Satellite-based detection of methane (CH₄) point sources is crucial in identifying and mitigating anthropogenic emissions of CH₄, a potent greenhouse gas. Previous studies have indicated the presence of CH₄ point source emissions from coal mines in Shanxi, China, which is an important source region with large CH₄ emissions, but a comprehensive survey has remained elusive. This study aims to conduct a survey of CH₄ point sources over Shanxi's coal mines based on observations of the Advanced Hyperspectral Imager (AHSI) on board the Gaofen-5B satellite (GF-5B/AHSI) between 2021 and 2023. The spectral shift in centre wavelength and change in full width at half-maximum (FWHM) from the nominal design values are estimated for all spectral channels, which are used as inputs for retrieving the enhancement of the column-averaged dry-air mole fraction of CH₄ (ΔXCH₄) using a matched-filter-based algorithm. Our results show that the spectral calibration on GF-5B/AHSI reduced estimation biases of the emission flux rate by up to 5.0 %. We applied the flood-fill algorithm to automatically extract emission plumes from ΔXCH₄ maps. We adopted the integrated mass enhancement (IME) model to estimate the emission flux rate values from each CH₄ point source. Consequently, we detected CH₄ point sources in 32 coal mines with 93 plume events in Shanxi province. The estimated emission flux rate ranges from 761.78 ± 185.00 to 12 729.12 ± 4658.13 kg h⁻¹. Our results show that wind speed is the dominant source of uncertainty contributing about 84.84 % to the total uncertainty in emission flux rate estimation. Interestingly, we found a number of false positive detections due to solar panels that are widely spread in Shanxi. This study also evaluates the accuracy of wind fields in ECMWF ERA5 reanalysis by comparing them with a ground-based meteorological station. We found a large discrepancy, especially in wind direction, suggesting that incorporating local meteorological measurements into the study CH₄ point source are important to achieve high accuracy. The study demonstrates that GF-5B/AHSI possesses capabilities for monitoring large CH₄ point sources over complex surface characteristics in Shanxi.

The regional uncertainty of the column-averaged dry air mole fraction of CO₂ (XCO₂) retrieved using different algorithms from the Greenhouse gases Observing SATellite (GOSAT) and its attribution are still not well understood. This paper investigates the regional performance of XCO₂ within a latitude band of 37–42° N segmented into 8 cells in a grid of 5° from west to east (80–120° E) in China, where typical land surface types and geographic conditions exist. The former includes desert, grassland and built-up areas mixed with cropland; and the latter includes anthropogenic emissions that change from small to large from west to east, including those from the megacity of Beijing. For these specific cells, we evaluate the regional uncertainty of GOSAT XCO₂ retrievals by quantifying and attributing the consistency of XCO₂ retrievals from four algorithms (ACOS, NIES, OCFP and SRFP) by intercomparison. These retrievals are then specifically compared with simulated XCO₂ from the high-resolution nested model in East Asia of the Goddard Earth Observing System 3-D chemical transport model (GEOS-Chem). We also introduce the anthropogenic CO₂ emissions data generated from the investigation of surface emitting point sources that was conducted by the Ministry of Environmental Protection of China to GEOS-Chem simulations of XCO₂ over the Chinese mainland. The results indicate that (1) regionally, the four algorithms demonstrate smaller absolute biases of 0.7–1.1 ppm in eastern cells, which are covered by built-up areas mixed with cropland with intensive anthropogenic emissions, than those in the western desert cells (1.0–1.6 ppm) with a high-brightness surface from the pairwise comparison results of XCO₂ retrievals. (2) Compared with XCO₂ simulated by GEOS-Chem (GEOS-XCO₂), the XCO₂ values from ACOS and SRFP have better agreement, while values from OCFP are the least consistent with GEOS-XCO₂. (3) Viewing attributions of XCO₂ in the spatio-temporal pattern, ACOS and SRFP demonstrate similar patterns, while OCFP is largely different from the others. In conclusion, the discrepancy in the four algorithms is the smallest in eastern cells in the study area, where the megacity of Beijing is located and where there are strong anthropogenic CO₂ emissions, which implies that XCO₂ from satellite observations could be reliably applied in the assessment of atmospheric CO₂ enhancements induced by anthropogenic CO₂ emissions. The large inconsistency among the four algorithms presented in western deserts which displays a high albedo and dust aerosols, moreover, demonstrates that further improvement is still necessary in such regions, even though many algorithms have endeavored to minimize the effects of aerosols scattering and surface albedo.

Forest fire risk is rising with climate warming, highlighting the need for timely monitoring and early warning. Satellite-based monitoring, currently a primary tool in remote sensing for fire detection, suffers from spatiotemporal gaps due to limited resolution and cloud cover. This study developed a novel smoke detection technology using operational S-band dual-polarization weather radar. By analyzing six forest fire cases in Zhejiang Province, China (2023), we established a filtering method using dual-polarization parameters, with thresholds set to a differential reflectivity (Z_DR) ≥ 3 dB and a cross-correlation coefficient (${\rho }_{HV}$) ≤ 0.7. This method effectively isolates fire-related echoes and, compared with geostationary satellites, enables more continuous monitoring; it also detects small and early-stage fires. Furthermore, radar-derived fire perimeters closely match satellite imagery, demonstrating its potential for real-time fire-spread tracking. The high spatiotemporal resolution and multi-parameter advantages of dual-polarization radar can complement satellite observations, offering vital support for early warning and real-time decision-making in fire management.

Timely detection of the spatial distribution of building damage in the immediate aftermath of a disaster is essential for guiding emergency response and recovery strategies. In January 2025, a large-scale wildfire struck the Los Angeles metropolitan area, with Altadena as one of the most severely affected regions. A timely and comprehensive understanding of the spatial distribution of building damage is essential for guiding rescue and resource allocation. In this study, we adopted the MambaBDA framework, which is built upon Mamba, a recently proposed state space architecture in the computer vision domain, and tailored it for spatio-temporal modeling of disaster impacts. The model was trained on the publicly available xBD dataset and subsequently applied to evaluate wildfire-induced building damage in Altadena, with pre- and post-disaster data acquired from WorldView-3 imagery captured during the 2025 Los Angeles wildfire. The workflow consisted of building localization and damage grading, followed by optimization to improve boundary accuracy and conversion to individual building-level assessments. Results show that about 28% of the buildings in Altadena suffered Major or Destroyed levels of damage. Population impact analysis, based on GHSL data, estimated approximately 3241 residents living in Major damage zones and 31,975 in Destroyed zones. These findings highlight the applicability of MambaBDA to wildfire scenarios, demonstrating its capability for efficient and transferable building damage assessment. The proposed approach provides timely information to support post-disaster rescue and recovery decision-making.