Global Navigation Satellite System Reflectometry Instrument Research Articles

Shipborne GNSS reflectometry for monitoring along-track significant wave height and wind speed

Shipborne Global Navigation Satellite System-Reflectometry (GNSS-R) for measuring along-track significant wave height (SWH) and wind speed are first investigated in this article. A real-time shipborne GNSS-R instrument was carried on the XUELONG-2 for the 38th Antarctic Scientific Expedition. The collected dataset covered the entire expedition of the XUELONG-2. The dependencies of the correlation time, normalized area, ratio of reflected-to-direct signal-to-noise ratio (SNR), spectral width, and peak frequency on SWH and wind speed are explored. It is found that the correlation time, spectral width, and peak frequency better depend on SWH and wind speed than other two observables, therefore, they are used to measure the SWH and wind speed. The elevation angle, SNR and ship velocity, also impact on the observables, thus their influences should be corrected. The geophysical model function (GMF) and neural network are used to develop retrieval models of SWH and wind speed. Neural networks provide better retrieval performances. The best root mean square errors (RMSEs) of the retrieved SWH and wind speed are 0.38 m and 2.49 m/s, with a temporal and spatial resolutions of 1 min and 600 m, respectively. The measurements from the shipborne GNSS-R also agree with the co-located European Centre for Medium-Range Weather Forecasts (ECMWF) and Jason-3 altimeter data.

Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals

Single-pass soil moisture retrieval has been a key objective of Global Navigation Satellite System-Reflectometry (GNSS-R) for the last decade. Achieving this goal will allow small satellites with GNSS-R payloads to perform such retrievals at high temporal resolutions. Properly modeling the soil surface roughness is key to providing high-quality soil moisture estimations. In the present work, the Physical Optics and Geometric Optics models of the Kirchhoff Approximation are implemented to the coherent and incoherent components of the reflectometry measurements collected by the SMAP radar receiver (SMAP-Reflectometry or SMAP-R). Two surface roughness products are retrieved and compared for a single-polarization approach, critical for single-polarization GNSS-R instruments that target soil moisture retrievals. Then, a polarization decoupling model is implemented for a dual-polarization retrieval approach, where the ratio between two orthogonal polarizations is evaluated to estimate soil moisture. Differences between linear and circular polarization ratios are evaluated using this decoupling parameter, and the theoretical soil moisture error with varying decoupling parameters is analyzed. Our results show a 1-sigma soil moisture error of 0.08 cm3/cm3 for the dual-polarization case for a fixed polarization decoupling value used for the whole Earth, and a 2-sigma error of 0.08 cm3/cm3 when the measured reflectivity and the VOD are used to estimate the polarization decoupling parameter.

Analysis of Polarimetric GNSS-R Airborne Data as a Function of Land Use

The objective of this study is to analyze GNSS-R data variations as a function of land cover using airborne measurements obtained with the GLObal Navigation Satellite System Reflectometry Instrument (GLORI), which is a polarimetric instrument. GNSS-R measurements were acquired at the agricultural Urgell site in Spain in July 2021. In situ measurements describing the soil and vegetation properties were then obtained simultaneously with flight measurements. The behavior of the observable copolarization (right-right) reflectivity Γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>RR</i></sub> and the cross-polarization (right-left) reflectivity Γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>RL</i></sub> as a function of land use is discussed. The distribution of coherent and incoherent components in the reflected power is estimated for different types of land cover.

Calibration Strategy for Compact Polarimetric GNSS-R Instruments

Polarimetric Global Navigation Satellite System - Reflectometry (GNSS-R) is being proposed by different teams as a solution to directly estimate soil moisture. Up to date, two approaches using polarimetric GNSS-R have been proposed to estimate soil moisture, using the ratio between the right-hand and left-hand circularly polarized received signals, and using the ratio between the linear horizontal and vertical components at a set of incidence angles, known as Hybrid Compact Polarimetric (HCP) GNSS-R. In this manuscript, the necessary calibrations of a received HCP GNSS-R signal are presented. The Stokes parameters of the signal are required to compensate all non-idealities. A methodology to calibrate the receiver effects, scene-antenna polarimetric misalignment, Faraday rotation, and transmitter non-idealities is proposed. Finally, the calibration performance is evaluated using several statistical parameters and polarimetric ratio models based on two different soil moisture products. Results show a correlation coefficient increase of 4.7% with respect to the model derived from the Soil Moisture Active Passive (SMAP) soil moisture product, and a 6.5% improvement with respect to the model derived from the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA-5) monthly average soil moisture product. Furthermore, the proposed calibrations show an unbiased root-mean-square error reduction of 6.3% and 6.8% for the SMAP soil moisture model and the ERA-5 model, respectively. Due to the SMAP antenna and pointing design, SMAP-Reflectometry (SMAP-R) is implicitly insensitive to some of the corrections. More significant corrections should be expected for future polarimetric GNSS-R instruments as European Space Agency (ESA) HydroGNSS.

