Estimation of above-ground biomass of winter wheat based on vegetation indexes and texture features of multispectral images captured by unmanned aerial vehicle

doi:10.3969/j.issn.1004-1524.20221748

Acta Agriculturae Zhejiangensis ›› 2023, Vol. 35 ›› Issue (12): 2966-2976.DOI: 10.3969/j.issn.1004-1524.20221748

• Biosystems Engineering • Previous Articles Next Articles

Estimation of above-ground biomass of winter wheat based on vegetation indexes and texture features of multispectral images captured by unmanned aerial vehicle

ZHU Yongji¹^,²(), TAO Xinyu¹^,², CHEN Xiaofang¹^,², SU Xiangxiang¹^,², LIU Jikai¹^,², LI Xinwei¹^,²^,^*()

1. College of Resource and Environment, Anhui Science and Technology University, Fengyang 233100, Anhui, China
2. Agricultural Waste Fertilizer Utilization and Cultivated Land Quality Improvement Engineering Research Center, Anhui Province, Fengyang 233100, Anhui, China

Received:2022-12-05 Online:2023-12-25 Published:2023-12-27

Abstract

Abstract:

In order to achieve efficient non-destructive monitoring of winter wheat biomass, a field experiment was carried out from 2020 to 2021. Multispectral images were collected at 6 key growth stages by DJI Phantom 4 Multispectral (P4M) unmanned aerial vehicle (UAV). The correlations within the above-ground biomass (AGB) of winter wheat, vegeatation indexes and texture features of the multispectral images were analyzed, the characteristic variables were screened, and the linear regression, partial least squares regression (PLSR), random forest (RF) methods were used to construct biomass estimation models with different combinations of characteristic variables. It was shown that the correlation between vegetation index and winter wheat AGB was higher than that between texture feature and winter wheat AGB. The combination of vegetation index and texture feature could effectively reduce the spectral saturation at growth stages, and improve the estimation accuracy. For the AGB estimation based on linear regression with the screened characteristic variable(s), the performace at booting and mature stages was good; while for the the AGB estimation based on PLSR and RF, the performance at heading stage was good. In general, texture features coupled with vegetation indexes could effectively improve the estimation accuracy of winter wheat AGB. Estimation of winter wheat AGB via consumer-grade UAV was feasible at small and medium scales.

Key words: unmanned aerial vehicle, multispectral, winter wheat, biomass, texture features, vegetation index

CLC Number:

S126

ZHU Yongji, TAO Xinyu, CHEN Xiaofang, SU Xiangxiang, LIU Jikai, LI Xinwei. Estimation of above-ground biomass of winter wheat based on vegetation indexes and texture features of multispectral images captured by unmanned aerial vehicle[J]. Acta Agriculturae Zhejiangensis, 2023, 35(12): 2966-2976.

Figures/Tables 7

References 36

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指标 Index	各时期与生物量的相关系数Coefficients of correlation with biomass at certain growth stage
指标 Index	拔节期 Jointing stage	孕穗期 Booting stage	抽穗期 Heading stage	开花期 Flowering stage	灌浆期 Filling stage	成熟期 Mature stage
Mean	-0.84^***	0.71^***	0.70^***	-0.66^***	-0.61^***	-0.55^***
Var	0.77^***	0.51^**	0.47^***	0.57^***	0.65^***	0.57^***
Ent	0.66^***	0.51^**	0.34^*	0.43^*	0.55^***	0.63^***
NDVI	0.82^***	0.64^***	0.69^***	0.69^***	0.75^***	0.84^***
DVI	0.83^***	0.72^***	0.72^***	0.70^***	0.78^***	0.82^***
RVI	0.80^***	0.76^***	0.77^***	0.78^***	0.80^***	0.79^***
GNDVI	0.82^***	0.67^***	0.71^***	0.72^***	0.77^***	0.85^***
NDRE	0.82^***	0.70^***	0.75^***	0.76^***	0.80^***	0.56^***
RECI	0.81^***	0.70^***	0.76^***	0.77^***	0.81^***	0.58^***
TCARI	0.80^***	0.77^***	0.75^***	0.76^***	0.80^***	0.76^***

指标 Index	各时期与生物量的相关系数Coefficients of correlation with biomass at certain growth stage
指标 Index	拔节期 Jointing stage	孕穗期 Booting stage	抽穗期 Heading stage	开花期 Flowering stage	灌浆期 Filling stage	成熟期 Mature stage
Mean	-0.84^***	0.71^***	0.70^***	-0.66^***	-0.61^***	-0.55^***
Var	0.77^***	0.51^**	0.47^***	0.57^***	0.65^***	0.57^***
Ent	0.66^***	0.51^**	0.34^*	0.43^*	0.55^***	0.63^***
NDVI	0.82^***	0.64^***	0.69^***	0.69^***	0.75^***	0.84^***
DVI	0.83^***	0.72^***	0.72^***	0.70^***	0.78^***	0.82^***
RVI	0.80^***	0.76^***	0.77^***	0.78^***	0.80^***	0.79^***
GNDVI	0.82^***	0.67^***	0.71^***	0.72^***	0.77^***	0.85^***
NDRE	0.82^***	0.70^***	0.75^***	0.76^***	0.80^***	0.56^***
RECI	0.81^***	0.70^***	0.76^***	0.77^***	0.81^***	0.58^***
TCARI	0.80^***	0.77^***	0.75^***	0.76^***	0.80^***	0.76^***

