Crop yield forecasting model integrating radiative transfer models with remote sensing data assimilation
A novel approach to crop yield forecasting that couples process-based crop models directly with Radiative Transfer Models, using vegetative indices and spectral signatures to improve predictability of crop biomass and yield. Validated at regional scale for the US Corn Belt.
Overview
This research proposes a novel methodology for improving crop yield forecasting by coupling process-based crop models with Radiative Transfer Models (RTMs). Rather than relying on pre-processed remote sensing products such as Leaf Area Index (LAI), which often suffer from low accuracy and precision due to complex conversion processes from raw satellite imagery, this approach integrates simpler vegetative indices or full spectral signatures directly into crop model forecasts. The goal is to improve the predictability of crop biomass and yield at a regional scale, with initial validation targeting the US Corn Belt at a 3 km x 3 km resolution.
Technical specifications
- Approach: Brings crop models to remote sensing data rather than processing remote sensing data into model-ready products
- Data integration: Couples process-based crop models with Radiative Transfer Models to assimilate vegetative indices or full spectral signatures
- Validation domain: Regional-scale testing across the US Corn Belt
- Spatial resolution: 3 km x 3 km grid
- Compute requirements: Cloud-based processing using Microsoft Azure infrastructure
- Key advantage: Avoids accuracy and precision losses associated with converting raw satellite imagery into derived crop variables such as LAI
Technology readiness level
The approach is currently at an early-to-mid research stage. Prior studies have demonstrated the feasibility of assimilating remote sensing products such as soil moisture and LAI into crop models, but this proposal advances the methodology by integrating RTMs directly. Future validation involves regional-scale testing for the Corn Belt, requiring software development and cloud compute resources to build and run the coupled model framework.
About University of Illinois, Urbana-Champaign
The University of Illinois Urbana‑Champaign is a flagship public research university with large‑scale research capacity and a broad academic portfolio. An on‑campus Research Park co‑locates corporate R&D teams and startups with faculty, while the National Center for Supercomputing Applications provides advanced computing and data capabilities for collaboration. Integration with a regional health system and an engineering‑based college of medicine enables clinical translation, and a long‑standing extension network links campus innovation to partners statewide. Research is supported by competitive federal funding from NSF, NIH, DOE, USDA, and DoD. A technology transfer office streamlines IP, licensing, and startups, complemented by incubators and prototyping in the Research Park.
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