Bi-LSTM Model for Time Series Leaf Area Index Estimation Using Multiple Satellite Products
文献类型：期刊论文
| 作者 | Liu, Tian1,2; Jin, Huaan1; Xie, Xinyao1; Fang, Hongliang2,3; Wei, Dandan4; Li, Ainong1 |
| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2022 |
| 卷号 | 19页码:5 |
| ISSN号 | 1545-598X |
| 关键词 | Bidirectional long short-term memory (Bi-LSTM) deep learning (DL) leaf area index (LAI) time series |
| DOI | 10.1109/LGRS.2022.3199765 |
| 通讯作者 | Jin, Huaan(jinhuaan@imde.ac.ar) |
| 英文摘要 | Time series leaf area index (LAI) is essential to studying vegetation dynamics and climate changes. The LAI at current status can be regarded as the accumulative consequence of the counterpart at prior times. Although the deep learning (DL) algorithm, long short-term memory (LSTM), can capture long-time dependencies from sequential satellite data for time series LAI estimation, it only uses the information at prior statuses and neglects the backward propagation of current vegetation change information. Thus, the LSTM-based LAI quality might be limited. In this letter, the bidirectional LSTM (Bi-LSTM) approach was proposed to integrate the information of multiple satellite products from both the past and future for temporal LAI retrieval. The fused values from Global Land Surface Satellite (GLASS), moderate-resolution imaging spectroradiometer (MODIS), and visible infrared imaging radiometer (VIIRS) LAI products, as well as MODIS reflectance in 2014-2015, serve as the output response and input for the Bi-LSTM training. Then, we compared the Bi-LSTM predictions with the counterparts from the LSTM, the fused LAI, and three products using independent validation datasets in 2016. Results illustrated that our proposed Bi-LSTM method achieved better performance with higher accuracy (R-2 = 0.84 and RMSE = 0.76) when compared to the LSTM estimation (R-2 = 0.83 and RMSE = 0.82) and LAI products (R-2 < 0.68 and RMSE > 1). Furthermore, our proposed method provided smoother and more continuous temporal profiles of LAI than other retrieval approaches. |
| WOS关键词 | MODIS ; LAI ; VEGETATION ; VALIDATION |
| 资助项目 | National Natural Science Foundation of China[42071352] ; National Key Research and Development Program of China[2020YFA0608702] ; Chinese Academy of Sciences Light of West China Program |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000849255500005 |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China ; Chinese Academy of Sciences Light of West China Program |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/182383]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Jin, Huaan |
| 作者单位 | 1.Chinese Acad Sci, Res Ctr Digital Mt & Remote Sensing Applicat, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.MNR, Land Satellite Remote Sensing Applicat Ctr, Beijing 100048, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Tian,Jin, Huaan,Xie, Xinyao,et al. Bi-LSTM Model for Time Series Leaf Area Index Estimation Using Multiple Satellite Products[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2022,19:5. |
| APA | Liu, Tian,Jin, Huaan,Xie, Xinyao,Fang, Hongliang,Wei, Dandan,&Li, Ainong.(2022).Bi-LSTM Model for Time Series Leaf Area Index Estimation Using Multiple Satellite Products.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19,5. |
| MLA | Liu, Tian,et al."Bi-LSTM Model for Time Series Leaf Area Index Estimation Using Multiple Satellite Products".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022):5. |
入库方式： OAI收割
来源：地理科学与资源研究所
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。