Low Earth orbit constellation design using a multi-objective genetic algorithm for GNSS reflectometry missions

Spaceborne global navigation satellite system reflectometry (GNSS-R) is an innovative bistatic radar remote sensing technique utilizing low Earth orbit (LEO) based GNSS-R instruments to acquire GNSS L-band opportunistic signals for measuring geophysical parameters. A GNSS-R LEO constellation with an optimization design for its specialized missions is very significant and necessary. However, the constellation design involves multi-parameter and multi-objective optimization, and the classical analytic solution is not capable of such a complicated issue. This study proposes a multi-objective LEO constellation design method with a genetic algorithm (GA) and presents a framework for designing two GNSS-R LEO constellations, termed “lower-latitude constellation” for typhoons and hurricanes observation in the tropics and “global constellation” for global geophysical parameter measurements. Then, the observation capability of both designed constellations is evaluated in terms of the number of reflection points, spatial coverage density, and revisit time to verify the GA efficiency in LEO constellation design. Results show that the two designed LEO constellations with high fitness function values possess optimal orbit parameter set configuration and outperform the existing CyGNSS constellations in observation performance. Compared with CyGNSS, the number of reflection points observed by the lower-latitude constellation and the global constellation increases by 38% and 45%, as well as the spatial coverage density increases by 28% and 36%. The revisit time for the lower-latitude constellation is reduced by 0.29 h, whereas the revisit time for the global constellation increases by one hour.

Vegetation Canopy Height Retrieval Using L1 and L5 Airborne GNSS-R

Vegetation canopy height is one of the important remote sensing parameters related to forests’ structure, and it can be related to the biomass and the carbon stock. Global Navigation Satellite System - Reflectometry (GNSS-R) has proven capable to retrieve vegetation information at a moderate resolution from space (20-65 km) using L1 C/A signals. In this study, data retrieved by the airborne Microwave Interferometric Reflectometer (MIR) GNSS-R instrument at L1 and L5 are compared to the Global Forest Canopy Height product, with a spatial resolution of 30 m. This work analyzes the waveforms measured at both bands, and the correlation of the waveform width and the reflectivity values to the canopy height product. A neural network algorithm is used for the retrieval, showing that the combination of the reflectivity and the waveform width allows to estimate the canopy height information at a very high resolution, with a root-mean-square error of 4.25 m and 4.07 m at L1 and L5, respectively, which is an error about 14% of the actual canopy height.

Airborne GNSS-R Polarimetric Multiincidence Data Analysis for Surface Soil Moisture Estimation Over an Agricultural Site

The objective of this study is to propose a mapping of surface soil moisture ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SSM</i> ) using airborne measurements based on the GLObal Navigation Satellite System Reflectometry Instrument (GLORI), a polarimetric instrument. GNSS-R measurements were acquired at the agricultural Urgell site in Spain in July 2021. In situ measurements describing the soil moisture and roughness and the vegetation cover leaf area index were then obtained simultaneously with flight measurements. An analysis of observable copolarization (right-right) reflectivity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\rm{\Gamma }}}_{RR}$</tex-math></inline-formula> and the cross-polarization (right-left) reflectivity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ {\Gamma }_{RL}$</tex-math></inline-formula> behaviors as a function of incidence angle is proposed, as is normalization of the reflectivity function of the incidence angle. The sensitivity of reflectivities is then proposed as a function of surface soil moisture. An empirical model with two variables, soil moisture and the normalized difference vegetation index ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NDVI</i> ), based on the principle of the tau-omega model is then considered for the inversion of GNSS-R reflectivity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\Gamma }_{RL}\ $</tex-math></inline-formula> and estimation of soil moisture. This model is calibrated and validated by a threefold cross-validation approach. A mapping of SSM at 100 m resolution is created with data from the studied site and three acquired flights.

