利用主被动微波数据联合反演土壤水分

doi:10.11867/j.issn.1001-8166.2009.07.0769

地球科学进展 ›› 2009, Vol. 24 ›› Issue (7): 769 -775. doi: 10.11867/j.issn.1001-8166.2009.07.0769

利用主被动微波数据联合反演土壤水分

赵天杰 ^1,2,张立新 ^1,2,蒋玲梅 ^1,2,陈权 ³,张志玉 ^1,2,张勇攀 ^1,2

1.北京师范大学地理学与遥感科学学院，北京 100875; 2.北京师范大学/中国科学院遥感应用研究所遥感科学国家重点实验室，北京 100875;
3.中国科学院对地观测与数字地球科学中心，北京 100190

收稿日期:2009-01-08 修回日期:2009-07-07 出版日期:2009-06-10
通讯作者: 赵天杰 E-mail:zhaotianjie@gmail.com
基金资助:
国家重点基础研究发展计划项目“陆表生态环境要素主被动遥感协同反演理论与方法”（编号：2007CB714400)和“被动遥感反射、辐射机理与参数反演”（编号：2007CB714403）;中国科学院西部行动计划（二期）项目“黑河流域遥感—地面观测同步试验与综合模拟平台建设”（编号：KZCX2-XB2-09）资助.

Joint Inversion of Soil Moisture Using Active and Passive Microwave Data

Zhao Tianjie ^1,2,Zhang Lixin ^1,2,Jiang Lingmei ^1,2,Chen Quan ³,Zhang Zhiyu ^1,2,Zhang Yongpan ^1,2

1.School of Geography and Remote Sensing Science, Beijing Normal University, Beijing 100875, China;
2.State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100875, China;
3.Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100190, China

Received:2009-01-08 Revised:2009-07-07 Online:2009-06-10 Published:2009-07-10

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在黑河中游干旱区水文试验的基础上，以临泽站为研究区域，探讨主被动微波数据联合反演土壤水分的方法。针对ALOS/PALSAR数据，使用AIEM理论模型计算地表的同极化后向散射系数，Oh半经验模型描述交叉极化散射特征，通过对大量后向散射模拟数据的分析，建立裸露地表粗糙度计算模型;利用模拟数据分析地表辐射亮温随土壤水分和粗糙度的变化规律，在此基础上构建NN模型结合粗糙度计算结果和辐射计飞行数据反演研究区域的土壤水分。地面同步测量数据的验证结果表明，该方法充分发挥了主被动微波数据各自的优势，同时避免了主被动协同过程中的尺度问题，为流域尺度的土壤水分监测提供了一种新的有效途径。

关键词: 主被动, 微波散射与辐射, 土壤水分

In this paper, on the basis of the Heihe hydrological drought experiments, the active and passive microwave data joint inversion method of soil moisture has been explored as Linze Station for the study area. For ALOS/PALSAR data, co-polarization backscattering coefficient was calculated using the theoretical model AIEM, and Oh semi-empirical model was used to describe the characteristics of cross-polarization scattering. By a large number of back-scattering simulation data analysis, a calculation model of surface roughness was established. With the use of simulation data, changes of surface radiation brightness temperature with soil moisture and roughness were analyzed. Based on this, a neural network model was built to combine roughness calculation results and the flight data, and soil moisture of the study area was estimated with the trained model. Verified results with synchronous measurement data showed that the method can give full play to the active and passive microwave data on their respective strengths, while avoiding the main problem of scaling issues with passive and active data. And it provides a new effective way for basin-scale monitoring of soil moisture.

Key words: Active and passive, Microwave scattering and emission, Soil moisture

中图分类号:

TP79
P334

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