GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation

Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling and retrieval process, as well as the extreme uncertainty of the experimental environment and equipment. To investigate the behavior of bistatic GNSS-R soil moisture retrieval process, two ground-truth measurements with dierent soil conditions were carried out and the performance of the input variables was analyzed from the mathematical statistical aspect. Moreover, the feature of XGBoost method was utilized as well. As a recently developed ensemble machine learning method, the XGBoost method just emerged for the classification of remote sensing and geographic data, to investigate the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. The variable contributions for the input data can also be seen and evaluated. The study of the paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting data

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

Remote Sensing in Land Applications by Using GNSS-Reflectometry

Global navigation satellite system-reflectometry (GNSS-R) as an efficient tool for remote sensing has gained increasing interests in the last two decades, due to its unique characteristics. It uses GNSS signals as sources of opportunity, providing precise, continuous, all-weather, and 24 hours’ detections, which play a key role in many land applications. The fundamental theoretical part of GNSS-R technique is examined at first. Then, GNSS-R methodologies applied in the soil moisture content, vegetation biomass sensing, and altimetry applications are also detailed. One retrieval method uses only RH (right-hand) reflected data. Another retrieval method for soil moisture content (SMC) aimed to calibrate the measurement by using water reflections, based on the bistatic equations with LH (left-hand) reflected and RH direct signals. The other method for SMC retrieval is related to the polarimetric ratio (PR), the ratio of LH/RH reflected signals can reveal the fluctuations of the SMC. Another vital parameter vegetation biomass was observed by using the variation of reflectivity of the LH and RH reflected components. Finally, the C/A code method was used for exploring the possibility to the altimetry estimation. The features of GNSS-R technique made it a promising remote sensing technique in hydrology, climatology carbon cycles, and other potential applications

GNSS Reflectometry and Remote Sensing: New Objectives and Results

The Global Navigation Satellite System (GNSS) has been a very powerful and important contributor to all scientific questions related to precise positioning on Earth's surface, particularly as a mature technique in geodesy and geosciences. With the development of GNSS as a satellite microwave (L-band) technique, more and wider applications and new potentials are explored and utilized. The versatile and available GNSS signals can image the Earth's surface environments as a new, highly precise, continuous, all-weather and near-real-time remote sensing tool. The refracted signals from GNSS Radio Occultation satellites together with ground GNSS observations can provide the high-resolution tropospheric water vapor, temperature and pressure, tropopause parameters and ionospheric total electron content (TEC) and electron density profile as well. The GNSS reflected signals from the ocean and land surface could determine the ocean height, wind speed and wind direction of ocean surface, soil moisture, ice and snow thickness. In this paper, GNSS remote sensing applications in the atmosphere, oceans, land and hydrology are presented as well as new objectives and results discussed.Comment: Advances in Space Research, 46(2), 111-117, 201

GNSS transpolar earth reflectometry exploriNg system (G-TERN): mission concept

The global navigation satellite system (GNSS) Transpolar Earth Reflectometry exploriNg system (G-TERN) was proposed in response to ESA's Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists. The primary objective of the mission is to quantify at high spatio-temporal resolution crucial characteristics, processes and interactions between sea ice, and other Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The objective is articulated through three key questions. 1) In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere, and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation, and melt)? 2) What are the impacts of extreme events and feedback mechanisms on sea ice evolution? 3) What are the effects of the cryosphere behaviors, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events? To contribute answering these questions, G-TERN will measure key parameters of the sea ice, the oceans, and the atmosphere with frequent and dense coverage over polar areas, becoming a “dynamic mapper”of the ice conditions, the ice production, and the loss in multiple time and space scales, and surrounding environment. Over polar areas, the G-TERN will measure sea ice surface elevation (<;10 cm precision), roughness, and polarimetry aspects at 30-km resolution and 3-days full coverage. G-TERN will implement the interferometric GNSS reflectometry concept, from a single satellite in near-polar orbit with capability for 12 simultaneous observations. Unlike currently orbiting GNSS reflectometry missions, the G-TERN uses the full GNSS available bandwidth to improve its ranging measurements. The lifetime would be 2025-2030 or optimally 2025-2035, covering key stages of the transition toward a nearly ice-free Arctic Ocean in summer. This paper describes the mission objectives, it reviews its measurement techniques, summarizes the suggested implementation, and finally, it estimates the expected performance.Peer ReviewedPostprint (published version

