High temporal resolution water distribution maps are essential for surface water monitoring because surface water exhibits significant inner-annual variation. Therefore, high-frequency remote sensing data are needed for surface water mapping. Dongting Lake, the second-largest freshwater lake in China, is famous for the seasonal fluctuations of its inundation extents in the middle reaches of the Yangtze River. It is also greatly affected by the Three Gorges Project. In this study, we used Sentinel-1 data to generate surface water maps of Dongting Lake at 10 m resolution. First, we generated the Sentinal-1 time series backscattering coefficient for VH and VV polarizations at 10 m resolution by using a monthly composition method. Second, we generated the thresholds for mapping surface water at 10 m resolution with monthly frequencies using Sentinel-1 data. Then, we derived the monthly surface water distribution product of Dongting Lake in 2016, and finally, we analyzed the inner-annual surface water dynamics. The results showed that: (1) The thresholds were −21.56 and −15.82 dB for the backscattering coefficients for VH and VV, respectively, and the overall accuracy and Kappa coefficients were above 95.50% and 0.90, respectively, for the VH backscattering coefficient, and above 94.50% and 0.88, respectively, for the VV backscattering coefficient. The VV backscattering coefficient achieved lower accuracy due to the effect of the wind causing roughness on the surface of the water. (2) The maximum and minimum areas of surface water were 2040.33 km²in July, and 738.89 km²in December. The surface water area of Dongting Lake varied most significantly in April and August. The permanent water acreage in 2016 was 556.35 km², accounting for 19.65% of the total area of Dongting Lake, and the acreage of seasonal water was 1525.21 km². This study proposed a method to automatically generate monthly surface water at 10 m resolution, which may contribute to monitoring surface water in a timely manner.

The three-dimensional (3D) geographic locations of street furniture such as traffic signs comprise the basic content of 3D city construction, and such information is indispensable for periodic statistics for road management and maintenance. This paper presents a novel solution for acquiring 3D information on small traffic signs based on Mobile Mapping System (MMS) data. Firstly, a lightweight backbone network called VGG-L under an optimized Faster Region-based Convolutional Neural Network (R-CNN) detection framework is proposed for the detection of small traffic signs. An Urban Traffic Sign Detection (UTSD) dataset is created based on panoramic images obtained from test areas. Detection results from the UTSD dataset show that VGG-L outperforms other popular networks and achieves a mean average precision of 75.4%, which is 4.2—14.8% higher than those of VGG16, MobileNet, ResNet, and YOLOv3. Secondly, a novel depth-map-based 3D spatial geo-location method is proposed for obtaining the 3D geographic locations of the objects. Then, a center-based method is proposed to automatically extract the final 3D vector of the target. Experimental results illustrate that the proposed method performs 3D positioning and vectorization of the milestones, circular and triangular traffic signs, accurately and effectively, achieving greater than 86% recall and precision for the three types of targets in the test areas. The experiments demonstrate that the overall 3D information acquisition scheme is feasible and has great application potential.

The ZiYuan3 (ZY3) is a civilian stereo surveying and mapping satellite from China operating under the framework of the Earth resources satellite series, and its objective is to fulfill 1:50,000 mapping and update largerscale fundamental geographic information products. This article introduces the ZY3-03 satellite's mission and payload specifications as well as its data utilization and distribution policy. In the future, the ZY3-03 will realize the demands of China's national economic and social development and play an important role in the fields of Earth observation and environmental protection.

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.