Xiaolin Zhu^a, Jin Chen^a,*, Feng Gao^b, Xuehong Chen ^a, Jeffrey G. Masek ^b

 

a State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China

b Biospheric Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

 

Due to technical and budget limitations, remote sensing instruments trade spatial resolution and swath width. As a result not one sensor provides both high spatial resolution and high temporal resolution. However, the ability to monitor seasonal landscape changes at fine resolution is urgently needed for global change science. One approach is to “blend” the radiometry from daily, global data (e.g. MODIS, MERIS, SPOTVegetation) with data from high-resolution sensors with less frequent coverage (e.g. Landsat, CBERS, ResourceSat). Unfortunately, existing algorithms for blending multi-source data have some shortcomings, particularly in accurately predicting the surface reflectance of heterogeneous landscapes. This study has developed an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on the existing STARFM algorithm, and has tested it with both simulated and actual satellite data. Results show that ESTARFM improves the accuracy of predicted fine-resolution reflectance, especially for heterogeneous landscapes, and preserves spatial details. Taking the NIR band as an example, for homogeneous regions the prediction of the ESTARFM is slightly better than the STARFM (average absolute difference [AAD] 0.0106 vs. 0.0129 reflectance units). But for a complex, heterogeneous landscape, the prediction accuracy of ESTARFM is improved even more compared with STARFM (AAD 0.0135 vs. 0.0194). This improved fusion algorithm will support new investigations into how global landscapes are changing across both seasonal and inter-annual timescales.

NIR–green–blue composites of MODIS surface reflectance (upper row) and Landsat ETM+ surface reflectance (lower row) images on January 25, February 26, and May 17,2002 respectively.

 

Published in Remote Sensing of Environment, 2010, 114: 2610–2623. Top Journal of Remote Sensing. Impact index 3.6 (2009).