The monitoring of Poyang Lake water area and storage changes using remote sensing and satellite gravimetry techniques is valuable for maintaining regional water resource security and addressing the challenges of global climate change. In this study, remote sensing datasets from Landsat images (Landsat 5, 7, 8 and 9) and three Gravity Recovery and Climate Experiment (GRACE) and Gravity Follow-on (GRACE-FO) mascon solutions were jointly used to evaluate the water area and storage changes in response to global and regional climate changes. The results showed that seasonal characteristics existed in the terrestrial water storage (TWS) and water area changes of Poyang Lake, with nearly no significant long-term trend, for the period from April 2002 to December 2022. Poyang Lake exhibited the largest water area in June and July every year and then demonstrated a downward trend, with relatively smaller water areas in January and November, confirmed by the estimated TWS changes. For the flood (August 2010) and drought (September 2022) events, the water area changes are 3032 km² and 813.18 km², with those estimated TWS changes 17.37 cm and −17.46 cm, respectively. The maximum and minimum Poyang Lake area differences exceeded 2700 km². The estimated terrestrial water storage changes in Poyang Lake derived from the three GRACE/GRACE-FO mascon solutions agreed well, with all correlation coefficients higher than 0.92. There was a significant positive correlation higher than 0.75 between the area and TWS changes derived from the two independent monitoring techniques. Therefore, it is reasonable to conclude that combined remote sensing with satellite gravimetric techniques can better interpret the response of Poyang Lake to climate change from the aspects of water area and TWS changes more efficiently.

The application of deep learning to seismic electric signal (SES) anomaly detection remains underexplored in geophysics. This study introduces the integration of a 1D convolutional neural network (1DCNN) with a self-attention mechanism to automate SES analysis in a station in a certain place in China. Utilizing physics-informed data augmentation, our framework adapts to real-world interference scenarios, including subway operations and tidal fluctuations. The model achieves an F1-score of 0.9797 on a 7-year dataset, demonstrating superior robustness and precision compared to traditional manual interpretation. This work establishes a practical deep learning solution for real-time geoelectric anomaly monitoring, offering a transformative tool for earthquake early warning systems.

Geomagnetically induced geoelectric fields can exhibit substantial spatial variability because regional subsurface electrical structures affect surface electromagnetic responses. This study characterized the induced geoelectric field across mainland China during the extreme geomagnetic storm of 10 May 2024, using observations from the China Geoelectric Network. We evaluated changes in field intensity, ratios between collinear short- and long-dipole measurements, and regional variations in polarization direction, with particular emphasis on the onset of the storm. The geoelectric response showed pronounced spatial heterogeneity, with substantial differences among stations during storm onset. At most stations, measurements from collinear short and long dipoles maintained consistent ratios, indicating stable local scaling between the two electrode configurations. In contrast, polarization directions inferred from the long-dipole measurements showed no uniform relationship with local land–sea orientations. High-density electrical exploration data further supported this interpretation. These findings indicate that local subsurface electrical structures play an important role in controlling geoelectric field measurements and their responses to geomagnetic disturbances. This study provides a quantitative assessment of storm-time geoelectric field variations across mainland China and establishes a basis for investigating regional electromagnetic phenomena associated with induced geoelectric fields.