Regression kriging as a workhorse in the digital soil mapper's toolbox
Regression kriging as a workhorse in the digital soil mapper's toolbox
复制标题
DOI：
10.1016/j.geoderma.2018.04.004
复制
发表时间：
2018-09-15
期刊：
影响因子：
6.1
通讯作者：
Grunwald, S.
中科院分区：
文献类型：
--
作者：
Keskin, H.;Grunwald, S.
迫切需要适当规模、合理可靠、明确和连续的空间土壤信息，以解决环境问题并确保生态系统服务在地方、区域和全球各级的可持续性。回归克立格(RK)是数字土壤制图工具箱中最流行、最实用、最稳健的混合空间内插技术之一，它可以在多尺度上模拟土壤在空间和时间上的分布格局。RK的一些理论和应用方面已经被讨论过，但是，还没有对影响RK绩效的基本因素进行量化的综述研究。本综述的材料来源于2004-2014年间高质量的国际土壤科学期刊：《土壤学》、《大地测量学》和《美国土壤科学学会》。总共检查了来自40篇不同文章的142个不同的模型。考虑以下标准来评估它们对RK预测效率的影响：i)土壤地理区域，ii)范围面积，iii)空间分辨率，iv)目标土壤性质和/或类别v)抽样设计，vi)抽样大小和密度，vii)样本深度)土壤环境因素作为预测因子，ix)转换方法，x)因子分析，xi)回归类型，xii)用于变异函数的模型，xii)块金与总槛比，xiv)空间自相关范围，XV)观测数据集的变异系数，十六)评估方法(请注意，在以前的出版物中，“验证”一词在儿科学出版物中广泛使用)和十)测定系数。讨论了RK的历史发展、当前RK研究的局限性和优势、RK的研究差距和未来趋势。一个主要发现是RK模型的精度与原始数据集中土壤性质的变化之间存在负相关关系。提出了一种新的改进的RK方法，用于土壤性质和分类的预测。
Appropriate scale, justifiably reliable, categorical and continuous spatial soil information is urgently needed to address environmental problems and ensure sustainability of ecosystem services at local, regional and global scales. Regression Kriging (RK) is one of the most popular, practical and robust hybrid spatial interpolation techniques in the digital soil mapper's toolbox that enables the modeling of soil distribution patterns at multiple scales in space and time. Several theoretical and applied aspects of RK have been discussed; however, there are no review studies, which quantify the essential factors affecting the performance of RK. Materials for this review were gathered from high-quality international soil science journals: Catena, Geoderma, and Soil Science Society of America from 2004 to 2014. A total of 142 different models from 40 different articles were examined. The following criteria were considered to evaluate their impacts on the prediction efficiency of RK: i) soil geographic region, ii) area of extent, iii) spatial resolution, iv) target soil properties and/or classes v) sampling design, vi) sampling size and density, vii) sample depth viii) soil-environmental factors as predictors, ix) methods of transformation, x) factor analysis, xi) regression type, xii) model used for variogram, xiii) nugget to total sill ratio, xiv) spatial autocorrelation range, xv) coefficient of variation of observed dataset, xvi) evaluation method (note that in previous publications the term 'validation' has been used extensively in publications in pedometrics) and xvii) coefficient of determination. The historical development of RK, limitations and strengths of current RK studies, research gaps, and future trends in RK are discussed. A major finding is the inverse relationship between the accuracy of RK models and the variation of soil properties in the original datasets. Novel modified RK methods are proposed for further investigation to predict soil properties and classes.