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Zheng Zhou holds a Ph.D. in Cartography and Geographic Information Science from Henan University and an M.S. in Information Technology from the University of Melbourne. His research focuses on the integration of InSAR technology with spatiotemporal big data and machine learning for geohazard monitoring and environmental analysis. He has been involved in studies of land subsidence, urban deformation, and groundwater dynamics, particularly in the context of rapidly developing urban regions. His work also explores the applications of remote sensing in agriculture and disaster risk assessment, aiming to enhance early warning capabilities and support sustainable resource management. His interdisciplinary approach bridges remote sensing, geodesy, and intelligent geospatial analysis.
Jiyuan Hu received the Dr. degree in the direction of Geodesy and Surveying Engineering from Wuhan University, Wuhan, Hubei Province, China, in 2020. He is currently affiliated with the School of Geographical Sciences, Henan University, China. His longstanding research focuses on 1) multi-source satellite remote sensing for geological hazard identification and cataloging, and 2) multi-temporal InSAR-based precision inversion of groundwater storage in agricultural irrigation zones.
Menglei Xie received the B.S. degree in Geographic Information Science from Shanghai Normal University, China, in 2024, for her studies on ecosystem health and urban landscape patterns. During her undergraduate stage, her research interests included spatial analysis, remote sensing, and urban ecological assessment, focusing on the spatiotemporal evolution of ecosystem conditions in rapidly urbanizing regions, particularly their response to landscape configuration and planning. She is currently pursuing the M.Sc. degree in Geographical Information Science at the University of Manchester, Manchester, United Kingdom. Her research is now focused on the application of remote sensing and spatial analysis techniques in environmental monitoring, including flood detection using SAR imagery and the spatial assessment of heat exposure and social vulnerability in urban areas, with an emphasis on climate risk, spatial equity, and urban adaptation strategies.
Jiayao Wang is an academician of the Chinese Academy of Engineering and a professor at Henan University. He is a renowned cartographer and expert in geographic information engineering, currently serving as director of the Henan Research Institute of Spatiotemporal Big Data Industry Technology. In the 1970s, he established China's first academic program in computer-assisted cartography and later pioneered programs in map databases and geographic information engineering. He has led more than 20 national and military scientific research projects in fields such as modern cartographic theory, GIS, spatial data infrastructure, digital cities, and spatiotemporal big data. He has published over 150 academic papers and 15 books, including several nationally recognized textbooks. His honors include the National Science and Technology Progress Award (Second Class), the Major Contribution Award for Military Technology, and multiple teaching and research awards at national and military levels. He has played a pivotal role in advancing the integration of traditional cartography with geoinformation technology in China.
Jiabei Wang received her bachelor's degree in 2023 from the Department of Surveying and Planning at Shangqiu Normal University, located in Shangqiu City, Henan Province, China. During her undergraduate studies, she systematically studied Geographic Information Science, Remote Sensing Science, and Surveying. She is currently pursuing a master's degree at the School of Geographical Sciences, Henan University, with a research focus in Cartography and Geographic Information Systems. Her current research primarily involves exploring the mechanisms of land subsidence using machine learning algorithms, as well as analyzing the influencing factors of land subsidence. Additionally, she is engaged in groundwater storage inversion research. By utilizing existing results on groundwater storage variation and regional crop planting structures, she aims to optimize water resource allocation and agricultural planting patterns.
Outline
Groundwater overexploitation in northern Henan Province has led to significant land subsidence and aquifer degradation. This methodologically driven study proposes a physically consistent framework that integrates Sentinel-1A-based Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data (2017-2022) with long-term groundwater head observations to invert elastic and inelastic skeletal storage coefficients and assess total groundwater storage (TGWS) changes. The framework is applied to the Anyang-Puyang Plain as a representative case study. MT-InSAR deformation time series were combined with Multi-channel Singular Spectrum Analysis (MSSA) decomposition and polynomial fitting to extract seasonal and long-term trends, enabling spatially distributed inversion of aquifer parameters. Results show strong spatial coupling between land subsidence and hydraulic head decline (maximum Pearson r is 0.993), with deformation dominated by inelastic compaction. The elastic storativity ranges from 0.00093 to 0.01596, whereas the inelastic storativity, ranging from 0.0362 to 0.0457 indicates irreversible compaction processes associated with a cumulative groundwater loss of approximately 3.01 × 108 m3. Based on the long-term groundwater level observations collected in this study and the inferred assumption of preconsolidation head, the TGWS loss reached -12.27 × 109 m3, with a mean annual rate of - 2.19 × 109 m3/yr and pronounced depletion in northern areas. Standard deviational ellipse (SDE) analysis revealed a north-westward shift of the depletion centre and enhanced spatial clustering. These findings provide critical hydromechanical insights and quantitative constraints for future groundwater regulation and aquifer recovery strategies in overdrawn regions.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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