Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data
文献类型：期刊论文
| 作者 | Yin, Gaofei1; Li, Jing1; Liu, Qinhuo1; Zhong, Bo1; Li, Ainong1 |
| 刊名 | Remote Sensing Letters
 |
| 出版日期 | 2016 |
| 卷号 | 7期号:8页码:771-780 |
| 关键词 | RX-ALGORITHM STATISTICS |
| 通讯作者 | Li, Jing (lijing01@radi.ac.cn) |
| 英文摘要 | Spatio-temporally continuous leaf area index (LAI) is required for surface process simulation, climate modelling and global change study. As a result of cloud contamination and other factors, the current LAI products are spatially and temporally discontinuous. A multi-sensor integration method was proposed in this paper to combine Terra-Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua-MODIS, FY (FengYun) 3A-MEdium Resolution Spectrum Imager (MERSI) and FY3B-MERSI data to improve LAI spatio-temporal continuity. It consists of a normalization algorithm to eliminate the difference between MODIS and MERSI data in spatial and spectral aspects, a daily LAI retrieval algorithm based on neural networks and a maximum value compositing algorithm. The feasibility of our LAI retrieval method to improve continuity was assessed at national scale (in China). Results show that (1) the combination of multi-sensor data can significantly improve LAI temporal continuity, especially for mountainous regions which are characterized by high frequency of cloud coverage; (2) the improvement in spatial continuity is obvious as can be seen from the increase of retrieval ratio, defined as the ratio of the number of retrieved pixels to the total number of pixels, from 0.78 for GEOV1 LAI product, and 0.88 for MOD15A2 LAI product to 0.98 for multi-sensor LAI product. © 2016 Informa UK Limited, trading as Taylor & Francis Group. |
| 学科主题 | Remote Sensing; Imaging Science & Photographic Technology |
| 类目[WOS] | Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20162602547026 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39465]  |
| 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. Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China 2. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 3. Joint Center for Global Change Studies (JCGCS), Beijing, China |
| 推荐引用方式 GB/T 7714 | Yin, Gaofei,Li, Jing,Liu, Qinhuo,et al. Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data[J]. Remote Sensing Letters,2016,7(8):771-780. |
| APA | Yin, Gaofei,Li, Jing,Liu, Qinhuo,Zhong, Bo,&Li, Ainong.(2016).Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data.Remote Sensing Letters,7(8),771-780. |
| MLA | Yin, Gaofei,et al."Improving LAI spatio-temporal continuity using a combination of MODIS and MERSI data".Remote Sensing Letters 7.8(2016):771-780. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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