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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 ( IF 11.1 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.rse.2020.112130
Junxiong Zhou , Jin Chen , Xuehong Chen , Xiaolin Zhu , Yuean Qiu , Huihui Song , Yunhan Rao , Chishan Zhang , Xin Cao , Xihong Cui

Abstract 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.

中文翻译:

六种典型时空融合方法对不同影响因素的敏感性:归一化差异植被指数时间序列重建的比较研究

摘要 已经开发了数十种时空融合方法来重建具有高空间分辨率和频繁覆盖的植被指数时间序列数据,用于监测地表动态。尽管已经进行了一些比较不同融合方法的研究,但选择合适的融合方法仍然具有挑战性,因为不可避免的影响因素往往被忽略。针对这一问题,本研究比较了六种典型的时空融合方法,包括基于Unmixing的数据融合(UBDF)、线性混合增长模型(LMGM)、时空自适应反射融合模型(STARFM)、Fit-FC(回归模型)拟合、空间滤波和残差补偿)、一对字典学习方法(OPDL)和灵活时空数据融合(FSDAF),基于模拟实验和理论分析,考虑了传感器之间的三个影响因素:几何重合失调、辐射测量不一致和空间分辨率。结果表明,当辐射不一致性可忽略不计时,Fit-FC 实现了最佳性能,对几何重合偏差的容忍度最强;因此,它是第一个推荐的混合归一化差异植被指数 (NDVI) 图像的算法。相反,如果辐射测量的不一致性不可忽略,FSDAF 可以产生最好的结果。这些发现可以帮助用户确定适合不同遥感数据集的方法,并为开发人员未来开发新方法提供指导。几何失配、辐射测量不一致和空间分辨率。结果表明,当辐射不一致性可忽略不计时,Fit-FC 实现了最佳性能,对几何重合偏差的容忍度最强;因此,它是第一个推荐的混合归一化差异植被指数 (NDVI) 图像的算法。相反,如果辐射测量的不一致性不可忽略,FSDAF 可以产生最好的结果。这些发现可以帮助用户确定适合不同遥感数据集的方法,并为开发人员未来开发新方法提供指导。几何失配、辐射测量不一致和空间分辨率。结果表明,当辐射不一致性可忽略不计时,Fit-FC 实现了最佳性能,对几何重合偏差的容忍度最强;因此,它是第一个推荐的混合归一化差异植被指数 (NDVI) 图像的算法。相反,如果辐射测量的不一致性不可忽略,FSDAF 可以产生最好的结果。这些发现可以帮助用户确定适合不同遥感数据集的方法,并为开发人员未来开发新方法提供指导。结果表明,当辐射不一致性可忽略不计时,Fit-FC 实现了最佳性能,对几何重合偏差的容忍度最强;因此,它是第一个推荐的混合归一化差异植被指数 (NDVI) 图像的算法。相反,如果辐射测量的不一致性不可忽略,FSDAF 可以产生最好的结果。这些发现可以帮助用户确定适合不同遥感数据集的方法,并为开发人员未来开发新方法提供指导。结果表明,当辐射不一致性可忽略不计时,Fit-FC 实现了最佳性能,对几何重合偏差的容忍度最强;因此,它是第一个推荐的混合归一化差异植被指数 (NDVI) 图像的算法。相反,如果辐射测量的不一致性不可忽略,FSDAF 可以产生最好的结果。这些发现可以帮助用户确定适合不同遥感数据集的方法,并为开发人员未来开发新方法提供指导。
更新日期:2021-01-01
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