Wang et al. (2026) A dual-mode UAV radar synergistic observation system and processing framework for high-resolution farmland imaging and soil moisture retrieval

Identification

• Journal: Agricultural Water Management
• Year: 2026
• Date: 2026-06-30
• Authors: D Wang, Pei Cao -, Xiaoying Song, Wenting Han
• DOI: 10.1016/j.agwat.2026.110590

Research Groups

• College of Mechanical and Electronic Engineering, Northwest A&F University
• Institute of Water-saving Agriculture in Arid Areas (IWSA), Northwest A&F University
• National Engineering Laboratory for Crop High-efficiency Water Use, Yangling 712100, China

Short Summary

This study develops a dual-mode microwave radar cooperative observation system mounted on an unmanned aerial vehicle (UAV) to achieve high-resolution farmland imaging and soil moisture retrieval. The system integrates side-looking and downward-looking configurations under a shared RTK-based geometric framework.

Objective

• To develop a lightweight UAV-borne microwave radar system that can support both side-looking SAR imaging and downward-looking soil moisture retrieval using a shared radar payload and RTK-based geometric reference.
• To improve farmland radar image focusing and geometric consistency by combining two-step motion compensation, minimum entropy autofocus, and SIFT-based mosaicking in the side-looking mode.
• To reduce the influence of UAV vertical motion on radar feature extraction and to extract layer-wise canopy- and soil-related backscattering features for RF-based soil moisture estimation in the downward-looking mode.

Study Configuration

• Spatial Scale: Field-scale observation with high spatial resolution (up to 0.1 m) for farmland imaging.
• Temporal Scale: Real-time data acquisition and processing for coordinated observation of both modes.

Methodology and Data

• Models used:
  • Improved range Doppler algorithm (RDA)
  • Minimum entropy autofocus (MEA)
  • Random forest (RF) model for soil moisture retrieval
  • SIFT-based mosaicking for geometrically consistent image registration
• Data sources: Raw radar echoes acquired by the PulsOn 440 ultra-wideband wireless transceiver module.

Main Results

• High-resolution farmland imaging with structural similarity index (SSIM) of 0.889 and root mean square error (RMSE) of 0.443 pixels in the side-looking mode.
• Soil moisture retrieval with RMSE values of 0.0213, 0.0110, and 0.0101 cm³/cm³ in Zones I–III, respectively, and correlation coefficients (R) of 0.6898, 0.7270, and 0.6308.

Contributions

• The proposed dual-mode framework can support field-scale soil moisture mapping, irrigation nonuniformity diagnosis, and precision irrigation decision-making in agricultural water management.
• The study addresses the limitations of traditional single-sensor systems by integrating a shared radar payload and RTK-based geometric reference for coordinated observation.

Funding

• This research was funded by the National Key Research and Development Program of China (Grant No. 2017YFC1501301).
• The authors acknowledge the support from the Northwest A&F University's Fundamental Research Funds for the Central Universities (Grant No. ZK2021033).

Citation

@article{Wang2026dualmode,
  author = {Wang, D and -, Pei Cao and Song, Xiaoying and Zhou, Yangshuo and Han, Wenting},
  title = {A dual-mode UAV radar synergistic observation system and processing framework for high-resolution farmland imaging and soil moisture retrieval},
  journal = {Agricultural Water Management},
  year = {2026},
  doi = {10.1016/j.agwat.2026.110590},
  url = {https://doi.org/10.1016/j.agwat.2026.110590}
}