vegetation (a)biotic stress, and the impacts of environmental conditions and management practices on ecosystem productivity. Remote sensing from spaceborne, unmanned/manned airborne, and proximal sensors provides unprecedented data sources for agriculture and forestry monitoring across scales. The synergy of hyperspectral, multispectral, thermal, LiDAR, or microwave data can thoroughly identify vegetation stress symptoms in near real-time and combined with modeling approaches to forecast ecosystem productivity. This session welcomes a wide range of contributions on remote sensing for sustainable agriculture and forestry including, but not limited to: (1) the development of novel sensing instruments and technologies; (2) the quantification of ecosystem energy, carbon, water, and nutrient fluxes across spatial and temporal scales; (3) the synergy of multi-source and multi-modal data; (4) the development and applications of machine learning, radiative transfer modeling, or their hybrid; (5) the integration of remotely sensed plant traits to assess ecosystem functioning and services; (6) the application of remote sensing techniques for vegetation biotic and abiotic stress detection; and (7) remote sensing to advance nature-based solutions in agriculture and forestry for climate change mitigation. This session is inspired by the cost action program, Pan-European Network of Green Deal Agriculture and Forestry Earth Observation Science (PANGEOS, https://pangeos.eu/), which aims to leverage state-of-the-art remote sensing technologies to advance field phenotyping workflows, precision agriculture/forestry practices and larger-scale operational assessments for a more sustainable management of Europe’s natural resources.