The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) has generated one of the most complete high-resolution digital topographic data sets of the world to date. The ASTER GDEM covers land surfaces between 83° N and 83° S at a spatial resolution of 1 arc-second (approximately 30 m at the equator). As an improvement over Shuttle Radar Topography Mission (SRTM) coverage, the ASTER GDEM will be a very useful product for many applications, such as relief analysis, hydrological studies, and radar interferometry. In this article, its absolute vertical accuracy in China was assessed at five study sites using ground control points (GCPs) from high-accuracy GPS benchmarks and also using a DEM-to-DEM comparison with the Consultative Group on International Agriculture Research Consortium for Spatial Information (CGIAR-CSI) SRTM DEM Version 4.1. It is demonstrated that the vertical accuracy of ASTER GDEM is 26 m (root mean square error (RMSE)) against GPS-GCPs, while for the SRTM DEM it is 23 m. Furthermore, height differences in the GDEM-SRTM comparison appear to be overestimated in the areas with a south or southwest aspect in the five study areas. To a certain extent, the error can be attributed to variations in heights due to land-cover effects and undefined inland waterbodies. But the ASTER GDEM needs further error-mitigating improvements to meet the expected accuracy specification. However, as for its unprecedented detail, it is believed that the ASTER GDEM offers a major alternative in accessibility to high-quality elevation data.

We reveal transient surface deformation following the 2017 M_w7.3 Sarpol Zahab (Iran) earthquake using Interferometric Synthetic Aperture Radar (InSAR) measurements. Based on the coseismic interferograms derived from the Advanced Land Observing Satellite‐2 (ALOS‐2) data, the preferred slip model of the earthquake has a centroid depth of 14.5 ± 4 km and suggests that a basement fault is most likely responsible for the 2017 earthquake in the northwest Zagros fold‐thrust belt zone. Two slip asperities with a maximum slip of 6 m separated by 16 km are observed in the best fitting slip model. The accumulated afterslip in the first month after the mainshock determined from the Sentinel‐1 postseismic interferograms reveals a slip distribution that lies immediately updip of the coseismic slip, implying that frictional properties of the fault vary along the rupture patch. The Bamo Mount in the earthquake area was uplifted by approximately 1 m during the earthquake. We suggest that local topographic growth from both seismic and aseismic faulting behaviors is common within the Zagros fold‐thrust belt zone based on our findings regarding the 2017 earthquake and other significant events in the region.

The integration of multi-source, multi-temporal, multi-band optical, and radar remote sensing images to accurately detect, extract, and monitor the long-term dynamic change of coastline is critical for a better understanding of how the coastal environment responds to climate change and human activities. In this study, we present a combination method to produce the spatiotemporal changes of the coastline in the Yellow River Delta (YRD) in 1980–2020 with both optical and Synthetic Aperture Radar (SAR) satellite remote sensing images. According to the measurement results of GPS RTK, this method can obtain a high accuracy of shoreline extraction, with an observation error of 71.4% within one pixel of the image. Then, the influence of annual water discharge and sediment load on the changes of the coastline is investigated. The results show that there are two significant accretion areas in the Qing 8 and Qingshuigou course. The relative high correlation illustrates that the sediment discharge has a great contribution to the change of estuary area. Human activities, climate change, and sea level rise that affect waves and storm surges are also important drivers of coastal morphology to be investigated in the future, in addition to the sediment transport.

The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances.

Floods are among the world's worst natural disasters, causing significant damage to people, infrastructure, and the economy. Since synthetic aperture radar (SAR) can work all-weather and is not affected by clouds and rain, the use of SAR for flood mapping and disaster assessment has obvious advantages. However, SAR images are highly susceptible to speckle noise, shadows, and distortions, which affects the accuracy of traditional water body extraction methods. To solve this problem, we designed a new model called WaterDetectionNet (WDNet) based on SAR remote sensing images and convolutional neural network, which has a strong water extraction capability for accurate flood mapping. In order to improve the generalization ability of the model, we used a semi-automatic strategy to generate the SAR dataset S1Water containing rich semantic information with diversity. Compared with the traditional machine learning and deep learning methods, we introduced a self-attention module to increase spatial and channel attention, and adaptively update the network weights, which improved the model performance and extraction accuracy of the real case study of Poyang Lake flood in 2020. The experimental results show that the accuracy, recall, intersection over union, and F1 score of the WDNet model were 0.986, 0.994, 0.974, and 0.987, respectively. This method is expected to provide a cost-effective alternative to global rapid flood mapping, improve the reliability of flood disaster analysis, and offer a reference for post-disaster emergency management.