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Remote Sens. 2015, 7(6), 6510-6534; doi:10.3390/rs70606510

Using RapidEye and MODIS Data Fusion to Monitor Vegetation Dynamics in Semi-Arid Rangelands in South Africa

1
Center for Remote Sensing of Land Surfaces, University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany
2
Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany
3
Remote Sensing Research Group, Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115 Bonn, Germany
4
Remote Sensing Centre, Environment and Resource Sciences, Department of Science, Information Technology, Innovation and the Arts, GPO Box 5078, Brisbane, Queensland 4001, Australia
5
Ecosystem Science and Sustainability, Colorado State University, NESB 108, 1499 Campus Delivery, Fort Collins, CO 805239, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 2 September 2014 / Revised: 20 March 2015 / Accepted: 7 April 2015 / Published: 26 May 2015
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Abstract

Image time series of high temporal and spatial resolution capture land surface dynamics of heterogeneous landscapes. We applied the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) algorithm to multi-spectral images covering two semi-arid heterogeneous rangeland study sites located in South Africa. MODIS 250 m resolution and RapidEye 5 m resolution images were fused to produce synthetic RapidEye images, from June 2011 to July 2012. We evaluated the performance of the algorithm by comparing predicted surface reflectance values to real RapidEye images. Our results show that ESTARFM predictions are accurate, with a coefficient of determination for the red band 0.80 < R2 < 0.92, and for the near-infrared band 0.83 < R2 < 0.93, a mean relative bias between 6% and 12% for the red band and 4% to 9% in the near-infrared band. Heterogeneous vegetation at sub-MODIS resolution is captured adequately: A comparison of NDVI time series derived from RapidEye and ESTARFM data shows that the characteristic phenological dynamics of different vegetation types are reproduced well. We conclude that the ESTARFM algorithm allows us to produce synthetic remote sensing images at high spatial combined with high temporal resolution and so provides valuable information on vegetation dynamics in semi-arid, heterogeneous rangeland landscapes. View Full-Text
Keywords: RapidEye; MODIS; ESTARFM; image fusion; data blending; vegetation dynamics; high spatial and temporal resolution; time series RapidEye; MODIS; ESTARFM; image fusion; data blending; vegetation dynamics; high spatial and temporal resolution; time series
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Tewes, A.; Thonfeld, F.; Schmidt, M.; Oomen, R.J.; Zhu, X.; Dubovyk, O.; Menz, G.; Schellberg, J. Using RapidEye and MODIS Data Fusion to Monitor Vegetation Dynamics in Semi-Arid Rangelands in South Africa. Remote Sens. 2015, 7, 6510-6534.

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