Learning to See More: UAS-Guided Super-Resolution of Satellite Imagery for Precision Agriculture

• URL: http://arxiv.org/abs/2505.21746v1
• Date: Tue, 27 May 2025 20:34:56 GMT
• Title: Learning to See More: UAS-Guided Super-Resolution of Satellite Imagery for Precision Agriculture
• Authors: Arif Masrur, Peder A. Olsen, Paul R. Adler, Carlan Jackson, Matthew W. Myers, Nathan Sedghi, Ray R. Weil,
• Abstract summary: Unmanned Aircraft Systems (UAS) and satellites are key data sources for precision agriculture, yet each presents trade-offs.<n>Satellite data offer broad spatial, temporal, and spectral coverage but lack the resolution needed for many precision farming applications.<n>This study presents a novel framework that fuses satellite and UAS imagery using super-resolution methods.
• Score: 0.6910389029249664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unmanned Aircraft Systems (UAS) and satellites are key data sources for precision agriculture, yet each presents trade-offs. Satellite data offer broad spatial, temporal, and spectral coverage but lack the resolution needed for many precision farming applications, while UAS provide high spatial detail but are limited by coverage and cost, especially for hyperspectral data. This study presents a novel framework that fuses satellite and UAS imagery using super-resolution methods. By integrating data across spatial, spectral, and temporal domains, we leverage the strengths of both platforms cost-effectively. We use estimation of cover crop biomass and nitrogen (N) as a case study to evaluate our approach. By spectrally extending UAS RGB data to the vegetation red edge and near-infrared regions, we generate high-resolution Sentinel-2 imagery and improve biomass and N estimation accuracy by 18% and 31%, respectively. Our results show that UAS data need only be collected from a subset of fields and time points. Farmers can then 1) enhance the spectral detail of UAS RGB imagery; 2) increase the spatial resolution by using satellite data; and 3) extend these enhancements spatially and across the growing season at the frequency of the satellite flights. Our SRCNN-based spectral extension model shows considerable promise for model transferability over other cropping systems in the Upper and Lower Chesapeake Bay regions. Additionally, it remains effective even when cloud-free satellite data are unavailable, relying solely on the UAS RGB input. The spatial extension model produces better biomass and N predictions than models built on raw UAS RGB images. Once trained with targeted UAS RGB data, the spatial extension model allows farmers to stop repeated UAS flights. While we introduce super-resolution advances, the core contribution is a lightweight and scalable system for affordable on-farm use.

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