The Shuttle Radar Topography Mission (SRTM) provides for the first time a near-global high-resolution digital elevation model (DEM) with great advantages of homogeneous quality and free availability. The last 10 years or so have seen rapid advances in the data processing and applications of SRTM DEM. From the perspective of SRTM, we present in this article a brief overview of the principles, datasets, void filling and accuracy of SRTM DEM first. Special emphasis is on its application advances in various research fields including, but not limited to, geology, geomorphology, water resources and hydrology, glaciology, evaluation of natural hazards and vegetation surveys. Problems that arose from the applications and the future research interests are also addressed. We hope this study will greatly facilitate the ease of use of SRTM DEM in extensive fields.

The Loess Plateau is a typical ecologically sensitive area that can easily be perturbed by the effects of human activities and global climate change. Therefore, it is necessary to develop tools to monitor the environmental quality in the LP quickly and accurately. To reveal the spatio-temporal changes in environmental quality in the LP from 2000 to 2020, we used the Moderate-Resolution Imaging Spectroradiometer (MODIS) products on the Google Earth Engine platform and constructed the remote sensing ecological index (RSEI) through principal component analysis (PCA). Then, Sen–Mann–Kendall methods were applied to determine the changing trend of the environmental quality of the LP. Finally, natural and anthropogenic factors affecting the environmental quality were probed using a geographical detector model. The results showed that: (1) the average RSEI values in 2000, 2010 and 2020 were 0.396, 0.468 and 0.511, respectively, displaying an upward trend from 2000 to 2020, with a growth rate of 0.005 ${\mathrm{year}}^{-1}$. The overall environment quality was moderate (0.4–0.6). (2) In terms of spatial distribution, the environmental quality was excellent in the southeast and poor in the northwest of the LP. The areas with improved environmental quality (84.51%) were located in all the counties, whereas the areas with degraded environmental quality (8.11%) occurred in the north and southeast of the study area. (3) Greenness, heat, wetness, dryness and land use types were prominent factors affecting RSEI throughout the study period; additionally, the total industrial gross domestic product showed a growing influence. The contribution of multi-factor interaction was stronger than that of single factors. The results will provide a reference and a new research perspective for local environmental protection and regional planning.

Soil moisture plays an important role in various applications such as hydrology, agronomy, climatology and ecology. Over the past few decades, images acquired by PolSAR (Polarimetric Synthetic Aperture Radar) systems have raised much interest in the remote sensing community. However, only a few studies have analysed the potential use of polarimetric parameters for the estimation of soil moisture. Taking Juyanze as the study area, two feature extraction methods, the simple polarimetric intensity and phase processing, and the polarimetric decomposition were used to extract different polarimetric parameters. The response of single, multiple, and combined polarimetric parameters to bare surface soil moisture was investigated based on linear and non-linear regression strategies in using fully polarimetric RADARSAT-2 SAR (Synthetic Aperture Radar) imagery and field measurements. The results indicate that each individual polarimetric parameter at C-band is not very sensitive to surface soil moisture under this investigation. However, with the increasing number of polarimetric parameters, the correlation between soil moisture and polarimetric parameters improves steadily and the root mean square error (RMSE) and mean absolute error (MAE) decreased gradually. Combined polarimetric parameters have little improvement on the correlation between soil moisture and polarimetric parameters. The non-linear regression method can improve the correlation to some extent. However, the preliminary results so far still do not satisfy the accuracy of using the polarimetric parameters to inverse soil moisture. Further investigations are required in the direction of a better characterization and exploitation of polarimetric SAR data for the retrieval of soil moisture in arid regions.