Research Papers

Predicting leaf area index in wheat using an improved empirical model

[+] Author Affiliations
Hanyue Chen

Chinese Academy of Sciences, The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, CAS Olympic S&T Park No. 20 Da Tun Road, P. O. Box 9718, Chao Yang, Beijing 100101, China

Graduate School of Chinese Academy of Sciences, Beijing 100039, China

Zheng Niu, Wenjiang Huang

Chinese Academy of Sciences, The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, CAS Olympic S&T Park No. 20 Da Tun Road, P. O. Box 9718, Chao Yang, Beijing 100101, China

Jilu Feng

University of Alberta, Center for Earth Observation Sciences, T6E 2E3, Alberta, Canada

J. Appl. Remote Sens. 7(1), 073577 (Apr 09, 2013). doi:10.1117/1.JRS.7.073577
History: Received May 21, 2012; Revised February 1, 2013; Accepted March 15, 2013
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Abstract.  Leaf area index (LAI) is typically estimated from remote sensing data acquired at nadir position. Known issues of this mono-angle approach include a saturation limit at intermediate values of LAI and inadequacy to represent any structural characteristics of vegetation. In this study, we present an improved LAI estimation model that incorporates multiangle reflectance data exploring the feasibility of addressing these issues, especially for LAI estimation in winter wheat. The improved model takes advantage of angular information in the normalized difference between hotspot and darkspot for improving LAI estimation by better accounting for foliage clumping. Four vegetation indices were also considered for LAI estimation, including three versions of the normalized difference vegetation index (NDVI) and the normalized hotspot-signature vegetation index (NHVI). A geometric-optical canopy model named Five-Scale was used to simulate a range of bidirectional reflectance for sensitivity analysis. The results indicated that better accuracy in LAI prediction was observed from our improved model than from NHVI or any NDVI. A validation with in situ measurements of LAI and bidirectional reflectance in the growth cycle of wheat indicated that the improved model provided the best correlation (R2=0.93) among all models, followed by the NHVI.

© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Hanyue Chen ; Zheng Niu ; Wenjiang Huang and Jilu Feng
"Predicting leaf area index in wheat using an improved empirical model", J. Appl. Remote Sens. 7(1), 073577 (Apr 09, 2013). ; http://dx.doi.org/10.1117/1.JRS.7.073577


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