归一化差异植被指数
加权
系列(地层学)
时间序列
反距离权重法
噪音(视频)
计算机科学
集合(抽象数据类型)
遥感
植被(病理学)
数据挖掘
算法
地质学
人工智能
机器学习
计算机视觉
物理
气候变化
图像(数学)
病理
海洋学
医学
古生物学
声学
程序设计语言
多元插值
双线性插值
作者
Jie Zhou,Jia Li,Mattijn van Hoek,Massimo Menenti,Jing Lu,G.C. Hu
标识
DOI:10.1109/igarss.2016.7729884
摘要
The applications of Multi-temporal Normalized Difference Vegetation Index (NDVI), a critical proxy for analysing vegetation dynamics, have been long hindered by prevalent noise. The Harmonic ANalysis of Time Series (HANTS) has been widely applied to reconstruct noise- and cloud-free NDVI time series data set from regional to global scales. The reconstruction performance of HANTS is severely dependent on the inherent parameter settings in HANTS. However, most applications set the parameters of HANTS based on users' experience. This study analysed the sensitivity of the reconstruction performance to several critical parameters and weighting scheme of HANTS. The results document the optimized parameter settings for global NDVI time series reconstruction.
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