Spatio-temporal patterns of soil salinity in Hetao Irrigation District based on spatio-temporal Kriging

: For maintaining crop yield in salt-affected dry agricultural settings, monitoring and analyzing spatio-temporal dynamics of soil salinity over broad areas is crucial yet challenging due to its high variability. The most popular techniques for evaluating spatial distribution patterns and temporal trends are classical statistical analysis and traditional geostatistical analysis, but they are not suitable for accurately capturing spatio-temporal trends of soil salinity due to irregular sampling time and inconsistent spatial position during sampling time. Spatio-temporal Kriging is an extension of spatial geostatistics to space- time geostatistics and may overcome this problem effectively because its model covariance/variance is a function of both space and time. However, its application in spatio-temporal modeling and prediction of regional soil salinity is still unclear. Based on 4582 soil salinity data of 0 - 1.8 m soil profiles from 68 monitoring locations in the Longsheng study area of Hetao Irrigation District, Inner Mongolia, spatio- temporal variation characteristics of regional soil salinity using a spatio- temporal geostatistical method, and spatio- temporal Kriging interpolation accuracy was compared with traditional spatial Kriging interpolation. Furthermore, the ability of spatio- temporal Kriging to obtain regional soil salinity dynamics was verified using less than half of the original monitoring locations. The results showed that the spatial variation coefficient of soil salt in the study area ranged from 0.43 to 1.14, which was categorized as medium to strong variability. Regional averaged soil salinity dynamics had obvious seasonal variation characteristics, and the root zone (0 - 0.6 m) soil salinity accumulated in the crop growing season and desalted in the fallow season, while the deep soil salinity (0.6 - 1.8 m) was the opposite. The sum-metric model can fit the temporal and spatial experience semi- variance of soil salinity well, and the root mean square error (RMSE) between the predicted value and observed value of soil salinity in each layer was less than 0.21 dS · m - 1 , which was 0.02 - 0.09 dS · m - 1 less than that of traditional spatial Kriging. The areas of different soil salinity determined by 32 sparse monitoring locations were in good agreement with those determined by all sampling sites, and the mean relative error between areas of different soil salinity for 0 - 0.6 m and 0.6 - 1.2 m were − 13.20 % and − 8.35%, respectively. Similarly, the respective RMSE were 466.67 hm 2 and 494.43 hm 2 and the determination coefficient ( R 2 ) were 0.79 and 0.72, indicating that spatial distribution of soil salinity obtained by sparse monitoring locations is consistent with the results of all sampling locations. Spatio- temporal Kriging significantly improves the prediction accuracy of soil salinity compared with ordinary Kriging, since it uses more information on soil salinity in time and space. The accurate estimation of spatio-temporal dynamics of soil salinity in the data set of sparse monitoring points was realized, which can greatly improve the monitoring efficiency of the spatio-temporal pattern of soil salinity in the region.