摘要
近年来,随着机器学习算法的发展,国内外专家学者致力于利用机器学习模型展开土壤盐渍化遥感相关研究,并取得了丰硕成果。本文利用文献计量可视化软件CiteSpace,对近十年来基于机器学习的土壤盐渍化遥感建模研究进行分析,确定了研究主题和热点,从机器学习算法、建模特征变量以及模型评价等方面评述研究进展,并针对当前研究热点对目前研究的局限性与发展趋势进行讨论。主要结论:1)机器学习算法在土壤盐渍化遥感建模中发挥着至关重要的作用,主要研究主题有机器学习算法及其精度研究、建模特征变量选择研究、遥感数据源选择对模型的影响研究、土壤盐渍化研究区域选择和基于机器学习的土壤盐渍化数字制图应用研究。2)目前的研究热点为辅助变量作为特征变量在模型构建中的应用、实测光谱数据与多源遥感光谱数据结合以及最佳机器学习算法选择。3)以2018年为节点,研究进展分为起步阶段和高速发展阶段,目前仍存在需要解决的挑战以提高模型的准确性。未来的研究方向应集中在云平台和机器学习在土壤盐碱化大范围、长期监测中的应用。
In recent years,with the development of machine learning algorithms,national and international experts and researchers have devoted themselves to the study of remote sensing of soil salinization using machine learning models and have achieved fruitful results.Using the bibliometric visualization software CiteSpace,this study reviewed the research themes of machine learning-based remote sensing modeling of soil salinization in the last decade.The main research themes from recent years were summarized,and research progress around machine learning algorithms,modeling feature variables,and model evaluation were discussed.Additionally,the study examined the limitations and development trends around current research themes.The main conclusions were:1)the main research themes included machine learning algorithms and their accuracy,modeling feature variable selection,the impact of remote sensing data source selection on models,selection of study areas for soil salinization,and the application of machine learning-based digital mapping of soil salinization.2)The current research themes were the application of covariates as feature variables in model construction,the combination of measured spectral data and multi-source remote sensing spectral data,and the selection of the most effective machine learning algorithms.3)Based on 2018,research progress could be divided into the initial stage and the high-speed development stage.Soil salinization remote sensing monitoring based on cloud platforms and machine learning will become a direction for future research development.
作者
张佘淑
赵军
ZHANG Sheshu;ZHAO Jun(College of Geography and Environmental Science,Northwest Normal University,Lanzhou 730070,Gansu,China)
出处
《草业科学》
CAS
CSCD
北大核心
2023年第11期2812-2821,共10页
Pratacultural Science
基金
石羊河下游水生态服务流与植被土壤响应过程GIS模拟项目(42161072)。
