AI-empowered hyperspectral sensing advances agricultural ground truthing across scales
Abstract
Sustainable intensification of the full agricultural production pipeline, from field breeding experiments, precision farming applications, and large-scale yield forecasting, to assessing environmental impact, is needed to ensure global food security and bioenergy production for a growing population. The first prerequisite to achieve this goal is to accurately, timely, and cost-effectively monitor agroecosystem variables including crop traits, soil properties, and management practices across scales from local to globe. However, conventional agricultural ground-truthing using field sampling, laboratory analysis, and/or grower surveys is time-consuming and costly to support direct upscaling to petascale satellite data for large-scale agriculture monitoring and applications. To alleviate the challenge of lacking high-resolution and quality-consistent large-scale agroecosystem variables, we developed an artificial intelligence empowered cross-scale hyperspectral sensing framework to integrate proximal, airborne, and spaceborne data to provide a scalable solution to upscale agriculture ground truth data to every field in Illinois, USA. Specifically, we deployed an airborne full-optical-range hyperspectral imaging system (0.5 m spatial resolution and 3-5 nm spectral resolution) to collect hyperspectral reflectance across Illinois. We first upscaled field observations to airborne hyperspectral data through radiative transfer process-guided machine learning. Then we utilized airborne hyperspectral estimates as quasi ground truth data to be integrated with multi-source satellite fusion data STAIR to derive agroecosystem variables for every individual field across Illinois. This cross-scaling sensing framework shows high accuracy to detect large-scale crop nitrogen content, tillage practices, and cover crops in Illinois, the heartland of US Corn Belt. We highlight that hyperspectral data from proximal, airborne, and new/forthcoming spaceborne missions provide great potential to empower agricultural ground truthing across scales to support food and bioenergy production.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMGC42D0726W