Estimating the characteristics of soil surface represents a significant area in applications such as hydrology, climatology, and agriculture. Signals transmitted from Global Navigation Satellite Systems (GNSSs) can be used for soil monitoring after reflection from the Earth's surface. In this paper, the feasibility of obtaining surface characteristics from the power ratio of left-hand (LH) reflected signal-to-noise ratio (SNR) over direct right-hand (RH) is investigated. The analysis was done regardless of the surface roughness and the incoherent components of the reflected power. First, the analysis was carried out on data collected during several in situ measurements in controlled environments with known characteristics. Then, further data were collected by a GNSS receiver prototype installed on a small aircraft and analyzed. This system was calibrated on the basis of signals reflected from water. The reflectivity and the estimated permittivity showed good correlation with the types of underlying terrain.

Estimation of Surface Characteristics Using GNSS LH-Reflected Signals: Land Versus Water

CANONE, Davide;
2016-01-01

Abstract

Estimating the characteristics of soil surface represents a significant area in applications such as hydrology, climatology, and agriculture. Signals transmitted from Global Navigation Satellite Systems (GNSSs) can be used for soil monitoring after reflection from the Earth's surface. In this paper, the feasibility of obtaining surface characteristics from the power ratio of left-hand (LH) reflected signal-to-noise ratio (SNR) over direct right-hand (RH) is investigated. The analysis was done regardless of the surface roughness and the incoherent components of the reflected power. First, the analysis was carried out on data collected during several in situ measurements in controlled environments with known characteristics. Then, further data were collected by a GNSS receiver prototype installed on a small aircraft and analyzed. This system was calibrated on the basis of signals reflected from water. The reflectivity and the estimated permittivity showed good correlation with the types of underlying terrain.
2016
9
10
4752
4758
http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443
Global Navigation Satellite System (GNSS) reflectometry; permittivity retrieval; signal-to-noise ratio (SNR); Computers in Earth Sciences; Atmospheric Science
Jia, Yan; Savi, Patrizia; Canone, Davide; Notarpietro, Riccardo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1619552
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