Soil moisture content (SMC) and above-ground biomass (AGB) are key parameters for the understanding of both the hydrological and carbon cycles. From an economical perspective, both SMC and AGB play a significant role in the agricultural sector, one of the most relevant markets worldwide. This paper assesses the sensitivity of Global Navigation Satellite System (GNSS) reflected signals to soil moisture and vegetation biomass from an experimental point of view. For that, three scientific flights were performed in order to acquire GNSS reflectometry (GNSS-R) polarimetric observations over a wide range of terrain conditions. The GNSS-R data were used to obtain the right-left and right-right reflectivity components, which were then georeferenced according to the transmitting GNSS satellite and receiver positions. It was determined that for low-altitude GNSS-R airborne platforms, the reflectivity polarization ratio provides a highly reliable observable for SMC due to its high stability with respect to surface roughness. A correlation coefficient of 0.93 and a sensitivity of 0.2 dB/SMC (%) were obtained for moderately vegetated fields with a surface roughness standard deviation below 3 cm. Similarly, the copolarized reflection coefficient shows a stable sensitivity to forest AGB with equal to 0.9 with a stable sensitivity of 1.5 dB/(100 t/ha) up to AGB values not detectable by other remote sensing systems.

In this paper, we introduce the concept, develop the theory, and demonstrate the advantages of fully focused coherent processing of pulse echoes from a nadir-looking pulse-limited radar altimeter. This process, similar to synthetic aperture radar (SAR) imaging systems, reduces the along-track resolution down to the theoretical limit equal to half the antenna length. We call this the fully focused SAR (FF-SAR) altimetry processing. The technique is directly applicable to SAR altimetry missions such as CryoSat-2, Sentinel-3, or Sentinel-6/Jason-CS. The footprint of an FF-SAR altimeter measurement is a narrow strip on the surface, which is pulse limited across track and SAR focused along track. Despite the asymmetry of the altimeter footprint, the fully focused technique may be useful for applications in which one needs to separate specific targets within highly heterogeneous scenes, such as in the case of sea ice lead detection, hydrology, and coastal altimetry applications. In addition, over rough homogeneous surfaces, such as the ocean or ice sheets, the improved multilooking capability of FF-SAR leads to a significant increase in the effective number of looks with respect to the delay/Doppler processing, resulting in better geophysical parameter estimation. The proposed processing technique was verified by processing full bit rate CryoSat-2 SAR mode data over the Svalbard transponder. Hydrology, sea ice, and open ocean applications are also demonstrated in this paper, showing the improvement of this technique with respect to conventional and delay/Doppler altimetry.

The use of Global Navigation Satellite Systems (GNSS) signals for remote sensing applications, generally referred to as GNSS-Reflectometry (GNSS-R), is gaining increasing interest among the scientific community as a remote sensing tool for land applications. This paper describes a long term experimental campaign in which an extensive dataset of GNSS-R polarimetric measurements was acquired over a crop field from a ground-based stationary platform. Ground truth ancillary data were also continuously recorded during the whole experimental campaign. The duration of the campaign allowed to cover a full crop growing season, and as a consequence of seasonal rains on the experimental area, data could be recorded over a wide variety of soil conditions. This enabled a study on the effects of different land bio-geophysical parameters on GNSS scattered signals. It is shown that significant power variations in the measured GNSS reflected signals can be detected for different soil moisture and vegetation development conditions. In this work we also propose a technique based on the combination of the reflected signal’s polarizations in order to improve the integrity of the observables with respect to nuisance parameters such as soil roughness.

The mean power of the reflected Global Navigation Satellite System (GNSS) signals acquired by a GNSS-Reflectometry (GNSS-R) receiver can be modeled through the integral bistatic radar equation by weighting the contributions of all scatterers on the surface by the system impulse response. The geophysical properties of the scattering surface affect the magnitude of the reflected navigation signals through the bistatic scattering coefficient which, in case the observed surface is land, is a function of the soil dielectric properties, surface roughness, and vegetation cover. In this paper, the GNSS-R signal simulator developed in the framework of the Land MOnitoring with Navigation signal (LEiMON) Project, supported by European Space Agency, is presented. The simulator is able to predict the power reflected by land, taking as input the system and observation parameters, as well as the land surface parameters. The latter are used to simulate both the coherent and the incoherent scattering, taking advantage of widely used theoretical models of bistatic scattering from bare soils and vegetated surfaces. First, the geometrical formulation is discussed, and then, the problem of polarization mismatch due to real antennas at circular polarization is faced following the polarization synthesis approach. Finally, a comparison with some experimental data collected during the LEiMON campaign is presented. The simulations display the same trend of the experimental data, thus showing that the simulator can be used as an efficient tool for the interpretation of GNSS-R measurements.

Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than −15 dB at all elevations above 45°, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than −30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics.

This paper investigates the global navigation satellite system-reflectometry (GNSS-R) measurements collected by the space GNSS receiver-remote sensing instrument (SGR-ReSI) on board the TechDemoSat-1 (TDS-1) satellite. The sensitivity of the SGR-ReSI measurements to the ocean surface winds and waves is characterized. The correlation with sea surface temperature (SST), wind direction, and rain is also investigated. The SGR-ReSI measurements exhibit clear sensitivity to wind speeds up to 20 m/s. There is also apparent sensitivity to 35 m/s wind speeds although the collocation dataset becomes sparser. A dependence on the swell is also observed for winds <;6 m/s. Additionally, a small correlation with SST is observed in which the slope of the SGR-ReSI measurements is positive for winds <;5 m/s, and reverses for winds >5 m/s. A weak wind direction signal was also observed, and an investigation of rain impacts did not conclusively confirm any influence on the data. These results are shown through an analysis of global statistics as well as an analysis of several case studies. This publicly released SGR-ReSI dataset provided a first opportunity to comprehensively investigate the sensitivity of GNSS-R measurements to various ocean surface parameters. The upcoming NASA cyclone global navigation satellite system will utilize a similar receiver to the SGI-ReSI; therefore, this data provides a valuable prelaunch insight.