Bayesian Maximum Entropy data fusion of field observed LAI and Landsat ETM+ derived LAI

Author

Li, Aihua ; Bo, Yanchen ; Chen, Ling

Author_Institution

Dept. of Geogr. & Remote Sensing, Beijing Normal Univ., Beijing, China

fYear

2011

fDate

24-29 July 2011

Firstpage

2617

Lastpage

2620

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.

Keywords

Bayes methods; data analysis; geophysical image processing; image resolution; maximum entropy methods; vegetation mapping; BIGFOOT project; Bayesian maximum entropy data fusion; ETM+ derived LAI surface; LANDSAT ETM+; coarser resolution satellite; field observation analysis; high resolution LAI reference maps; in situ measurement method; leaf area index; Data models; Earth; Entropy; Measurement uncertainty; Remote sensing; Satellites; Uncertainty; Bayesian Maximum Entropy; LAI; fusion; uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International

Conference_Location

Vancouver, BC

ISSN

2153-6996

Print_ISBN

978-1-4577-1003-2

Type

conf

DOI

10.1109/IGARSS.2011.6049739

Filename

6049739