数字土壤属性制图研究进展

Research Progress on Digital Soil Property Mapping

土壤是维持人类生存与发展的关键自然资源,其属性的空间分布对粮食安全、水资源保护、生态多样性、气候变化等全球性问题具有重要意义。为更有效地利用土壤资源,需要对其属性进行精确的数字化描述。传统土壤属性制图方法因其局限性,已不能满足现代精准农业和生态模拟的需求。数字土壤属性制图作为一种新兴技术,能够更精准地预测土壤养分的空间分布特征。目前,数字土壤属性制图的研究主要体现在地统计学方法、数理统计方法和机器学习模型的应用。地统计学方法通过分析土壤属性的空间自相关性,模拟其空间分布规律,并通过克里金法等进行空间预测。数理统计方法则主要用于探索土壤属性与环境因素之间的关系,构建预测模型。机器学习,如决策树、随机森林、人工神经网络、支持向量机等,通过构建模型预测土壤属性,并在土壤分类、养分预测等方面表现出较高准确性。然而,土壤属性空间分布的预测受样点布设方法的影响较大。因此,合理设计采样点的位置和数量,以及采用适当的布局方式,对于提高预测结果的准确性和可靠性至关重要。未来,数字土壤属性制图的研究将朝向多尺度、技术融合、人工智能化、精细化、动态更新等方向发展,以满足农业生产和土地资源管理的精准化需求。

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.