Estimating leaf area index of wheat at the booting stage using GF-2 data: A case study of Langfang City,Hebei Province

doi:10.6046/gtzyyg.2018.01.27

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Abstract

Leaf area index (LAI) is an important agricultural parameter to assess crop growing status for production estimation. Due to its very high spatial resolution, GF-2 can be used as a new source of remote sensing data for crop monitoring. It is particularly valuable to develop approaches for LAI estimation using GF-2 data. In this paper, the study of LAI estimation for wheat at the booting stage in Wanzhuang Town, Langfang City of Hebei Province in North China Plain is presented. Canopy LAI of wheat at the booting stage was measured over an experimental field in the town. Regression analysis method was performed with LAI and four different vegetation indexes, and the neural network method combined with the PROSAIL model was also considered. The results show that the best LAI retrieval regression model is the binomial model of normalized difference vegetation index (NDVI). The correlation coefficient (R²) and root mean square error (RMSE) are 0.719 3 and 0.393 6 respectively. Compared with the regression analysis method, the accuracy is greatly improved by using neural network method,and the R² and RMSE reached 0.900 8 and 0.273 2 respectively. Neural network method has feasibility and applicability based GF-2 data at wheat booting stage, which would provide a reference scheme for LAI inversion from high spatial resolution satellite image.

Keywords vegetation index neural network leaf area index(LAI) inversion method precision validation

TP79

Issue Date: 08 February 2018

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	Kun LU
	Qingyan MENG
	Yunxiao SUN
	Zhenhui SUN
	Linlin ZHANG

Cite this article:

Kun LU,Qingyan MENG,Yunxiao SUN, et al. Estimating leaf area index of wheat at the booting stage using GF-2 data: A case study of Langfang City,Hebei Province[J]. Remote Sensing for Land & Resources, 2018, 30(1): 196-202.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.01.27 OR https://www.gtzyyg.com/EN/Y2018/V30/I1/196

Tab.1 Band setting of GF-2

Fig.1 Spectral curves of vegetation reflectivity after FLAASH atmospheric correction

植被指数类型	计算公式^①
NDVI^[16]	$NDVI = ρ nir - ρ red ρ nir + ρ red$
比值植被指数^[17]	$RVI = ρ nir ρ red$
土壤调节植被指数^[18]	$SAVI = ρ nir - ρ red ρ nir + ρ red + L (L + 1)$
EV $I 219$	$EV I 2 = 2.5 ρ nir - ρ red (ρ nir + 2.4 ρ red) + 1$

Tab.2 Vegetation indexes used in this paper

Fig.2 Sketch map for structure of BP-ANN

Fig.3 VI-LAI regression fitting

植被指数回归模型	拟合方程	R²	RMSE
NDVI-LAI	y = 11.312 x - 2.495 9	0.700 1	0.348 7
	y = 0.251 1 $e 5.123 x$	0.705 8	0.335 3
	y = 4.615 5 ln x + 6.287	0.683 7	0.358 2
	y = 30.586 x²-14.286 x+2.786 8	0.718 5	0.337 9
	RVI-LAI	y=1.560 8 x - 1.649	0.641 5	0.381 3
y =0.374 1 $e 0.700 5 x$		0.635 2	0.368 8
y =3.719 9 ln x - 1.127 2		0.615 3	0.395 0
y=0.664 3 x²-1.742 6 x+2.380 7		0.664 9	0.368 6
SAVI-LAI		y =5.918 6 x - 1.513	0.601 8	0.545 5
	y =0.383 5 $e 2.716 4 x$	0.623 2	0.382 5
	y =3.400 5 ln x + 3.807 7	0.561 1	0.421 9
	y =15.543 x²-13.027 x+4.155 4	0.656 9	0.373 0
	EVI₂-LAI	y =4.219 2 x-0.946 3	0.615 0	0.395 2
y =0.501 7 $e 1.924 6 x$		0.629 0	0.375 9
y =2.867 3 ln x+3.07		0.569 8	0.417 7
y =6.989 9 x²-5.985 1 x+2.681 3		0.659 2	0.371 8

Tab.3 Regression models establishment

Fig.4 Flow chart of BP-ANN model

Tab.4 Results of neural network test

Fig.5 Comparison of LAI inversion results

Fig.6 Verification of LAI inversion results

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