Unraveling the Spatial Heterogeneity of Land Subsidence in the Yellow River Delta: A Spatially Adaptive Ensemble Learning Approach

The dataset utilized in this study integrates multi-source geospatial and environmental data to investigate land subsidence in the Yellow River Delta. The primary subsidence measurements were derived from 83 scenes of Sentinel-1A Single Look Complex (SLC) SAR images acquired between March 2017 and June 2023, processed using the Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique. This processing yielded high-precision annual subsidence rates at 30 m spatial resolution, with accuracy validated against leveling survey data (R² = 0.877, RMSE = 17.24 mm/yr).To characterize the driving factors of subsidence, nine predictive variables were compiled from multiple sources: (1) topographic factors (elevation, slope, and sine/cosine-transformed aspect) derived from the ALOS Digital Elevation Model; (2) lithological classes obtained from regional geological maps, encoded via one-hot transformation; (3) anthropogenic activity indicators, including Euclidean distance to salt pans and binary salt pan mask (vectorized from 1:310,000 scale maps) and cropland proportion extracted from 2022 land cover data; and (4) hydrometeorological variables comprising current-month and one-month lagged precipitation, serving as proxies for groundwater recharge due to limited in-situ well observations.All predictor layers were resampled to 30 m resolution to maintain consistency with InSAR subsidence data. Spatial features were enhanced through nonlinear transformations: inverse transformation of salt pan distance to model decay effects, and square root transformation of local topographic variance (computed within 3×3 moving windows) to characterize micro-topographic influences. The complete dataset comprises approximately 1.7 million sample points covering the eastern Yellow River Delta, providing comprehensive spatial coverage of both subsidence measurements and potential controlling factors.

本研究所使用的数据集整合多源地理空间与环境数据，旨在探究黄河三角洲的地面沉降问题。核心沉降测量数据源自2017年3月至2023年6月间获取的83景Sentinel-1A单视复数（Single Look Complex, SLC）合成孔径雷达（Synthetic Aperture Radar, SAR）影像，并采用小基线集干涉合成孔径雷达（Small BAseline Subset Interferometric Synthetic Aperture Radar, SBAS-InSAR）技术完成处理。经该流程处理后，得到空间分辨率为30米的高精度年度沉降速率，并通过水准测量数据验证了精度（决定系数R²=0.877，均方根误差RMSE=17.24 mm/yr）。为表征地面沉降的驱动因素，本研究从多源渠道收集了9类预测变量：1. 地形因子：由ALOS数字高程模型（Digital Elevation Model, DEM）提取的高程、坡度，以及经正弦/余弦变换后的坡向；2. 岩性类别：源自区域地质图，通过独热编码完成向量化编码；3. 人类活动指标：包括至盐田的欧氏距离、二元盐田掩码（从1:310000比例尺地图矢量化得到），以及从2022年土地覆盖数据中提取的耕地占比；4. 水文气象变量：包含当月及滞后1个月的降水量。由于原位观测井数据有限，该变量被用作地下水补给的替代指标。所有预测因子图层均被重采样至30米分辨率，以与InSAR沉降数据保持空间一致性。通过非线性变换强化空间特征：对盐田距离进行逆变换以模拟衰减效应，对3×3移动窗口内计算得到的局地地形方差进行平方根变换，以此表征微地形的影响。完整数据集涵盖黄河三角洲东部区域，共计约170万个样本点，全面覆盖了沉降测量数据与潜在控制因子的空间分布范围。