Spatiotemporal fusion through the best linear unbiased estimator to generate fine spatial resolution NDVI time series

ABSTRACT

Spatiotemporal fusion (STF) technologies are commonly used to acquire high spatiotemporal resolution remote sensing observations. However, most STF technologies fail to consider the nonlinear variation in vegetation in the time domain. Based on the Best Linear Unbiased Estimator (BLUE), this paper proposed a novel STF algorithm (referred to BLUE) which accounts for the phenological characteristics of vegetation. First, annual time series of normalized difference vegetation index (NDVI) data with high spatial resolution but low temporal resolution is fitted using a double logistic function and used as the background field. Then, NDVI data with low spatial resolution but high temporal resolution is used as the observation field. The information in the background and observation fields is fused using the BLUE to obtain high spatiotemporal resolution NDVI data. The proposed algorithm was used to produce dense time series of 30 m resolution NDVI data for a 10 km × 10 km experimental area in 2014. The experimental results demonstrate that the accuracy of fusion results from the proposed BLUE method are higher than those from the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and Linear Mixing Growth Model (LMGM), especially when the temporal component of surface heterogeneity is dominant. The proposed algorithm has broad prospects in vegetation monitoring at high spatiotemporal resolution.

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Erratum

Acknowledgments

We would like to thank Xiaolin Zhu for sharing the code of ESTARFM, and Yuhan Rao for sharing the code of LMGM.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (No. 41631180 No. 41601403, No. 41571373 and No. 41531174), the National Key Research and Development Program of China (No. 2016YFA0600103), the CAS ‘Light of West China’ Program and the Youth Talent Team Program of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (No. SDSQB-2015-02)