谢彦辉等:Modeling Microwave Emission from Short Vegetation-Covered Surfaces
被阅读 1035 次
2015-12-11
Modeling Microwave Emission from Short Vegetation-Covered Surfaces
作者:Xie, YH (Xie, Yanhui)[ 1,2 ] ; Shi, JC (Shi, Jiancheng)[ 1,3 ] ; Lei, YH (Lei, Yonghui)[ 1,3 ] ; Li, YQ (Li, Yunqing)[ 1,2 ]
Remote Sensing
卷: 7  期: 10  页: 14099-14118
DOI: 10.3390/rs71014099
出版年: OCT 2015
 
摘要
Owing to the temporal and spatial variability of the emissivity spectra, problems remain in the interpretation and application of satellite passive microwave data over vegetation-covered surfaces. The commonly used microwave land emissivity model, developed by Weng et al. (2001) and implemented into the community radiative transfer model (CRTM), treats vegetation-covered surfaces as a three-layer medium. This simplification comes at the cost of accuracy. In this study, to reduce bias in the modeling of microwave emissions from short vegetation-covered surfaces, two modifications are made. First, vegetation was considered as a multilayered medium including leaves and stems to simulate volumetric absorption and scattering. The results suggest that the calculated brightness temperatures well agree with field experiments under different incidence angles for low soil moisture and sparse crop cover. On the other hand, large errors from the measurements are found for high soil moisture content and dense crop cover. Second, the advanced integral equation model (AIEM) was also used to improve the simulation of reflectivity from rough soil surfaces. Comparisons with field experimental data show that the determination coefficient between the calculated and measured brightness temperatures significantly increased and the root-mean-square errors remarkably decreased. The average improvement using the proposed approach is about 80% and 59% in accuracy for the vertical and horizontal polarization, respectively.
 
通讯作者地址: Shi, JC (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[ 2 ] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[ 3 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China