Spatial upscaling of remotely sensed leaf area index based on discrete wavelet transform
文献类型：期刊论文
| 作者 | Chen, Hong1,2; Wu, Hua1,2; Li, Zhao-Liang1,2,3; Tang, Bo-hui1,2; Tang, Ronglin1,2; Yan, Guangjian4 |
| 刊名 | INTERNATIONAL JOURNAL OF REMOTE SENSING
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| 出版日期 | 2019 |
| 卷号 | 40期号:5-6页码:2343-2358 |
| ISSN号 | 0143-1161 |
| DOI | 10.1080/01431161.2018.1460515 |
| 通讯作者 | Wu, Hua(wuhua@igsnrr.ac.cn) |
| 英文摘要 | Leaf area index (LAI), a crucial parameter of vegetation structure, provides key information for Earth's surface process simulations and climate change research from local to global scale. However, when the LAI retrieval model built at local scale (high resolution) is directly applied at a large scale (low resolution), a spatial scaling bias may be caused. The magnitude of this bias depends on the non-linearity of retrieval model and heterogeneity of land surface. Various spatial upscaling algorithms have been developed to correct for this scaling bias. In this study, we try to explore the potential application of wavelet transform in spatial upscaling. Hence, an algorithm based on the relation between the bias rate in scaling and the detail lost rate in discrete wavelet transform (DWT) was proposed to eliminate scaling bias at a large scale. To evaluate the proposed algorithm, three sites with different degrees of heterogeneity from Validation of Land European Remote Sensing Instruments database were chosen. Using Systeme Probatoire d'Observation dela Tarre, Operational Land Imager, and corresponding ground measurements, the performances of the proposed algorithm were further quantitatively analysed. Additionally, the upscaling accuracy between the algorithm based on Taylor Series Expansion (TSE) and that based on DWT was compared. Generally speaking, the root mean square error (RMSE) and relative error (RE) of retrieved LAI induced by the scale bias can be greatly reduced after correction with those two algorithms. Over high heterogeneous landscape, the upscaling performance is more obvious. When the corresponding synchronous priori knowledge is available, the proposed DWT-based algorithm has reached a comparative accuracy with the TSE-based algorithm. The RE can decrease from 13.54% to 3.47% and RMSE from 0.36 to 0.09 over the selected heterogeneous landscape. When the synchronous priori knowledge is not available, the proposed DWT-based algorithm outperforms the TSE-based algorithm. The RE and RMSE can decrease from 22.98% and 0.49 to 7.97% and 0.13, respectively. However, unlike the TSE-based algorithm, the proposed DWT-based algorithm is simpler and not constrained by the characteristic of the retrieval model. These results indicate that it is feasible to successfully correct for the scaling bias by using the proposed DWT-based spatial upscaling algorithm. |
| WOS关键词 | DIFFERENCE VEGETATION INDEX ; SCALE ; SURFACE ; HETEROGENEITY ; RETRIEVAL ; LAI ; TEMPERATURE ; FRACTION ; MODELS ; IMPACT |
| 资助项目 | National Natural Science Foundation of China[41531174] ; National Natural Science Foundation of China[41471297] ; National Natural Science Foundation of China[41331171] ; International Science & Technology Cooperation Program of China[2014DFE10220] ; Innovation Project of LREIS[O88RA801YA] |
| WOS研究方向 | Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000464043900045 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Natural Science Foundation of China ; International Science & Technology Cooperation Program of China ; Innovation Project of LREIS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/48096]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wu, Hua |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr, Key Lab Agr Remote Sensing, Beijing, Peoples R China 4.Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Hong,Wu, Hua,Li, Zhao-Liang,et al. Spatial upscaling of remotely sensed leaf area index based on discrete wavelet transform[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2019,40(5-6):2343-2358. |
| APA | Chen, Hong,Wu, Hua,Li, Zhao-Liang,Tang, Bo-hui,Tang, Ronglin,&Yan, Guangjian.(2019).Spatial upscaling of remotely sensed leaf area index based on discrete wavelet transform.INTERNATIONAL JOURNAL OF REMOTE SENSING,40(5-6),2343-2358. |
| MLA | Chen, Hong,et al."Spatial upscaling of remotely sensed leaf area index based on discrete wavelet transform".INTERNATIONAL JOURNAL OF REMOTE SENSING 40.5-6(2019):2343-2358. |
入库方式： OAI收割
来源：地理科学与资源研究所
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