As a by-product of Interferometric Synthetic Aperture Radar (SAR, InSAR) technique, interferometric coherence is a measure of the decorrelation noise for InSAR observation, where the lower the coherence value, the more serious the decorrelation noise. In the densely vegetated area, the coherence value could be too low to obtain any valuable signals, leading to the degradation of InSAR performance and the possible waste of expensive SAR data. Normalized Difference Vegetation Index (NDVI) value is a measure of the vegetation coverage and can be estimated from the freely available optical satellite images. In this paper, a multi-stage model is established to quantitatively estimate the decorrelation noise for vegetable areas based on Landsat-derived NDVI prior to the acquisition of SAR data. The modeling process is being investigated with the L-band ALOS-1/PALSAR-1 data and the Landsat-5 optical data acquired in the Meitanba area of Hunan Province, China. Furthermore, the reliability of the established model is verified in the Longhui area, which is situated near the Meitanba area. The results demonstrate that the established model can quantitatively estimate InSAR decorrelation associated with the vegetation coverage.

Luanchuan mining area, which is located in Henan Province, China and characterized by undulating topography and large precipitation, is vulnerable to the landslide disasters. The observations from the space-borne radar (ascending and descending TerraSAR-X) images and the ground-based radar (GPRI-II) images are integrated to monitor the surface deformations in Luanchuan mining area. Before the integration of the observations from multi-source SAR images, the coordinate systems of multi-source images are required to be unified by geographical projection. However, the accuracy of the commonly used geographical coding will be degraded by external DEM errors and inaccurate satellite orbit, which makes it difficult to achieve the high-precision registration of space-borne and ground-based radar scenes. Therefore, in this paper the iterative closest point (ICP) method is introduced to register the space-borne and ground-based images, yielding sub-pixel registration accuracy. Reasonable weights are then assigned to multi-source observations through Strain Model and Variance Component Estimation (SM-VCE) method, from which the high-precision three-dimensional deformations can be reconstructed. The results show that the maximum deformation rates in the east-west, north-south and vertical directions of Luanchuan mining area from July 20, 2019, to August 1, 2019, are about 0.2, 0.4 and 0.7 mm/day, respectively. The deformations are mainly on the loose slags in the Luanchuan pit rather than the bedrock, as a result of the accumulation of waste slags. Some suggestions on the installation location of ground-based radar are also provided to better combine the space-borne and ground-based observations.

The Yuanjiang Basin in Northwestern Hunan is a landslide-prone region due to its complex geological features and dense vegetation. Conventional single-polarization muti-temporal InSAR (MT-InSAR) methods often fail in such areas because of severe decorrelation, leading to reduced accuracy and coverage in monitoring. To address these limitations, this study proposes an innovative landslide detection framework using the muti-temporal polarimetric InSAR (MT-PolInSAR) method. This approach improves the density and precision of deformation measurements by optimizing polarimetric and temporal dimensions. Leveraging fully polarimetric ALOS-2 data acquired from May 2021 to June 2022, 32 potential deformation sites were identified, including 18 landslide-prone areas and 8 sites showing other deformation types, with average deformation rates between −4 and −2 cm/year. Field validation confirmed an identification accuracy of 81.25%, demonstrating the robustness of fully polarimetric long-wavelength SAR data for landslide monitoring in densely vegetated regions. This method offers a significant advancement in the detection and assessment of landslide hazards in challenging environments.

The polarimetric phase optimization method has been integrated into the multitemporal synthetic aperture radar interferometry (MT-InSAR) framework to enhance phase quality and deformation coverage, known as multitemporal polarimetric InSAR (MT-PolInSAR) technology. However, most existing MT-PolInSAR methods optimize phase separately in the temporal and polarimetric dimensions, failing to leverage the interdimensional relationships fully. This article proposes a novel multipolarization optimization method, which achieves one-step phase optimization by joining temporal and polarimetric dimensions based on a joint probability density function and maximum likelihood estimation (MLE). Additionally, a no-threshold regularization is employed to strengthen the stability of the multipolarization covariance matrix. The proposed approach has been validated through synthetic and real quad-polarization datasets. Regarding the real data, ALOS-2/PARSAR-2 from the Fengjie landslide in China and Radarsat-2 data from the Barcelona airport in Spain are used. The experimental outcomes demonstrate that our proposed approach significantly diminishes phase noise while increasing the density of measurement points.

Traditional Selection of Homogeneous Pixels (SHP) methods insufficiently utilize polarization information and rely on extensive statistical computations from large datasets. When applied to mountainous or densely vegetated regions with low coherence, these methods are susceptible to noise interference, which reduces the interpretation reliability. In this study, we propose ViTPolSHP———the Vision Transformer(ViT)-based Polarimetric Synthetic Aperture Radar (PolSAR) Selection of Homogeneous Pixels algorithm for selecting homogeneous pixels in multi-temporal PolSAR imagery. The ViTPolSHP algorithm leverages the temporal average Yamaguchi four-component decomposition features for classification and integrates the terrain characteristics and geometric distortions to derive homogeneous pixel sets even with limited datasets, presenting a promising solution for SAR/InSAR data processing in complex mountainous terrains.