Signal-to-Noise Ratio Analyses of Spaceborne GNSS-Reflectometry from Galileo and BeiDou Satellites

In recent years, Global Navigation Satellite System Reflectometry (GNSS-R) technology has made considerable progress with the increasing of GNSS-R satellites in orbit, the improvements of GNSS-R data processing technology, and the expansion of its geophysical applications. Meanwhile, with the modernization and evolution of GNSS systems, more signal sources and signal modulation modes are available. The effective use of the signals at different frequencies or from new GNSS systems can improve the accuracy, reliability, and resolution of the GNSS-R data products. This paper analyses the signal-to-noise ratio (SNR) of the GNSS-R measurements from Galileo and BeiDou-3 (BDS-3) systems, which is one of the important indicators to measure the quality of GNSS-R data. The multi-GNSS (GPS, Galileo and BDS-3) complex waveform products generated from the raw intermediate frequency data from TechDemoSat-1 (TDS-1) satellite and Cyclone Global Navigation Satellite System (CYGNSS) constellation are used for such analyses. The SNR and normalized SNR (NSNR) of the reflected signals from Galileo and BDS-3 satellites are compared to these from GPS. Preliminary results show that the GNSS-R SNRs from Galileo and BDS-3 are ∼1–2 dB lower than the GNSS-R measurements from GPS, which could be due to the power of the transmitted power and the bandwidth of the receiver. In addition, the effect of coherent integration time on GNSS-R SNR is also assessed for different GNSS signals. It is shown that the SNR of the reflected signals can be improved by using longer coherent integration time (∼0.4–0.8 dB with 2 ms coherent integration and ∼0.6–1.2 dB with 4 ms coherent integration). In addition, it is also shown that the SNR can be improved more efficiently (∼0.2–0.4 dB) for reflected BDS-3 and Galileo signals than for GPS. These results can provide useful references for the design of future spaceborne GNSS-R instrument compatible with reflections from multi-GNSS constellations.

Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment

Global Navigation Satellite System—Reflectometry (GNSS-R) has already proven its potential for retrieving a number of geophysical parameters, including soil moisture. However, single-pass GNSS-R soil moisture retrieval is still a challenge. This study presents a comparison of two different data sets acquired with the Microwave Interferometer Reflectometer (MIR), an airborne-based dual-band (L1/E1 and L5/E5a), multiconstellation (GPS and Galileo) GNSS-R instrument with two 19-element antenna arrays with four electronically steered beams each. The instrument was flown twice over the OzNet soil moisture monitoring network in southern New South Wales (Australia): the first flight was performed after a long period without rain, and the second one just after a rain event. In this work, the impact of surface roughness and vegetation attenuation in the reflectivity of the GNSS-R signal is assessed at both L1 and L5 bands. The work analyzes the reflectivity at different integration times, and finally, an artificial neural network is used to retrieve soil moisture from the reflectivity values. The algorithm is trained and compared to a 20-m resolution downscaled soil moisture estimate derived from SMOS soil moisture, Sentinel-2 normalized difference vegetation index (NDVI) data, and ECMWF Land Surface Temperature.

First spaceborne demonstration of BeiDou-3 signals for GNSS reflectometry from CYGNSS constellation

The full constellation of Chinese Global Navigation Satellite System (GNSS) BeiDou-3 has been deployed completely and started fully operational service. In addition to providing global Positioning, Navigation and Timing (PNT) services, the BeiDou-3 satellites transmissions can also be used as the sources of illumination for Earth Observation (EO) with a bistatic radar configuration. This innovative EO concept, known as GNSS reflectometry (GNSS-R), allows to measure the Earth surface characteristics at high resolution via the reflected L-band radar signals collected by a constellation of small, low cost and low Earth orbiting satellites. For the first time in orbit, earth reflected BeiDou-3 signal has been detected from the limited sets of raw data collected by the NASA’s Cyclone GNSS (CYGNSS) constellation. The feasibility of spaceborne BeiDou-3 reflections on two typical applications, including sea surface wind and flooding inundation detection, has been demonstrated. The methodology and results give new strength to the prospect of new spaceborne GNSS-R instruments and missions, which can make multi-GNSS reflectometry observations available to better capture rapidly changing weather systems at better spatio-temporal scales.

