scholarly journals Computation Approach for Quantitative Dielectric Constant from Time Sequential Data Observed by CYGNSS Satellites

2021 ◽  
Vol 13 (11) ◽  
pp. 2032
Author(s):  
Junchan Lee ◽  
Sunil Bisnath ◽  
Regina S.K. Lee ◽  
Narin Gavili Kilane
Keyword(s):  
Dielectric Constant ◽  
Soil Moisture ◽  
Global Navigation Satellite System ◽  
Measurement Data ◽  
Satellite System ◽  
Computation Method ◽  
Navigation Satellite ◽  
Local Soil ◽  
Global Navigation ◽  
Global Navigation Satellite

This paper describes a computation method for obtaining dielectric constant using Global Navigation Satellite System reflectometry (GNSS-R) products. Dielectric constant is a crucial component in the soil moisture retrieval process using reflected GNSS signals. The reflectivity for circular polarized signals is combined with the dielectric constant equation that is used for radiometer observations. Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission, an eight-nanosatellite constellation for GNSS-R, are used for computing dielectric constant. Data from the Soil Moisture Active Passive (SMAP) mission are used to measure the soil moisture through its radiometer, and they are considered as a reference to confirm the accuracy of the new dielectric constant calculation method. The analyzed locations have been chosen that correspond to sites used for the calibration and validation of the SMAP soil moisture product using in-situ measurement data. The retrieved results, especially in the case of a specular point around Yanco, Australia, show that the estimated results track closely to the soil moisture results, and the Root Mean Square Error (RMSE) in the estimated dielectric constant is approximately 5.73. Similar results can be obtained when the specular point is located near the Texas Soil Moisture Network (TxSON), USA. These results indicate that the analysis procedure is well-defined, and it lays the foundation for obtaining quantitative soil moisture content using the GNSS reflectometry results. Future work will include applying the computation product to determine the characteristics that will allow for the separation of coherent and incoherent signals in delay Doppler maps, as well as to develop local soil moisture models.

scholarly journals Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1031 ◽  
Author(s):  
Yuanlan Wen ◽  
Jun Zhu ◽  
Youxing Gong ◽  
Qian Wang ◽  
Xiufeng He
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Global Navigation Satellite System ◽  
Orbit Determination ◽  
Measurement Data ◽  
Satellite System ◽  
Navigation Satellite ◽  
Satellite Link ◽  
Global Navigation ◽  
Global Navigation Satellite

To keep the global navigation satellite system functional during extreme conditions, it is a trend to employ autonomous navigation technology with inter-satellite link. As in the newly built BeiDou system (BDS-3) equipped with Ka-band inter-satellite links, every individual satellite has the ability of communicating and measuring distances among each other. The system also has less dependence on the ground stations and improved navigation performance. Because of the huge amount of measurement data, the centralized data processing algorithm for orbit determination is suggested to be replaced by a distributed one in which each satellite in the constellation is required to finish a partial computation task. In the present paper, the balanced extended Kalman filter algorithm for distributed orbit determination is proposed and compared with the whole-constellation centralized extended Kalman filter, the iterative cascade extended Kalman filter, and the increasing measurement covariance extended Kalman filter. The proposed method demands a lower computation power; however, it yields results with a relatively good accuracy.

Progress in Time Series Soil Moisture Retrievals Using the Cyclone Global Navigation Satellite System Mission

2021 ◽  
Author(s):  
Mohammad Al-Khaldi ◽  
Joel Johnson ◽  
Scott Gleason
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Global Navigation Satellite System ◽  
Satellite System ◽  
Retrieval Algorithm ◽  
Navigation Satellite ◽  
Gps Satellites ◽  
Wide Range ◽  
Global Navigation ◽  
Global Navigation Satellite

