基于SBAS与DS-SBAS的小浪底库区沉降监测对比

doi:10.13474/j.cnki.11-2246.2026.0203

测绘通报 ›› 2026, Vol. 0 ›› Issue (2): 12-17.doi: 10.13474/j.cnki.11-2246.2026.0203

• 水陆环境监测与资源评估 • 上一篇下一篇

基于SBAS与DS-SBAS的小浪底库区沉降监测对比

李鹏豪¹, 李爱国¹, 张诗玉²

1. 河南理工大学测绘与国土信息工程学院, 河南焦作 454000;
2. 商丘师范学院测绘与规划学院, 河南商丘 476000

收稿日期:2025-07-28 发布日期:2026-03-12
作者简介:李鹏豪(1995—),男,硕士,助理工程师,主要从事InSAR技术的应用研究工作。E-mail:lipenghao@163.com
基金资助:
国家自然科学基金(U1304404)

A comparison study on land subsidence monitoring in the Xiaolangdi Reservoir area based on SBAS and DS-SBAS

LI Penghao¹, LI Aiguo¹, ZHANG Shiyu²

1. School of Surveying and land information Engineering, Henan Polytechnic University, Jiaozuo 454000, China;
2. School of Surveying and Planning, Shangqiu Normal University, Shangqiu 476000, China

Received:2025-07-28 Published:2026-03-12

摘要/Abstract

摘要： 小浪底水库是黄河水沙调控的核心,对其进行沉降监测是保障其长期有效运行的关键。为准确获取小浪底水库的沉降信息,本文分别采用常规SBAS技术和DS-SBAS技术对2018—2024年60景Sentinel-1A卫星影像进行了时序干涉处理,并对两者结果进行对比。结果表明,SBAS与DS-SBAS得到的同经纬度监测点沉降速率相关系数为0.96,表明两种监测结果一致性较高;DS-SBAS提取的高相干点数量比SBAS提取的提高约54%,对沉降速率为0～5 mm/a的监测效果更加明显。因此,DS-SBAS可作为库区沉降监测的有效手段。

关键词: SBAS, DS-SBAS, SBAS-InSAR, 地面沉降, 小浪底水库

Abstract: Xiaolangdi Reservoir is the core of water and sediment regulation in the Yellow River.The subsidence monitoring of the reservoir is crucial to ensuring its long-term and effective operation.To accurately obtain the settlement information of Xiaolangdi Reservoir,conventional SBAS (small baseline subset)technology and DS-SBAS (distributed scatterer small baseline subset)technology are respectively used for time-series interferometric processing of 60 Sentinel-1A satellite images from 2018 to 2024,and their results are compared.The results show that the correlation coefficient of settlement rates at monitoring points with the same longitude and latitude obtained by SBAS and DS-SBAS is 0.96,indicating a high consistency between the two monitoring results.The number of high-coherence points extracted by DS-SBAS is approximately 54% higher than that by SBAS,and its monitoring effect is more significant in the settlement rate range of 0～5 mm/a.Therefore,DS-SBAS can be used as an effective method for settlement monitoring in the reservoir area.

Key words: SBAS, DS-SBAS, SBAS-InSAR, land settlement, Xiaolangdi Reservoir

中图分类号:

P237

李鹏豪, 李爱国, 张诗玉. 基于SBAS与DS-SBAS的小浪底库区沉降监测对比[J]. 测绘通报, 2026, 0(2): 12-17.

LI Penghao, LI Aiguo, ZHANG Shiyu. A comparison study on land subsidence monitoring in the Xiaolangdi Reservoir area based on SBAS and DS-SBAS[J]. Bulletin of Surveying and Mapping, 2026, 0(2): 12-17.

