Soil organic carbon density spatial heterogeneity-considered Kriging interpolation model

Abstract

The invention discloses a soil organic carbon density spatial heterogeneity-considered Kriging interpolation model. According to the Kriging interpolation model, influences, on common Kriging methods, of low-spatial heterogeneity and spatial abnormal values are reduced by combining land utilization information; and according to different trend term functions, the Kriging interpolation model comprises a virtual variable regression Kriging method, a mean centralized Kriging method and a median centralized Kriging method. By utilizing the model, high-precision SOCD prediction and mapping are realized, and finally, an optimum prediction method and a prediction result thereof are selected according to model evaluation indexes. The reliability, prediction rationality, prediction precision and decision coefficient R2 of the model are remarkably improved; and meanwhile, the model realizes high-precision SOCD spatial mapping, so that significance is provided for realizing precision agriculture and improving the agricultural production efficiency.

Description

technical field [0001] The invention belongs to the technical field of soil quality evaluation, and relates to a soil organic carbon density (SOCD) spatial distribution prediction and mapping method, in particular to a land use-Kriging interpolation model considering the spatial heterogeneity of soil organic carbon density. Background technique [0002] Soil organic carbon density (SOCD) is an important index for evaluating soil quality, which profoundly affects the physical and chemical properties of soil. Accurately describing the spatial distribution characteristics of regional soil organic carbon density and completing the accurate mapping of SOCD spatial distribution are of great significance for realizing precision agriculture, guiding agricultural production and improving agricultural production efficiency. Ordinary Kriging (OK), as a linear and unbiased optimal spatial interpolation method, can obtain the spatial distribution characteristics of regionalized variables...

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Problems solved by technology

In the study of soil attribute kriging interpolation combined with land use information, some scholars carry out spatial partitioning according to land use types, realize sub-area spatial interpolation respectively and combine the results, but this method is mostly used for land use types with large spatial dimensions. In the case of continuous area (Wu Qian et al., 2017); in addition, some scholars use the method of combining mean centering and Kriging (Gu Chengjun et al., 2014) or dummy variable regression The Rig method (Wen Wen et al., 2015) predicts the soil attribute value space, but there is no model that can unify these two methods and compare the prediction accuracy of the two methods; in addition, when the correction set exists When there are spatial outliers, the prediction accuracy of these two methods needs to be improved

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