Learning to See More: UAS-Guided Super-Resolution of Satellite Imagery for Precision Agriculture
Arif Masrur, Peder A. Olsen, Paul R. Adler, Carlan Jackson, Matthew W. Myers, Nathan Sedghi, Ray R. Weil
TL;DR
The paper tackles the challenge of achieving high spatial and rich spectral information for precision agriculture by fusing UAS and satellite data through a spectral-spatial-temporal super-resolution framework. It introduces spectral extension (RGB UAS augmented with Red Edge/NIR simulated via hyperspectral UAS data), spatial extension (training on high-resolution targets to sharpen satellite imagery in unflown areas), and temporal extension (high-resolution data for different times), with a core SRCNN-based approach and NNLS spectral alignment to mimic Sentinel-2 spectral responses. The approach demonstrates that reconstructed sub-meter Sentinel-2-like imagery can boost biomass and nitrogen predictions by up to $18 ext{ extpercent}$ and $31 ext{ extpercent}$ respectively, while significantly reducing the need for extensive UAS flights and expensive hyperspectral sensors. The method generalizes across regions and crops and remains effective when cloud-free satellite data are unavailable, offering a practical, cost-effective path to scalable precision farming. Collectively, these findings imply that targeted, spectrum-aware fusion of UAS and satellite imagery can deliver actionable agronomic insights with substantial operational savings.
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.