Detecting 3D Anomalies in Soil Water from Saline-Alkali Land of Yellow River Delta Using Sampling Data

Understanding soil water in the saline-alkali lands is crucial for sustainable agriculture and ecological restoration. Existing studies have largely focused on macroscopic distribution and associated interpolation techniques, which complicates the precise identification of localized anomalous regions. To address this limitation, this study proposes a novel three-dimensional detection method for localized soil water anomalies (3D-SWLA). Utilizing soil water sampling data, a comprehensive three-dimensional soil water cube is constructed through 3D Empirical Bayesian Kriging (3D EBK). We introduce the Soil Water Local Anomaly Index (SWLAI) and apply a second-order difference method to effectively identify and filter anomalous voxels. Then, the 3D Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to cluster Soil Water Anomalous Voxels (SWAVs), thereby delineating three-dimensional Local Anomalous Soil Water Areas (LASWAs) with precision and robustness. A series of experiments were conducted in Kenli to validate the proposed methodology. The results reveal that 3D-SWLA successfully identified a total of 8 Local Anomalous Soil Water Areas (LASWAs), four of which—classified as large-scale anomalies (area > 1.0 km2)—are predominantly concentrated in the northeastern coastal zone and the southern salt fields. The largest among them, LASWA-1, spans 1.8 km2 with a vertical depth ranging from 0 to 35 cm and an average soil water content of 0.36. Another significant anomaly, LASWA-8, covers 1.5 km2, extends to a depth of 0–60 cm, and exhibits a higher average water content of 0.42, reflecting distinct hydrological dynamics in these regions. Additionally, 4 smaller LASWAs (area < 1.0 km2) are spatially distributed along the northeastern irrigation channels, indicating localized moisture accumulation likely influenced by agricultural water management.