## 1 Introduction  
Land subsidence is a geological phenomenon characterized by the gradual lowering of the Earth's surface, primarily resulting from the compaction of unconsolidated strata under natural and anthropogenic pressures [1]. Often referred to as a \"chronic disease\" hinderingsustainable urban development [2], land subsidence has become an increasingly critical issue in China. Recent national-scale assessments indicate that over 30 major and medium-sized cities experienced substantial land subsidence between 2015 and 2022, affecting nearly 30% of the urban population [3]. The associated impacts, such as infrastructure damage, building deformation, and increased flood risk, pose serious threats to socioeconomic stability and public safety [4].  
Among the many contributing factors, long-term overexploitation of groundwater is widely recognized as the dominant driver of land subsidence [5]. Excessive withdrawal of groundwater reduces pore water pressure within aquifer systems [6], resulting in soil compaction and surface deformation [7]. Understanding the dynamic response of ground deformation to groundwater level fluctuations is essential for revealing the mechanisms underlying subsidence processes.  
Various geodetic and statistical methods have been employed to study land subsidence. Satellite-based interferometric synthetic aperture radar (InSAR) techniques [8], particularly the Small Baseline Subset InSAR (SBAS-InSAR), have proven highly effective in monitoring spatiotemporal ground deformation with high accuracy over large areas [9]. However, the time series of subsidence signals are often non-stationary, influenced by multiple geophysical factors, and embedded with noise [9]. Classical statistical tools such as the Pearson correlation coefficient or the Fourier transform have limitations in such contexts due to sensitivity to outliers and poor adaptability to non-stationary data [9; 10; 11]. To address these challenges, the time-lagged cross-correlation (TLCC) method has been proposed to quantify delayed relationships between variables [12; 13], and the seasonal-trend decomposition using the LOESS (STL) algorithm has been employed to extract meaningful trends and seasonal components from complex geophysical time series [14]. Despite recent advancements, there remains a lack of detailed understanding regarding the seasonal lag response of land subsidence to groundwater fluctuations, particularly in agricultural regions with intensive irrigation practices. Moreover, few studies have integrated STL decomposition with TLCC analysis to interpret subsidence dynamics in a physically meaningful way.  
To address these gaps, this study aims to achieve the following: investigate the spatiotemporal evolution of land subsidence in Dangshan County, Anhui Province, from 2018 to 2024, using SBAS-InSAR techniques on 207 Sentinel-1A SAR images; decompose the surface deformation time series using STL to identify seasonal characteristics; quantify the time-lagged response between surface deformation and groundwater level variations using TLCC analysis; and compare seasonal behaviors between residential and agricultural areas and explore the underlying causes of observed differences. Dangshan County was selected due to its well-documented history of significant groundwater extraction and associated land subsidence. By integrating remote sensing, statistical decomposition, and correlation analysis, this study provides new insights into the seasonal mechanisms driving land subsidence in groundwater-stressed regions.