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Cai, Qingkong
- Retrieval of Leaf Area Index of Winter Wheat at Different Growth Stages Using Continuous Wavelet Analysis
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Authors
Affiliations
1 College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, CN
2 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, CN
1 College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, CN
2 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, CN
Source
Nature Environment and Pollution Technology, Vol 13, No 3 (2014), Pagination: 491-498Abstract
Leaf area index (LAI) is one of the most basic parameters to characterize the vegetation canopy structure, and is widely used in monitoring crop growth, yield estimation and other fields. Therefore, accurate estimation of LAI has great significance for agricultural precision fertilization and protecting agricultural ecological environment. However, few studies have attempted to estimate LAI of winter wheat using the continuous wavelet analysis (CWA), particularly at different growth stages. This paper aims at studying the spectral estimation of LAI by applying CWA into canopy spectra of 190 samples observed at Guanzhong Plain in China. Two partial least square regression (PLSR) models using six wavelet features and the optimal spectral indices were constructed and compared respectively. Results indicated that the model using wavelet features combination had a considerable improvement than the spectral indices combination for the whole validation dataset. When the validation dataset was separated according to the growth stage, the predictive performance of the wavelet features combination performed well at both growth stages, while the spectral indices combination had not achieved the same effect. The results showed that CWA approach could derive more robust wavelet features to growth stage variation, and wavelet features were more effective than the spectral indices for predicting LAI of winter wheat at different growth stages.Keywords
Leaf Area Index, Continuous Wavelet Analysis, Winter Wheat, Hyperspectral Remote Sensing, Agricultural Ecological Environment.- Analysis of the Relationship between Land Surface Temperature and Land Cover Changes Using Multi-Temporal Satellite Data
Abstract Views :150 |
PDF Views:0
Authors
Affiliations
1 College of Civil Engineering, Henan University of Engineering, Zhengzhou 451191, CN
2 College of Human and Social Sciences, Henan University of Engineering, Zhengzhou 451191, CN
1 College of Civil Engineering, Henan University of Engineering, Zhengzhou 451191, CN
2 College of Human and Social Sciences, Henan University of Engineering, Zhengzhou 451191, CN