## 5 Conclusions  
In this paper, we used the cokriging interpolation to calculate the groundwater depth data in the study area. At the same time, MHRU was divided by DEM and land coverage. Following this, spatial autocorrelation analysis was carried out using MHRU as the carrier, and the corresponding remote sensing inversion index was selected for geographically weighted regression. The main variables were groundwater depth in 2005, 2009, 2013, and 2017, and data that could spatially affect groundwater depth, such as NDVI, precipitation, and hydrogeological conditions, were used as covariates. The groundwater depth data are presented in space through collaborative kriging interpolation. In the four-year interpolation results, the groundwater depth data displayed little change on average, and the groundwater depth varies between 2 and 5 m.  
The groundwater burial depth data after coordinated kriging interpolation was used to convert raster data into vector data spatially using the hydrological response unit model. Through hydrological analysis, the HRUs were demarcated and re-divided according to the land use type of the study area, which was the MHRU, totalling 551. Spatial autocorrelation analysis was carried out based on the assignment of the MHRU. In the four-year data, there were 135 high-value clusters and 114 low-value clusters. The overall groundwater burial depth shows aggregation characteristics, and the groundwater confluence process shows a gradually orderly state. The overall analysis of the spatial-temporal differentiation characteristics of groundwater depth in the study area was then carried out. The groundwater has an obvious spatial differentiation pattern between the east and west, mainly from the land cover of the mountainous forest belt in the west and the farmland and dune belt in the east, as well as the difference in topography and landform. The groundwater table shows a trend of high in the west and low in the east. The average change of groundwater depth in the time series is not large but shows a gradual trend of accumulation, indicating the obstacles that hinder the recharge of groundwater are gradually being eliminated. According to the accumulation characteristics through spatial autocorrelation analysis, the land cover type is associated with the accumulation characteristics of groundwater depth, and the characteristics of forest land, cultivated land, and bare land are prominent; therefore, a geographically weighted regression model of groundwater depth and NDVI, NDDI, and NRI was established and the spatial location information was combined. The NDVI representing the forest land and the adjusted R\({}^{2}\) of the groundwater depth is 0.67. NRI representing the cultivated land and the adjusted R\({}^{2}\) of the groundwater depth is 0.8675. NDDI representing the bare land and the adjusted R\({}^{2}\) of the groundwater depth is 0.7875. The band index representing the land type has a good fitting effect with the groundwater burial depth, and the groundwater resources that cannot be directly monitored could be simulated and predicted by diversified land type characteristics; therefore, the development and utilization of groundwater resources in KAPZs should be combined with the above characteristics, rational planning, key development, and effective use. The effective use of groundwater can better maintain the stability of the water circulation system and provide better ecological service functions, which is of great significance for optimizing the ecological pattern of ECZ. Conceptualization, T.N. and J.Y. ; methodology, T.N. ; software, Q.Y. ; validation, T.N. and D.Y. ; formal analysis, Y.H. ; investigation, S.L. ; resources, X.M. ; data curation, Q.L. ; writing--original draft preparation, T.N. ; writing--review and editing, T.N. ; visualization, T.N. ; supervision, D.Y. ; project administration, D.Y. ; funding acquisition, D.Y. All authors have read and agreed to the published version of the manuscript.