A sampling strategy for remotely sensed LAI product validation over heterogeneous land surfaces

Zeng, Y; Li, J; Liu, Q; Li, L; Xu, B; Yin, G; Peng, J

A sampling strategy for remotely sensed LAI product validation over heterogeneous land surfaces

Zeng, Y Li, J Liu, Q Li, L

Xu, B Yin, G Peng, J

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Publication Type:: Journal Article
Citation:: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7 (7), pp. 3128 - 3142
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Zeng, Y	en_US
dc.contributor.author	Li, J	en_US
dc.contributor.author	Liu, Q	en_US
dc.contributor.author	Li, L https://orcid.org/0000-0001-9983-6681	en_US
dc.contributor.author	Xu, B	en_US
dc.contributor.author	Yin, G	en_US
dc.contributor.author	Peng, J	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7 (7), pp. 3128 - 3142	en_US
dc.identifier.issn	1939-1404	en_US
dc.identifier.uri	http://hdl.handle.net/10453/36136
dc.description.abstract	The development of efficient and systematic ground-based spatial sampling strategies is critical for the validation of medium-resolution satellite-derived leaf area index (LAI) products, particularly over heterogeneous land surfaces. In this paper, a new sampling strategy based on high-resolution vegetation index prior knowledge (SSVIP) is proposed to generate accurate LAI reference maps over heterogeneous areas. To capture the variability across a site, the SSVIP is designed to 1) stratify the nonhomogeneous area into zones with minimum within-class variance; 2) assign the number of samples to each zone using Neyman optimal allocation; and 3) determine the spatial distribution of samples with a nearest neighbor index. The efficiency of the proposed method was examined using different vegetation types and pixel heterogeneities. The results indicate that the SSVIP approach can properly divide a heterogeneous area into different vegetation cover zones. Whereas the LAI reference maps generated by SSVIP attain the target accuracy of 0.1 LAI units in cropland and broadleaf forest sites, the current sampling strategy based on vegetation type has a root mean square error (RMSE) of 0.14 for the same number of samples. SSVIP was compared with the current sampling strategy at 24 VALERI sites, and the results suggested that samples selected by SSVIP were more representative in the feature space and geographical space, which further indicated the reasonable validation over heterogeneous land surfaces. © 2014 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE120103022
dc.relation.ispartof	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing	en_US
dc.relation.isbasedon	10.1109/JSTARS.2014.2312231	en_US
dc.title	A sampling strategy for remotely sensed LAI product validation over heterogeneous land surfaces	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.for	050206 Environmental Monitoring	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0909 Geomatic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	closed_access
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

The development of efficient and systematic ground-based spatial sampling strategies is critical for the validation of medium-resolution satellite-derived leaf area index (LAI) products, particularly over heterogeneous land surfaces. In this paper, a new sampling strategy based on high-resolution vegetation index prior knowledge (SSVIP) is proposed to generate accurate LAI reference maps over heterogeneous areas. To capture the variability across a site, the SSVIP is designed to 1) stratify the nonhomogeneous area into zones with minimum within-class variance; 2) assign the number of samples to each zone using Neyman optimal allocation; and 3) determine the spatial distribution of samples with a nearest neighbor index. The efficiency of the proposed method was examined using different vegetation types and pixel heterogeneities. The results indicate that the SSVIP approach can properly divide a heterogeneous area into different vegetation cover zones. Whereas the LAI reference maps generated by SSVIP attain the target accuracy of 0.1 LAI units in cropland and broadleaf forest sites, the current sampling strategy based on vegetation type has a root mean square error (RMSE) of 0.14 for the same number of samples. SSVIP was compared with the current sampling strategy at 24 VALERI sites, and the results suggested that samples selected by SSVIP were more representative in the feature space and geographical space, which further indicated the reasonable validation over heterogeneous land surfaces. © 2014 IEEE.

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http://hdl.handle.net/10453/36136