Sustainable agriculture faces the challenges of scaling solutions and gathering sufficient data for monitoring plant characteristics, environmental conditions, and management practices. Remote sensing from spaceborne, unmanned/manned airborne, and proximal sensors provides unprecedented data sources for agriculture monitoring across scales. The synergy of hyperspectral, multispectral, thermal, LiDAR, or microwave data can thoroughly identify crop stress and predict agroecosystem productivity with ecosystem modeling. This session welcomes a wide range of contributions on remote sensing for sustainable agriculture 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 applications of machine learning, radiative transfer modeling, or their hybrid; (4) the integration of remotely sensed plant traits to assess agroecosystem functioning and services; and (5) remote sensing to advance nature-based solutions in agriculture for climate change mitigation.

• B - Biogeosciences
• EP - Earth and Planetary Surface Processes
• H - Hydrology
• IN - Informatics