Paper Info
Title
Bayesian Maximum Entropy data fusion of field observed LAI and Landsat ETM+ derived LAI
Abstract
Accurate high resolution LAI reference maps are necessary for the validation of coarser resolution satellite derived LAI products. In this paper, an efficient method for combining field observations and Landsat ETM+ derived LAI is proposed based on the Bayesian Maximum Entropy paradigm to get more accurate reference maps. This method can take account of the uncertainties associated with field observations and linear relationship between the ETM+ LAI and in situ measurements to perform a nonlinear prediction of the interest variable. A comparison with ETM+ derived LAI surfaces in three validation sites from the BIGFOOT project showed that the RMSE can be reduced by this approach, indicating a promising method in fusing different sources and different types of data.
Year
DOI
Venue
2011
10.1109/IGARSS.2011.6049739
IGARSS
Keywords
Field
DocType
fusion,field observation analysis,bayes methods,image resolution,bayesian maximum entropy,bigfoot project,uncertainty,data analysis,bayesian maximum entropy data fusion,landsat etm+,coarser resolution satellite,leaf area index,geophysical image processing,maximum entropy methods,high resolution lai reference maps,etm+ derived lai surface,vegetation mapping,lai,in situ measurement method,measurement uncertainty,high resolution,data models,satellites,data model,entropy,earth,remote sensing,data fusion
Leaf area index,Satellite,Computer science,Remote sensing,Measurement uncertainty,Mean squared error,Sensor fusion,Principle of maximum entropy,Image resolution,Bayesian probability
Conference
Volume
Issue
ISSN
null
null
2153-6996
ISBN
Citations 
PageRank 
978-1-4577-1003-2
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Aihua Li100.34
Yanchen Bo2166.61
Ling Chen353.99