短波红外垂直失水指数观测误差估计方法及其同化方案

发展了一个基于短波红外垂直失水指数的土壤水分观测误差空间分析方法,据此改进了以遥感和生态过程模型为基础的两阶段土壤水分同化方案.通过实地同化实验,证明了改进的土壤水分同化方案能较好地体现空间植被覆盖和数量的异质性导致的表层土壤水分的空间变异,进一步提高了遥感数据在空间上的同化精度....

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Bibliographic Details
Published in红外与毫米波学报 Vol. 30; no. 6; pp. 526 - 530
Main Author 朱琳 秦其明 王金梁 刘明超
Format Journal Article
LanguageChinese
Published 国家卫星气象中心,北京,100081%北京大学地球与空间科学学院遥感所,北京,100871 2011
Subjects
BEPS模型
土壤水分
数据同化
短波红外垂直失水指数
观测误差
短波红外垂直失水指数
观测误差
土壤水分
数据同化
BEPS模型
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ISSN1001-9014

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Summary:发展了一个基于短波红外垂直失水指数的土壤水分观测误差空间分析方法,据此改进了以遥感和生态过程模型为基础的两阶段土壤水分同化方案.通过实地同化实验,证明了改进的土壤水分同化方案能较好地体现空间植被覆盖和数量的异质性导致的表层土壤水分的空间变异,进一步提高了遥感数据在空间上的同化精度.
Bibliography:New method for estimating the observation error in the shortwave infrared perpendicular water stress index was presented.The new method was implemented in the two stage data assimilation scheme.From the data assimilation experiment,it was demonstrated that the improved data assimilation scheme can fairly reveal spatial variations of surface soil moisture resulted from the spatial and the quantitative heterogeneous of vegetation.Thus the accuracy of the assimilation was further improved.
shortwave infrared perpendicular water stress index（SPSI）; observation error; soil moisture; data assimilation; boreal ecosystem productivity simulator（BEPS）
ZHU Lin,QIN Qi-Ming,WANG Jin-Liang,LIU Ming-Chao（1.National Satellite Meteorological Center of China,Beijing 100081,China; 2.Institute of Remote Sensing and GIS,Peking University,Beijing 100871,China）
31-1577/TN
ISSN:1001-9014