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Proceedings Paper

A methodology to estimate representativeness of LAI station observation for validation: a case study with Chinese Ecosystem Research Network (CERN) in situ data
Author(s): Baodong Xu; Jing Li; Qinhuo Liu; Yelu Zeng; Gaofei Yin
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Paper Abstract

Leaf Area Index (LAI) is known as a key vegetation biophysical variable. To effectively use remote sensing LAI products in various disciplines, it is critical to understand the accuracy of them. The common method for the validation of LAI products is firstly establish the empirical relationship between the field data and high-resolution imagery, to derive LAI maps, then aggregate high-resolution LAI maps to match moderate-resolution LAI products. This method is just suited for the small region, and its frequencies of measurement are limited. Therefore, the continuous observing LAI datasets from ground station network are important for the validation of multi-temporal LAI products. However, due to the scale mismatch between the point observation in the ground station and the pixel observation, the direct comparison will bring the scale error. Thus it is needed to evaluate the representativeness of ground station measurement within pixel scale of products for the reasonable validation. In this paper, a case study with Chinese Ecosystem Research Network (CERN) in situ data was taken to introduce a methodology to estimate representativeness of LAI station observation for validating LAI products. We first analyzed the indicators to evaluate the observation representativeness, and then graded the station measurement data. Finally, the LAI measurement data which can represent the pixel scale was used to validate the MODIS, GLASS and GEOV1 LAI products. The result shows that the best agreement is reached between the GLASS and GEOV1, while the lowest uncertainty is achieved by GEOV1 followed by GLASS and MODIS. We conclude that the ground station measurement data can validate multi-temporal LAI products objectively based on the evaluation indicators of station observation representativeness, which can also improve the reliability for the validation of remote sensing products.

Paper Details

Date Published: 8 November 2014
PDF: 15 pages
Proc. SPIE 9260, Land Surface Remote Sensing II, 926023 (8 November 2014); doi: 10.1117/12.2068845
Show Author Affiliations
Baodong Xu, Institute of Remote Sensing and Digital Earth (China)
Univ. of Chinese Academy of Sciences (China)
Jing Li, Institute of Remote Sensing and Digital Earth (China)
Qinhuo Liu, Institute of Remote Sensing and Digital Earth (China)
Yelu Zeng, Institute of Remote Sensing and Digital Earth (China)
Univ. of Chinese Academy of Sciences (China)
Gaofei Yin, Institute of Remote Sensing and Digital Earth (China)
Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 9260:
Land Surface Remote Sensing II
Thomas J. Jackson; Jing Ming Chen; Peng Gong; Shunlin Liang, Editor(s)

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