Agricultural production is increasingly vulnerable to climate variability, extreme weather, and growing resource limitations. To better understand these challenges and support adaptation, regional crop modeling has become an essential tool for assessing agricultural productivity, food and water security, and the impacts of climate variability and change. At the same time, the growing availability of satellite observations provides unprecedented opportunities to better constrain, calibrate, and validate crop model simulations. This session focuses on recent methodological and applied advances in linking regional crop models with Earth observation datasets to improve predictive accuracy and robustness.
We invite contributions that address:
• Advances in regional crop modeling frameworks (process-based, statistical, and hybrid)
• Integration of AI/ML techniques within a remote sensing and crop modeling framework
• Data assimilation techniques and model parameterization strategies
• Integration of remote sensing data into regional crop modeling systems
• Seasonal yield forecasting and the role of improved initial conditions via data assimilation
• Uncertainty quantification of regional crop model output
• Applications to water use, irrigation, and agro-hydrological monitoring
• Applications to assess and optimize climate change adaptation strategies
• Benchmarking and intercomparison of crop models with remote sensing data
This session brings together researchers in crop modeling, remote sensing, climate science, and data assimilation to advance integration across disciplines and tackle global challenges in agriculture and food security.