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Retrieval and scale effect analysis of LAI over typical farmland from UAV-based hyperspectral data
Author(s): Xiaohua Zhu; Chuanrong Li; Lingli Tang; Lingling Ma
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Paper Abstract

In this article, a multiple dimensional Look-Up-Table (LUT) is generated based on the PROSAIL model for multiple crops LAI estimation from unmanned aerial vehicle hyperspectral data. Based on Taylor expansion method (TEM) and computational geometry model (CGM), a scale transfer model considering both difference between inter-class and intraclass is constructed for scale effect analysis of LAI inversion over inhomogeneous surface. The results indicate that, 1) the LUT method based on classification and parameter sensitive analysis is useful for LAI retrieval of corn, potato, sunflower and melon on Baotou test site, with correlation coefficient R2 of 0.85 and root mean square error RMSE of 0.41m2/m2. 2) The scale effect of LAI is becoming obvious with the decrease of resolution, and scale difference between inter-classes is higher than that of intra-class, which can be corrected efficiently by the scale transfer model established based Taylor expansion and Computational geometry. 3) Further research could focus on multiple scale remote sensing products validation based on the scale transfer model mentioned above.

Paper Details

Date Published: 21 October 2019
PDF: 6 pages
Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111490K (21 October 2019); doi: 10.1117/12.2535478
Show Author Affiliations
Xiaohua Zhu, Key Lab. of Quantitative Remote Sensing Information Technology (China)
Aerospace Information Research Institute (China)
Chuanrong Li, Key Lab. of Quantitative Remote Sensing Information Technology (China)
Aerospace Information Research Institute (China)
Lingli Tang, Key Lab. of Quantitative Remote Sensing Information Technology (China)
Aerospace Information Research Institute (China)
Lingling Ma, Key Lab. of Quantitative Remote Sensing Information Technology (China)
Aerospace Information Research Institute (China)


Published in SPIE Proceedings Vol. 11149:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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