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Spatial upscaling of remotely sensed leaf area index based on discrete wavelet transform
Chen, Hong1,2; Wu, Hua1,2; Li, Zhao-Liang1,2,3; Tang, Bo-hui1,2; Tang, Ronglin1,2; Yan, Guangjian4
2019
Source PublicationINTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN0143-1161
Volume40Issue:5-6Pages:2343-2358
Corresponding AuthorWu, Hua(wuhua@igsnrr.ac.cn)
AbstractLeaf 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.
DOI10.1080/01431161.2018.1460515
WOS KeywordDIFFERENCE VEGETATION INDEX ; SCALE ; SURFACE ; HETEROGENEITY ; RETRIEVAL ; LAI ; TEMPERATURE ; FRACTION ; MODELS ; IMPACT
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Natural Science Foundation of China ; International Science & Technology Cooperation Program of China ; Innovation Project of LREIS
WOS Research AreaRemote Sensing ; Imaging Science & Photographic Technology
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000464043900045
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/48097
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWu, Hua
Affiliation1.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
Recommended Citation
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.
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