雄安新区2015-2022地面沉降分布数据库
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为了研究雄安新区地面沉降的发育规律，研究团队利用2015-2022年Sentinel-1A卫星的监测数据（来自欧空局哥白尼数据中心https://dataspace.copernicus.eu/），采用SBAS-InSAR技术提取了雄安新区该时间段内的地面沉降，具体过程如下：首先，根据短基线原则，将数据分组形成多个干涉子集；然后，利用外部参考数字高程模型（DEM）数据来模拟并去除每个干涉子集中的地形相位，以提取地表形变信息，通过处理后的数据生成时间序列差分干涉图集，然后对时间序列数据进行相位解缠，以获取各个干涉子集中的相干目标的相位信息；采用滤波方法或多项式模型进行去除误差相位；最后，利用最小二乘原理获得地表时间序列形变信息。然后，团队将InSAR监测处理结果导入ArcGIS平台，建立了雄安新区地面沉降分布数据库。通过ArcGIS平台，可以查看雄安新区的地面沉降的分布情况。基于该数据库，揭示了雄安新区的地面沉降发育规律并形成了分析报告。本数据集数据量为64.3GB。
To investigate the developmental patterns of land subsidence in Xiong'an New Area, the research team utilized Sentinel-1A satellite monitoring data acquired between 2015 and 2022 (from the European Space Agency's Copernicus Data Center, https://dataspace.copernicus.eu/), and applied the SBAS-InSAR technique to extract land subsidence information for Xiong'an New Area over this period. The specific processing workflow is as follows: First, the raw data were grouped into multiple interferometric subsets according to the short baseline principle. Next, external reference digital elevation model (DEM) data were employed to simulate and remove the topographic phase within each interferometric subset, thereby extracting surface deformation information. Time-series differential interferogram sets were then generated from the processed data, followed by phase unwrapping of the time-series data to obtain the phase information of coherent targets in each interferometric subset. Error phases were subsequently eliminated using filtering methods or polynomial models. Finally, surface time-series deformation information was derived via the least squares principle. The team then imported the processed InSAR monitoring results into the ArcGIS platform and established a land subsidence distribution database for Xiong'an New Area. The spatial distribution of land subsidence in Xiong'an New Area can be visualized through this platform. Based on this database, the developmental patterns of land subsidence in Xiong'an New Area were revealed, and an analytical report was compiled. The total data volume of this dataset is 64.3 GB.
