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.

LuTan-1(LT-1) has finished its in-orbit test in 2023. The inorbit test results show that LT-1 performed well and fulfilled the needs of the users. In order to use the data operationally for natural resources monitoring, especially for geohazard monitoring, we have established the application system for LT-1. In this paper, we have introduced the application system including the geometric technology, the standard products generation method, the basic deformation products generation method. Demonstrations of LT-1 are also provided to support the applications of the satellite data.

The Kinematics of rock glaciers is a key indicator for studying periglacial environment hydrology, climate, and disasters. Differential Interferometric Synthetic Aperture Radar (DInSAR) has been demonstrated to be an effective technique for detecting the deformation of rock glaciers. In this study, we present an investigation of the potential of the Lutan-1 (LT-1) SAR constellation for rock glacier topography mapping and deformation monitoring over a periglacial environment with complex topography using Stacking InSAR methods. Firstly, a high-resolution digital surface model (DSM) is generated by the bistatic formation data of the LT-1 SAR constellation. Compared with the ICESat GLAS points elevation product distributed in the study area, the R2reached 0.96, and the root mean square error (RMSE) reached 1.6m, indicating the accuracy of the LT-1 derived DSM product. Then, the deformation velocities of the rock glaciers were acquired from the Stacking InSAR method over the study area. The results indicated that the LOS deformation velocities of rock glaciers in the study area ranged from −0.8 m/y to approximately 0.6 m/y, with an average velocity amplitude of 0.06 m/y. With 241 of the rock glaciers exhibit active zones. The results demonstrated that the LT-1 SAR constellation could provide a new SAR data source for periglacial landform topography mapping and deformation monitoring.

A collapse accident occurred in the Alxa left Banner open-pit mining area of Inner Mongolia on February 22, 2023, causing heavy casualties and property losses. Multi-temporal pre-collapse and post-collapse images were acquired by the Lutan-1 SAR satellites, which is the first group L-band SAR constellation in China. The subsidence information of pre-collapse and post-collapse were extracted and analyzed, which provides certain knowledge for post-disaster accident handling and emergency response. Due to the extensive mining activities, the topography of open-pit coal mine area generally changes obviously. TanDEM and Gaofen-7 DEM were employed for deformation monitoring. The results indicated that time-efficient DEM is necessary for open-pit mining area monitoring. At the same time, based on the subsidence information detected before the disaster with high spatial and temporal resolutions, combining with prior information and expert interpretation, it is potential to provide certain support for the early hidden danger identification.

The L-band differential interferometric SAR satellite, also named as LuTan-1, is the first bistatic spaceborne L-band SAR constellation for multiple applications in China. The Datong, Yulin and Liupanshui coalfields are the main coal production base in China which produce over 100 million tons coal each year and play a significant role for the development of national economy. However, a series of issues such as ground subsidence, landslides and damage of structures are induced by extensive coal mining. Therefore, the preliminary assessment of LuTan-1 SAR data for mining deformation monitoring, including Datong, Yulin and Liupanshui coalmines in China, were implemented in this research. Owing to the high revisit ability and long wavelength of LuTan-1 satellites, obvious subsidence was detected for different coalmines using LuTan-1 SAR data. In comparison with the simultaneous leveling measurements, deformation monitoring with cm accuracy were achieved over the study area.

Lutan-l (LT-1) is the first Chinese SAR satellite constellation that provide continuous deformation images covering the whole China. Three deformation products are designed to conduct the geohazard monitoring tasks. The first is DInSAR deformation field product which can be used for general geohazards investigation. The second is stacking deformation velocity field that can be used for geohazards screening. The third is deformation time-series obtained from multi-temporal InSAR and is specially designed for the continuous deformation regions to discover the deformation rules in temporal domain. In this paper, the LT-1 key features for deformation monitoring are provided. Product examples are shown with the Setninel-1A SAR images covering Datong City, Shanxi province. The three deformation products are expected to be used for geohazard monitoring in China.

2-D phase unwrapping (PU) is one of the key steps in the processing of interferometric synthetic aperture radar (InSAR) data. PU in large-gradient areas has always been a fundamental problem. The phase continuity assumption makes it difficult to obtain ideal PU results in large-gradient areas. To address this issue, in this letter, we propose a phase slicing 2-D PU method using the L1-norm that can effectively reduce the phase gradient interval. First, the study area is analyzed using an external digital elevation model (DEM) and interferometric phase information to obtain the number of phase slices and assisted phases. Then, we cut the interferometric phase to obtain the sliced interferometric phase and use the single-baseline (SB) L1-norm PU method to unwrap the sliced interferometric phase. Finally, the unwrapped phase is recovered by summing the sliced unwrapped phases and assisted phases. Experiments are conducted with simulated and TanDEM-X InSAR data sets. Compared with the representative SB-PU method, the proposed method can effectively avoid the problem of large gradients in PU.

Slow-moving target detection in strong clutter background is a critical issue for the ground-based radar system. To detect the slow-moving target effectively, a novel multi-scan joint target detection method via principal component analysis (RPCA) is proposed. For radar echoes, there are two useful properties: 1) Stationary ground clutters have low-rank property, since the clutters in adjacent scan intervals are almost similar; 2) Moving targets have the sparse characteristic, due to their variation of position and sparsely distributed. Thanks to these two properties, moving targets can be separated from the stationary clutters via RPCA. Compared with the moving target indicator (MTI) method, the experimental results demonstrate that the proposed algorithm not only can suppress clutters effectively, but also preserve the moving target as much as possible.

Radio frequency interference (RFI) has been a pervasive and critical issue for space-borne synthetic aperture radar (SAR). The presence of RFI could lead to incorrect image interpretation and biased parameter retrieval, making the RFI detection a necessity to preserve overall data quality. In this paper, we propose an approach for detecting RFI signals in SAR raw data using time-frequency semantic analysis. Employing the U-Net convolutional neural network enables identification of target echoes and RFI signatures in 2D time-frequency representation with high probability. The detection process is realized without setting predefined thresholds, and could achieve superior performance without requiring large number of training samples.

An AIEM based soil moisture retrieval strategy for bare agricultural area was developed in this study. In order to solve the ill-posed problem for soil moisture retrieval, multi-sensor SAR observations were integrated to estimate soil moisture based on the AIEM simulation. Soil surface parameters were derived using the least square method. The influence of surface roughness for soil moisture estimation was removed. Based on the in situ measurement and multi-sensor SAR images acquired on October 2015 over agricultural area, quantitative evaluation of the developed method was implemented. The results indicate that accurate soil moisture was obtained over the study area. The root mean square error of the estimated result is less than 0.05 cm3/cm3.

Gaofen-3 (GF-3) is the only in-orbit civilian SAR satellite of China. The satellite is specially designed for ocean observation. The coherence is not exclusively considered during satellite designing phase. In this paper, we conduct the preliminary coherence assessment of GF-3 SAR data. Decoherence sources of GF-3 consists of seven components. Baseline decoherence, signal-to-noise decoherence as well as the volume decoherence affects the coherence severely. While the last four components, i.e., volume decoherence, ambiguity decoherence, Doppler spectral decoherence and processing decoherence contributes to the coherence values slightly. We select 13 pairs of GF-3 SAR data and analyze the coherence. Results of the selected data show that the coherence values after multi-looking, flattening, elevation phase removal and filtering exceed 0.4 even at the dessert regions, making it potential in InSAR related applications such as DEM generation and deformation monitoring.