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

Two effective machine learning-aided sea ice monitoring methods are investigated using 42 months of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data collected by the TechDemoSat-1 (TDS-1). The two-dimensional delay waveforms with different Doppler spread characteristics are applied to extract six features, which are combined to monitor sea ice using the decision tree (DT) and random forest (RF) algorithms. Firstly, the feature sequences are used as input variables and sea ice concentration (SIC) data from the Advanced Microwave Space Radiometer-2 (AMSR-2) are applied as targeted output to train the sea ice monitoring model. Hereafter, the performance of the proposed method is evaluated through comparing with the sea ice edge (SIE) data from the Special Sensor Microwave Imager Sounder (SSMIS) data. The DT- and RF-based methods achieve an overall accuracy of 97.51% and 98.03%, respectively, in the Arctic region and 95.46% and 95.96%, respectively, in the Antarctic region. The DT- and RF-based methods achieve similar accuracies, while the Kappa coefficient of RF-based approach is slightly larger than that of the DT-based approach, which indicates that the RF-based method outperforms the DT-based method. The results show the potential of monitoring sea ice using machine learning-aided GNSS-R approaches.

Section: Introductionmentioning

confidence: 99%

Machine Learning-Aided Sea Ice Monitoring Using Feature Sequences Extracted from Spaceborne GNSS-Reflectometry Data

Zhu

Tao

et al. 2020

“…The traditional selection method generally uses the SNR peak as the index of data filtering. For example, in [26], the threshold of SNR peak is set to 0 dB, and the DDMs with SNR peak greater than the threshold are selected; in [29,49], the SNR peak threshold is set to 3 and 4 dB, respectively. Although the above SNR peak method can be effectively used for DDM selection, there are some deficiencies in threshold selection.…”

Section: Methodsmentioning

confidence: 99%

“…Model-1 and Model-2 are derived from the training data in this paper, while the piecewise function in Model-3 is modified on the basis of [26,29]. They are expressed as follows:…”

Section: Model-1 Model-2 and Model-3mentioning

confidence: 99%

Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms

Zhu

et al. 2020

This paper focuses on sea surface wind speed estimation based on cyclone global navigation satellite system reflectometry (GNSS-R) data. In order to extract useful information from delay-Doppler map (DDM) data, three delay waveforms are presented for wind speed estimation. The delay waveform without Doppler shift is defined as central delay waveform (CDW), and the integral of the delay waveforms with different Doppler shift values is defined as integral delay waveform (IDW), while the difference between normalized IDW (NIDW) and normalized CDW (NCDW) is defined as differential delay waveform (DDW). We first propose a data filtering method based on threshold setting for data quality control. This method can select good-quality DDM data by adjusting the root mean square (RMS) threshold of cleaned DDW. Then, the normalized bistatic radar scattering cross section (NBRCS) and the leading edge slope (LES) of IDW are calculated using clean DDM data. Wind speed estimation models based on NBRCS and LES observations are then developed, respectively, and on this basis, a combination wind speed estimation model based on determination coefficient is further proposed. The CYGNSS data and ECMWF reanalysis data collected from 12 May 2020 to 12 August 2020 are used, excluding data collected on land, to evaluate the proposed models. The evaluation results show that the wind speed estimation accuracy of the piecewise function model based on NBRCS is 2.3 m/s in terms of root mean square error (RMSE), while that of the double-parameter and triple-parameter models is 2.6 and 2.7 m/s, respectively. The wind speed estimation accuracy of the double-parameter and triple-parameter models based on LES is 3.3 and 2.5 m/s. The results also demonstrate that the RMSE of the combination method is 2.1 m/s, and the coefficient of determination is 0.906, achieving a considerable performance gain compared with the individual NBRCS- and LES-based methods.

“…In the past decade, spaceborne GNSS-R has undergone rapid development with successfully deployed satellite missions, such as the UK Technology Demonstration Satellite-1 (TDS-1, launched in July 2014), NASA's cyclone global navigation satellite system (CYGNSS, launched in December 2016), China's Bufeng-1 A/B mission (launched in June 2019) [5]. Although all missions were originally designed for ocean surface wind speed retrieval, they also provided a large number of land observations for terrestrial remote sensing applications, such as soil moisture retrieval, forest biomass estimation, and wetland extent detection [6].…”

Section: Introductionmentioning

confidence: 99%

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

Dong

Jin

2021

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.