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.

The successful launch of the GF-4 satellite has greatly enhanced wide-swath and high-frequency imaging capabilities, both of which are urgently needed in the fields of ship detection and monitoring. In this letter, the applicability of GF-4 satellite data for ship recognition and maritime traffic surveillance was investigated. It is concluded that the movement of ships, the velocity and direction, for instance, can be detected by using either a single GF-4 image that has short time lags between bands, or time-series GF-4 images that have a short interval between their acquisition times, implying that GF-4 satellite images have the potentiality for the monitoring of moving ships.

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.

Oil spill pollution is a major environmental threat for many countries in the world, which can cause serious damage to marine environment. Synthetic aperture radar (SAR) has become a valuable tool for marine oil spill monitoring, because of its all-weather and all-day capabilities. However, interpretation of marine SAR imagery is often ambiguous, and some other look-alike features often pose a fundamental challenge to the identification of oil spills and make the discrimination between oil spills and the look-alikes become a necessary procedure. In this paper, co-occurrence matrix method is employed to extract textural features of marine SAR image first, then these features are analyzed and optimized, and then support machine vector (SVM) method is used to identify oil spills in SAR images. Experiments on several SAR images show that method proposed in this paper can improve the detection and identification of oil spill in SAR images.

This study presents a near-real-time water stress monitoring framework for tropical heterogeneous landscapes by integrating optical and radar remote sensing data within the Google Earth Engine platform. Five complementary indices, vertical transmit/vertical receive–vertical transmit/horizontal receive (VV/VH) ratio, Dual Polarimetric Radar Vegetation Index (DpRVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Ratio Drought Index (RDI), were analyzed across three contrasting agricultural systems: paddy, sugarcane, and rubber, revealing distinct phenological and water stress dynamics. Radar-derived structural indices captured patterns of biomass accumulation and canopy development, with VV/VH values ranging from 4.2 to 12.3 in paddy and 5.4 to 6.0 in rubber. In parallel, optical moisture indices detected crop physiological stress; for instance, NDMI dropped from 0.26 to 0.06 during drought in sugarcane. Cross-index analyses demonstrated strong complementarity; synchronized VV/VH and RDI peaks characterized paddy inundation, whereas lagged NDMI–VV/VH responses captured stress-induced defoliation in rubber trees. Temporal profiling established crop-specific diagnostic signatures, with DpRVI peaking at 0.75 in paddy, gradual RDI decline in sugarcane, and NDMI values of 0.2–0.3 in rubber. The framework provides spatially explicit, temporally continuous, and cost-effective monitoring to support irrigation, drought early warning, and agricultural planning. Multi-year validation and field-based calibration are recommended for operational implementation.

High-resolution remote-sensing images can be used in human activity analysis and criminal activity monitoring, especially in sparsely populated zones. In this paper, we explore the applicability of China’s Gaofen satellite images in the land cover classification of Xinjiang, China. First of all, the features of spectral reflectance and a normalized radar cross section (NRCS) for different types of land covers were analyzed. Moreover, the seasonal variation of the NRCS in SAR (Synthetic Aperture Radar) images for the study area, Dunkuotan Village of Yuli County, China, was demonstrated by the GEE (Google Earth Engine) platform accordingly. Finally, the CART (classification and regression trees) algorithm of a DT (decision tree) was applied to investigate the classification of land cover in the western area of China when both optical and SAR images were employed. An overall classification accuracy of 83.15% with a kappa coefficient of 0.803 was observed by using GF-2/GF-3 images (2017–2021) in the study area. The DT-based classification procedure proposed in this investigation proved that Gaofen series remote-sensing images can be engaged to effectively promote the routine workflow of the administrative department.