GNSS-R Sea Ice Detection Based on Linear Discriminant Analysis

Abstract

Global Navigation Satellite System-Reflectometry (GNSS-R) is one of the main technologies used for sea ice remote sensing detection, and is based on the multipath interference effect of satellite signals. To improve the GNSS-R sea ice detection performance in terms of accuracy, robustness to noise, and data utilization, a linear discriminant analysis (LDA)-based method was proposed in this paper. Delay-Doppler maps (DDMs) collected from TechDemoSat-1 (TDS-1) were employed as input and classified into different types based on the signal-to noise ratio (SNR) related to the noise effect. For low-effect-noise DDMs, the LDA-based sea-ice detection method presented an accuracy of 95.03%, verifying the feasibility of LDA-based GNSS-R sea-ice detection. For the middle noise effect and high noise effect DDMs, the LDA-based method is more robust to noise effects than the convolutional neural network (CNN) method. Although the detection accuracy decreased when the SNR decreased or integral delay waveform average (IDWA) increased, the LDA-based method was more robust than the CNN-based one. The data utilization and melting period were also analyzed to account for variations in detection accuracy. The LDA-based method used 67.82% more data than previous experiments with threshold IDWA≤58210.32 and SNR>-17.48dB. The melting periods were analyzed based on the noise, SNR, surface reflectivity, and permittivity. When the status of sea ice changes, outliers of surface reflectivity appear, the average permittivity varies in [10, 60], and the detection accuracy decreases during the melting period of sea ice. The results show that the correlation coefficient with the National Oceanic and Atmospheric Administration (NOAA) data is up to 0.93, with different threshold IDWA or IDWA. The LDA-based method predicted results that greatly matched the sea ice distribution from the NOAA data.