Sensitivity to Soil Moisture and Observation Geometry of Spaceborne GNSS-R Delay-Doppler Maps

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Thanks to the successful operations of the UK TDS-1 and NASA CYGNSS GNSS-R missions, a wealth of Delay-Doppler Maps (DDM) are being measured from the ocean, but also from land reflections. Using the land reflected DDM, several studies are being conducted to retrieve the land geophysical parameters, such as soil moisture, vegetation depth, and biomass. Although they have shown the dependence of the land geophysical parameters on the DDM, it is also shown that many other parameters impact the DDM. This work presents the impacts of some parameters on the DDM. For the systematical and efficient study, an E2E simulator is used. The simulator generates the synthesized DDM reflected over land varying the input parameters, which are the specular point position on the Earth, the elevation angle at the specular points, soil moisture, etc. From the simulation results, the relation between the input parameters and the DDM is individually analyzed, providing the clue to the retrieval algorithm of the geophysical parameters.Peer ReviewedPostprint (author's final draft

Sensitivity of GNSS-R spaceborne observations to soil moisture and vegetation

Global navigation satellite systems-reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multistatic radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from ground-based and airborne experiments, but studies using space-borne data are still preliminary due to the limited amount of data, collocation, footprint heterogeneity, etc. This study presents a sensitivity study of TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e., vegetation covers) and for a wide range of soil moisture and normalized difference vegetation index (NDVI) values. Despite the scattering in the data, which can be largely attributed to the delay-Doppler maps peak variance, the temporal and spatial (footprint size) collocation mismatch with the SMOS soil moisture, and MODIS NDVI vegetation data, and land use data, experimental results for low NDVI values show a large sensitivity to soil moisture and a relatively good Pearson correlation coefficient. As the vegetation cover increases (NDVI increases) the reflectivity, the sensitivity to soil moisture and the Pearson correlation coefficient decreases, but it is still significant.Postprint (author's final draft

Vegetation canopy height retrieval using L1 and L5 airborne GNSS-R

Vegetation canopy height (CH) 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 proved 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 CH product, with a spatial resolution of 30 m. This work analyzes the waveforms (WFs) measured at both bands, and the correlation of the waveform width and the reflectivity values to the CH 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 CH information at a very high resolution, with a root-mean-square error (RMSE) of 4.25 and 4.07 m at L1 and L5, respectively, which is an error about 14% of the actual CH.Postprint (updated version

On the Response of Polarimetric GNSS-Reflectometry to Sea Surface Roughness

Reflectometry of Global Navigation Satellite Systems (GNSS) signals from the ocean surface has provided a new source of observations to study the ocean-atmosphere interaction. We investigate the sensitivity and performance of GNSS-Reflectometry (GNSS-R) data to retrieve sea surface roughness (SSR) as an indicator of sea state. A data set of one-year observations in 2016 is acquired from a coastal GNSS-R experiment in Onsala, Sweden. The experiment exploits two sea-looking antennas with right- and left-hand circular polarizations (RHCP and LHCP). The interference of the direct and reflected signals captured by the antennas is used by a GNSS-R receiver to generate complex interferometric fringes. We process the interferometric observations to estimate the contributions of direct signals and reflections to the total power. The power estimates are inverted to the SSR using the state-of-the-art model. The roughness measurements from the RHCP and LHCP links are evaluated against match-up wind measurements obtained from the nearest meteorological station. The results report on successful roughness retrieval with overall correlations of 0.76 for both links. However, the roughness effect in LHCP observations is more pronounced. The influence of surrounding complex coastlines and the wind direction dependence are discussed. The analysis reveals that the winds blowing from land have minimal impact on the roughness due to limited fetch. A clear improvement of roughness estimates with an overall correlation of 0.82 is observed for combined polarimetric observations from the RHCP and LHCP links. The combined observations can also improve the sensitivity of GNSS-R measurements to the change of sea state

Desert roughness retrieval using CYGNSS GNSS-R data

The aim of this paper is to assess the potential use of data recorded by the Global Navigation Satellite System Reflectometry (GNSS-R) Cyclone Global Navigation Satellite System (CYGNSS) constellation to characterize desert surface roughness. The study is applied over the Sahara, the largest non-polar desert in the world. This is based on a spatio-temporal analysis of variations in Cyclone Global Navigation Satellite System (CYGNSS) data, expressed as changes in reflectivity (G). In general, the reflectivity of each type of land surface (reliefs, dunes, etc.) encountered at the studied site is found to have a high temporal stability. A grid of CYGNSS G measurements has been developed, at the relatively fine resolution of 0.03° x 0.03°, and the resulting map of average reflectivity, computed over a 2.5-year period, illustrates the potential of CYGNSS data for the characterization of the main types of desert land surface (dunes, reliefs, etc.). A discussion of the relationship between aerodynamic or geometric roughness and CYGNSS reflectivity is proposed. A high correlation is observed between these roughness parameters and reflectivity. The behaviors of the GNSS-R reflectivity and the Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR) backscattering coeffcient are compared and found to be strongly correlated. An aerodynamic roughness (Z0) map of the Sahara is proposed, using four distinct classes of terrain roughness

