Surface water is the most important resource and environmental factor in maintaining human survival and ecosystem stability; therefore, timely accurate information on dynamic surface water is urgently needed. However, the existing water datasets fall short of the current needs of the various organizations and disciplines due to the limitations of optical sensors in dynamic water mapping. The advancement of the cloud-based Google Earth Engine (GEE) platform and free-sharing Sentinel-1 imagery makes it possible to map the dynamics of a surface water body with high spatial-temporal resolution on a large scale. This study first establishes a water extraction method oriented towards Sentinel-1 Synthetic Aperture Radar (SAR) data based on the statistics of a large number of samples of land-cover types. An unprecedented high spatial-temporal water body dataset in China (HSWDC) with monthly temporal and 10-m spatial resolution using the Sentinel-1 data from 2016 to 2018 is developed in this study. The HSWDC is validated by 14,070 random samples across China. A high classification accuracy (overall accuracy = 0.93, kappa coefficient = 0.86) is achieved. The HSWDC is highly consistent with the Global Surface Water Explorer dataset and water levels from satellite altimetry. In addition to the good performance of detecting frozen water and small water bodies, the HSWDC can also classify various water cover/uses, which are obtained from its high spatial-temporal resolution. The HSWDC dataset can provide more detailed information on surface water bodies in China and has good application potential for developing high-resolution wetland maps.

High-quality road network information plays a vital role in regional economic development, disaster emergency management and land planning. To date, studies have primarily focused on sampling flat urban roads, while fewer have paid attention to road extraction in mountainous regions. Compared with road extraction in flat regions, road extraction in mountainous regions suffers more interference, due to shadows caused by mountains and road-like terrain. Furthermore, there are more practical problems involved when researching an entire region rather than at the sample level. To address the difficulties outlined regarding mountain road extraction, this paper takes Jiuzhaigou county in China as an example and studies road extraction in practical applications. Based on deep learning methods, we used a multistage optimization method to improve the extraction effect. First, we used the contrast limited adaptive histogram equalization (CLAHE) algorithm to attenuate the influence of mountain shadows and improve the quality of the image. Then the road was extracted by the improved DSDNet network. Finally, the terrain constraint method is used to reduce the false detection problem caused by the terrain factor, and after that the final road extraction result is obtained. To evaluate the effect of road extraction comprehensively, we used multiple data sources (i.e., points, raster and OpenStreetMap data) in different evaluation schemes to verify the accuracy of the road extraction results. The accuracy of our method for the three schemes was 0.8631, 0.8558 and 0.8801, which is higher than other methods have obtained. The results show that our method can effectively solve the interference of shadow and terrain encountered in road extraction over mountainous regions, significantly improving the effect of road extraction.