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.

The world’s largest macroalgae bloom (also known as green tide) has been reported since the 29th Olympic Games in 2008, which is verified as the fast reproduction of floating green macroalgae (Ulva prolifera). It is helpful to assess the biomass of macroalgae for the government of marine environment protection. In this study, the synchronization cruise experiment was firstly introduced, which aimed to investigate the biomass evaluation of Ulva prolifera in the Yellow Sea of China. The Floating Algae Index by Polarimetric SAR image (FAIPS) was then proposed. Finally, the floating algae biomass evaluation model was demonstrated and verified, which showed an exponential relationship between FAIPS and wet biomass per area (kg/m²) of macroalgae. The model proposed in this paper can be used in the biomass assessment of floating algae in the presence of polarimetric SAR images, regardless of daylight and cloud coverage over the sea surface.

Ground deformation monitoring is pivotal for enhancing urban resilience and mitigating geohazards. This study presents a synergistic monitoring framework integrating 26 Sentinel-1A (C-band) and 16 LuTan-1 (L-band) SAR scenes acquired between December 2023 and August 2025 to characterize the deformation dynamics in Beijing. Utilizing SBAS-InSAR, we first established a regional deformation baseline using Sentinel-1A observations, identifying critical subsidence and uplift zones in the eastern plains. Subsequently, high-resolution (3 m) LT-1 data were leveraged to achieve refined spatiotemporal characterization of these deformation hotspots. Validation against ground leveling benchmarks confirmed that both satellites yield high accuracy. LuTan-1 (RMSE = 3.810 mm/a) shows slightly better agreement with the ground leveling data than Sentinel-1A (RMSE = 4.853 mm/a). Analysis of the spatiotemporal patterns derived from InSAR revealed that the study area is characterized by widespread gene uplift (averaging ~10 mm/a), interspersed with acute localized subsidence exceeding 40 mm/a. Correlation analysis demonstrates a high spatiotemporal coupling between the extent and rate of surface uplift and groundwater level recovery. To further investigate these dynamics, Terzaghi’s effective stress principle is employed to quantify the contribution of groundwater level fluctuations to the observed surface deformation. A Parametric Harmonic Model was implemented to decouple elastic and trend components, and attribution analysis confirms that the continuous recovery of groundwater levels is the fundamental driver of the regional surface uplift. The inverted elastic skeletal storativity (S_ke), ranging from 1.587 × 10⁻³ to 9.184 × 10⁻³, reveals that regional surface uplift is predominantly driven by the elastic rebound of aquifer systems following groundwater recovery. In contrast, localized subsidence anomalies observed at large-scale engineering construction sites, landfill facilities, major expressway corridors, and high-density residential areas are independent of groundwater fluctuations, instead they are primarily attributed to anthropogenic stressors. This study elucidates a dual-drive mechanism, which comprising macroscopic hydrogeological rebound and localized anthropogenic disturbance, providing a robust scientific basis for differentiated urban hazard management.

Emergency forecasts benefit from scene detail when it constrains the relevant physical process and reaches decision-makers in time. This critical review examines flood inundation, wildfire spread and marine drift through 148 sources and 16 purpose-selected study families. Within this selected corpus, four families develop dedicated 3D conversion, chiefly for hydraulic-domain or fuel-material preparation; the coding rule excludes routine terrain input and existing ocean analyses. Forecast cases mainly update states, controls or initial trajectories. Hydraulic connectivity can alter local inundation. Corrected fire fronts remain exposed to propagation error, while wider marine search regions improve coverage at increased search cost. These mechanisms explain why geometric accuracy and analysis fit alone cannot establish predictive value. We propose a framework that selects scene attributes, update targets and observation timing jointly. Matched alternatives share a future response quantity, information cutoff and resource budget; deployment-specific tolerances connect their comparison to evacuation requirements and search capacity. Physically coupled neural representations require calibrated material properties and an online delivery-time account. The proposed tests assess the forecast effect of additional scene detail, the persistence of a correction and the time left for response.