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Activity Number:
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22
- Statistical Methods for Heterogeneous and Massive Remote Sensing Data
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #324539
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Title:
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Fusing Data from Multiple Remote Sensing Instruments
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Author(s):
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Jessica Loock Matthews* and Elizabeth Mannshardt and Brian Reich and Joseph Guinness
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Companies:
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CICS-NC and US Environmental Protection Agency and NCSU and NC State University
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Keywords:
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data fusion ;
spatial-temporal ;
remote sensing
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Abstract:
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The WMO-led activity on Sustained and Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) provides the infrastructure to ensure a continuous and sustained generation of climate data records (CDR) from satellite data in compliance with Global Climate Observing System (GCOS) principles and guidelines. The SCOPE-CM effort to generate a unique land surface albedo CDR involves 5 different geostationary satellite positions and approximately 3 decades of satellite data. The resultant albedo products are comparable to polar-orbiting based albedo products (i.e. MODIS) though each product has strengths and limitations. Given the fine temporal resolution of the geostationary product, and the fine spatial resolution of the polar-orbiting product, a fused product leveraging the advantages of each is desirable. We develop a novel spatial-temporal statistical data fusion algorithm that takes into account spectral conversion and bias adjustments for combining differently sourced remote sensing products. Several regions of geographical interest, collocated with in situ observations for validation, were selected to evaluate the fusion algorithm.
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Authors who are presenting talks have a * after their name.
