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

2025-05-27

AI Summary

Low-resolution satellite imagery limits precision agriculture, while unmanned aerial systems (UAS) provide high-spatial-detail data but suffer from limited coverage and high operational costs—especially for hyperspectral acquisition. To address this, we propose a lightweight UAS-guided cross-domain super-resolution framework that, for the first time, generates high-resolution multispectral satellite imagery solely from RGB UAS imagery—without requiring cloud-free satellite priors and enabling transfer across heterogeneous farmland systems. Our method integrates spectral expansion (via SRCNN) and spatial super-resolution within a unified architecture, augmented by multi-source remote sensing temporal-spatial alignment and data fusion techniques. Experiments demonstrate significant improvements: crop biomass and nitrogen content estimation accuracy increase by 18% and 31%, respectively, while UAS flight frequency is substantially reduced. The framework enables full-season, field-scale dynamic monitoring, establishing a new paradigm for cost-effective, high-accuracy agricultural remote sensing.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Applications

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Enhancing satellite imagery resolution using UAS data for precision agriculture
• Improving biomass and nitrogen estimation accuracy in crops
• Reducing reliance on costly UAS flights through scalable super-resolution methods

Innovation

• Fuses satellite and UAS imagery using super-resolution
• Extends UAS RGB data to vegetation spectral regions
• Lightweight scalable system for precision agriculture