This paper presents an overview of intelligent reflection technologies, including backscatter communication (BackCom) and reconfigurable intelligent surfaces (RIS), and their integration in 6G non-terrestrial networks (NTN) for Internet of Things (IoT) applications. The exponential growth in IoT devices necessitates efficient and energy-saving communication mechanisms to enable intelligent collaboration, which these technologies provide. We explore the benefits of RIS and BackCom in 6G NTN IoT environments, showing their potential to facilitate future applications. A case study of an RIS-enhanced bistatic BackCom scheme tailored for 6G NTN IoT is introduced, demonstrating considerable capacity improvements from integrating RIS with BackCom. Simulation results validate the effectiveness of RIS in significantly boosting BackCom performance. Finally, we discuss research opportunities to advance the implementation of intelligent reflection technologies from theoretical concepts to practical 6G NTN IoT solutions, involving optimization of RIS placement, channel estimation, and radio resource management. Our comprehensive analysis underscores intelligent reflection as an indispensable capability in 6G NTN IoT.

The shadow statistical method (SSM) used for extracting the significant wave height (SWH) from X-band marine radar images was further investigated because of its advantage of not requiring an external reference for calibration. Currently, a fixed shadow segmentation threshold is utilized to extract the SWH from a radar image based on the SSM. However, the retrieval accuracy of the SWH is not ideal for low wind speeds since the echo intensity of sea waves rapidly decays over distance. In order to solve this problem, an adaptive shadow threshold, which varies with echo intensity over distance and can accurately divide the radar image into shadow and nonshadow areas, is adopted to calculate the wave slope (WS) based on the texture feature of the edge image. Instead of using the averaged WS, the wave slope feature vector (WSFV) is constructed for retrieving the SWH since the illumination ratio and the calculated WS in the azimuth are different for shore-based radar images. In this paper, the SWH is calculated based on the constructed WSFV and classical support vector regression (SVR) technology. The collected 222 sets of X-band marine radar images with an SWH range of 1.0∼3.5 m and an average wind speed range of 5∼10 m/s were utilized to verify the performance of the proposed approach. The buoy record, which was deployed during the experiment, was used as the ground truth. For the proposed approach, the mean bias (BIAS) and the mean absolute error (MAE) were 0.03 m and 0.14 m when the ratio of the training set to the test set was 1:1. Compared to the traditional SSM, the correlation coefficient (CC) of the proposed approach increased by 0.27, and the root mean square error (RMSE) decreased by 0.28 m.