Using least squares support vector machines to estimate time series leaf area index

Liang Dong; Xie Qiaoyun; Huang Wenjiang; Peng Dailiang; Yang Xiaohua; Huang Linsheng; Hu Yong

The multi-temporal leaf area index (LAI) data retrieved from remote sensing images have been widely used in climate simulation, crop growth monitoring and etc. However,there might be some missing data owing to temporal resolution, weather and some other factors. The support vector machine (SVM) is a kind of machine learning algorithm that has excellent properties. The least squares support vector machine (LS-SVM) algorithm is an improved algorithm of SVM. In this paper, the LS-SVM and SVM models were used to predict the LAI time series products of MODIS data of Naqu in year 2011, based on The multi-temporal leaf area index (LAI) data retrieved from remote sensing images have been widely used in climate simulation, crop growth monitoring and etc. However,there might be some missing data owing to temporal resolution, weather and some other factors. The support vector machine (SVM) is a kind of machine learning algorithm that has excellent properties. The least squares support vector machine (LS-SVM) algorithm is an improved algorithm of SVM. In this paper, the LS-SVM and SVM models were used to predict the LAI time series products of MODIS data of Naqu in year 2011, based on the MODIS LAI from 2003 to 2011. The results show that LS-SVM method performs better than SVM method. Therefore the predicted LAI data is proved to be very supportive for making up for the loss of remote sensing LAI time-series data, the LS-SVM method proposed in this study is significant to improve the quality of the LAI time series remote sensing products.

1. Key Laboratory of Intelligent Computer & Signal Processing,Ministry of Education,Anhui University,Hefei 230039,China;

2. School of Electronic and Information Engineering,Anhui University,Hefei 230039,China;

3. The State Key Laboratoryof Remote Sensing Science,Laboratory of Digital Earth Sciences,Institute of Remote Sensing and Digital Earth,Chinese Academy ofSciences,Beijing 100094,China;

4. Space Weather Center Meteorological and Hydrographic Department,Beijing 100094,China

Received Date: 2013-05-10

Rev Recd Date: 2013-06-25

Publish Date: 2014-01-25

Key words:

least squares support vector machine (LS-SVM) /
support vector machine (SVM) /
leaf area index(LAI) /
time series /
MODIS

Abstract: The multi-temporal leaf area index (LAI) data retrieved from remote sensing images have been widely used in climate simulation, crop growth monitoring and etc. However,there might be some missing data owing to temporal resolution, weather and some other factors. The support vector machine (SVM) is a kind of machine learning algorithm that has excellent properties. The least squares support vector machine (LS-SVM) algorithm is an improved algorithm of SVM. In this paper, the LS-SVM and SVM models were used to predict the LAI time series products of MODIS data of Naqu in year 2011, based on The multi-temporal leaf area index (LAI) data retrieved from remote sensing images have been widely used in climate simulation, crop growth monitoring and etc. However,there might be some missing data owing to temporal resolution, weather and some other factors. The support vector machine (SVM) is a kind of machine learning algorithm that has excellent properties. The least squares support vector machine (LS-SVM) algorithm is an improved algorithm of SVM. In this paper, the LS-SVM and SVM models were used to predict the LAI time series products of MODIS data of Naqu in year 2011, based on the MODIS LAI from 2003 to 2011. The results show that LS-SVM method performs better than SVM method. Therefore the predicted LAI data is proved to be very supportive for making up for the loss of remote sensing LAI time-series data, the LS-SVM method proposed in this study is significant to improve the quality of the LAI time series remote sensing products.

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Reference (23)

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Using least squares support vector machines to estimate time series leaf area index

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