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

Content Provider	IEEE Xplore Digital Library
Author	Aihua Li Yanchen Bo Ling Chen
Copyright Year	2011
Description	Author affiliation: State Key Laboratory of Remote Sensing Science, Department of Geography and Remote Sensing, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, China, 100875 (Aihua Li; Yanchen Bo; Ling Chen)
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.
Starting Page	2617
Ending Page	2620
File Size	392156
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049739
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Remote sensing Uncertainty Satellites Earth Measurement uncertainty Entropy Data models uncertainty Bayesian Maximum Entropy fusion LAI
Content Type	Text
Resource Type	Article

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