生育时期 Growth stage	回归方程 Regression equation	植被指数 Vegetation index	R²	RMSE/(kg·hm^-2)	MAE/(kg·hm^-2)
拔节期Jointing stage	Y=5903.1X-264.18	DVI	0.72	291.69	227.02
孕穗期Booting stage	Y=151.98X+2630.5	TCARI	0.56	1 483.98	1 255.93
抽穗期Heading stage	Y=288.81X+2876.2	RVI	0.66	1 625.48	1 285.38
开花期Flowering stage	Y=531.35X+3407.3	RVI	0.59	2 790.83	2 165.32
灌浆期Filling stage	Y=0.7919X+2569.8	RECI	0.78	2 228.92	1 783.60
成熟期Mature stage	Y=53795X-14256	GNDVI	0.72	3 186.10	2 550.66

生育时期 Growth stage	回归方程 Regression equation	植被指数 Vegetation index	R²	RMSE/(kg·hm^-2)	MAE/(kg·hm^-2)
拔节期Jointing stage	Y=5903.1X-264.18	DVI	0.72	291.69	227.02
孕穗期Booting stage	Y=151.98X+2630.5	TCARI	0.56	1 483.98	1 255.93
抽穗期Heading stage	Y=288.81X+2876.2	RVI	0.66	1 625.48	1 285.38
开花期Flowering stage	Y=531.35X+3407.3	RVI	0.59	2 790.83	2 165.32
灌浆期Filling stage	Y=0.7919X+2569.8	RECI	0.78	2 228.92	1 783.60
成熟期Mature stage	Y=53795X-14256	GNDVI	0.72	3 186.10	2 550.66

生育期 Growth stage	回归方程 Regression equation	R²	RMSE/(kg·hm^-2)	MAE/(kg·hm^-2)
拔节期Jointing stage	Y=-88.129×X_Mean+1 538.180×X_DVI+4 396.559	0.72	290.24	225.70
孕穗期Booting stage	Y=-179.779×X_Mean+230.607×X_TCARI+6 275.049	0.57	1 470.04	1 246.08
抽穗期Heading stage	Y=-402.281×X_Mean+523.738×X_RVI+11 134.649	0.69	1 537.85	1 179.70
开花期Flowering stage	Y=621.788×X_Mean+889.089×X_RVI-21 373.639	0.63	2 659.75	2 285.44
灌浆期Filling stage	Y=653.858×X_Var+10 446.017×X_RECI+1 338.706	0.79	2 109.77	1 683.14
成熟期Mature stage	Y=14 332.459×X_Ent+47 702.454×X_GNDVI-38 563.559	0.74	3111.02	2 596.56

Estimation of above-ground biomass of winter wheat based on vegetation indexes and texture features of multispectral images captured by unmanned aerial vehicle

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生育期 Growth stage	植被指数Vegetation indexes						植被指数+纹理特征Vegetation indexes+texture features
	建模集Modeling set			验证集Validation set			建模集Modeling set			验证集Validation set
	R²	RMSE/ (kg· hm^-2)	MAE/ (kg· hm^-2)	R²	RMSE/ (kg· hm^-2)	MAE/ (kg· hm^-2)	R²	RMSE/ (kg· hm^-2)	MAE/ (kg· hm^-2)	R²	RMSE/ (kg· hm^-2)	MAE/ (kg· hm^-2)
拔节期	0.93	151.48	112.68	0.46	486.82	363.89	0.98	77.61	63.17	0.56	453.26	368.62
Jointing stage
孕穗期	0.64	1 334.05	1 179.94	0.60	1 372.66	1 068.87	0.64	1 340.06	1 144.86	0.62	1 343.03	1 033.66
Booting stage
抽穗期	0.69	1 543.34	1 172.91	0.66	2 864.57	2 255.11	0.78	1 289.56	984.51	0.82	2 428.92	1 847.91
Heading stage
开花期	0.62	2 677.76	2 200.77	0.51	2 632.76	2 349.72	0.75	2 179.16	1 841.78	0.57	2 592.52	1 815.60
Flowering stage
灌浆期	0.76	2 263.58	1 792.08	0.51	4 007.59	3 437.22	0.89	1 543.50	1 416.67	0.53	3 751.17	3 144.52
Filling stage
成熟期	0.82	2 582.25	2 024.81	0.69	3 196.87	2 788.14	0.84	2 422.84	1 966.21	0.73	2 949.30	2 479.46
Mature stage

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