文章摘要
基于遥感技术的土壤质地空间预测方法研究进展
Research progress in soil texture spatial prediction methods based on remote sensing technology
投稿时间:2022-10-12  修订日期:2022-11-17
DOI:10.13254/j.jare.2022.0719
中文关键词: 遥感,土壤质地,空间分布,预测方法,模型,精度分析
英文关键词: remote sensing, soil texture, spatial distribution, prediction method, model, precision analysis
基金项目:安徽省科技重大专项(202003a06020002)
作者单位E-mail
汪甜甜 安徽农业大学资源与环境学院 农田生态保育与污染防控安徽省重点实验室, 合肥 230036  
丁琪洵 安徽农业大学资源与环境学院 农田生态保育与污染防控安徽省重点实验室, 合肥 230036  
梅帅 安徽农业大学资源与环境学院 农田生态保育与污染防控安徽省重点实验室, 合肥 230036  
汤萌萌 安徽农业大学资源与环境学院 农田生态保育与污染防控安徽省重点实验室, 合肥 230036  
江文娟 安徽农业大学资源与环境学院 农田生态保育与污染防控安徽省重点实验室, 合肥 230036  
王强 安徽农业大学资源与环境学院 农田生态保育与污染防控安徽省重点实验室, 合肥 230036  
马友华 安徽农业大学资源与环境学院 农田生态保育与污染防控安徽省重点实验室, 合肥 230036 yhma2020@qq.com 
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中文摘要:
      土壤质地影响土壤持水持肥性和透气性,进而驱动一系列与土壤有关的物理化学过程,结合高效快速的遥感技术预测土壤质地空间分布,对土壤质量评价与农业生产规划具有重要的理论和实践意义。本文从遥感预测土壤质地的数据、方法和模型的应用出发,介绍了用于土壤质地遥感预测的雷达、地形和植被指数等辅助数据,提出了光谱响应、特征波长选择和遥感解译这三种基于遥感特征预测土壤质地空间分布的方法,梳理了统计学、地统计学和机器学习这三类模型与遥感结合对土壤质地空间预测的应用效果,总结了几种典型方法的优缺点与适用情况,并分析了遥感预测土壤质地的应用条件和精度验证方法,最后提出未来研究需侧重于深入提取各种遥感光谱特征、利用遥感技术获取多类型环境变量和开发土壤物理属性与数据驱动机器学习特征相结合的多算法混合模型,旨在为开展不同区域尺度下土壤质地空间预测研究提供依据与技术支撑。
英文摘要:
      Soil texture affects soil water content and fertilizer and air permeability, and drives multiple physical and chemical processes occurring in the soil. Developing efficient and fast remote sensing technologies to predict the spatial distribution of soil texture has important theoretical and practical applications in soil quality evaluation and agricultural production planning. This paper discusses the application of remote sensing data, methods and models to predict soil texture, and the use of auxiliary data such as radar, terrain, and vegetation index data for soil texture prediction by remote sensing. The paper also proposes three methods for predicting the spatial distribution of soil texture based on remote sensing characteristics; namely, spectral response, characteristic wavelength selection, and remote sensing interpretation. It also focuses on the application effects of statistics, geostatistics, and machine learning models combined with remote sensing on spatial prediction of soil texture. The advantages, disadvantages, and application of several typical methods are compared, and the application conditions and accuracy verification methods of remote sensing prediction of soil texture are analyzed. Future research needs to focus on in-depth extraction of various remote sensing spectral features, acquisition of multiple types of environmental variables by remote sensing technologies, and development of a multi-algorithm hybrid model combining soil physical properties and data-driven machine learning features, so as to provide a basis and technical support for the spatial prediction of soil texture at different regional scales.
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