Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and “Blend-then-Index” Approaches

Jarihani, Abdollah A., McVicar, Tim R., Van Niel, Thomas G., Emelyanova, Irina V., Callow, John N. and Johansen, Kasper (2014) Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and “Blend-then-Index” Approaches. Remote Sensing, 6 10: 9213-9238. doi:10.3390/rs6109213

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Author Jarihani, Abdollah A.
McVicar, Tim R.
Van Niel, Thomas G.
Emelyanova, Irina V.
Callow, John N.
Johansen, Kasper
Title Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and “Blend-then-Index” Approaches
Journal name Remote Sensing   Check publisher's open access policy
ISSN 2072-4292
Publication date 2014-09-26
Year available 2014
Sub-type Article (original research)
DOI 10.3390/rs6109213
Open Access Status DOI
Volume 6
Issue 10
Start page 9213
End page 9238
Total pages 26
Place of publication Basel, Switzerland
Publisher MDPI AG
Collection year 2015
Language eng
Formatted abstract
The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) “Index-then-Blend” (IB); and (ii) “Blend-then-Index” (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R2; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm.
Keyword Data fusion
Blending
Multispectral indices
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Official 2015 Collection
 
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Citation counts: TR Web of Science Citation Count  Cited 16 times in Thomson Reuters Web of Science Article | Citations
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Created: Mon, 30 Mar 2015, 13:01:54 EST by Genna Apted on behalf of School of Geography, Planning & Env Management