On the performance of remote sensing time series reconstruction methods - A spatial comparison

	On the performance of remote sensing time series reconstruction methods - A spatial comparison
	Zhou, Jie 1; Jia, Li 1; Menenti, Massimo 1; Gorte, Ben 1
刊名	REMOTE SENSING OF ENVIRONMENT
	2016
卷号	187 页码:367-384
关键词	DECIDUOUS BROADLEAF FOREST GROSS PRIMARY PRODUCTION INTERANNUAL VARIABILITY SPRING PHENOLOGY CARBON-DIOXIDE ECOSYSTEM PRODUCTIVITY VEGETATION PHENOLOGY EDDY-COVARIANCE SATELLITE DATA CO2 EXCHANGE
通讯作者	Jia, L (reprint author), Inst Remote Sensing & Digital Earth, CAS Olymp S&T Pk,20 Datun Rd,POB 9718, Beijing 100101, Peoples R China.
英文摘要	The satellite observed Normalized Difference Vegetation Index (NDVI) time series, which describe the temporal and spatial variability of global terrestrial vegetation, are inevitably contaminated by clouds, aerosol, snow and ice cover. In general all these conditions yield negative deviations in the time series of NDVI. Many time series reconstruction models have been developed to eliminate effect of the negative deviations and most of them perform differently in different applications and regions. The Harmonic Analysis (HA), Double logistic (DL), Asymmetric Gaussian (AG), Whittaker smoother (WS) and Savitzky-Golay filter (SG) are five of the most widely used time series reconstruction models owing to their simplicity of implementation or the capability to extract phenological metrics from the time series. The performance of these models varies with the NDVI signal and the noise distribution, however, and until now there is no consensus on which method outperforms all others under all situations. Since the NDVI signal and the noise distribution are highly dependent on regional climate and land cover, the reconstruction performance is expected to be spatially variable. Thus this study compared the five reconstruction models at pixel scale to provide practical and biome- specific recommendations for future time series reconstruction applications. Specifically, the 14 years raw daily reflectance data and ancillary Quality Assessment (QA) information from the MODIS sensor were used to generate pixel reference series and noisy series. Then the five candidate models were applied to both reference series and noisy series and three reconstruction performance metrics i.e. Overall Reconstruction Error (ORE), Fitting Related Error (FRE), and Normalized Noise Related Error (NNRE), were calculated. Finally, the performance of the five candidate reconstruction models was evaluated by applying the three metrics. The preliminary results showed that when considering ORE only, the Asymmetric Gaussian model outperforms other models over most areas of high latitude boreal region, while the Savitzky-Golay model gives the best reconstruction performance in tropical and subtropical regions. The FRE and the NNRE helped to reveal the main error sources in the reconstruction in different regions. The comparison method developed and applied in this study led to suggest adaptive selection of the best reconstruction model for specific NDVI signals and noise distribution. (C) 2016 Elsevier Inc. All rights reserved.
学科主题	Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology
类目[WOS]	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
收录类别	SCI
语种	英语
WOS记录号	WOS:000390494000026
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39155]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1.Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China 2.Joint Ctr Global Change Studies, Beijing 100875, Peoples R China 3.Delft Univ Technol, Delft, Netherlands
推荐引用方式 GB/T 7714	Zhou, Jie,Jia, Li,Menenti, Massimo,et al. On the performance of remote sensing time series reconstruction methods - A spatial comparison[J]. REMOTE SENSING OF ENVIRONMENT,2016,187:367-384.
APA	Zhou, Jie,Jia, Li,Menenti, Massimo,&Gorte, Ben.(2016).On the performance of remote sensing time series reconstruction methods - A spatial comparison.REMOTE SENSING OF ENVIRONMENT,187,367-384.
MLA	Zhou, Jie,et al."On the performance of remote sensing time series reconstruction methods - A spatial comparison".REMOTE SENSING OF ENVIRONMENT 187(2016):367-384.