Abstract
Soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK) are important indicators of soil fertility when undertaking a quality evaluation. Obtaining a high-precision spatial distribution map of soil nutrients is of great significance for the differentiated management of nutrient resources and reducing non-point source pollution. However, the spatial heterogeneity of soil nutrients lead to uncertainty in the modeling process. To determine the best interpolation method, terrain, climate, and vegetation factors were used as auxiliary variables to participate in the investigation of soil nutrient spatial modeling in the present study. We used the mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and accuracy (Acc) of a dataset to comprehensively compare the performance of four different geospatial techniques: ordinary kriging (OK), regression kriging (RK), geographically weighted regression kriging (GWRK), and multiscale geographically weighted regression kriging (MGWRK). The results showed that the hybrid methods (RK, GWRK, and MGWRK) could improve the prediction accuracy to a certain extent when the residuals were spatially correlated; however, this improvement was not significant. The new MGWRK model has certain advantages in reducing the overall residual level, but it failed to achieve the desired accuracy. Considering the cost of modeling, the OK method still provides an interpolation method with a relatively simple analysis process and relatively reliable results. Therefore, it may be more beneficial to design soil sampling rationally and obtain higher-quality auxiliary variable data than to seek complex statistical methods to improve spatial prediction accuracy. This research provides a reference for the spatial mapping of soil nutrients at the farmland scale.
Highlights
Soil nutrient indicators are key indicators for the evaluation of soil quality
Bangroo et al [14] used regression kriging (RK) to study the influence of topographic factors on the spatial distribution of soil organic carbon (SOC) and total soil nitrogen (TSN); the authors found that the RK method was significantly better than the ordinary kriging (OK) model in predicting SOC and TSN in the case of residuals with a moderate degree of spatial autocorrelation
Based on the above analysis, we drew spatial distribution maps of the soil nutrient content in the study area based on different geospatial technologies
Summary
Studying the spatial distribution of soil nutrients is the basis for understanding regional soil quality conditions, adjusting management measures and various material inputs, and obtaining maximum benefits [1,2,3]. Sustainability 2021, 13, 3270 geographically weighted regression kriging (GWRK)) [7] Among these methods, the OK method is the most widely used geostatistical method. The sampling density and method have a considerable influence on the interpolation accuracy of OK [10] This method does not incorporate environmental factors into the model, which are closely related to soil nutrients. These limitations lead to the simulation of the spatial distribution of soil properties in complex landscapes being greatly restricted [11,12]
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