High Wind Speed Inversion Model of CYGNSS Sea Surface Data Based on Machine Learning

Abstract

In response to the deficiency of the detection capability of traditional remote sensing means (scatterometer, microwave radiometer, etc.) for high wind speed above 25 m/s, this paper proposes a GNSS-R technique combined with a machine learning method to invert high wind speed at sea surface. The L1-level satellite-based data from the Cyclone Global Navigation Satellite System (CYGNSS), together with the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) data, constitute the original sample set, which is processed and trained with Support Vector Regression (SVR), the combination of Principal Component Analysis (PCA) and SVR (PCA-SVR), and Convolutional Neural Network (CNN) methods, respectively, to finally construct a sea surface high wind speed inversion model. The three models for high wind speed inversion are certified by the test data collected during Typhoon Bavi in 2020. The results show that all three machine learning models can be used for high wind speed inversion on sea surface, among which the CNN method has the highest inversion accuracy with a mean absolute error of 2.71 m/s and a root mean square error of 3.80 m/s. The experimental results largely meet the operational requirements for high wind speed inversion accuracy.

Global Sea Surface Height Measurement From CYGNSS Based on Machine Learning

“…Zhansheng Chen, Weiliang Liu, Shanghai Aerospace Space Technology Co., LTD, 201109 Shanghai, China (e-mail: czs@sastspace.com; lwl@sastspace.com). [17] combined principal component analysis (PCA), support vector regression (SVR), PCA combined SVR (PCA-SVR) method, and the convolutional neural network (CNN) method, respectively, thus constructing a sea surface high wind speed inversion model. Zhang et al [18] analyzed CYGNSS data and used the support vector machine (SVM) method for sea surface wind direction inversion.…”

Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data

“…After our research about the sea surface wind speed inversion model of the CYGNSS sea surface data based on Machine Learning [32], this paper studies the sea surface wind direction retrieval model of space-borne GNSS-R based on SVM. The data comes from CYGNSS Full DDM data, CYGNSS L1 data and ECMWF reanalysis datasets from 2019 to 2020.…”

Statistical and Machine Learning Models for Remote Sensing Data Mining—Recent Advancements

“…After the rigorous review process, a total of five papers have been accepted for publication in this issue. The selected papers either deal with the core challenges, such as missing data handling, noisy label distillation, feature-level fusion, etc., in remote sensing data analyses [3][4][5], or these highlight on various critical real-world problems, including oil spill detection [6], and high wind speed inversion [7].…”

“…In addition to discussion on the technical challenges being faced during remote sensing image data processing, this Special Issue also includes papers on some critical applications of remote sensing data analytics [6,7]. For example, in the fourth article of this Special Issue, Almulihi et al [6] have presented the application of SAR Image analysis in oil spill detection.…”

“…The traditional ways of detecting wind speed using remote sensing imaging technology are often found to be failed when the wind speed is high. The study made by Zhang et al [7], as included in this Special Issue, reveals that machine learning techniques can be effectively employed as the complements of these conventional RS technology-based models. Experimentations on multi-sourced RS data show that machine learning schemes of Support Vector Regression (SVR), combined Principal Component Analysis (PCA) and SVR (PCA-SVR), and Convolutional Neural Network (CNN) can be certainly useful for improving the accuracy in high wind speed inversion on sea surface, where CNNs are promising models in this area.…”