<p>NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission has continued to provide measurements of land surface specular scattering since its launch in December 2016. CYGNSS’s operates in a GNSS-R configuration in which  CYGNSS satellites together with GPS satellites form a bistatic radar geometry with GPS satellites acting as transmitters and CYGNSS satellites acting as receivers. The fundamental GNSS-R measurement obtained using the CYGNSS observatories is the delay-Doppler map (DDM), from which normalized radar cross section (NRCS) estimates are derived. The sensitivity of CYGNSS measurements to a wide range of surface properties has motivated their use for soil moisture retrievals.</p><p>This presentation reports an updated analysis of soil moisture retrieval errors using a previously reported time series soil moisture retrieval algorithm that considers a  multi-year CYGNSS dataset. The presentation also reports recent progress in which further simplifications to the proposed algorithm are introduced that limit its need for ancillary soil moisture data and promote use in an operational capacity. This is accomplished, in part, through the incorporation of a recently developed global Level-1 coherence detection methodology and the use of a soil moisture climatology.</p><p>Soil moisture is sensed using a time-series retrieval in which NRCS ratios derived from CYGNSS measurements are used to form a system of equations that can be solved for a times series of surface reflectivities. While the NRCS exhibits a dependence on a wide range of properties such as soil moisture, soil composition, vegetation cover, and surface roughness, NRCS ratios in consecutive acquisitions, at sufficiently low latency, exhibit a direct proportionality to reflectivity ratios that are a function of soil permittivity and therefore soil moisture. The dependence of NRCS ratios on reflectivity facilitates a location dependent inversion of reflectivity to soil moisture through a dielectric mixing model. The use of NRCS ratios however results in N-1 equations for the N soil moistures in the time series, thereby necessitating the incorporation of additional information typically expressed in terms of maximum and/or minimum soil moisture (or reflectivity) values over the time series when solving the system. These values can be obtained either from ancillary data from other systems or from a soil moisture climatology as incorporated in this presentation.</p><p>Retrieved moisture values from the updated algorithm are compared against observed values reported by the Soil Moisture Active Passive (SMAP) mission. The findings suggest that there exists potential for using GNSS-R systems for global soil moisture retrievals with an RMS error on the order of 0.06 cm<sup>3</sup>/cm<sup>3</sup> over varied terrain. The dependence of the algorithm’s retrieval error on land cover class, soil texture, and moisture variability trends will be reported in detail in this presentation.</p>

scholarly journals Evaluation of the Land GNSS-Reflected DDM Coherence on Soil Moisture Estimation from CYGNSS Data

2021 ◽  
Vol 13 (4) ◽  
pp. 570
Author(s):  
Zhounan Dong ◽  
Shuanggen Jin
Keyword(s):  
Soil Moisture ◽  
Global Navigation Satellite System ◽  
Land Surface ◽  
Satellite System ◽  
Navigation Satellite ◽  
Global Navigation ◽  
Incoherent Component ◽  
Global Navigation Satellite ◽  
The Impact ◽  
Moisture Estimation

With the development of spaceborne global navigation satellite system-reflectometry (GNSS-R), it can be used for terrestrial applications as a promising remote sensing tool, such as soil moisture (SM) retrieval. The reflected L-band GNSS signal from the land surface can simultaneously generate coherent and incoherent scattering, depending on surface roughness. However, the contribution of the incoherent component was directly ignored in previous GNSS-R land soil moisture content retrieval due to the hypothesis of its relatively small proportion. In this paper, a detection method is proposed to distinguish the coherence of land GNSS-R delay-Doppler map (DDM) from the cyclone global navigation satellite system (CYGNSS) mission in terms of DDM power-spreading features, which are characterized by different classification estimators. The results show that the trailing edge slope of normalized integrated time-delay waveform presents a better performance to recognize coherent and incoherent dominated observations, indicating that 89.6% of CYGNSS land observations are dominated by the coherent component. Furthermore, the impact of the land GNSS-Reflected DDM coherence on soil moisture retrieval is evaluated from 19-month CYGNSS data. The experiment results show that the influence of incoherent component and incoherent observations is marginal for CYGNSS soil moisture retrieval, and the RMSE of GNSS-R derived soil moisture reaches 0.04 cm3/cm3.

scholarly journals Comprehensive Evaluation of Using TechDemoSat-1 and CYGNSS Data to Estimate Soil Moisture over Mainland China

2020 ◽  
Vol 12 (11) ◽  
pp. 1699 ◽  
Author(s):  
Ting Yang ◽  
Wei Wan ◽  
Zhigang Sun ◽  
Baojian Liu ◽  
Sen Li ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Soil Moisture ◽  
Global Navigation Satellite System ◽  
Mainland China ◽  
Satellite System ◽  
Navigation Satellite ◽  
Data Set ◽  
Global Navigation ◽  
Global Navigation Satellite

