The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was successfully launched. Due to its small spot size, multibeam configuration, high sampling rate, and strong immunity to terrain slopes, it has been regarded as a powerful tool for forest resources surveying and managing. However, the ICESat-2 photon cloud data contain considerable background photons, which discretely distribute in the background space of signal photons. Therefore, it is necessary to filter these noise photons. In this study, photons are divided into three categories: signal photons, noise photons far away from signal photons, and noise photons adjacent to signal photons. Based on the existing research, forward and backward elliptical distances were used to express the spatial relationship between two photons, and backward local density (BLD) was used to describe the density distribution of the photons. However, the single statistical parameter cannot clearly distinguish three types of photon cloud. Therefore, forward local density (FLD) and neighboring forward local density difference (NFLDD) also were defined to describe the density distribution of the photons. Finally, by combining the support vector machine (SVM), the above three density parameters were used to classify the photons by signal and noise photons. The proposed method was validated with photon cloud data acquired by the Simulated Advanced Terrain Laser Altimeter System (MATLAS), the Multiple Altimeter Beam Experimental Lidar (MABEL), and the ICESat-2 systems over different forested areas. The results demonstrated that the proposed method can well remove the noise photons and retain the signal photons without depending on any statistical assumptions or thresholds. The comprehensive accuracy of the three test sites was 0.99, 0.98, and 0.99, respectively, which was higher than those of the existing method. In addition, the total errors corresponding to the three test sites were about 0.4%, 0.5%, and 1.0% respectively, which were lower than those of the existing method.

Highways built in permafrost regions are susceptible to deformations and instability of the roadbed caused by climatic factors. Long-term deformation monitoring is essential to reveal the freeze-thaw-related deformations. When using Interferometric Synthetic Aperture Radar (InSAR) for permafrost highway monitoring, the majority of different physical phase components are usually considered as equally weighted, and the permafrost deformation-related components are mostly modeled with an empirical mathematical model. This may induce uncertainty and difficulties to remove the atmospheric delay and orbital error, which affects both the accuracy and efficiency of deformation estimation. To address these limitations, we propose an independent component analysis (ICA) based method for estimating the deformation of highways in permafrost regions (HPICA). In HPICA, the Fast ICA is utilized to separate the original InSAR unwrapped phases, and then the extracted freeze-thaw deformation-related components are modeled considering the climatic factors. The simulated experiments show that the spatial ICA can more accurately separate the deformation-related signals from the mixed signals than that of temporal ICA. The Maduo section of Gongyu Highway on the Tibetan Plateau was selected as a study area in the real-data experiment. The results showed the maximum cumulative settlement spanning January 2020 to January 2022 was up to –140.8 mm. A comparative analysis indicated that the modeling accuracy of HPICA is with significant improvement. Besides, HPICA could reveal the boundaries of different permafrost regions according to the nature of permafrost, thus assisting in spatial classification of different types of soil regions.

Health monitoring is important for densely distributed urban infrastructures, particularly in cities undergoing rapid economic progress. Permanent scatterer interferometry (PSI) is an advanced remote sensing observation technique that is commonly used in urban infrastructure monitoring. However, the rapid construction of infrastructures may easily cause a loss of coherence for radar interferometry, inducing a low density of effective permanent scatterer (PS) points, which is the main limitation of PSI. In order to address these problems, a novel time-series synthetic aperture radar interferometry (InSAR) process based on the adaptive window homogeneous pixel selection and phase optimization (AWHPSPO) algorithm and thermal expansion linear model (TELM) is proposed. Firstly, for homogeneous point selection, information on both the time-series intensity and deformation phases is considered, which can compensate for the defects of insufficient homogeneous samples and low phase quality in traditional distributed scatterer interferometric synthetic aperture radar (DS-InSAR) processing. Secondly, the physical, thermal expansion component, which reflects the material properties of the infrastructures, is introduced into the traditional linear model, which can more rationally reflect the temporal evolution of deformation variation, and the thermal expansion coefficients can be estimated simultaneously with the deformation parameters. In order to verify our proposed algorithm, the Orange Island area in Changsha City, China, was selected as the study area in this experiment. Three years of its historical time-series deformation fields and thermal expansion coefficients were regenerated. With the use of high-resolution TerraSAR-X radar satellite images, a maximum accumulated settlement of 12.3 mm and a minor uplift of 8.2 mm were detected. Crossvalidation with small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) results using Sentinel 1A data proved the reliability of AWHPSPO. The proposed algorithm can provide a reference for the control of the health and safety of urban infrastructures.

Long-term deformation prediction and quantitative brine estimation are of great significance for safety precautions and environmental protection in salt mining areas. Traditional Interferometric Synthetic Aperture Radar (InSAR) combined with Probability Integral Method (PIM) has the following limitations: mathematical empirical models are mostly used in InSAR deformation modelling, which ignore the mechanisms of underground mining thus may limit the accuracy of deformation observations; InSAR deformation models have theoretical contradictions and inconsistencies with the future prediction models; PIM is not applicable to the deformation induced by salt solution mining. In this study, a novel Water-Solution Kinetic (WSK) InSAR model was proposed to replace the traditional InSAR models. It considers the principles of substance diffusion and mass transfer of salt, thus can be used directly for the quantitative prediction of both the deformations and extracted brine. Least Squares with Inequality Constraints (LSIC) was introduced to solve the unknown WSK parameters based on the InSAR phases to improve the computational efficiency. Two salt mines in China were selected for the experiments. The results show that the maximum predicted deformation was 296 mm in XR and 155 mm in HR, and the brine composition was estimated to be 0.406 and 1.477 million tons per year, respectively. Compared to traditional Static-PIM, the accuracy was improved by 48.3% and 54.5%, respectively. The results indicated that WSK is more precise and reasonable for predicting the deformation in salt mining areas, which can provide data reference for the management of salt mines.

The penetration depth of Synthetic Aperture Radar (SAR) signals is a critical parameter for assessing subsurface characteristics and detecting concealed structures. Although numerous empirical and scattering models have been proposed to estimate signal penetration depth (SPD), there remains a lack of understanding of SPD in various settings. In this study, we deployed a ground-based SAR system equipped with L-band (1.5 GHz) and S-band (3.0 GHz) SAR sensors and a microwave anechoic chamber to carry out experimental studies of SPD in soil with different volumetric water content (VWCs) and in hard surfaces of varying thicknesses of gravel and asphalt. At an incidence angle of 40°, the laboratory measurements indicate that the L-band SPDs in homogeneous clay with VWC levels of 4% and 18% are approximately 45–50 cm and 30–35 cm. The L-band signal can penetrate asphalt up to 9 cm, gravel up to 5 cm, and a composited layer of 2 cm gravel and 7 cm asphalt. The S-band can penetrate loose and compacted clay of 20‒25 cm and 15‒20 cm, respectively. The SPD in silt loam was about 15‒20 cm.