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Comparing different smoothing methods to detect double-cropping rice phenology based on LAI products - a case study in the Hunan province of China
Wang, Chenzhi1,2,3,4; Zhang, Zhao1,2,3,4; Chen, Yi5; Tao, Fulu5,6; Zhang, Jing1,2,3,4; Zhang, Wen4
2018-10-02
Source PublicationINTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN0143-1161
Volume39Issue:19Pages:6405-6428
Corresponding AuthorZhang, Zhao(zhangzhao@bnu.edu.cn)
AbstractMany studies have demonstrated the remarkable potential of assimilating remotely sensing leaf area index (LAI) products into crop models in estimating regional crop yield. To ensure the temporal consistency between crop models and remote-sensing system, it is prerequisite to derive the crop phenology information from the LAI products. However, previous studies mainly detected the phenology through the vegetation index (VI). Although some pieces of research applied LAI in phenology monitoring for trees and shrubs, fewer focused on crops, especially those with two or three growing seasons annually. Thus, which smoothing algorithm methods are suitable to obtain phenology of double-cropping rice and their difference in smoothing for crops are still unknown. Based on the Global Land Surface Satellite (GLASS)LAI products, we applied four favourite smoothing algorithms (Asymmetric Gaussian fitting, Double Logistic fitting, Savitzky-Golay filter, and Wavelet-based Filter method) to reduce noise and reconstruct the LAI profile and then detected the phenological information of double-cropping rice in Hunan Province. Compared with ground actual observations, we found that two fitting methods are not suitable to smooth double-cropping rice LAI, while the wavelet method performed the best. Based on the wavelet method, we estimated the phenological information of double-cropping rice at different regional scales as well and the results reflected that the accuracy of regional estimation is also acceptable. This study implied that the wavelet method is rather suitable to detect phenological information of crops from LAI products, which provides narrow gaps between two growing season. Our contribution can benefit researchers who focus on agriculture or remote sensing, especially those who would like to assimilate remotely sensed information into crop growth models.
DOI10.1080/01431161.2018.1460504
WOS KeywordLEAF-AREA INDEX ; WINTER-WHEAT YIELD ; PHOTOSYNTHETICALLY ACTIVE RADIATION ; SATELLITE SENSOR DATA ; MODIS TIME-SERIES ; WAVELET TRANSFORM ; GLOBAL PRODUCTS ; MEKONG DELTA ; WOFOST MODEL ; LANDSAT TM
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41571493] ; National Natural Science Foundation of China[41571088] ; National Natural Science Foundation of China[31561143003] ; Academy of Finland[277403] ; Academy of Finland[292836] ; Academy of Finland[268277] ; Academy of Finland[292944] ; State Key Laboratory of Earth Surface Processes and Resource Ecology
Funding OrganizationNational Natural Science Foundation of China ; Academy of Finland ; State Key Laboratory of Earth Surface Processes and Resource Ecology
WOS Research AreaRemote Sensing ; Imaging Science & Photographic Technology
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000449971700018
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52497
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhang, Zhao
Affiliation1.Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, 19 Xinjiekou St, Beijing 100875, Peoples R China
2.Beijing Normal Univ, Acad Disaster Reduct & Emergency Management, Minist Civil Affairs, Beijing, Peoples R China
3.Beijing Normal Univ, Minist Educ, Beijing, Peoples R China
4.Beijing Normal Univ, Fac Geog Sci, Beijing, Peoples R China
5.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
6.Nat Resources Inst Finland Luke, Helsinki, Finland
Recommended Citation
GB/T 7714
Wang, Chenzhi,Zhang, Zhao,Chen, Yi,et al. Comparing different smoothing methods to detect double-cropping rice phenology based on LAI products - a case study in the Hunan province of China[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2018,39(19):6405-6428.
APA Wang, Chenzhi,Zhang, Zhao,Chen, Yi,Tao, Fulu,Zhang, Jing,&Zhang, Wen.(2018).Comparing different smoothing methods to detect double-cropping rice phenology based on LAI products - a case study in the Hunan province of China.INTERNATIONAL JOURNAL OF REMOTE SENSING,39(19),6405-6428.
MLA Wang, Chenzhi,et al."Comparing different smoothing methods to detect double-cropping rice phenology based on LAI products - a case study in the Hunan province of China".INTERNATIONAL JOURNAL OF REMOTE SENSING 39.19(2018):6405-6428.
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