Remote Sensing and Agroecosystem Modeling to Support Sustainable Nitrogen Management in the Midwest

Cross-scale sensing to integrate ground measurements, airborne hyperspectral sensing, and NASA satellite Earth observation to monitor crop nitrogen in three I states.

Project Summary

This project aims to improve sustainable nutrient management in the Midwest to benefit crop production, reduce fertilizer use, and protect the environment. Dr. Guan and his team will use advanced tools like satellite remote sensing, AI and agroecosystem models to assist farmers in handling nitrogen management in their fields. They are developing a framework to integrate satellite Earth observations, like Harmonized Landsat and Sentinel-2 (HLS) products, with proximal, hyperspectral airborne sensors to to derive field-level crop nitrogen in the U.S. Midwest. This cross-scaling framework has a high potential to serve as a scalable solution to quantify high-resolution crop nitrogen at the regional scale and to support sustainable fertilization management. By closely tracking nitrogen levels in crops, they will determine the optimal rate, type, time, and location for nitrogen fertilizer applications.

Study Area:

The Midwest

Earth Observations Used

Harmonized Landsat and Sentinel-2 (HLS)

Multi-source satellite fusion data (STAIR)

Xiang Zhu

University of Illinois Urbana- Champaign

Yiwei An

University of Illinois Urbana- Champaign

Lead Institution

Project Lead

Kaiyu Guan

University of Illinois Urbana- Champaign

Project Team:

Sheng Wang

University of Illinois Urbana- Champaign

Bing Peng

University of Illinois Urbana- Champaign