The spatiotemporal pattern of soil moisture is of great significance for the understanding of the water exchange between the land surface and the atmosphere. The two-satellite constellation of the Sentinel-1 mission provides C-band synthetic aperture radar (SAR) observations with high spatial and temporal resolutions, which are suitable for soil moisture monitoring. In this paper, we aim to assess the capability of pattern analysis based on the soil moisture retrieved from Sentinel-1 time-series data of Dahra in Senegal. The look-up table (LUT) method is used in the retrieval with the backscattering coefficients that are simulated by the advanced integrated equation Model (AIEM) for the soil layer and the Michigan microwave canopy scattering (MIMICS) model for the vegetation layer. The temporal trend of Sentinel-1A soil moisture is evaluated by the ground measurements from the site at Dahra, with an unbiased root-mean-squared deviation (ubRMSD) of 0.053 m³/m³, a mean average deviation (MAD) of 0.034 m³/m³, and an R value of 0.62. The spatial variation is also compared with the existing microwave products at a coarse scale, which confirms the reliability of the Sentinel-1A soil moisture. The spatiotemporal patterns are analyzed by empirical orthogonal functions (EOF), and the geophysical factors that are affecting soil moisture are discussed. The first four EOFs of soil moisture explain 77.2% of the variance in total and the primary EOF explains 66.2%, which shows the dominant pattern at the study site. Soil texture and the normalized difference vegetation index are more closely correlated with the primary pattern than the topography and temperature in the study area. The investigation confirms the potential for soil moisture retrieval and spatiotemporal pattern analysis using Sentinel-1 images.

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.

The backscattering coefficient ${\sigma }^0$ of a rice field is closely related to the amplitude, power, and phase of its radar backscattered signals. An investigation of the statistics of indoor full-polarization scatterometric and synthetic aperture radar (SAR) measurements on rice fields in the Laboratory of Target Microwave Properties (LAMP) is implemented in terms of the amplitude, power, and phase difference of backscattered signals. The validity and accuracy of LAMP measured data are studied and confirmed for the first time. The Rayleigh fading model and phase difference statistical model are both validated by the experimental data. Continuous microwave spectrum is obtained after spatial and frequency averaging over N independent scatterometric samples and full-polarization images are generated by applying a focusing algorithm to the SAR data. Comparisons between scatterometric results and SAR images with three resolutions of rice field scene are conducted with respect to amplitude and co-pol phase difference (CPD) statistics, as well as backscattering coefficients. The results show that the measured statistics of a rice field scene are in good agreement with those calculated by theoretical formulas. Spatial and frequency averaging of scatterometric data can increase N and thus improve the estimation accuracy of the backscattering coefficients. SAR images show a shift to the near range due to the intrinsic height of the rice plants and the probable existence of the double bounce scattering between vertical rice stems and the water surface considering the measurement geometry. The measured amplitude statistics of the SAR images approach a Rayleigh distribution with reduction of the resolution cell size while the size has little effect on the CPD statistics. The differences between backscattering coefficients extracted from the scatterometric data and SAR images confirm a 1-dB calibration accuracy in power of the LAMP measurement system.

Using microwave remote sensing to invert forest parameters requires clear canopy scattering characteristics, which can be intuitively investigated through scattering measurements. However, there are very few ground-based measurements on forest branches, needles, and canopies. In this study, a quantitative analysis of the canopy branches, needles, and ground contribution of Masson pine scenes in C-, X-, and Ku-bands was conducted based on a microwave anechoic chamber measurement platform. Four canopy scenes with different densities by defoliation in the vertical direction were constructed, and the backscattering data for each scene were collected in the C-, X-, and Ku-bands across eight incidence angles and eight azimuth angles, respectively. The results show that in the vertical observation direction, the backscattering energy of the C- and X-bands was predominantly contributed by the ground, whereas the Ku-band signal exhibited higher sensitivity to the canopy structure. The backscattering energy of the scene was influenced by the incident angle, particularly in the cross-polarization, where backscattering energy increased with larger incident angles. The scene’s backscattering energy was influenced by the scattering and extinction of canopy branches and needles, as well as by ground scattering, resulting in a complex relationship with canopy density. In addition, applying orientation correction to the polarization scattering matrix can mitigate the impact of the incident angle and reduce the decomposition energy errors in the Freeman–Durden model. In order to ensure the reliability of forest parameter inversion based on SAR data, a greater emphasis should be placed on physical models that account for signal scattering and the extinction process, rather than relying on empirical models.