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林业资源管理 ›› 2015, Vol. 0 ›› Issue (4): 151-156.doi: 10.13466/j.cnki.lyzygl.2015.04.026

• 研究简报 • 上一篇    下一篇

土壤水分遥感反演研究进展徐 沛,张 超(西南林业大学,昆明 650224)

徐沛, 张超   

  1. 西南林业大学,昆明 650224
  • 出版日期:2015-08-28 发布日期:2020-12-01
  • 通讯作者: 张超(1980-),男,河北唐山人,副教授,博士,主要从事森林经理学研究。Email:zhchgis@126.com
  • 作者简介:徐沛(1989-),女,陕西西安人,在读硕士,主要从事林业遥感研究。Email:xupeigis@126.com
  • 基金资助:
    国家自然科学基金项目(31460195)

Progress of Research on Retrieval of Soil Moisture Based on Remote Sensing

XU Pei, ZHANG Chao   

  1. Southwest Forestry University,Kunming 650224,China
  • Online:2015-08-28 Published:2020-12-01

摘要: 近年来,随着人们对全球气候变化的逐渐重视,环境遥感领域中的土壤水分遥感反演技术已成为研究热点和前沿之一。通过对区域宏观土壤水分进行遥感反演,对于研究植被生长状况、农作物生长发育及产量预估、气候变化及环境响应机制等提供基础依据,具有重要的理论意义。在广泛了解和分析国内外土壤水分遥感反演研究进展的基础上,分别从可见光-近红外法、热红外法和微波遥感法3个方面总结和归纳了目前土壤水分遥感反演的主要方法,分析了各方法的原理与特点,讨论了国内外在该领域研究方面存在的主要技术问题,最后从4个方面对基于遥感技术的土壤水分反演研究进行了展望。

关键词: 土壤水分, 反演模型, 光学遥感, 微波遥感

Abstract: In recent years,with the increase of the people’s attention to the global climate change,the technology of retrieval of soil moisture based on remote sensing in the filed of environmental remote sensing has become one of the research focuses and cutting edges.By using remote sensing to retrieve the condition of soil moisture in the macro region,the study of vegetation growth conditions,crop growth and yield estimates,climate change and environmental response mechanism to provide basic basis,has important theoretical significance.In this paper,based on the understanding and analysis of the domestic and foreign research progress in Remote Sensing Inversion of soil moisture in the three aspects of visible light and near infrared,thermal infrared and microwave remote sensing method,summarized the main methods of remote sensing inversion of soil moisture,analyzed the principle and characteristics of each method,discussed the main technical questions that existed at home and abroad in this field,and look into the future possible prospect of the developing trend of soil moisture monitoring from four aspects.

Key words: soil moisture, retrieval model, optical remote sensing, microwave remote sensing

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