The Global Navigation Satellite System-Scattering (GNSS-S) radar has a wide coverage and strong concealment, enabling large-scale and long-term monitoring of sea surface targets. However, its signal power is extremely low and susceptible to sea clutter interference. To address the challenge of detecting and tracking moving targets in complex maritime environments using low-resolution radar, this paper proposes a method for extracting moving target trajectories from GNSS-S radar under low signal-to-noise ratio (SNR) conditions. The method constructs a feature plane consisting of statistical and polarization characteristics, based on the unique distribution of different motion targets in this plane, the distinction between sea clutter and multi-motion targets is carried out using machine learning algorithms, and finally the trajectory association of the targets is achieved by the Kalman filter, and the tracking correctness can reach more than 93.89%. Compared with the tracking method based on high-resolution imaging targets, this technique does not require complex imaging operations, and only requires certain processing on the radar echo, which has the advantages of easy operation and high reliability.

Soil moisture is a key variable governing land–atmosphere water and energy exchanges. This letter proposes a novel soil moisture retrieval technique based on GNSS-S (Global Navigation Satellite System–Scattering) radar. Unlike conventional GNSS-R systems that are restricted to specular reflection points, the proposed GNSS-S radar exploits distributed GNSS backscattering to enable continuous bistatic imaging of surface soil moisture. The system is mounted on an airborne or spaceborne platform and uses navigation satellites as illuminators of opportunity. GNSS-S imaging is achieved through signal-level preprocessing, backprojection-based image formation, geometric correction, and relative radiometric calibration. Airborne experiments demonstrate that continuous surface imaging with a spatial resolution of approximately 100 m × 100 m can be achieved using L-band GNSS signals. Comparative analysis with in-situ soil moisture measurements shows consistent spatial distribution trends, and quantitative evaluation reveals moderate to strong correlations depending on bistatic geometry. These results validate the feasibility of GNSS-S radar for area-wide soil moisture monitoring and highlight its potential as a complementary technique to conventional active SAR systems.