Evaluation of L-band GPS signal attenuation to multiple vegetations using ground-based measurements

ABSTRACT

Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technique that can be regarded as a bistatic radar system. GNSS-R uses GNSS signals as signal sources and obtains the Earth’s surface environmental parameters, such as soil moisture (SM), by receiving the L-band microwave signal reflected from the Earth’s surface. However, surface vegetation could be one of the main factors influencing the accuracy of GNSS-R land applications since plants, including branches and leaves, attenuate GNSS signals. Additionally, the evaluation of signal attenuation caused by the plant canopy is quite difficult. In this paper, we study the attenuations of received L1- and L2-band GPS signals to the vegetation leaf area index (LAI) for different types of plants. The relationship between the attenuation of the GPS signal-to-noise ratio (SNR) (both above and below the canopy) and the LAI is established through field experiments. The results show that the mean SNR received in the L2 band is lower than that in the L1 band for each satellite but with a larger standard deviation (SD). The sensitivities of L1- and L2-band signals to the LAI are revealed, revealing greater sensitivity and a relatively good Pearson correlation coefficient (R) for lower elevation angles and vegetation biomass. In addition, the sensitivity and R of L2-band signals to the LAI are lower than those of L1-band signals. This study is significantly valuable for improving the quantitative representation of error estimates for GNSS-R SM retrieval. The established model can be employed in GNSS land applications and aid in solving signal surface-scattering problems in which accurate signal estimates are important.

KEYWORDS:

• Global Navigation Satellite System-Reflectometry (GNSS-R)
• Soil Moisture Retrieval
• Signal-to-Noise Ratio (SNR)
• Vegetation, Leaf Area Index (LAI)

Acknowledgements

The authors would like to thank the America Li-COR, Beijing Ligaotai, and Shanghai ComNav technology companies for technical support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [42001375, 42001362] and by the Shanghai Leading Talent Project (Grant No. E056061) and Strategic Priority Research Program Project of the Chinese 460 Academy of Sciences [Grant No. XDA23040100]