论文

沙漠地区微波地表发射率和土壤质地关系分析

  • 吴莹- ,
  • WENG Fuzhong ,
  • 王振会- ,
  • YAN Banghua
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  • 南京信息工程大学 气象灾害省部共建教育部重点实验室, 江苏 南京210044;南京信息工程大学 大气物理学院, 江苏 南京210044;NOAA/NESDIS/Center for Satellite Application and Research, College Park, MD 20742, USA;NOAA/NESDIS/OSPO/Satellite Products Branch, College Park,? MD 20742, USA

网络出版日期: 2013-04-28

Analysis on the Relationship between Microwave Land Surface Emissivity and Soil Texture in Desert Region

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Online published: 2013-04-28

摘要

利用微波地表发射率模型, 模拟计算了沙漠地区石英、 砂岩、 花岗岩和石灰石不同粒子尺度、 砂土含量和土壤含水量时的地表发射率频谱, 并用先进微波扫描辐射计-地球观测系统AMSR—E资料对塔克拉玛干沙漠地区进行了地表发射率卫星反演和模型模拟计算的对比研究, 分析了该地区不同地表类型和矿物成分的微波地表发射率特征。结果表明, 微波地表发射率和地表类型密切相关, 并且随着土壤含水量、 土壤质地、 矿物成分和土壤粒子尺度的不同存在显著变化, 成为现有微波地表发射率模型用于沙漠地区模拟计算结果的误差主要来源。根据沙漠地区准确的土壤类型、 土壤组分比例、 粒径尺度和矿物颗粒等信息对近地表土壤进行了辐射传输过程的分层分析, 为改进现有微波地表发射率计算模型提供理论依据。

本文引用格式

吴莹- , WENG Fuzhong , 王振会- , YAN Banghua . 沙漠地区微波地表发射率和土壤质地关系分析[J]. 高原气象, 2013 , 32(2) : 481 . DOI: 10.7522/j.issn.1000-0534.2012.00046

Abstract

The characteristics of microwave land surface emissivity different types of soil and different minerals in desert region is studied. Emissivities of four kinds of the most abundant desert materials, i.e. quartz, sandstone, granite and limestone with different particle size, sand fractions and soil moistures are simulated using a microwave land surface emissivity model. And also, the simulated emissivity of Taklimakan Desert is compared with satellite retrieval values from AMSR-E measurements, the result shows that the microwave land surface emissivity is closely related with surface types, and also significantly vary with soil moisture, soil texture, mineral component and soil particle size, which are the main sources of errors of current land emissivity simulation. Moreover, the results provide a theoretical basis for the further improving the existing microwave land surface emissivity model by stratifying near surface soil and analyzing radiative transfer process of multi-layer soil with accurate soil types, soil textural components, particle size scales and mineral material datasets in desert region.

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