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Leaf Area Index Estimation of Winter Pepper Based on Canopy Spectral Data and Simulated Bands of Satellite

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Geo-Spatial Knowledge and Intelligence (GRMSE 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 698))

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Abstract

Leaf area index (LAI) is an important indicator of crop growth status. In this paper, the relationships between canopy reflectance at 400–2500 nm and leaf area index (LAI) in pepper crop were studied. 102 pair of canopy reflectance and LAI of pepper were collected in 2014–2015. Reflectance of canopy were measured in the field over a spectral range of 400–2500 nm. Simultaneously, the LAI were collected by the LAI-2000. Estimation models of LAI were developed based on the whole spectrum range by partial least squares regression (PLSR) and support vector regression (SVR), respectively. Then the field canopy spectra were resampled according to the band response functions of seven satellite sensors. They were the Vegetation and environment monitoring on a new micro-satellite (VENμS), Worldview-2 (WV-2), RapidEye-1 (RE-1), HJ1/CCD1, Sentinel-2, Landsat 8/OLI and GaoFen (GF) 1/WFV1. The values of common used spectral indices were calculated based on the simulated sensor bands, respectively. Prediction models were also developed based on the spectral indices and simulated bands. The results showed that the PLSR model by whole spectrum had the good accuracy of LAI estimation with the R2c = 0.726, RMSEc = 0.462, R2cv = 0.635, RMSEcv = 0.538. For the simulated satellite datasets, the better LAI estimation were obtained by Sentinel-2 and Venμs bands with the R2cv greater than 0.600 and RMSEcv less than 0.557. The Estimation model by simulated WV-2 bands, and RE-1 bands had the lowest performance with the R2cv between 0.50 and 0.55, and RMSEcv between 0.600 and 0.623. The inversion results demonstrated the potential of the multispectral remote sensing data to calibrate the LAI estimation model of winter pepper for the precision agriculture application.

Work was supported by the National Natural Science Foundation of China (No. 41301401), the Guangdong Natural Science Foundation (No. 2015A030313805) and the Guangdong Science & Technology Plan Foundation (Nos. 2015A030303013 and 2013B020501006).

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Authors and Affiliations

  1. Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Key Laboratory of Remote Sensing & GIS Application & Engineer Technology Research Center for Remote Sensing Big Data Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou, 510070, China

    Dan Li, Hao Jiang, Shuisen Chen, Chongyang Wang, Siyu Huang & Wei Liu

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  2. Hao Jiang
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  4. Chongyang Wang
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  5. Siyu Huang
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Correspondence to Shuisen Chen .

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  1. Beijing Institute of Technology, Beijing, China

    Hanning Yuan

  2. Beijing Institute of Technology, Beijing, China

    Jing Geng

  3. Wuhan University, Wuhan, China

    Fuling Bian

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Li, D., Jiang, H., Chen, S., Wang, C., Huang, S., Liu, W. (2017). Leaf Area Index Estimation of Winter Pepper Based on Canopy Spectral Data and Simulated Bands of Satellite. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-10-3966-9_57

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