2026-10-13T00:39:12Z https://www.scidb.cn/oai

10.57760/sciencedb.29484 2026-03-10T14:53:30Z

2026-03-10 Unraveling the Spatial Heterogeneity of Land Subsidence in the Yellow River Delta: A Spatially Adaptive Ensemble Learning Approach doi:10.57760/sciencedb.29484 en 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. Land subsidence; SBAS-InSAR; Yellow River Delta; Machine learning; Spatial heterogeneity; Brine mining; Groundwater resource. Jianyu Li PUBLIC https://api.github.com/licenses/cc0-1.0 dataset http://www.doi.org/10.3390/rs18101549 Science Data Bank