Inversion of leaf area index in Heihe Oasis based on CASI data

doi:10.6046/gtzyyg.2017.04.27

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Abstract As the vegetation canopy’s important parameter, the leaf area index (LAI) has important significance for crop growth monitoring and yield estimation. In this study, the authors used the hyperspectral compact airborne spectrographic imager (CASI) data of Zhangye Oasis experimental area in Heihe River Basin as the experiment object and relied on physical and statistical model to estimate the inversion of the LAI. The process is as follows: First, the optimal linear regression model is established by using the normalized difference vegetation index (NDVI) and the corresponding measured LAI data. Then the physical model is adopted based on the combination of the mixed pixel decomposition model and the multiple scattering vegetation canopy model. With the linear regression model as the reference, the multiple scattering vegetation canopy model is modified, and the semi-empirical LAI inversion model is constructed. Finally, the fitting effects of the models are compared with each other. The results show that the semi-empirical model is the best model for LAI inversion in oasis area and its estimation accuracy of R² increases significantly to 0.89. This study provides technical support for the estimation of crop leaf area index in high precision, and will further promote the study and application of quantitative remote sensing theory about precision agriculture.

Keywords GF-2 satellite image Beidou satellite navigation system GPS ortho-rectification accuracy validation rational function model

TP751.1

Issue Date: 04 December 2017

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	JINAG Wei
	HE Guojin
	LONG Tengfei
	YIN Ranyu
	SONG Xiaolu
	YUAN Yiqin
	LING Saiguang

Cite this article:

JINAG Wei,HE Guojin,LONG Tengfei, et al. Inversion of leaf area index in Heihe Oasis based on CASI data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 179-184.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.04.27 OR https://www.gtzyyg.com/EN/Y2017/V29/I4/179

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