First Assessment of Geophysical Sensitivities from Spaceborne Galileo and BeiDou GNSS-Reflectometry Data Collected by the UK TechDemoSat-1 Mission

The UK’s TechDemoSat-1 (TDS-1), launched 2014, has demonstrated the use of global positioning system (GPS) signals for monitoring ocean winds and sea ice. Here it is shown, for the first time, that Galileo and BeiDou signals detected by TDS-1 show similar promise. TDS-1 made seven raw data collections, recovering returns from Galileo and BeiDou, between November 2015 and March 2019. The retrieved open ocean delay Doppler maps (DDMs) are similar to those from GPS. Over sea ice, the Galileo DDMs show a distinctive triple peak. Analysis, adapted from that for GPS DDMs, gives Galileo’s signal-to-noise ratio (SNR), which is found to be inversely sensitive to wind speed, as for GPS. A Galileo track transiting from open ocean to sea ice shows a strong instantaneous SNR response. These results demonstrate the potential of future spaceborne constellations of GNSS-R (global navigation satellite system–reflectometry) instruments for exploiting signals from multiple systems: GPS, Galileo, and BeiDou.

Can the Accuracy of Sea Surface Salinity Measurement be Improved by Incorporating Spaceborne GNSS-Reflectometry?

Sea surface salinity (SSS) can be measured by L-band (1.4 GHz) radiometry. However, the L-band brightness temperature is sensitive to ocean surface roughness, so that the precise knowledge of sea state can help to improve the accuracy of SSS retrievals. Cyclone Global Navigation Satellite System (CyGNSS) mission measures tropical ocean wind speeds, which can provide further knowledge about sea state. This letter, by investigating the sensitivity of brightness temperature derived from two L-band radiometry satellite missions [i.e., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP)] to CyGNSS data, first explores the potential of using spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) to improve the accuracy of SSS measurements. The statistical results from two-year CyGNSS data show that the systematic uncertainties of the brightness temperature up to 0.5 K in the SMOS/SMAP can be minimized at low wind speed, which proves the concept that CyGNSS wind speed data can be used to improve the accuracy of SSS retrievals across the oceans. The findings strongly suggest that the spaceborne GNSS-R instruments could be utilized as cost-effective hosted payloads in designing future ocean remote sensing missions.

Generic Performance Simulator of Spaceborne GNSS-Reflectometer for Land Applications

Nowadays, the space missions employing the global navigation satellite system reflectometry (GNSS-R) are UK TDS-1, NASA CYGNSS, and the Chinese BuFeng-1A/B twin satellites, part of the first Chinese global navigation satellite system reflectometry (GNSS-R) satellite mission. They provide delay-Doppler map (DDM) measurements reflected from the land as well as the ocean. Using land reflected DDMs, several studies have been conducted to retrieve land geophysical parameters, such as soil moisture and biomass. Despite the clear dependence on these parameters, many other parameters impact the DDMs as well, such as topography, surface roughness, etc. The impact of these perturbing factors must be analyzed, and modeled in various conditions. This article presents a comprehensive end-to-end simulator that can generate synthetic DDMs reflected over land. It is an extension of a previously developed simulator validated for ocean applications. This simulator is very generic, and it includes numerous configurable parameters such as arbitrary scattering geometry (transmitter and receiver positions and speeds), arbitrary GPS and Galileo transmitted signals and frequencies, GNSS-R instrument antenna and receiver errors, as well as surface topography, roughness, soil moisture, vegetation cover, etc. The sensitivities of GNSS-R observables with respect to soil moisture and vegetation are obtained and compared to previous experimental results, and synthetic DDMs are compared and validated against TDS-1 ones.

Sea Surface Wind Speed Retrieval from the First Chinese GNSS-R Mission: Technique and Preliminary Results

Launched on 5 June 2019, the BuFeng-1 A/B twin satellites were part of the first Chinese global navigation satellite system reflectometry (GNSS-R) satellite mission. In this letter, a brief introduction of the BF-1 mission and its preliminary results of sea surface wind retrieval are presented. Empirical fully developed sea (FDS) geophysical model functions (GMFs) relating the normalized bistatic radar cross-section to the sea surface wind speed are proposed for the BF-1 GNSS-R instruments. The FDS GMFs are derived from the collocated BF-1 observations, the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, and the advanced scatterometer (ASCAT) satellite observations. The preliminary tests reveal that the root-mean-square error (RMSE) between the derived wind speed and the reanalysis is 2.63 m/s for wind speeds in the range of 0.5–40.5 m/s. Further comparisons with the ASCAT observations and mooring buoys show that the RMSEs are 2.04 m/s and 1.77 m/s, respectively, at low-to-moderate wind speeds. This study demonstrates the effectiveness of BF-1 and provides a basis for the future GMF development of the BF-1 A/B mission.

Intercomparison of Soil Moisture Retrieved from GNSS-R and from Passive L-Band Radiometry at the Valencia Anchor Station.

