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Schmidt, M., Udelhoven, T., Gill, T. and Röder, A. (2012) Long Term Data Fusion for a Dense Time Series Analysis with MODIS and Landsat Imagery in an Australian Savanna. J. Appl. Remote Sens., 6, 63512. https://doi.org/10.1117/1.JRS.6.063512

has been cited by the following article:

• TITLE: Comparison of Spatiotemporal Fusion Models for Producing High Spatiotemporal Resolution Normalized Difference Vegetation Index Time Series Data Sets

  AUTHORS: Zhizhong Han, Wenya Zhao

  KEYWORDS: Spatiotemporal Fusion, NDVI, Time Series, STVIFM

  JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.7, July 10, 2019

  ABSTRACT: It has a great significance to combine multi-source with different spatial resolution and temporal resolution to produce high spatiotemporal resolution Normalized Difference Vegetation Index (NDVI) time series data sets. In this study, four spatiotemporal fusion models were analyzed and compared with each other. The models included the spatial and temporal adaptive reflectance model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion model (FSDAF), and a spatiotemporal vegetation index image fusion model (STVIFM). The objective of is to: 1) compare four fusion models using Landsat-MODIS NDVI image from the Banan district, Chongqing Province; 2) analyze the prediction accuracy quantitatively and visually. Results indicate that STVIFM would be more suitable to produce NDVI time series data sets.