Fusion of MODIS and landsat-8 surface temperature images: a new approach.
Here, our objective was to develop a spatio-temporal image fusion model (STI-FM) for enhancing temporal resolution of Landsat-8 land surface temperature (LST) images by fusing LST images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS); and implement the developed algorithm over a heterogeneous semi-arid study area in Jordan, Middle East. The STI-FM technique consisted of two major components: (i) establishing a linear relationship between two consecutive MODIS 8-day composite LST images acquired at time 1 and time 2; and (ii) utilizing the above mentioned relationship as a function of a Landsat-8 LST image acquired at time 1 in order to predict a synthetic Landsat-8 LST image at time 2. It revealed that strong linear relationships (i.e., r2, slopes, and intercepts were in the range 0.93-0.94, 0.94-0.99; and 2.97-20.07) existed between the two consecutive MODIS LST images. We evaluated the synthetic LST images qualitatively and found high visual agreements with the actual Landsat-8 LST images. In addition, we conducted quantitative evaluations of these synthetic images; and found strong agreements with the actual Landsat-8 LST images. For example, r2, root mean square error (RMSE), and absolute average difference (AAD)-values were in the ranges 084-0.90, 0.061-0.080, and 0.003-0.004, respectively.
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. NW, Calgary, Alberta, Canada.
Khaled
K
Hazaymeh
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. NW, Calgary, Alberta, Canada.
Quazi K
QK
Hassan
D000465:Algorithms; D004784:Environmental Monitoring / Q000295:instrumentation / Q000379:methods*; D007597:Jordan; D063809:Satellite Imagery; D013696:Temperature