本研究針對雲林地區長期且嚴重的地層下陷問題，整合 Sentinel-1A 時序雷達影像、GNSS 垂直位移資料、磁環分層監測井與地下水位觀測井等多源資料，建立一套分層彈性儲水係數推估與儲水變化量空間化分析流程。研究首先透過 GMTSAR 軟體完成 2016–2024 年之 InSAR 累積與年均變形圖，顯示研究區中西部下陷明顯，年最大沉陷速率高達 6.5 cm/yr。為提升變形資料準確性，本研究以 GNSS–InSAR 差值建立誤差場，並應用 Kriging 進行修正，進一步整合磁環分層井沉陷量與地下水位變化，分別於三層主要含水層推估其分層彈性儲水係數，建立區域空間分布。結合分層彈性儲水係數值與InSAR位移圖，並利用 IDW 進行區域內插，建立分層空間化模型，推估各層可回復儲水變化量。
在修正後的變形資料基礎上，本研究結合磁環分層井沉陷量與地下水位變化，於三層主要含水層分別推估彈性儲水係數，並建立其區域分布。進一步將彈性儲水係數與 InSAR 位移圖結合，利用 IDW 內插建立分層空間化模型，以推估不同含水層的 可回復儲水變化量，分析結果顯示，第二含水層（F2）為主要貢獻層，其平均 ∆V_r 達 27.15 mm，佔總量比例最高；第一含水層（F1）平均 ∆V_r 為 16.59 mm；第三含水層（F3）平均 ∆V_r 為 14.24 mm，變化幅度最小，顯示第三含水層含水層對可回復儲水量的影響有限，整體比較結果呈現 F2 ＞ F1 ＞ F3，凸顯 F2 在雲林地區可回復儲水變化量最大。
本研究突破傳統僅以單一含水層估算的限制，首次建立「分層∆V_r貢獻度」之空間分布觀測，補足了傳統井測點不足的問題，並能更精確地揭示不同含水層在地層下陷與地下水儲量變化中的貢獻差異。研究成果不僅具創新性，也對雲林地區地下水管理與地層下陷防治具有重要的實務應用價值。

This study addresses the severe land subsidence in Yunlin County by integrating Sentinel-1A time-series radar imagery, GNSS vertical displacement data, magnetic-ring layered monitoring wells, and groundwater level observations. A comprehensive workflow was developed to estimate elastic storage coefficientsfor individual aquifer layers and to quantify spatial variations in recoverable groundwater storage change.Time-series InSAR analysis using GMTSAR produced cumulative deformation and annual subsidence maps for 2016–2024, revealing significant subsidence in the central-western region, with maximum annual rates reaching 6.5 cm/yr. GNSS–InSAR displacement differences were used to construct an error field, and Kriging interpolation was applied for pixel-level corrections, improving the absolute accuracy of InSAR-derived deformation data.
The results show that the second aquifer (F2) contributes most significantly to recoverable storage changes, with an average ∆V_r of 27.15 mm, followed by F1 (16.59 mm) and F3 (14.24 mm), indicating a limited response of deeper strata. These findings highlight the dominant role of F2 in groundwater storage dynamics.
This research establishes the first spatially explicit assessment of layered ∆V_r contributions in Yunlin County, overcoming limitations of traditional single-layer estimations. The outcomes provide a scientific foundation for improved groundwater management and effective subsidence mitigation strategies.

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