publication . Article . Other literature type . 2017

A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions

Liao, Chunhua; Wang, Jinfei; Pritchard, Ian; Liu, Jiangui; Shang, Jiali;
Open Access English
  • Published: 04 Nov 2017 Journal: Remote Sensing (issn: 2072-4292, Copyright policy)
  • Publisher: MDPI AG
Abstract
Time series vegetation indices with high spatial resolution and high temporal frequency are important for crop growth monitoring and management. However, due to technical constraints and cloud contamination, it is difficult to obtain such datasets. In this study, a spatio-temporal vegetation index image fusion model (STVIFM) was developed to generate high spatial resolution Normalized Difference Vegetation Index (NDVI) time-series images with higher accuracy, since most of the existing methods have some limitations in accurately predicting NDVI in heterogeneous regions, or rely on very computationally intensive steps and land cover maps for heterogeneous regions...
Subjects
free text keywords: spatio-temporal, data fusion, Landsat, MODIS, NDVI, time-series, Science, Q, Sensor fusion, Vegetation, Temporal database, Geology, Land cover, Remote sensing, Normalized Difference Vegetation Index, Pixel, Image resolution, Image fusion
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