Paper Info
Title
A modified vegetation index based algorithm for thermal imagery sharpening
Abstract
Land surface temperature (LST) at both high spatial and high temporal resolution is required for routine monitoring of surface energy fluxes. Disaggregating LST to the NDVI-pixel resolution is possible because of significant inverse relationship between LST and vegetation indices. A modified algorithm (SWISF) has been proposed for thermal imagery sharpening, in which multiple least-squares regression relationships between LST and vegetation indices were acquired for bins of pixels with different soil wetness index values. Applying both SWISF and Distrad which is originally proposed by Kustas et al. to simulated thermal maps at 360 m resolution and sharpening down to 90 m shows that the new algorithm slightly outperform the old one. Moreover, DisTrad does not have the ability to consider the fact that two pairs of pixels with the same NDVI difference may have distinct LST difference under different soil moisture conditions, while SWISF algorithm could consider it to some extent.
Year
DOI
Venue
2010
10.1109/IGARSS.2010.5651428
IGARSS
Keywords
Field
DocType
vegetation index,geophysical techniques,swisf algorithm,image sharpening,thermal imagery sharpening,distrad,land surface temperature,geophysical image processing,modified vegetation index,ndvi pixel resolution,soil wetness index,vegetation,remote sensing,temporal resolution,pixel,spatial resolution,least square,indexes,soil moisture,surface energy,indexation
Sharpening,Soil science,Vegetation,Regression,Computer science,Remote sensing,Algorithm,Normalized Difference Vegetation Index,Pixel,Water content,Temporal resolution,Image resolution
Conference
ISSN
ISBN
Citations 
2153-6996 E-ISBN : 978-1-4244-9564-1
978-1-4244-9564-1
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Ling Chen153.99
Guangjian Yan214038.69
Huazhong Ren37323.56
Aihua Li400.34