融合升降轨的极化干涉SAR三层模型植被高度反演方法

沈鹏; 汪长城; 朱建军; 高晗; 付海强; 解清华; 王赛; 何帅帅

doi:10.11947/j.AGCS.2017.20170122

测绘学报 >

2017 , Vol. 46 >Issue 11: 1868 - 1879

DOI: https://doi.org/10.11947/j.AGCS.2017.20170122

摄影测量学与遥感

融合升降轨的极化干涉SAR三层模型植被高度反演方法

沈鹏 ,
汪长城 ,
朱建军 ,
高晗 ,
付海强 ,
解清华 ,
王赛 ,
何帅帅

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1. 中南大学地球科学与信息物理学院, 湖南长沙 410083;
2. 中南大学有色金属成矿预测教育部重点实验室, 湖南长沙 410083

沈鹏(1994-),男,硕士生,研究方向为极化干涉SAR数据处理。E-mail:shenpengcsu@163.com

收稿日期: 2017-03-22

修回日期: 2017-09-08

网络出版日期: 2017-12-05

基金资助

国家自然科学基金（41531068；41371335；41671356）；湖南省自然科学基金（2016JJ2141）；湖南省重点研发计划（2016SK2003）；欧空局数据合作计划（14655）；中南大学研究生自主探索创新项目（2017zzts549）

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Vegetation Height Inversion Method with Three-layer Model by Fusing the Ascending and Descending PolInSAR Data

SHEN Peng ,
WANG Changcheng ,
ZHU Jianjun ,
GAO Han ,
FU Haiqiang ,
XIE Qinghua ,
WANG Sai ,
HE Shuaishuai

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1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University), Ministry of Education, Changsha 410083, China

Received date: 2017-03-22

Revised date: 2017-09-08

Online published: 2017-12-05

Supported by

The National Natural Science Foundation of China (Nos. 41531068 41371335 41671356) The National Natural Science Foundation of Hunan Province of China (No. 2016JJ2141) The Planned Science and Technology Project of Hunan Province, China (No. 2016SK2003) PA-SB ESA EO Project Campaign (No. 14655) Innovation Foundation for Postgraduate of Central South University (No. 2017zzts549)

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摘要

森林参数的获取不仅可以估算地表生物量和林下地形，还有助于研究全球碳循环和分析全球气候变化。极化干涉SAR植被参数反演算法一般是基于随机地体两层模型（RVoG），但是当实际植被有着冠层、树干层和地表层的明显三层结构时，植被参数反演精度就会变差；另外，由于机载SAR系统数据的近距远距垂直向波数差异较大，导致试验结果存在着由其引起的系统误差。针对这两个问题，本文提出了一种融合升降轨的极化干涉SAR三层模型植被参数反演方法。该方法首先采用三层植被RVoG模型修正微波在穿透植被时的散射过程；然后采用融合升降轨道数据的方式削弱其系统误差；最后，采用非线性迭代平差的反演算法来进行植被高度反演。为了验证该方法的有效性，采用了德国宇航局DLR提供的BioSAR2008项目的两景升轨及两景降轨E-SAR P波段全极化SAR数据进行试验，并采用3组反演策略进行比较分析。结果表明，三层植被模型能够更好地描述植被散射过程；同时，新方法有效降低了由垂直向波数引起的系统误差，提高了树高反演精度。

关键词： RVoG(random volume over ground); 植被高度; 三层植被模型; 三阶段算法; 升降轨; 垂直向波数

本文引用格式

沈鹏 , 汪长城 , 朱建军 , 高晗 , 付海强 , 解清华 , 王赛 , 何帅帅 . 融合升降轨的极化干涉SAR三层模型植被高度反演方法[J]. 测绘学报, 2017 , 46(11) : 1868 -1879 . DOI: 10.11947/j.AGCS.2017.20170122

Abstract

The acquisition of forest parameters can not only estimate the surface biomass and underlying topography,but also contribute to the study of global carbon cycle and global climate change.Vegetation parameter inversion algorithm with polarimetric interferometric SAR (PolInSAR) is generally based on the two-layer RVoG(random volume over ground) model.However,when the actual vegetation has three-layer structure of canopy,trunk layer and surface layer,the vegetation parameters inversion accuracy will decrease.As the vertical effective wave number difference between the near and far range in the case of airborne SAR system is large,it will bring the system error to the final inversion results.To solve these two problems,this paper proposes an algorithm of three-layer vegetation parameters inversion by fusing the ascending and descending PolInSAR data.The proposed method uses the three-layer RVoG model to correct scattering process of radar echo in vegetation.Then it combines the ascending and descending PolInSAR datasets to weaken the system errors; Finally,we use the non-linear iteration adjustment for tree height inversion.In order to validate the proposed algorithm,two ascending and two descending P-band full polarization SAR data acquired by ESAR airborne platform under the German space agency (DLR) BioSAR 2008 campaign are utilized and other three inversion strategies are used for comparison and analysis.The results prove the correctness of the three vegetation model,and the proposed method reduces the system error caused by the vertical effective wave number and improves the precision of tree height inversion.

Key words： random volume over ground; vegetation height; three-layer vegetation model; three-stage algorithm; ascending and descending; vertical wave number

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