Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction

REMOTE SENSING OF ENVIRONMENT

卷:252

Article

Zhou, Junxiong^1,2  Chen, Jin^1,2  Chen, Xuehong^1,2  Zhu, Xiaolin³  Qiu, Yuean^1,2  Song, Huihui⁴  Rao, Yunhan⁵  Zhang, Chishan^1,2  Cao, Xin^1,2  Cui, Xihong^1,2 

[1] Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China

[2] Beijing Normal Univ, Fac Geog Sci, Inst Remote Sensing Sci & Engn, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China

[3] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong, Peoples R China

[4] Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Big Data Anal Technol, Nanjing 210044, Peoples R China

[5] North Carolina State Univ, North Carolina Inst Climate Studies, Asheville, NC 28805 USA

关键词: Spatiotemporal fusion;    Normalized difference vegetation index (NDVI);    Geometric misregistration;    Radiometric inconsistency;    Spatial resolution ratio;   

DOI  :  10.1016/j.rse.2020.112130

来源: Elsevier

【 摘 要 】

Dozens of spatiotemporal fusion methods have been developed to reconstruct vegetation index time-series data with both high spatial resolution and frequent coverage for monitoring land surface dynamics. Although several studies comparing the different fusion methods have been conducted, selecting the suitable fusion methods is still challenging, as inevitable influential factors tend to be neglected. To address this problem, this study compared six typical spatiotemporal fusion methods, including the Unmixing-Based Data Fusion (UBDF), Linear Mixing Growth Model (LMGM), Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), One Pair Dictionary-Learning method (OPDL), and Flexible Spatiotemporal DAta Fusion (FSDAF), based on simulation experiments and theoretical analysis considering three influential factors between sensors: geometric misregistration, radiometric inconsistency, and spatial resolution ratio. The results indicate that Fit-FC achieved the best performance with the strongest tolerance to geometric misregistration when radiometric inconsistency was negligible; thus, it is the first recommended algorithm for blending normalized difference vegetation index (NDVI) imagery. Instead, the FSDAF could generate the best results if radiometric inconsistency was non-negligible. These findings could help users determine the method that is appropriate for different remote sensing datasets, and provide guidelines for developers in the future development of novel methods.

【 授权许可】

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