4.7 Article

Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models

期刊

出版社

ELSEVIER
DOI: 10.1016/j.isprsjprs.2019.02.013

关键词

UAV-multispectral image; Rice; Radiative transfer model; Bayesian network; Leaf area index; Canopy chlorophyll content

资金

  1. National Key RD Program [2018YFD0300805]
  2. Science and Technology Support Program of Jiangsu [BE2016375]
  3. Jiangsu Collaborative Innovation Center for Modern Crop Production

向作者/读者索取更多资源

The radiative transfer model (RTM) simulates forward spectral reflectance of vegetation and is used to estimate physical parameters using backwards inversion. However, differentiation of spectral reflectances may be hampered due to model parameter combinations, and the cost function within RTM that calculates statistical distance may lead to inconsistent inversions. Bayesian network (BN) is a probabilistic model that is used to solve problems of model ambiguity and incompleteness. Here, we constructed a model to estimate rice growth parameters using data collected by an unmanned aerial vehicle (UAV). We collected rice canopy spectral information using a MiniMCA-6 multispectral camera fitted to an UAV that was used to determine BN structure using parameters derived from the PROSAIL model. We calculated conditional probability distributions of different observed combinations of rice canopy chlorophyll content (CCC) and leaf area index (LAI) and a look up table of maximum conditional probabilities of rice growth parameters based on BN was developed. Results indicated that most accurate inversions of LAI and CCC as BN nodes were achieved at reflectances of 720 nm, 800 nm under the red normalized difference vegetation index and at reflectances of 550, 720, 800 nm under the modified simple ratio index, respectively. Compared with the cost function inversion method, the BN method mitigated the ill posed problem of inversion and obtained higher inversion accuracy with model test R-2, RRMSE, and RE values of 0.81, 0.31, and 0.38, and 0.83, 0.36, and 0.43 for LAI and CCC, respectively. We conclude that application of the BN method to the inversion process of crop RTM could improve inversion accuracy of estimation of crop parameters.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据