Optimising view angles for the estimation of leaf area index via entropy-difference analysis

Journal of Earth system science

Qiang Liu1 3¹  Yanjuan Yao1 2¹  Qinhuo Liu1¹  Yanhua Gao1 2¹ 

[1] Institute of Remote Sensing Applications (IRSA), Chinese Academy of Sciences (CAS), Beijing, China.$$

关键词: Information evaluation;    multi-angular/spectral remote sensing;    entropy-difference analysis;    leaf area index inversion.;   

DOI  :  

学科分类：天文学(综合)

来源: Indian Academy of Sciences

【 摘 要 】

It is important to evaluate the information content of remote sensing data in order to synthetically use multi-source remote sensing data to improve the accuracy and consistency of land surface parameter retrieval. This paper presents a technique for information content evaluation of multi-spectral/angular remote sensing data for the leaf area index (LAI) inversion, the method of entropy-difference analysis.The proposed method is based on a numerical evaluation of the entropy of the observed dataset to learn how much variation in observation is caused by the variation in LAI. The relationship between remote sensing information and the LAI inversion accuracy is validated based on the model-simulated canopy reflectance data and the experiment data. We make the following observation: the larger the entropydifference for canopy reflectance data, the higher the LAI inversion accuracy. That is, choosing a good combination of observation angles is sometimes more important than simply increasing the number of observations. The presented technique may be useful in designing and evaluating quantitative remote sensing algorithms and products.

【 授权许可】

Unknown