Digital soil mapping with adaptive consideration of the applicability of environmental covariates over large areas
Digital soil mapping with adaptive consideration of the applicability of environmental covariates over large areas
复制标题
数字土壤测绘，自适应考虑大面积环境协变量的适用性
DOI：
10.1016/j.jag.2022.102986
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发表时间：
2022-09
影响因子：
7.5
通讯作者：
A-Xing Zhu
中科院分区：
文献类型：
--
作者：
Nai-Qing Fan;Fang-He Zhao;Liang-Jun Zhu;Cheng-Zhi Qin;A-Xing Zhu
•自适应考虑协变量适用性可以改善土壤预测。•地形条件很好地指示了土壤-环境关系的空间变化。•预测不确定性考虑相似性和协变量适用性。•协变量适用性参数在这个大的研究区域是不敏感的。有效利用环境协变量表征土壤-环境关系是数字土壤制图成功的关键。在数字土壤制图中使用环境协变量的典型方法是考虑研究区域的整体地理特征选择多种环境协变量，并考虑这些协变量在整个区域具有一致的适用性。然而，这种做法忽略了这样一个事实，即每个环境协变量在表征土壤-环境关系方面的适用性在复杂的环境条件下是不同的，特别是在大片地区。提出了一种利用土壤-环境关系自适应考虑协变量适用性的大面积数字土壤制图方法。利用新设计的模糊函数，从地形条件出发，量化各协变量在各位置的适用性。然后将协变量适用性作为重要权重，整合到已有的代表性方法iPSM（个体预测土壤制图）中。分别在iPSM相似性计算和土壤估计阶段进行积分，得到两种新方法：iPSM对所有协变量适用性的加权方法（iPSM_WCovar_all）和iPSM对极限协变量适用性的加权方法（iPSM_WCovar_limit）。极限协变量是指两个位置之间具有最小相似性的协变量，约束了整体相似性。实验在中国安徽省进行。两种方法预测表层土壤有机质含量的结果均优于原始的iPSM方法和随机森林克里格方法。iPSM_WCovar_all、iPSM_WCovar_limit、iPSM和随机森林克里格方法的均方根误差分别为8.14、8.00、8.88和9.65 g/kg，平均绝对误差分别为6.48、6.31、6.61和6.82 g/kg。这两种方法都优于iPSM方法和另一种常用的随机森林克里格方法。此外，在不同的参数设置下，性能都是稳定的。实验结果表明，自适应考虑协变量适用性的方法在数字土壤制图中是可行和有效的。
• Adaptively considering covariate applicability can improve soil prediction. • Terrain conditions well indicate spatial variation of soil–environment relations. • Prediction uncertainty considers both similarity and covariate applicability. • Parameters for covariate applicability are insensitivity in this large study area. The effective use of environmental covariates in characterizing soil–environment relationships is key to successful digital soil mapping. The typical way to use environmental covariates in digital soil mapping is by selecting diverse environmental covariates considering the overall geographical characteristics of the study area and considering these covariates to have consistent applicability across the whole area. However, this practice ignores the fact that the applicability of each environmental covariate in characterizing soil–environment relationships varies over complex environmental conditions, especially in large areas. This study proposed a method to adaptively consider covariate applicability in large-area digital soil mapping using soil–environment relationships. The applicability of each covariate at each location was quantified from the terrain conditions using the newly designed fuzzy functions in the study. Then the covariate applicability was regarded as the importance weight and integrated into an existing representative method, iPSM (individual predictive soil mapping). The integration was separately performed at the similarity calculation and soil estimation stages of iPSM to generate two new methods: iPSM weighting on the applicability of all covariates (iPSM_WCovar_all), and iPSM weighting on the applicability of the limiting covariate (i.e., the covariate with the minimum similarity between two locations that constrains the overall similarity) (iPSM_WCovar_limit). Experiments were carried in Anhui Province, China. The two new methods were used to predict the soil organic matter content of topsoil and outperformed the original iPSM and random forest kriging methods. The root mean square error of the iPSM_WCovar_all, iPSM_WCovar_limit, iPSM and random forest kriging methods were 8.14, 8.00, 8.88 and 9.65 g/kg, respectively, while the mean absolute error of those methods were 6.48, 6.31, 6.61 and 6.82 g/kg. Both proposed methods outperformed the iPSM method and the other commonly used method, i.e., random forest kriging. Moreover, the performance was stable under different parameter settings. Experimental results indicate that the idea of adaptively considering covariate applicability in digital soil mapping is feasible and effective.
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影响因子：
4.2
作者：
A. Zhu;J. Liu;F. Du;S. Zhang;C. Qin;J. Burt;T. Behrens;T. Scholten
通讯作者：
A. Zhu;J. Liu;F. Du;S. Zhang;C. Qin;J. Burt;T. Behrens;T. Scholten
影响因子：
1.3
作者：
A. Zhu
通讯作者：
A. Zhu
DOI：
10.1081/e-ess3-120044271
发表时间：
2018-10
期刊：
Handbook of Soil Sciences (Two Volume Set)
影响因子：
--
作者：
M. R. P. Rad;N. Toomanian;F. Khormali
通讯作者：
M. R. P. Rad;N. Toomanian;F. Khormali
DOI：
10.1007/978-0-387-75961-6
发表时间：
2008-08
期刊：
--
影响因子：
--
作者：
G. Nason
通讯作者：
G. Nason
影响因子：
18.3
作者：
Rossel, R. A. Viscarra;Lee, J.;Richards, A.
通讯作者：
Richards, A.