This paper presents two empirical models, the low wind bagged trees (LWBT) and high wind bagged trees (HWBT) ensemble models to estimate ocean surface wind speed using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data. The models are empirically trained using NASA’s Cyclone GNSS (CYGNSS) mission level 1 data (version 2.1). The truth label for the LWBT model is the wind speed product derived from European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 and Global Data Assimilation System (GDAS), while the label for the HWBT model is wind speed measurements from Stepped Frequency Microwave Radiometer (SFMR). Testing results show that the LWBT and HWBT models achieved global wind speed retrieval root-mean-square-error (RMSE) of ~1.5 m/s and ~1.4 m/s, respectively, corresponding to an improvement of 29% and 65% with respect to the CYGNSS Level 2 standard wind speed product. The maximum bias is reduced by 65% and 60% for LWBT and HWBT over the Level 2 wind speeds, respectively. Two typhoon case studies are presented to corroborate the model performances and their retrieved wind speeds are consistent with reports from World Meteorological Organization (WMO) and with the measurement provided by the Huangmao Zhou (HMZ) weather station. 

A Bagged-Tree Machine Learning Model for High and Low Wind Speed Ocean Wind Retrieval From CYGNSS Measurements

Using Cyclone Global Navigation Satellite System (CYGNSS) L1 data with large amount and wide coverage, this paper establishes a sea surface wind direction retrieval model based on three machine learning algorithms. Wind direction will cause the asymmetry of Delay Doppler Map (DDM). Based on this, this paper extracts two angle characteristic parameters from DDM. Compared with CYGNSS full DDM, L1 compact DDM has a reduced dimension. Therefore, this paper expands more characteristic parameters, including L1 parameters and geophysical parameters such as wind speed, mean sea surface pressure (MSL), sea surface temperature (SST). Wind speed, direction, MSL and SST are from the European Centre for Medium-Range Weather Forecasts (ECMWF). After data preprocessing, the experimental data set is generated. Based on this data set, this paper establishes SVM, BP and CNN wind direction retrieval models and verifies their model performance and generalization performance. In addition, a filter that can optimize the accuracy of CNN model is constructed, and the retrieval effect under different wind direction intervals is further studied. The results show that the accuracy of CNN model for L1 data is higher than that of SVM and BP, and the retrieval error of global sea surface wind direction after filtering is less than 20°. The accuracy difference of different wind direction intervals also has a significant impact on the wind direction retrieval results. 

Wind Direction Retrieval From CYGNSS L1 Level Sea Surface Data Based on Machine Learning

During the last few decades, the remarkable progress in the field of satellite remote sensing (RS) technology has enabled us to capture coarse, moderate to high-resolution earth imagery on weekly, daily, and even hourly intervals [...]

/pdf/statistical-and-machine-learning-models-for-remote-sensing-2hlxfn4s.pdf

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

Adaptive graph neural network based South China Sea seawater temperature prediction and multivariate uncertainty correlation analysis

Global navigation satellite system reflectometry (GNSS-R) Delay-Doppler map measures the sea surface roughness, which has recently been applied to retrieve sea surface wind speed. However, current studies on GNSS-R wind speed retrieval only use the spatial domain of the delay-Doppler map without considering the variations patterns in the map, which is regarded as frequency domain information of the map. In this study, we propose a joint frequency-spatial-domain wind speed retrieval network (FSNet) based on reflectivity data provided by the Cyclone Global Navigation Satellite System (CyGNSS) mission. We construct a matchup dataset between the CyGNSS satellite data and ECMWF model data from January 1, 2018, to December 31, 2019. The wind speed range is 0–25 m/s. Using the proposed FSNet, frequency and spatial features are simultaneously extracted. The frequency domain feature supplements the spatial-domain information of the mid and high-level features in the neural network. Rather than directly concatenating the frequency-domain features with the spatial-domain features, we designed a feature fusion module to fuse frequency and spatial features for wind speed retrieval adaptively. Experiments show that our FSNet wind speed retrieval has a root mean square error (RMSE) of 1.63 m/s for a wind range of 0-25 m/s. This accuracy is 25.4% better than the operational algorithm provided by the CyGNSS Level 2 wind speed product. For a higher wind range of 16-25m/s, FSNet performed even better, improving the RMSE by 31%. 

Exploiting Frequency-Domain Information of GNSS Reflectometry for Sea Surface Wind Speed Retrieval

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.

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

In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°.

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

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

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