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Tobias Weigel

Bio: Tobias Weigel is an academic researcher. The author has contributed to research in topics: GNSS applications & Wind speed. The author has co-authored 1 publications.

Papers
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Journal ArticleDOI
TL;DR: In this article, the capability of deep learning, especially, for an operational wind speed data derivation from the measured Delay-Doppler Maps (DDMs) is characterized, and the best architecture is determined on a validation set and is evaluated over a completely blind dataset from a different time span than that of the training data to validate the generality of the model for operational usage.

13 citations


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Journal ArticleDOI
TL;DR: In this article , seven machine learning methods are applied for wind speed retrieval, i.e., regression trees (Binary Tree), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM), ANN (Artificial neural network), stepwise linear regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made.
Abstract: This paper focuses on sea surface wind speed estimation using L1B level v3.1 data of reflected GNSS signals from the Cyclone GNSS (CYGNSS) mission and European Centre for Medium-range Weather Forecast Reanalysis (ECMWF) wind speed data. Seven machine learning methods are applied for wind speed retrieval, i.e., Regression trees (Binary Tree (BT), Ensembles of Trees (ET), XGBoost (XGB), LightGBM (LGBM)), ANN (Artificial neural network), Stepwise Linear Regression (SLR), and Gaussian Support Vector Machine (GSVM), and a comparison of their performance is made. The wind speed is divided into two different ranges to study the suitability of the different algorithms. A total of 10 observation variables are considered as input parameters to study the importance of individual variables or combinations thereof. The results show that the LGBM model performs the best with an RMSE of 1.419 and a correlation coefficient of 0.849 in the low wind speed interval (0–15 m/s), while the ET model performs the best with an RMSE of 1.100 and a correlation coefficient of 0.767 in the high wind speed interval (15–30 m/s). The effects of the variables used in wind speed retrieval models are investigated using the XGBoost importance metric, showing that a number of variables play a very significant role in wind speed retrieval. It is expected that these results will provide a useful reference for the development of advanced wind speed retrieval algorithms in the future.

9 citations

Journal ArticleDOI
TL;DR: A review on spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) can be found in this article , where the basic methods and techniques in the retrieval of a number of geophysical parameters and the detection of several objects are discussed.
Abstract: This article presents a review on spaceborne Global Navigation Satellite System Reflectometry (GNSS-R), which is an important part of GNSS-R technology and has attracted great attention from academia, industry and government agencies in recent years. Compared with ground-based and airborne GNSS-R approaches, spaceborne GNSS-R has a number of advantages, including wide coverage and the ability to sense medium- and large-scale phenomena such as ocean eddies, hurricanes and tsunamis. Since 2014, about seven satellite missions have been successfully conducted and a large number of spaceborne data were recorded. Accordingly, the data have been widely used to carry out a variety of studies for a range of useful applications, and significant research outcomes have been generated. This article provides an overview of these studies with a focus on the basic methods and techniques in the retrieval of a number of geophysical parameters and the detection of several objects. The challenges and future prospects of spaceborne GNSS-R are also addressed.

8 citations

Journal ArticleDOI
TL;DR: In this article , the authors investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R data and proposed a data fusion approach based on particle swarm optimization (PSO).
Abstract: Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) technology has opened a new window for ocean remote sensing because of its unique advantages, including short revisit period, low observation cost, and high spatial-temporal resolution. In this article, we investigated the potential of estimating swell height from delay-Doppler maps (DDMs) data generated by spaceborne GNSS-R. Three observables extracted from the DDM are introduced for swell height estimation, including delay-Doppler map average (DDMA), the leading edge slope (LES) of the integrated delay waveform (IDW), and trailing edge slope (TES) of the IDW. We propose one modeling scheme for each observable. To improve the swell height estimation performance of a single observable-based method, we present a data fusion approach based on particle swarm optimization (PSO). Furthermore, a simulated annealing aided PSO (SA-PSO) algorithm is proposed to handle the problem of local optimal solution for the PSO algorithm. Extensive testing has been performed and the results show that the swell height estimated by the proposed methods is highly consistent with reference data, i.e., the ERA5 swell height. The correlation coefficient (CC) is 0.86 and the root mean square error (RMSE) is 0.56 m. Particularly, the SA-PSO method achieved the best performance, with RMSE, CC, and mean absolute percentage error (MAPE) being 0.39 m, 0.92, and 18.98%, respectively. Compared with the DDMA, LES, TES, and PSO methods, the RMSE of the SA-PSO method is improved by 23.53%, 26.42%, 30.36%, and 7.14%, respectively.

5 citations

Journal ArticleDOI
Jinwei Bu, Kegen Yu, Xiaoqing Zuo, Jun Ni, Yongfa Li 
TL;DR: Wang et al. as mentioned in this paper proposed an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net, which considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model.
Abstract: Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth’s surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds.

3 citations

Journal ArticleDOI
TL;DR: In this article , the authors provide a comprehensive review of soil moisture retrieved by two space-based GNSS-R missions (TDS-1 and CYGNSS) to show the general past trends, gaps, and opportunities for soil moisture monitoring through GNSSR observations.
Abstract: ABSTRACT Accurate knowledge of soil moisture is critical for hydrological and agricultural applications such as agricultural irrigation management, drought characterization, and flood detection. Researchers have attempted to provide soil moisture using various methods and techniques. Traditionally, the amount of soil moisture was based on field measurements. On the other hand, remote sensing satellites have been widely used to provide continuous soil moisture measurements worldwide, encountering problems such as the lack of simultaneous spatial and temporal sampling rates and dependence on weather conditions. However, in recent decades, GNSS signals reflected from the Earth’s surface (GNSS-R technique) have been increasingly used for soil moisture monitoring, due to the numerous advantages it offers. This paper aims to provide a comprehensive review of soil moisture retrieved by two space-based GNSS-R missions (TDS-1 and CYGNSS) to show the general past trends, gaps, and opportunities for soil moisture monitoring through GNSS-R observations.

2 citations