参考文献

[1] 朱建军,李志伟,胡俊.InSAR变形监测方法与研究进展[J].测绘学报,2017,46(10):1717-1733.
[2] 费新峰,田野,赵超英,等.基于多时相InSAR技术的黄河上游龙羊峡库区不稳定边坡识别与形变监测[J].地球科学与环境学报,2023,45(3):578-589.
[3] 武俐,来梦媛,杨萌,等.小浪底水库早春溶解性有机质光谱特征对浮游植物群落变化的响应[J].生态学报,2025,45(4):1736-1747.
[4] 刘宁.大江大河防洪关键技术问题与挑战[J].水利学报,2018,49(1):19-25.
[5] 赵君,朱金仓.黄土高塬沟壑区固沟保塬工程对流域水流路径的影响[J].地下水,2024,46(5):284-287.
[6] 张炜,张伟胜,张东升,等.采动覆岩活动规律的"空-地"监测技术[J].中国矿业大学学报,2018,47(6):1212-1223.
[7] SEIDEL M,MARZAHN P,LUDWIG R.Dyke monitorin by the means of persistent scattering interferometry at the coast of northern Germany[J].ISPRS-International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2016,XLI-B8:169-173.
[8] 尹宏杰,朱建军,李志伟,等.基于SBAS的矿区形变监测研究[J].测绘学报,2011,40(1):52-58.
[9] ROSEN P A,HENSLEY S,JOUGHIN I R,et al.Synthetic aperture radar interferometry[J].Proceedings of the IEEE,2000,88(3):333-382.
[10] 张倩,马悦,周洪月,等.基于InSAR技术的天津局部地表沉降特征分析[J].测绘通报,2024(2):74-79.
[11] BERARDINO P,FORNARO G,LANARI R,et al.A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(11):2375-2383.
[12] FERRETTI A,PRATI C,ROCCA F.Permanent scatterers in SAR interferometry[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(1):8-20.
[13] ZHANG Zhengjia,WANG Chao,WANG Mengmeng,et al.Surface deformation monitoring in Zhengzhou city from 2014 to 2016 using time-series InSAR[J].Remote Sensing,2018,10(11):1731.
[14] 杨洋,贾洪果,柏铮航,等.联合机器学习与SBAS-InSAR的青藏高原冻土形变监测[J].测绘通报,2025(4):45-50.
[15] HUSSAIN S,PAN Bin,AFZAL Z,et al.Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram highway,Northern Pakistan[J].Scientific Reports,2023,13:7485.
[16] 张诗玉,张姿旗,李雯雯,等.融合PS与DS方法的2019-2023年郑州城区地表形变研究[J].测绘通报,2025(2):127-131.
[17] 张敏,张春晋,马东方,等.小浪底水库运用以来黄河下游冲刷发展规律[J].人民黄河,2025,47(8):83-89.
[18] 谢瑾博,曾毓金,张明华,等.气候变化和人类活动对中国东部季风区水循环影响的检测和归因[J].气候与环境研究,2016,21(1):87-98.
[19] 王宗润,汤小芸.基于TOPSIS方法改进的多属性决策模型:最小化偏好反转[J].控制与决策,2021,36(1):216-225.

基于SBAS与DS-SBAS的小浪底库区沉降监测对比

A comparison study on land subsidence monitoring in the Xiaolangdi Reservoir area based on SBAS and DS-SBAS

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	陈元非, 王磊. 基于SBAS-InSAR和水准数据融合的采动沉陷盆地边界稳健识别方法[J]. 测绘通报, 2026, 0(2): 60-67.
[2]	林羊, 于冰, 田馨, 张冠军, 刘成, 甘俊, 李广宇. 京雄城际铁路沿线InSAR地表沉降监测与影响因素分析[J]. 测绘通报, 2026, 0(2): 46-53.
[3]	曹芮菡, 李新, 周定杰, 喜文飞, 黄广才, 王瑞亭, 郭蓁. 联合ALOS-2和Sentinel-1的复杂山区潜在滑坡识别[J]. 测绘通报, 2026, 0(1): 65-71.
[4]	李景洮, 张双成, 盛磊, 张毅, 强菲, 聂腾, 刘宁, 闻繁. 时序InSAR与普适型GNSS用于西安地面沉降监测研究[J]. 测绘通报, 2026, 0(1): 18-24,46.
[5]	莫丹丹, 火久元. 基于SBAS-InSAR技术的喀喇昆仑公路盖孜河谷段形变监测与影响因子分析[J]. 测绘通报, 2025, 0(8): 32-42.
[6]	李若凡, 李勇发, 左小清, 黄成, 邢明泽, 李永宁, 张荐铭, 石超, 顾晓娜. 兰坪县InSAR形变监测与滑坡灾害早期识别[J]. 测绘通报, 2025, 0(7): 40-45.
[7]	李文东, 叶禹, 李霞, 魏伟, 辛存林. 融合SBAS-InSAR形变与机器学习模型的滑坡易发性评价[J]. 测绘通报, 2025, 0(7): 126-131,146.
[8]	熊春宝, 安贺雯, 苏广利. 大型引水工程地面沉降PS-InSAR监测与分析[J]. 测绘通报, 2025, 0(7): 19-25,72.
[9]	汪晨煜, 彭军还, 薛跃明, 李旭, 张炎. 融合InSAR与信息量-逻辑回归耦合模型的滑坡动态危险性评价[J]. 测绘通报, 2025, 0(6): 43-48.
[10]	高浩亮, 李德伟, 李朋朋, 李晓红, 狄桂栓. 黄河三角洲海岸线变化与地面沉降监测分析[J]. 测绘通报, 2025, 0(6): 103-108,122.
[11]	王身丽, 刘毅, 韩昊, 杜勇. 联合SBAS-InSAR和DS-InSAR的特高压输电通道形变监测与预测[J]. 测绘通报, 2025, 0(6): 130-135,141.
[12]	杨洋, 贾洪果, 柏铮航, 刘宇辰. 联合机器学习与SBAS-InSAR的青藏高原冻土形变监测[J]. 测绘通报, 2025, 0(4): 45-50.
[13]	尚依炜, 熊俊楠, 贾倩, 罗思远, 王启盛, 曹依帆. 融合升降轨SBAS-InSAR技术的冕宁县滑坡隐患识别与分析[J]. 测绘通报, 2025, 0(4): 58-62.
[14]	邵亚璇, 马晶, 韩莉莉, 姚冠宇. 基于SBAS-InSAR技术的地表沉降时间序列分析与预测[J]. 测绘通报, 2025, 0(4): 51-57,62.
[15]	周天翔, 董景龙, 沈强. 基于SBAS-InSAR技术的济南市地铁沿线地表沉降时空分析[J]. 测绘通报, 2025, 0(4): 109-113.