A Bayesian network algorithm for retrieving the characterization of land surface vegetation
A Bayesian network algorithm for retrieving the characterization of land surface vegetation
复制标题
用于检索地表植被特征的贝叶斯网络算法
DOI：
10.1016/j.rse.2007.03.031
复制
发表时间：
2008-03
影响因子：
13.5
通讯作者：
Li, Xiaowen
中科院分区：
文献类型：
--
作者：
Wan, Huawei;Zhou, Guoqing;Qu, Yonghua;Wang, Jindi;Li, Xiaowen
提出了一种基于贝叶斯网络的陆面植被生化和生物物理参数遥感反演方法。贝叶斯网络是一个统一的知识推理过程，它可以合并来自多个来源的信息，包括遥感和先验知识。利用该反演方法，利用模拟数据和田间实测数据估算了冬小麦叶绿素a和叶绿素B含量（Ca B）以及叶面积指数（LAI）。结果表明，利用模拟数据进行估算是准确的，LAI和Cab的均方根误差分别为0.54 m2/m2和4.5 μg/cm 2。在验证的估计对现场测量，它被发现，目标参数的先验知识提高了估计的准确性，在从0.73至0.22 m2/m2的LAI和9.6至4.0 μg/cm 2的Cab的RMSE。在这种混合反演方案中，贝叶斯推理产生后验概率分布，该后验概率分布可以将推断结果的属性作为反演结果中包含的更新信息来揭示。利用熵计算了感兴趣参数的后验信息修正量。包括更多数据可以允许检索关于一般参数的更多信息。还观察到一些观察角度的数据略微减少了感兴趣参数的信息。还发现来自这些视角的数据对参数不太敏感。这里提出的方法也验证了使用LandSat ETM+图像提供的大脚项目。当用于映射LAI与ETM+图像，所提出的方法与RMSE为0.70和0.67的相关性产生了一个略好于经验回归的结果。
A hybrid inversion technique based on Bayesian network is proposed for estimating the biochemical and biophysical parameters of land surface vegetation from remotely sensed data. A Bayesian network is a unified knowledge-inferring process that can incorporate information derived from multiple sources including remote sensing and information derived from a priori knowledge. Using this inversion approach, content of chlorophyll a and chlorophyll b (Cab) and leaf area index (LAI) of winter wheat were estimated from data derived from simulations as well as field measurements. Estimations from the simulated data proved accurate, with root mean square errors (RMSEs) of 0.54 m2/m2in LAI and 4.5 μg/cm2in Cab. In validating the estimates against field measurements, it was found that prior knowledge of target parameters improved the accuracy of estimates, in terms of RMSEs from 0.73 to 0.22 m2/m2in LAI and 9.6 to 4.0 μg/cm2in Cab. Bayesian inference in this hybrid inversion scheme produces a posterior probability distribution, which can reveal such properties of the inferred results as updated information contained in the inversion result. Using entropy, the revision of posterior information about the parameters of interest was calculated. Including more data may allow more information to be retrieved about parameters in general. Exceptions were also observed where data from some viewing angles slightly reduced the information on the parameters of interest. It was also found that data from these viewing angles were less sensitive to the parameters. The method proposed here was also validated using LandSat ETM+ imagery provided by the BigFoot project. When used for mapping LAI with ETM+ imagery, the proposed method with an RMSE of 0.70 and a correlation of 0.67 produced a slightly better result than that from empirical regression.
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影响因子：
13.5
作者：
Combal, B;Baret, F;Wang, L
通讯作者：
Wang, L
影响因子：
--
作者：
SHANNON, CE
通讯作者：
SHANNON, CE
影响因子：
13.5
作者：
C. Walthall;W. Dulaney;Martha C. Anderson;J. Norman;H. Fang;S. Liang
通讯作者：
C. Walthall;W. Dulaney;Martha C. Anderson;J. Norman;H. Fang;S. Liang
影响因子：
2.6
作者：
Peng Gong;R. Pu;John R. Miller
通讯作者：
Peng Gong;R. Pu;John R. Miller
DOI：
10.1109/tgrs.2005.848412
发表时间：
2005-08-01
影响因子：
8.2
作者：
Kalácska, M;Sánchez-Azofeifa, A;Boerlage, B
通讯作者：
Boerlage, B