现代农业科技Issue(23)：133-142,149,11.DOI:10.3969/j.issn.1007-5739.2024.23.034
数字土壤属性制图研究进展
Research Progress on Digital Soil Property Mapping
摘要
Abstract
Soil is a crucial natural resource for sustaining human survival and development,and the spatial distribution of its properties is of great significance for global issues such as food security,water resource protection,biodiversity,and climate change.In order to utilize soil resources more effectively,precise digital descriptions of their properties are necessary.Traditional soil property mapping methods,due to their limitations,can no longer meet the needs of modern precision agriculture and ecological modeling.Digital soil property mapping,as an emerging technology,enables more accurate predictions of the spatial distribution characteristics of soil nutrients.Current research on digital soil property mapping focuses mainly on the application of geostatistical methods,mathematical statistical methods,and machine learning models.Geostatistical methods simulate the spatial distribution patterns of soil properties by analyzing their spatial autocorrelation and make spatial predictions using techniques such as Kriging method.Mathematical statistical methods are primarily used to explore the relationships between soil properties and environmental factors and to construct predictive models.Machine learning,such as decision tree,random forest,artificial neural network,and support vector machine,predict soil properties by constructing models and demonstrate high accuracy in soil classification and nutrient prediction.However,the prediction of the spatial distribution of soil properties is significantly influenced by the sampling design.Therefore,the rational design of the location and number of sampling points,as well as the adoption of appropriate layouts,is crucial for improving the accuracy and reliability of prediction results.In the future,research on digital soil property mapping will trend towards multi-scale analysis,technological integration,artificial intelligence,refinement,and dynamic updates,so as to meet the precise needs of agricultural production and land resource management.关键词
土壤属性/数字土壤属性制图/环境变量/地统计学/数理统计/机器学习/样点分布/研究进展Key words
soil property/digital soil property mapping/environmental variable/geostatistics/mathematical statistics/machine learning/sampling point distribution/research progress分类
农业科技引用本文复制引用
应纯洋,周晓天,张代维,梅帅,马友华,吴雷..数字土壤属性制图研究进展[J].现代农业科技,2024,(23):133-142,149,11.基金项目
安徽省科技重大专项"现代农业遥感监测系统构建与产业化应用"(202003a06020002). （202003a06020002）
