土壤粒径空间预测方法研究综述  

A review of spatial prediction methods for soil particle-size fractions

作　　者：史文娇[1,2,3] 张沫 SHIWenjiao;ZHANG Mo(Key Laboratory of Land Surface Pattern and Simulation of CAS,Institute of Geographic Sciences and Natural Resources Research,CAS,Beijing 100101,China;State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,CAS,Beijing 100101,China;College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院地理科学与资源研究所中国科学院陆地表层格局与模拟院重点实验室,北京100101 [2]中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101 [3]中国科学院大学资源与环境学院,北京100049

出　　处：《地理学报》2022年第11期2890-2901,共12页Acta Geographica Sinica

基　　金：中国科学院战略性先导科技专项(XDA23100202,XDA20040301);国家自然科学基金项目(41930647);资源与环境信息系统国家重点实验室开放基金。

摘　　要：土壤粒径(砂粒、粉粒和黏粒)是各种陆表过程和生态系统服务评估等模型的关键参数。作为一种土壤成分数据,土壤粒径的空间预测方法有和为1(或100%)等特殊要求,其空间分布精度受预测方法影响较大。本文针对土壤粒径相较于其他土壤属性的特殊性,提出了土壤粒径空间预测方法框架,综述了土壤粒径数据变换、空间插值和精度验证等系列方法,总结了提升土壤粒径空间预测精度的各种途径,包括通过有效的数据变换改善数据分布、结合数据分布特点选择合适的预测方法、结合辅助变量提升制图精度和分布合理性、使用混合模型提升插值精度、使用多成分联合模拟模型提升预测的系统性等。最后,提出了今后土壤粒径空间预测方法研究的未来方向,包括从考虑数据变换原理和机制角度改善数据分布、发展多成分联合模拟模型和高精度曲面建模方法,以及引入土壤粒径函数曲线并与随机模拟结合等。Soil particle-size fractions(PSFs),including sand,silt,and clay,are key parameters for land-surface process simulation and ecosystem service evaluation.More accurate interpolation of soil PSFs can help better understand the simulation of the above models.As compositional data,soil PSFs have special demands of the constant sum(1 or 100%)in the interpolation process,and the spatial distribution accuracy is mostly affected by the performance of spatial prediction methods.Here,we provided a framework for the spatial prediction of soil PSFs,and reviewed a series of methods in the steps of this framework,including methods of log-ratio transformation of soil PSFs(additive log-ratio,centered logratio,symmetry log-ratio,and isometric log-ratio methods),spatial interpolators of soil PSFs(geostatistical methods,regression models,and machine learning models),validation methods(probability sampling,data splitting,and cross-validation)and indices for accuracy assessments in soil PSF interpolation and soil texture classification(rank correlation coefficient,mean error,root mean square error,mean absolute error,coefficient of determination,Aitchison distance,standardized residual sum of squares,overall accuracy,Kappa coefficient,and precision-recall curve)and uncertainty analysis(prediction interval,confidence interval,standard deviation,and confusion index).In addition,we summarized several ways to improve the prediction accuracy of soil PSF,such as normalizing the data distributions through effective data transformation,choosing suitable prediction methods based on the data distribution characteristics,improving mapping accuracy and distribution reasonability through the combination of auxiliary data,improving interpolation accuracy through hybrid models or joint modeling for multi-components.Finally,we proposed the future research fields of the spatial prediction methods of soil PSFs,including considering the principles and mechanisms of data transformation,developing joint simulation models and high accuracy surface modeling

关 键 词：土壤粒径 土壤质地 地统计 机器学习 高精度曲面建模 

分 类 号：S152[农业科学—土壤学]