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a new opportunity for land observation. This study is the first to compare and evaluate the performance of the only two spaceborne GNSS-R satellite missions whose data are publicly available, i.e., the UK’s TechdemoSat-1 (TDS-1) and the US’s Cyclone Global Navigation Satellite System (CYGNSS), for sensitivity analysis with SMAP SM on a daily basis and soil moisture (SM) estimates on a monthly basis over Mainland China. For daily sensitivity analysis, the two data were matched up and compared for the period (i.e., May 2017 through April 2018) when they coexisted (R = 0.561 vs R = 0.613). For monthly SM estimates, a back-propagation artificial neural network (BP-ANN) was used to construct a model using data from more than two years. The model was subsequently used to derive long-term and continuous SM maps over Mainland China. The results showed that TDS-1 and CYGNSS agree and correlate very well with the SMAP SM in Mainland China (R = 0.676, MAE = 0.052 m3m-3, and ubRMSE = 0.060 m3m-3 for TDS-1; R = 0.798, MAE = 0.040 m3m-3, and ubRMSE = 0.062 m3m-3 for CYGNSS). The retrieved results were further validated using monthly in situ SM data from dense sites across Mainland China. It was found that the SM derived from the TDS-1/ CYGNSS also correlated well with in situ SM (R = 0.687, MAE = 0.066 m3m-3, and ubRMSE = 0.056 m3m-3 for TDS-1; R = 0.724, MAE = 0.052 m3m-3, and ubRMSE = 0.053 m3m-3 for CYGNSS). The results in this study suggested that TDS-1/CYGNSS and the upcoming spaceborne GNSS-R mission could be new and powerful data sources to produce SM data set at a large scale and with relatively high precision.

scholarly journals Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link

2019 ◽  
Author(s):  
Yuanlan Wen ◽  
Jun Zhu ◽  
Youxing Gong ◽  
Qian Wang ◽  
Xiufeng He
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Global Navigation Satellite System ◽  
Orbit Determination ◽  
Measurement Data ◽  
Satellite System ◽  
Navigation Satellite ◽  
Satellite Link ◽  
Global Navigation ◽  
Global Navigation Satellite

To keep the global navigation satellite system functional during extreme conditions, it is a trend to employ autonomous navigation technology with inter-satellite link. As in the newly built BeiDou system (BDS-3) equipped with Ka-band inter-satellite links, every individual satellite has the ability of communicating and measuring distances among each other. The system also has less dependence on the ground stations and improved navigation performance. Because of the huge amount of measurement data, centralized data processing algorithm for orbit determination is suggested to be replaced by a distributed one in which each satellite in the constellation is required to finish a partial computation task. In current paper, the balanced extended Kalman filter algorithm for distributed orbit determination is proposed and compared with whole-constellation centralized extended Kalman filter, iterative cascade extended Kalman filter, and increasing measurement covariance extended Kalman filter. The proposed method demands a lower computation power however yields results with a relatively good accuracy.

scholarly journals OutFin, a multi-device and multi-modal dataset for outdoor localization based on the fingerprinting approach

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fahad Alhomayani ◽  
Mohammad H. Mahoor
Keyword(s):  
Global Navigation Satellite System ◽  
Urban Areas ◽  
Satellite System ◽  
Reference Points ◽  
Navigation Satellite ◽  
Data Types ◽  
Entry Barrier ◽  
Promising Alternative ◽  
Global Navigation ◽  
Global Navigation Satellite

AbstractIn recent years, fingerprint-based positioning has gained researchers’ attention since it is a promising alternative to the Global Navigation Satellite System and cellular network-based localization in urban areas. Despite this, the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions constitutes a high entry barrier for studies. As an effort to overcome this barrier and foster new research efforts, this paper presents OutFin, a novel dataset of outdoor location fingerprints that were collected using two different smartphones. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 reference points. Each site is different in terms of its visibility to the Global Navigation Satellite System and reference points’ number, arrangement, and spacing. Before OutFin was made available to the public, several experiments were conducted to validate its technical quality.

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