In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and a dual-pol down-looking antenna for receiving LHCP (left-hand circular polarization) and RHCP (right-hand circular polarization) reflected signals from the soil surface. Data were collected from the three different antennas through the two channels of Oceanpal GNSS-R receiver and, in addition, calibration was performed to reduce the impact from the differing channels. Reflectivity was thus measured, and soil moisture could be retrieved. The ESA (European Space Agency)-funded ELBARA-II (ESA L Band Radiometer II) is an L-band radiometer with two channels with 11 MHz bandwidth and respective center frequencies of 1407.5 MHz and 1419.5 MHz. The ELBARAII antenna is a large dual-mode Picket horn that is 1.4 m wide, with a length of 2.7 m with −3 dB full beam width of 12° (±6° around the antenna main direction) and a gain of 23.5 dB. By comparing GNSS-R and ELBARA-II radiometer data, a high correlation was found between the LHCP reflectivity measured by GNSS-R and the horizontal/vertical reflectivity from the radiometer (with correlation coefficients ranging from 0.83 to 0.91). Neural net fitting was used for GNSS-R soil moisture inversion, and the RMSE (Root Mean Square Error) was 0.014 m3/m3. The determination coefficient between the retrieved soil moisture and in situ measurements was R2 = 0.90 for Oceanpal and R2 = 0.65 for Elbara II, and the ubRMSE (Unbiased RMSE) were 0.0128 and 0.0734 respectively. The soil moisture retrievals by both L-band remote sensing methods show good agreement with each other, and their mutual correspondence with in-situ measurements and with rainfall was also good.

Spatiotemporal Evaluation of GNSS-R Based on Future Fully Operational Global Multi-GNSS and Eight-LEO Constellations

Spaceborne GNSS-R (global navigation satellite system reflectometry) is an innovative and powerful bistatic radar remote sensing technique that uses specialized GNSS-R instruments on LEO (low Earth orbit) satellites to receive GNSS L-band signals reflected by the Earth’s surface. Unlike monostatic radar, the illuminated areas are elliptical regions centered on specular reflection points. Evaluation of the spatiotemporal resolution of the reflections is necessary at the GNSS-R mission design stage for various applications. However, not all specular reflection signals can be received because the size and location of the GNSS-R antenna’s available reflecting ground coverage depends on parameters including the on-board receiver antenna gain, the signal frequency and power, the antenna face direction, and the LEO’s altitude. Additionally, the number of available reflections is strongly related to the number of GNSS-R LEO and GNSS satellites. By 2020, the Galileo and BeiDou Navigation Satellite System (BDS) constellations are scheduled to be fully operational at global scale and nearly 120 multi-GNSS satellites, including Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) satellites, will be available for use as illuminators. In this paper, to evaluate the future capacity for repetitive GNSS-R observations, we propose a GNSS satellite selection method and simulate the orbit of eight-satellite LEO and partial multi-GNSS constellations. We then analyze the spatiotemporal distribution characteristics of the reflections in two cases: (1) When only GPS satellites are available; (2) when multi-GNSS satellites are available separately. Simulation and analysis results show that the multi-GNSS-R system has major advantages in terms of available satellite numbers and revisit times over the GPS-R system. Additionally, the spatial density of the specular reflections on the Earth’s surface is related to the LEO inclination and constellation construction.

Feasibility of GNSS-R Ice Sheet Altimetry in Greenland Using TDS-1

Radar altimetry provides valuable measurements to characterize the state and the evolution of the ice sheet cover of Antartica and Greenland. Global Navigation Satellite System Reflectometry (GNSS-R) has the potential to complement the dedicated radar altimeters, increasing the temporal and spatial resolution of the measurements. Here we perform a study of the Greenland ice sheet using data obtained by the GNSS-R instrument aboard the British TechDemoSat-1 (TDS-1) satellite mission. TDS-1 was primarily designed to provide sea state information such as sea surface roughness or wind, but not altimetric products. The data have been analyzed with altimetric methodologies, already tested in aircraft based experiments, to extract signal delay observables to be used to infer properties of the Greenland ice sheet cover. The penetration depth of the GNSS signals into ice has also been considered. The large scale topographic signal obtained is consistent with the one obtained with ICEsat GLAS sensor, with differences likely to be related to L-band signal penetration into the ice and the along-track variations in structure and morphology of the firn and ice volumes The main conclusion derived from this work is that GNSS-R also provides potentially valuable measurements of the ice sheet cover. Thus, this methodology has the potential to complement our understanding of the ice firn and its evolution.

GLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring.

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.