Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 491-495, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-491-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 491-495, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-491-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

A COMPARATIVE ANALYSIS OF SPATIOTEMPORAL DATA FUSION MODELS FOR LANDSAT AND MODIS DATA

K. Hazaymeh and A. Almagbile K. Hazaymeh and A. Almagbile
  • Department of Geography, Faculty of Arts, Yarmouk University, Irbid 21163, Jordan

Keywords: Surface Reflectance, Remote Sensing, Coleambally, Landsat-like, STI-FM

Abstract. In this study, three documented spatiotemporal data fusion models were applied to Landsat-7 and MODIS surface reflectance, and NDVI. The algorithms included the spatial and temporal adaptive reflectance fusion model (STARFM), sparse representation based on a spatiotemporal reflectance fusion model (SPSTFM), and spatiotemporal image-fusion model (STI-FM). The objectives of this study were to (i) compare the performance of these three fusion models using a one Landsat-MODIS spectral reflectance image pairs using time-series datasets from the Coleambally irrigation area in Australia, and (ii) quantitatively evaluate the accuracy of the synthetic images generated from each fusion model using statistical measurements. Results showed that the three fusion models predicted the synthetic Landsat-7 image with adequate agreements. The STI-FM produced more accurate reconstructions of both Landsat-7 spectral bands and NDVI. Furthermore, it produced surface reflectance images having the highest correlation with the actual Landsat-7 images. This study indicated that STI-FM would be more suitable for spatiotemporal data fusion applications such as vegetation monitoring, drought monitoring, and evapotranspiration.