GNSS Reflectometry for land surface monitoring and buried object detection

Global Navigation Satellite System Reflectometry (GNSS-R) is attracting growing interest nowadays for several remote sensing applications. As a bistatic radar, the transmitter and the receiver are not co-located and in the special case of GNSS-R, the GNSS satellites are acting as transmitters and the receiver can be mounted either in a static position or onboard a aircraft or low orbit satellite. Various information about the surface from where the GNSS signals are reflected or scattered can be extracted by means of reflected signal strength, code delay, carrier phase delay, interference with direct GNSS signals and so on. Possible applications cover soil moisture retrieval, ice topography and thickness detection, snow depth estimation, vegetation coverage, sea state monitoring such as sea wind and surface roughness, sea salinity… In this work, soil moisture retrieval was mostly focused on. Hardware including antennas and receivers was studied and designed. Our first strategy of soil moisture retrieval is to apply a single Left Hand Circular Polarization (LHCP) antenna for reflected signal reception. Therefore multiple types of antennas such as the helix antenna, the patch antenna and several commercial antennas were designed, simulated or tested in the anechoic chamber. Two receiver solutions were used in our group and both of them apply the SiGe GPS frontend. The first solution is a PC based one: the collection and store of the raw incoming reflected GPS signals were done by the NGrab software (designed by NAVSAS Group of Politecnico di Torino) installed in a standard PC. The other solution was developed in our group and it is operated by a single Hackberry board, which consists of power supply, storage subsystem and customized Linux Debian operating system. The light weight and small size enable this compact receiver to perform flight measurement onboard UAVs. Both of the above mentioned receivers only store raw sampled data and no real time signal processing is performed on board. Post processing is done by Matlab program which makes correlations in both time and frequency domain with incoming signals using the local generated GPS C/A code replica. The so-called Delay Doppler Map (DDM) is therefore generated through this correlation. Signal to Noise Ratio (SNR) can be calculated through Delay Waveform (DW) which is extracted from DDM at the Doppler frequency where the correlation peak exists. Received signal power can be obtained knowing the noise power which is given in a standard equation. In order to better plan a static measurement and to georeference specular points on the surface, programs for georeferencing specular points on either Google Maps or an x-y plane centered at the receiver position were developed. Fly dynamics in terms of roll, pitch and yaw influencing the antenna gain due to the variation of incident angles were also studied in order to compensate the gain to the received signal. Two soil moisture retrieval algorithms were derived corresponding to two receiving schemes. The first one is for the receiving of only LHCP reflected signals. In this case, the surface is assumed to be perfectly smooth and the received signal is seen to consist of only coherent component caused by specular reflection. Dielectric constant can be retrieved from the processed SNR. Two measurement campaigns were carried out using this single LHCP system. The first campaign is a flight measurement overflown a big portion of rice fields when most of the fields were flooded. It was a test measurement on the SNR sensitivity to water/no-water surfaces and an attempt of dielectric constant retrieval was also performed. SNR showed good sensitivity to the surface water content and dielectric constant was also checked to be reasonable. The second campaign is in static positions and it includes two experiments. This campaign initially aimed at testing the sensitivity of the compact receiver to different surface moisture. Results of both SNR and retrieved dielectric constant showed to be coherent with the surface moisture changes. The other retrieval algorithm is for the receiving of both LHCP and RHCP reflected signals concurrently. The cross polarization power ratio (LHCP/RHCP) is believed to be independent of surface roughness by several previous studies and this idea was also verified during the deriving process for either specular reflection case (only coherent component) or diffuse scattering condition (incoherent component). For diffuse scattering, three well known models were applied which are the Kirchhoff Approximation in stationary-phase approximation (Kirchhoff Geometrical Optics, KGO), Kirchhoff Approximation in Physical Optics Approximation (KPO) and Small Perturbation Method (SPM). These three models cover different roughness surfaces from very rough (KGO) to slightly rough surfaces (SPM). All the derived results of cross polarization ratio for the three models were verified to be independent of surface properties and depend on only dielectric constant of soil and incident angle. A new application of GNSS-R technique for the possibility of detection of buried objects was firstly investigated by our group. It has the potential use for man-made mines detection in the military field. Two measurement campaigns were carried out and the variation of the SNR level due to the presence of a metallic object was investigated. The first measurement campaign was performed in a static condition on a sandy terrain to check the functionality of the system. And the presence of the metallic object was detected also in the case of wet terrain. In the second measurement campaign, the antenna was moving along a given path and the possibility of detecting the object dimensions was highlighted. The results show the possibility of adopting this technique on board a remotely controlled UAV for metal object and even its dimension detection. A measurement of snow depth attempting to relate it to reflected LHCP SNR is briefly presented and discussed in Chapter 7

Soil moisture estimation synergy using GNSS-R and L-Band microwave radiometry data from FSSCat/FMPL-2

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM.This work was supported by the 2017 ESA S3 challenge and Copernicus Masters overall winner award (“FSSCat” project). This work was (partially) sponsored by project SPOT: Sensing with Pioneering Opportunistic Techniques grant RTI2018-099008-B-C21 / AEI / 10.13039/501100011033, and by the Unidad de Excelencia Maria de Maeztu MDM-2016-0600. This work was also (partially) sponsored by the Spanish Ministry of Science and Innovation through the project ESP2017-89463-C3, by the Centro de Excelencia Severo Ochoa (CEX2019-000928-S), and by the CSIC Plataforma Temática Interdisciplinar de Teledetección (PTI-Teledetect). Joan Francesc Munoz-Martin received support from the grant for the recruitment of early-stage research staff FI-DGR 2018 of the AGAUR - Generalitat de Catalunya (FEDER), Spain; Christoph Herbert received the support of a fellowship from “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/DI18/11660050 and funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 713673; David Llavería received support from an FPU fellowship from the Spanish Ministry of Education FPU18/06107.Peer ReviewedPostprint (published version

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security