An Introduction to the HydroGNSS GNSS Reflectometry Remote Sensing Mission

HydroGNSS (Hydrology using Global Navigation Satellite System reflections) has been selected as the second European Space Agency (ESA) Scout earth observation mission to demonstrate the capability of small satellites to deliver science. This article summarizes the case for HydroGNSS as developed during its system consolidation study. HydroGNSS is a high-value dual small satellite mission, which will prove new concepts and offer timely climate observations that supplement and complement the existing observations and are high in ESAs earth observation scientific priorities. The mission delivers the observations of four hydrological essential climate variables as defined by the global climate observing system using the new technique of GNSS reflectometry. These will cover the world's land mass to 25 km resolution, with a 15-day revisit. The variables are soil moisture, inundation or wetlands, freeze/thaw state, and above-ground biomass

Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach

Global Navigation Satellite System-Reflectometry (GNSS-R) as a microwave remote sensing technique can retrieve the Earth’s surface parameters using the GNSS reflected signal from the surface. These reflected signals convey the surface features and therefore can be utilized to detect certain physical properties of the reflecting surface such as soil moisture content (SMC). Up to now, a serial of electromagnetic models (e.g., bistatic radar and Fresnel equations, etc.) are employed and solved for SMC retrieval. However, due to the uncertainty of the physical characteristics of the sites, complexity, and nonlinearity of the inversion process, etc., it is still challenging to accurately retrieve the soil moisture. The popular machine learning (ML) methods are flexible and able to handle nonlinear problems. It can dig out and model the complex interactions between input and output and ultimately make good predictions. In this paper, two typical ML methods, specifically, random forest (RF) and support vector machine (SVM), are employed for SMC retrieval from GNSS-R data of self-designed experiments (in situ and airborne). A comprehensive simulated dataset involving different types of soil is constructed firstly to represent the complex interactions between the variables (reflectivity, elevation angle, dielectric constant, and SMC) for the requirement of training ML regression models. Correspondingly, the main task of soil moisture retrieval (regression) is addressed. Specifically, the post-processed data (reflectivity and elevation angle) from sensor acquisitions are used to make predictions by these two adopted ML methods and compared with the commonly used GNSS-R retrieval method (electromagnetic models). The results show that the RF outperforms the SVM method, and it is more suitable for handling the inversion problem. Moreover, the RF regression model built by the comprehensive dataset demonstrates satisfactory accuracy and strong universality, especially when the soil type is not uniform or unknown. Furthermore, the typical task of detecting water/soil (classification) is discussed. The ML algorithms demonstrate a high potential and efficiency in SMC retrieval from GNSS-R data

Single-pass soil moisture retrievals using GNSS-R: lessons learned

In this paper, an algorithm to retrieve surface soil moisture from GNSS-R (Global Navigaton Satellite System Reflectometry) observations is presented. Surface roughness and vegetation effects are found to be the most critical ones to be corrected. On one side, the NASA SMAP (Soil Moisture Active and Passive) correction for vegetation opacity (multiplied by two to account for the descending and ascending passes) seems too high. Surface roughness effects cannot be compensated using in situ measurements, as they are not representative. An ad hoc correction for surface roughness, including the dependence with the incidence angle, and the actual reflectivity value is needed. With this correction, reasonable surface soil moisture values are obtained up to approximately a 30° incidence angle, beyond which the GNSS-R retrieved surface soil moisture spreads significantly.This work has been funded by the Spanish MCIU and EU ERDF project (RTI2018-099008-B-C21) “Sensing with pioneering opportunistic techniques” and grant to ”CommSensLab-UPC” Excellence Research Unit Maria de Maeztu (MINECO grant MDM-2016-600), and by a Doctorat Industrial grant from ICGC.Peer ReviewedPostprint (published version

Remote Sensing of Forest Biomass Using GNSS Reflectometry

In this study, the capability of Global Navigation Satellite System Reflectometry in evaluating forest biomass from space has been investigated by using data coming from the TechDemoSat-1 (TDS-1) mission of Surrey Satellite Technology Ltd. and from the Cyclone Satellite System (CyGNSS) mission of NASA. The analysis has been first conducted using TDS-1 data on a local scale, by selecting five test areas located in different parts of the Earth's surface. The areas were chosen as examples of various forest coverages, including equatorial and boreal forests. Then, the analysis has been extended by using CyGNSS to a global scale, including any type of forest coverage. The peak of the Delay Doppler Map calibrated to retrieve an "equivalent" reflectivity has been exploited for this investigation and its sensitivity to forest parameters has been evaluated by a direct comparison with vegetation optical depth (VOD) derived from the Soil Moisture Active Passive L-band radiometer, with a pantropical aboveground biomass (AGB) map and then with a tree height (H) global map derived from the Geoscience Laser Altimeter System installed on-board the ICEsat satellite. The sensitivity analysis confirmed the decreasing trend of the observed equivalent reflectivity for increasing biomass, with correlation coefficients 0.31 ≤ R ≤ 0.54 depending on the target parameter (VOD, AGB, or H) and on the considered dataset (local or global). These correlations were not sufficient to retrieve the target parameters by simple inversion of the direct relationships. The retrieval has been therefore based on Artificial Neural Networks making it possible to add other inputs (e.g., the incidence angle, the signal to noise ratio, and the lat/lon information in case of global maps) to the algorithm. Although not directly correlated to the biomass, these inputs helped in improving the retrieval accuracy. The algorithm was tested on both the selected areas and globally, showing a promising ability to retrieve the target parameter, either AGB or H, with correlation coefficients R ≃ 0.8