Research Papers

Estimating canopy leaf area index in the late stages of wheat growth using continuous wavelet transform

[+] Author Affiliations
Yan Huang

Nanjing University, International Institute for Earth System Science, Nanjing, Jiangsu 210023, China

Nanjing University, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing, Jiangsu 210023, China

Qingjiu Tian

Nanjing University, International Institute for Earth System Science, Nanjing, Jiangsu 210023, China

Nanjing University, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing, Jiangsu 210023, China

Lei Wang

Nanjing University, International Institute for Earth System Science, Nanjing, Jiangsu 210023, China

Nanjing University, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing, Jiangsu 210023, China

Ningxia University, Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in North-western China of Ministry of Education, Yinchuan 750021, China

Jun Geng

Nanjing University, International Institute for Earth System Science, Nanjing, Jiangsu 210023, China

Nanjing University, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing, Jiangsu 210023, China

Chunguang Lyu

Nanjing University, International Institute for Earth System Science, Nanjing, Jiangsu 210023, China

Nanjing University, Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing, Jiangsu 210023, China

J. Appl. Remote Sens. 8(1), 083517 (Nov 17, 2014). doi:10.1117/1.JRS.8.083517
History: Received March 20, 2014; Revised July 27, 2014; Accepted October 6, 2014
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Abstract.  The existing hyperspectral vegetation indices used for estimating the canopy leaf area index (LAI) of winter wheat (Triticum aestivum L.) performed well, but the use of such indices at late growth stages can lead to inaccurate results. To improve the performance of LAI models for wheat in late growth stages, the continuous wavelet transform (CWT) method was applied in this study and used to decompose the canopy reflectance and its first derivative into wavelet coefficients. The correlation scalograms of wavelet coefficients and the LAI were then constructed and used to extract the top 1% correlated region as the wavelet feature. The canopy LAI estimation model for late growth wheat was established at last and compared with models based on 12 different types of hyperspectral vegetation indices. The results showed that, compared with the estimation models using the hyperspectral vegetation indices (for which the R2 values were all less than 0.15 and the root-mean-square errors (RMSEs) were greater than 1), the CWT-based canopy LAI estimation model for late growth wheat had obvious improvements in accuracy (maximum R2 of 0.53 and minimum of RMSE of 0.78). Hence, this new method shows promise for use in agricultural and ecological applications.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Yan Huang ; Qingjiu Tian ; Lei Wang ; Jun Geng and Chunguang Lyu
"Estimating canopy leaf area index in the late stages of wheat growth using continuous wavelet transform", J. Appl. Remote Sens. 8(1), 083517 (Nov 17, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083517


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