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

Remote Sensing

Andreas Tewes¹  Frank Thonfeld¹  Michael Schmidt³  Roelof J. Oomen⁴  Xiaolin Zhu²  Olena Dubovyk¹  Gunter Menz¹  Jürgen Schellberg¹  Clement Atzberger⁵ 

[1] Center for Remote Sensing of Land Surfaces, University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany; E-Mails:;Ecosystem Science and Sustainability, Colorado State University, NESB 108, 1499 Campus Delivery, Fort Collins, CO 805239, USA; E-Mail:;Remote Sensing Centre, Environment and Resource Sciences, Department of Science, Information Technology, Innovation and the Arts, GPO Box 5078, Brisbane, Queensland 4001, Australia; E-Mail:;Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany; E-Mail:Center for Remote Sensing of Land Surfaces, University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany;

关键词: RapidEye;    MODIS;    ESTARFM;    image fusion;    data blending;    vegetation dynamics;    high spatial and temporal resolution;    time series;   

DOI  :  10.3390/rs70606510

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.