With the development of the Chinese BeiDou satellite navigation system, the applications of BeiDou-reflected (BeiDou-R) signals will play a key role in Global Navigation Satellite System-reflected (GNSS-R) signals. In this paper, we describe the theory of code-level ocean surface altimetry using BeiDou-R signals. Two BeiDou-R coastal experiments (lake and ocean experiments in China) were performed using direct righthanded circularly polarized (D-RHCP) antenna, reflected lefthanded circularly polarized (R-LHCP) antenna, and reflected right-handed circularly polarized (R-RHCP) antenna. This is the first research on BeiDou-R ocean altimetry performance analysis. The lake experiment demonstrated the potential availability of water surface altimetry using BeiDou-R signals. We found that the resulting error (0.11 m) from the BeiDou geostationary Earth orbit (GEO) satellite signals was much smaller than that (1.61 m) from the inclined geosynchronous orbit (IGSO) satellite signals, and thus verified that R-LHCP signals from high-elevation satellites were more suitable for altimetry. The ocean surface altimetry was performed on China East Sea for 28 h. The predicted results of ocean surface height using R-LHCP signals from BeiDou GEO satellites were in good agreement with the field measured data, and the root-mean squared (rms) height precision can reach 0.37 m. A better performance of GEO observations compared to IGSO observations was found for coastal setups. In case of airborne setups with different multipath settings, the result may be different.Index Terms-BeiDou-reflected (BeiDou-R) signals, code-level, geostationary Earth orbit (GEO), ocean surface altimetry.

For preventing the effects of sea ice disaster, traditional methods for detecting sea ice have some disadvantages, such as inadequate robustness against weather and high cost of real-time detection. This paper evaluates the usage of Global Positioning System (GPS)-reflected signals for accurate real-time Earth observations to study the changes in the sea surface state through remote sensing (RS). GPS L1 signals received after reflection from Bohai Bay were analyzed for their sea-ice content. The results are in good agreement with a reflected power ratio model [the ratio of the direct right-hand circular polarization (RHCP) signals and the reflected left-handed circular polarization (LHCP) signals] and sea ice concentration. The average of the reflected power ratio on the sea ice surface (0.41) is much smaller than that on the sea water surface (2.61).

Sea ice is one of the most critical marine disasters, especially in the polar and high latitude regions. Hyperspectral image is suitable for monitoring the sea ice, which contains continuous spectrum information and has better ability of target recognition. The principal bottleneck for the classification of hyperspectral image is a large number of labeled training samples required. However, the collection of labeled samples is time consuming and costly. In order to solve this problem, we apply the active learning (AL) algorithm to hyperspectral sea ice detection which can select the most informative samples. Moreover, we propose a novel investigated AL algorithm based on the evaluation of two criteria: uncertainty and diversity. The uncertainty criterion is based on the difference between the probabilities of the two classes having the highest estimated probabilities, while the diversity criterion is based on a kernelk-means clustering technology. In the experiments of Baffin Bay in northwest Greenland on April 12, 2014, our proposed AL algorithm achieves the highest classification accuracy of 89.327% compared with other AL algorithms and random sampling, while achieving the same classification accuracy, the proposed AL algorithm needs less labeling cost.

Sea surface height (SSH) retrieval based on spaceborne global navigation satellite system reflectometry (GNSS-R) usually uses the GNSS-R geometric principle and delay-Doppler map (DDM). The traditional method condenses the DDM information into a single scalar measure and requires error model correction. In this paper, the idea of using machine learning methods to retrieve SSH is proposed. Specifically, two widely-used methods, Principal Component Analysis combined with Support Vector Regression (PCA-SVR) and Convolution Neural Network (CNN), are used for verification and comparative analysis based on the observation data provided by Techdemosat-1 (TDS-1). According to the DDM inversion method, ten features from TDS-1 Level 1 data are selected as inputs; The SSH verification model based on the Danmarks Tekniske Universitet (DTU) 15 ocean wide mean SSH model and the DTU global ocean tide model is used as output verification of SSH. For the hyperparameters in the machine learning model, a grid search strategy is used to find the optimal values. By analyzing the TDS-1 data from 31 GPS satellites, the mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R 2 ) of the PCA-SVR inversion model are 0.61 m, 1.72 m and 99.56%, respectively; and the MAE, RMSE and R 2 of the CNN inversion model is 0.71 m, 1.27 m and 99.76%, respectively. In addition, the time required to train the PCA-SVR and CNN inversion models is also analyzed. Overall, the technique proposed in this paper can be confidently applied to SSH inversion based on TDS-1 data.

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.