IGSNRR OpenIR
A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine
Kong, Dongdong1; Zhang, Yongqiang2; Gu, Xihui3; Wang, Dagang1
2019-09-01
Source PublicationISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN0924-2716
Volume155Pages:13-24
Corresponding AuthorZhang, Yongqiang(zhangyq@igsnrr.ac.cn)
AbstractObtaining high quality remote sensing vegetation index that is not noticeably affected by abiotic factors is critical for agricultural, ecological, climate and hydrological studies. Here we developed a computationally efficient and well-performed denoising method for reconstructing remote sensing vegetation indices, namely wWHd (weighted Whittaker with dynamic parameter lambda in spatial). The single parameter lambda is automatically estimated for every pixel by a multiple linear regression. Weights updating and the inherit nature of Whittaker make Whittaker robust for contaminations. We applied the wWHd on the Google Earth Engine (GEE) platform for reconstructing 500 m resolution enhanced vegetation index (EVI) time series from moderate-resolution imaging spectroradiometer (MODIS) at global scale and for the period of 2000-2017. To demonstrate its robustness, wWHd was compared with four well-known denoising methods, i.e. Fourier-based approach (Fourier), Savitzky-Golay filter (SG), Asymmetric Gaussian (AG) and double logistic (DL) at 16,000 randomly selected sites. All approaches were evaluated using two indices at each site: (1) root mean square error (RMSE) between observed best quality EVI and gap-filled EVI series, and (2) roughness of gap-filled EVI series. Results show that wWHd has an RMSE (indicating fidelity) of similar to 0.032, which is similar to Fourier and SG at similar to 90% sampled sites, but outperforms (similar to 0.02 less in the RMSE) AG and DL at similar to 45% and similar to 25% sampled sites. Among the four, wWHd has the lowest (best) roughness of similar to 0.003. These performances demonstrate that wWHd balances fidelity and roughness well. Another advantage is that the wWHd is computationally more efficient than others, and is currently the only one denoising method deployed on the GEE. Our results suggest that it is promising to use the proposed wWHd method for processing remote sensing vegetation indices with high spatiotemporal resolution and the reconstructed EVI product should be widely used by global community.
KeywordRemote sensing vegetation index EVI Time series reconstruction Whittaker Google Earth Engine
DOI10.1016/j.isprsjprs.2019.06.014
WOS KeywordLAND-SURFACE PHENOLOGY ; VEGETATION INDEX ; HARMONIC-ANALYSIS ; TIBETAN PLATEAU ; NDVI ; TEMPERATURE ; PERFORMANCE ; EXTRACTION ; REGRESSION ; CHINA
Indexed BySCI
Language英语
Funding ProjectIGSNRR Supporting Fund[YJRCPT2019-101] ; National Key Research and Development Program of China[2018YFA0605603] ; National Natural Science Foundation of China[41501037] ; International Program for Ph.D. Candidates, Sun Yat-Sen University ; CAS Pioneer Hundred Talents Program
Funding OrganizationIGSNRR Supporting Fund ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; International Program for Ph.D. Candidates, Sun Yat-Sen University ; CAS Pioneer Hundred Talents Program
WOS Research AreaPhysical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000482526600002
PublisherELSEVIER
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/69736
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhang, Yongqiang
Affiliation1.Sun Yan Sen Univ, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
3.China Univ Geosci, Sch Environm Studies, Dept Atmospher Sci, Wuhan 430074, Hubei, Peoples R China
Recommended Citation
GB/T 7714
Kong, Dongdong,Zhang, Yongqiang,Gu, Xihui,et al. A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2019,155:13-24.
APA Kong, Dongdong,Zhang, Yongqiang,Gu, Xihui,&Wang, Dagang.(2019).A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,155,13-24.
MLA Kong, Dongdong,et al."A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 155(2019):13-24.
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