Crop classification is an important part of crop management and yield estimation. In recent years, neural networks have made great progress in synthetic aperture radar (SAR) crop classification. However, the insufficient number of labeled samples limits the classification performance of neural networks. In order to solve this problem, a new crop classification method combining geodesic distance spectral similarity measurement and a one-dimensional convolutional neural network (GDSSM-CNN) is proposed in this study. The method consisted of: (1) the geodesic distance spectral similarity method (GDSSM) for obtaining similarity and (2) the one-dimensional convolutional neural network model for crop classification. Thereinto, a large number of training data are extracted by GDSSM and the generalized volume scattering model which is based on radar vegetation index (GRVI), and then classified by 1D-CNN. In order to prove the effectiveness of the GDSSM-CNN method, the GDSSM method and 1D-CNN method are compared in the case of a limited sample. In terms of evaluation and verification of methods, the GDSSM-CNN method has the highest accuracy, with an accuracy rate of 91.2%, which is 19.94% and 23.91% higher than the GDSSM method and the 1D-CNN method, respectively. In general, the GDSSM-CNN method uses a small number of ground measurement samples, and it uses the rich polarity information in multi-temporal fully polarized SAR data to obtain a large number of training samples, which can quickly improve the accuracy of classification in a short time, which has more new inspiration for crop classification.

LuTan-1 (LT-1) is the first civil L-band synthetic aperture radar (SAR) satellite constellation and comprises two identical satellites. LT-1 is designed to fulfill two main requirements, one of which the main tasks is to provide digital surface model (DSM) products covering the areas that are not available using optical satellites. The second task is to provide deformation products to support the geohazard monitoring task. For the first task, LT-1 provides a novel noninterrupted imaging technology to facilitate phase synchronization. For the second task, the orbit maintenance is implemented using a newly proposed in-plane offset semimajor axis and out-of-plane control triggered strategy. Besides, we provide the system performance analysis for the two main tasks. Finally, the first results are produced with root-mean-square-error (RMSE) of the DSM less than 0.7 m in the flat region and 6.7 m in the mountainous region. The RMSE of the first deformation products is less than 2.7 mm in the test region. The results indicate a favorable potential for terrain mapping and deformation monitoring applications.

In cold mountain environments, rock glaciers and talus represent common periglacial landforms. Accurate monitoring of their activity is crucial for understanding alpine kinematics. Presently, spaceborne SAR satellites monitor periglacial landform deformations mainly utilizing medium-resolution data. However, capturing surface structure deformations accurately and comprehending the movement mechanisms remain challenging. This study employs high-resolution GF-7 optical stereo images and Radarsat-2 SAR data, introducing a multi-baseline persistent scatterer and distributed scatterer combined MT-InSAR method to identify rock glaciers and talus landforms, as well as to analyze their deformations. Initially, rock glaciers and talus are outlined using GF-7 optical images, and Digital Surface Models (DSM) are extracted. The developed MT-InSAR method then detects the line of sight (LOS) and slope-parallel deformations of these landforms. Radarsat-2 monitoring reveals that 47.5% of identified rock glaciers are classified as active, while talus deformations are less active compared to rock glaciers. By utilizing high-resolution optical and SAR satellite data, we first documented the intricate deformation features within standard rock glaciers, such as the front, lateral margins, and optionally ridge-and-furrow deformations, as well as their interaction with the surrounding terrain topography. This discovery offers evidence of gravity-driven forces impacting rock glacier movement. We have also conducted a deformation analysis of the talus, obtaining spatial deformation characteristics of typical talus. It was observed that the deformation of the talus is jointly influenced by the conditions of surface debris cover and the slope of the terrain. This study highlights the value of high-resolution optical and SAR satellites in studying periglacial geomorphology dynamics.

Phase unwrapping (PU) has been a key step in the processing of interferometric synthetic aperture radar (InSAR) data, and its processing accuracy will directly affect the reconstruction results of digital elevation models (DEMs). The traditional single-baseline (SB) PU must be calculated under continuity assumptions. However, multi-baseline (MB) PU can get rid of the limitation of continuity assumption, so reasonable results can be obtained in regions with large gradient changes. However, the poor noise robustness of MBPU has always been a key problem. To address this issue, we transplant three Bayesian filtering methods with a two-stage programming approach (TSPA), and propose corresponding MBPU models. First, we propose a gradient-estimation method based on the first step of TSPA, and then the corresponding PU model is determined according to different Bayesian filtering. Finally, the wrapped phase can be obtained by unwrapping, one by one, using an effective quality map based on heapsort. These methods can improve the robustness of the MBPU methods. More significantly, this paper establishes a novel TSPA-based Bayesian filtering MBPU framework for the first time. This is of great significance for broadening the research of MBPU. The proposed methods experiments on simulated and real MB InSAR datasets. From the results, we can see that the TSPA-based Bayesian filtering MBPU framework can significantly improve the robustness of the MBPU method.