Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection
文献类型：期刊论文
| 作者 | Yin, Gaofei1; Li, Jing1; Liu, Qinhuo1; Fan, Weiliang1; Xu, Baodong1; Zeng, Yelu1; Zhao, Jing1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2015 |
| 卷号 | 7期号:4页码:194-202 |
| 通讯作者 | Li, J (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China. |
| 英文摘要 | Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval. If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model's potential to retrieve LAI is limited by the availability of a priori information on RT model parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types. |
| 研究领域[WOS] | Remote Sensing |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000354789300054 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38238]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1.[Yin, Gaofei 2.Li, Jing 3.Liu, Qinhuo 4.Fan, Weiliang 5.Xu, Baodong 6.Zeng, Yelu 7.Zhao, Jing] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 8.[Yin, Gaofei 9.Li, Jing 10.Liu, Qinhuo] JCGCS, Beijing 100875, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Gaofei,Li, Jing,Liu, Qinhuo,et al. Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection[J]. REMOTE SENSING,2015,7(4):194-202. |
| APA | Yin, Gaofei.,Li, Jing.,Liu, Qinhuo.,Fan, Weiliang.,Xu, Baodong.,...&Zhao, Jing.(2015).Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection.REMOTE SENSING,7(4),194-202. |
| MLA | Yin, Gaofei,et al."Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection".REMOTE SENSING 7.4(2015):194-202. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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