The Sentinel-1 constellation can provide numerous high-resolution C-band synthetic aperture radar (SAR) data with long-term continuity and freely, thus showing a cost-effective solution for the coastal monitoring at high or moderate spatial resolutions. The major goal is to improve estimates of shallow water depth for SAR applications. We present an algorithm that is based on the linear dispersion relation between water depth and swell parameters like swell wavelength, direction, and period to estimate shallow water depth using multitemporal SAR data with a short repeating cycle. This is accomplished via circular convolution and Kalman filter that provides both the estimates and a measure of their uncertainty at each location. The introduced algorithm is tested on four Sentinel-1 interferometric wide swath (IW) mode SAR images over the coastal region of Fujian Province, China. The retrieved water depth both from multitemporal SAR images and different single SAR images show general agreement with water depth from an official electronic navigational chart. All comparisons indicate that the proposed method is feasible and multitemporal SAR data have great potential in bathymetric surveying.

Fully polarimetric synthetic aperture radar (SAR) can provide detailed information on scattering mechanisms that could enable the target or structure to be identified. This paper presents a method to detect underwater topography in coastal areas using high resolution fully polarimetric SAR data, while less prior information is required. The method is based on the shoaling and refraction of long surface gravity waves as they propagate shoreward. First, the surface scattering component is obtained by polarization decomposition. Then, wave fields are retrieved from the two-dimensional (2D) spectra by the Fast Fourier Transformation (FFT). Finally, shallow water depths are estimated from the dispersion relation. Applicability and effectiveness of the proposed methodology are tested by using C-band fine quad-polarization mode RADARSAT-2 SAR data over the near-shore area of the Hainan province, China. By comparing with the values from an official electronic navigational chart (ENC), the estimated water depths are in good agreement with them. The average relative error of the detected results from the scattering mechanisms based method and single polarization SAR data are 9.73% and 11.53% respectively. The validation results indicate that the scattering mechanisms based methodology is more effective than only using the single polarization SAR data for underwater topography detection, and will inspire further research on underwater topography detection with fully polarimetric SAR data.

Biodiversity in China is analyzed based on the components of the Dynamic Habitat Index (DHI). First, observed field survey based spatial patterns of species richness including threatened species are presented to test their linear relationship with remote sensing based DHI (2001–2010 MODIS). Areas with a high cumulative DHI component are associated with relatively high species richness, and threatened species richness increases in regions with frequently varying levels of the cumulative DHI component. The analysis of geographical and statistical distributions yields the following results on interdependence, polarization and change detection: (1) The decadal mean Cumulative Annual Productivity (DHI-\(\overline{cum}\) < 4) in Northwest China and (DHI-\(\overline{cum}\) > 4) in Southeast China are in a stable (positive) relation to the Minimum Annual Apparent Cover (DHI-\(\overline{min}\)) and is positively (negatively) related to the Seasonal Variation of Greenness (DHI-\(\overline{sea}\)); (2) The decadal tendencies show bimodal frequency distributions aligned near DHI-\(\overline{min}\)~0.05 and DHI-\(\overline{sea}\)~0.5 which separated by zero slopes; that is, regions with both small DHI-min and DHI-sea are becoming smaller and vice versa; (3) The decadal tendencies identify regions of land-cover change (as revealed in previous research). That is, the relation of strong and significant tendencies of the three DHI components with climatic or anthropogenic induced changes provides useful information for conservation planning. These results suggest that the spatial-temporal dynamics of China’s terrestrial species and threatened species richness needs to be monitored by first and second moments of remote sensing based information of the DHI.

This paper presents a shallow water depth estimation methodology using S-band Synthetic Aperture Radar (SAR) data from the HJ-1C satellite. It is based on the shoaling and refraction of long surface gravity waves as they propagate shoreward. A two-scale Bragg scattering model is used to describe the imaging process of long waves by SAR. By computing the Fast Fourier Transformation (FFT) for the selected sub image, wavelength and direction of the long wave can be retrieved from the two-dimensional (2D) spectra with wave tracking technology. Shallow water depths are then obtained from the linear dispersion relation with the calculated angular wave frequency obtained from other sources or first guesses of initial water depths or wave periods. Applicability and effectiveness are tested in the near-shore area of the Fujian province, China. Comparison between the derived results and water depths from an Electronic Navigational Chart (ENC) indicates that HJ-1C SAR is capable of higher resolution underwater topography detection, and the methodology can be used for shallow water depth estimation with good accuracy. The average absolute error and average relative error of the estimated results is 0·86 m and 11·05%, respectively.

The Loess Plateau, covering approximately 640,000 km², has experienced the most severe soil erosion in the world. A greening tendency has been noticed since implementing the Grain to Green Program (GTGP), which may prevent further soil erosion. Therefore, understanding the underpinning basis of greening stability and persistence is important for sustainable improvement. Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) datasets for 1982–2013 were used to investigate the temporal stability and persistent time (PT) of vegetation over the Loess Plateau, utilizing the coefficient of variation (CV) and the estimation of tendencies of vegetation greening starting from the selected reference conditions. Two periods from 1982 to 1999 (as the reference period) and 2000 to 2013 were selected by considering the GTGP since 1999. The results indicate that: (1) A significant increase in vegetation cover occurred in the low NDVI area (NDVI < 0.3), with a high fluctuation from 2000 to 2013 compared with the reference period. Moreover, the fluctuation in vegetation is more related to precipitation variation since 1999. (2) Most areas recovered in the greening trend of the first period starting in 2009, occurring in 28.7% (2628 of 9148) of the total area. (3) The revegetated areas have a low PT and a high $C{V}_{vi}$, that is, the revegetated areas need a long time to recover from disturbances. Therefore, we identify the sensitive areas with PT = 4; further management needs to be implemented for sustainable development in these areas. These results provide a method to quantify the stability and persistence of the complex interactions between vegetation greenness and environmental changes, particularly in fragile areas.

The performance of deep learning models largely depends on the scale and quality of training data. However, acquiring sufficient, high-quality samples for specific observation scenarios is often challenging due to high acquisition costs. Unlike optical imagery, synthetic aperture radar (SAR) target images exhibit strong nonlinear scattering variations with changing azimuth angles, making conventional data augmentation methods such as cropping or rotation ineffective. To tackle these challenges, this paper introduces an Azimuth-Continuously Controllable Generative Adversarial Network (ACC-GAN), which incorporates a continuous azimuth conditional variable to achieve precise azimuth-controllable target generation from dual-input SAR images. Our key contributions are threefold: (1) a continuous azimuth control mechanism that enables precise interpolation between arbitrary azimuth angles; (2) a dual-discriminator framework combining similarity and azimuth supervision to ensure both visual realism and angular accuracy; and (3) conditional batch normalization integrated with adaptive feature fusion to maintain scattering consistency. Experiments on the MSTAR dataset demonstrate that ACC-GAN effectively captures nonlinear azimuth-dependent transformations, generating high-quality images that improve downstream classification accuracy and validate its practical value for SAR data augmentation.