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Proceedings Paper

GNSS-R sensor sensitivity to soil moisture and vegetation biomass and comparison with SAR data performance
Author(s): S. Paloscia; E. Santi; G. Fontanelli; S. Pettinato; A. Egido; M. Caparrini; E. Motte; L. Guerriero; N. Pierdicca; N. Floury
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Paper Abstract

In this paper the results obtained from an experiment focused on the capabilities of GNSS-R sensors for land applications are described. This experiment was carried out within the framework of two ESA projects devoted to the investigation of GNSS-R signal over land: LEiMON(Land Monitoring with Navigation Signals) and GRASS (GNSS Reflectometry Analysis for BiomaSS Monitoring). The latter project consisted in the analysis of GNSS-R signal collected by a dual polarization sensor installed on an aircraft which flew over a test area in Italy with agricultural fields and poplar plots. It has been observed that the LR reflection coefficient was sensitive to changes in the surface soil moisture, with a total variation of about 6 dB between the dry and wet seasons, within an interval between -8/-17 dB.Whereas, the RR reflection coefficient was generally very low for all surfaces, in the range -20/-25 dB, with an increasing trend with incidence angle. LR reflection coefficient was directly related to the main parameters of soil and vegetation, namely soil moisture and vegetation biomass and rather good sensitivity to these parameters was observed. The sensitivity to soil moisture was of about 0.25dB/%soil moisture. These results have been compared with those obtained in the LEiMON project showing a good agreement. A clear correlation was also observed between LR reflection coefficient and poplar biomass, especially at steep incidence angles (17-23°). The observed sensitivity was of about 1.0dB/(50-100t/ha of dry biomass). These results have been subsequently compared with those obtained at the same frequency (L-band) with SAR sensors. From the comparison it was observed that the sensitivity of GNSS-R signal is generally lower than that one of SAR, except for the case of forest biomass. The obtained results suggest good prospects of GNSS-R especially for soil moisture and forest biomass monitoring, also in view of the increasing availability of GNSS constellations and the potential synergy with other Earth Observation sensors like SAR’s.

Paper Details

Date Published: 17 October 2013
PDF: 7 pages
Proc. SPIE 8891, SAR Image Analysis, Modeling, and Techniques XIII, 88910I (17 October 2013); doi: 10.1117/12.2029382
Show Author Affiliations
S. Paloscia, Istituto di Fisica Applicata Nello Carrara, CNR (Italy)
E. Santi, Istituto di Fisica Applicata Nello Carrara, CNR (Italy)
G. Fontanelli, Istituto di Fisica Applicata Nello Carrara, CNR (Italy)
S. Pettinato, Istituto di Fisica Applicata Nello Carrara, CNR (Italy)
A. Egido, Starlab (Spain)
M. Caparrini, Starlab (Spain)
E. Motte, Starlab (Spain)
L. Guerriero, Univ. degli Studi di Roma Tor Vergata (Italy)
N. Pierdicca, Univ. degli Studi di Roma La Sapienza (Italy)
N. Floury, European Space Agency, ESTEC (Netherlands)

Published in SPIE Proceedings Vol. 8891:
SAR Image Analysis, Modeling, and Techniques XIII
Claudia Notarnicola; Simonetta Paloscia; Nazzareno Pierdicca, Editor(s)

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