An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities
文献类型：期刊论文
| 作者 | Zeng, Yelu1; Li, Jing1; Liu, Qinhuo1; Qu, Yonghua1; Huete, Alfredo R.1; Xu, Baodong1; Yin, Geofei1; Zhao, Jing1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2015 |
| 卷号 | 7期号:2页码:57-65 |
| 通讯作者 | Li, J (reprint author), Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, 20A Datun Rd, Beijing 100101, Peoples R China. |
| 英文摘要 | A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer scale is an important step in the validation process for moderate-resolution leaf area index (LAI) products. Current LAI-sampling strategies are unable to consider the vegetation seasonal changes and are better suited for single-day LAI product validation, whereas the increasingly used wireless sensor network for LAI measurement (LAINet) requires an optimal sampling strategy across both spatial and temporal scales. In this study, we developed an efficient and robust LAI Sampling strategy based on Multi-temporal Prior knowledge (SMP) for long-term, fixed-position LAI observations. The SMP approach employed multi-temporal vegetation index (VI) maps and the vegetation classification map as a priori knowledge. The SMP approach minimized the multi-temporal bias of the VI frequency histogram between the ESUs and the entire site and maximized the nearest-neighbor index to ensure that ESUs were dispersed in the geographical space. The SMP approach was compared with four sampling strategies including random sampling, systematic sampling, sampling based on the land-cover map and a sampling strategy based on vegetation index prior knowledge using the PROSAIL model-based simulation analysis in the Heihe River basin. The results indicate that the ESUs selected using the SMP method spread more evenly in both the multi-temporal feature space and geographical space over the vegetation cycle. By considering the temporal changes in heterogeneity, the average root-mean-square error (RMSE) of the LAI reference maps can be reduced from 0.12 to 0.05, and the relative error can be reduced from 6.1% to 2.2%. The SMP technique was applied to assign the LAINet ESU locations at the Huailai Remote Sensing Experimental Station in Beijing, China, from 4 July to 28 August 2013, to validate three MODIS C5 LAI products. The results suggest that the average R-2, RMSE, bias and relative uncertainty for the three MODIS LAI products were 0.60, 0.33, -0.11, and 12.2%, respectively. The MCD15A2 product performed best, exhibiting a RMSE of 0.20, a bias of -0.07 and a relative uncertainty of 7.4%. Future efforts are needed to obtain more long-term validation datasets using the SMP approach on different vegetation types for validating moderate-resolution LAI products in time series. |
| 研究领域[WOS] | Remote Sensing |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000352400900007 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38274]  |
| 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.[Zeng, Yelu 2.Li, Jing 3.Liu, Qinhuo 4.Qu, Yonghua 5.Xu, Baodong 6.Yin, Geofei 7.Zhao, Jing] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China 8.[Zeng, Yelu 9.Xu, Baodong 10.Yin, Geofei] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zeng, Yelu,Li, Jing,Liu, Qinhuo,et al. An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities[J]. REMOTE SENSING,2015,7(2):57-65. |
| APA | Zeng, Yelu.,Li, Jing.,Liu, Qinhuo.,Qu, Yonghua.,Huete, Alfredo R..,...&Zhao, Jing.(2015).An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities.REMOTE SENSING,7(2),57-65. |
| MLA | Zeng, Yelu,et al."An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities".REMOTE SENSING 7.2(2015):57-65. |
入库方式： OAI收割
来源：遥感与数字地球研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。