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Projecte llegit

Títol: Feasibility Study of Drone-Based GNSS-R for Soil Moisture Estimation

Estudiants que han llegit aquest projecte:

Director/a: ESPONA DONÉS, MARGARIDA

Departament: MAT

Títol: Feasibility Study of Drone-Based GNSS-R for Soil Moisture Estimation

Data inici oferta: 25-03-2026     Data finalització oferta: 20-07-2026


Estudis d'assignació del projecte:
    MU DRONS
Tipus: Individual
 
Lloc de realització: Fora UPC    
 
        Supervisor/a extern: M. Eulàlia Parés Calaf
        Institució/Empresa: CTTC
        Titulació del Director/a: PhD Aerospace Science and Technology
 
Paraules clau:
UAS, GNSS, Reflectometry, Soil moisture
 
Descripció del contingut i pla d'activitats:
The main objective of this study is the integration of GNSS-R technology with drones to perform soil moisture measurements. GNSS-R (Global Navigation Satellite System Reflectometry) is a remote sensing technique that enables the characterization of certain properties of the Earth's surface by comparing the direct GNSS signal received from satellites with the signal reflected by the surface. Traditionally, data acquisition for GNSS-R applications has been mainly performed using Low Earth Orbit (LEO) satellites. In this project, we aim to improve the spatial resolution of the acquired data.

The study will focus on improving the results achieved in previous research, particularly with regard to the accuracy of the data obtained using a GNSS receiver mounted on a drone. The first step will be to identify a GNSS receiver capable of providing higher-precision measurements (centimeter-level accuracy) while remaining suitable for integration into a drone platform. The second step will be to perform soil moisture measurements under different moisture conditions using the drone-based GNSS-R system in order to validate the proposed methodology.

Regarding the drone platform, different antenna placement configurations will be evaluated, as several factors must be considered to minimize interference. These include payload weight, which affects the drone's flight performance, and electromagnetic interference generated by the drone itself, which may degrade the quality of the GNSS signals.
 
Overview (resum en anglès):
This project investigates the feasibility of implementing the Global Navigation Satellite System-Reflectometry (GNSS-R) technique using a drone as an observation platform to detect soil moisture variations. This technique consists of comparing a direct GNSS signal with a reflected signal in order to obtain information about the reflecting surface. GNSS-R has been implemented using different observation platforms, with drones being the focus of this project due to their potential applications in remote sensing.

There are different techniques for soil moisture estimation, some of which require the collection of soil samples or the use of dedicated sensing instruments. GNSS-R is a remote sensing technique that allows soil moisture conditions to be investigated without direct soil sampling, which may provide advantages in certain scenarios.

The project was conducted through two main experimental phases. First, the GNSS-R system, consisting of two GNSS receivers, two dual-band antennas and a Raspberry Pi for data acquisition, was validated and integrated onto a drone. Two antenna configurations were tested and evaluated in terms of flight stability, satellite visibility, signal-to-noise ratio (SNR) and positioning performance. The dual-level configuration provided better overall performance and was therefore selected for the subsequent soil moisture experiment. In the second phase, four flights were conducted over a predefined observation area under progressively increasing soil moisture conditions. Direct and reflected GNSS observations were simultaneously acquired and analysed using SNR as the main signal-quality parameter.

The results showed that the reflected signal generally presented lower SNR than the direct signal, while the reflected SNR tended to increase as volumetric water content (VWC) increased. Consequently, the differential SNR between direct and reflected signals generally decreased with increasing soil moisture. Satellite-level analysis of fifteen common satellites showed a predominantly decreasing differential SNR trend. Pearson correlation analysis also revealed predominantly negative relationships between VWC and differential SNR, with ten satellites presenting moderate to strong negative correlations. Overall, the results provide an initial indication that drone-based GNSS-R observations may be sensitive to soil moisture variations.

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