基于连续小波变换估测干旱胁迫下玉米籽粒产量

doi:10.3724/SP.J.1006.2024.33030

Abstract

Abstract:

The use of hyperspectral remote sensing technology to monitor crop water status and grain yield is important for regulating crop growth, optimizing water management and improving yield formation. Zhenghong 505 was selected as the maize variety in this study, to analyze the quantitative relationship between canopy water content (CWC) and grain yield of maize at jointing stage (V6), tasseling stage (VT), and filling stage (R2), four drought stress treatments (well-watered, mild, intermediate and severe drought) were conducted in the experimental fields of Ya’an and Renshou in Sichuan Province from 2018 to 2019. The spectral reflectance data were processed using vegetation indices and continuous wavelet transform, and a linear regression method was used to construct a quantitative CWC inversion model to explore the effectiveness of CWC as a bridge to establish a spectral inversion model for maize grain yield estimation. The results showed that the CWC estimation models using wavelet features was better than that of vegetation indices, and the linear regression models constructed with wavelet features gaus3_770,64, rbio3.3_1635,2 and rbio3.3_838,2 at the V6, VT, and R2 stages had high test accuracy with the R² of 0.770, 0.291, and 0.233, respectively. The linear regression models established between CWC and maize grain yield all reached highly significant levels (P < 0.01), with R² of 0.596, 0.366 and 0.439 at the V6, VT, and R2 stages, respectively. The yield prediction model based on the basis of spectral reflectance was the best validated with the wavelet feature gaus3_770,64 (R² = 0.577, RMSE = 1.625 t hm^-2) at V6 stage, which can be used as the best period for predicting maize grain yield. Therefore, the “spectral reflectance-canopy water content-yield” modeling method proposed in this study can achieve an accurate estimation of maize grain yield and provide a theoretical basis for future large-scale monitoring of maize productivity.

Key words: maize, grain yield, canopy water content, vegetation indices, wavelet features

ZOU Jia-Qi, WANG Zhong-Lin, TAN Xian-Ming, CHEN Liao-Yuan, YANG Wen-Yu, YANG Feng. Estimation of maize grain yield under drought stress based on continuous wavelet transform[J].Acta Agronomica Sinica, 2024, 50(4): 1030-1042.

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References 48

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试验编号Exp. No.	年份和地点 Year and site	试验载体 Experimental carrier	玉米品种 Maize cultivar	试验处理 Treatment
试验1 Exp. 1	2018, 雅安 2018, Ya’an	干旱池 Drought pool	正红505 Zhenghong 505	水分等级(占田间持水量百分比) 正常水分(60%-70%), 轻度干旱(45%-55%), 中度干旱(30%-40%), 重度干旱(15%-25%) Moisture degree (percentage of field capacity) WW (60%-70%), MD (45%-55%), ID (30%-40%), SD (15%-25%)
试验2 Exp. 2	2018, 仁寿 2018, Renshou	大田 Field	正红505 Zhenghong 505	倾斜农田(坡度/°) 斜坡顶部, 斜坡中间, 斜坡下方 Sloping farmland (gradient/°) Top of the slope, middle of the slope, under the slope
试验3 Exp. 3	2019, 雅安 2019, Ya’an	干旱池 Drought pool	正红505 Zhenghong 505	水分等级(占田间持水量百分比) 正常水分(60%-70%), 轻度干旱(45%-55%), 中度干旱(30%-40%), 重度干旱(15%-25%) Moisture degree (percentage of field capacity) WW (60%-70%), MD (45%-55%), ID (30%-40%), SD (15%-25%)

植被指数 Vegetation indices	公式 Formula	参考文献 Reference
水分指数Water index (WI)	$\frac{{{R}_{900}}}{{{R}_{970}}}$	[29]
水分胁迫指数Moisture stress index (MSI)	$\frac{{{R}_{1599}}}{{{R}_{819}}}$	[30-31]
比值植被指数Ratio vegetation index (RVI)	$\frac{{{R}_{i}}}{{{R}_{j}}}$	[32]
差值植被指数Difference vegetation index (DVI)	${{R}_{i}}-{{R}_{j}}$	[33]
归一化差值植被指数Normalized difference vegetation index (NDVI)	$\frac{\left( {{R}_{i}}-{{R}_{j}} \right)}{\left( {{R}_{i}}+{{R}_{j}} \right)}$	[34]

参数 Parameters	数据集 Datasets	生育期 Growth stages	样本数 Number of samples	最大值Maximum	最小值Minimum	均值 Mean	标准差Standard deviation	变异系数Coefficient of variation (%)
冠层含水量 Canopy water content (kg m^-2)	建模集 Calibration set	V6	44	0.468	0.051	0.222	0.115	51.7
		VT	48	1.246	0.479	0.847	0.215	25.4
		R2	38	1.198	0.581	0.889	0.151	16.9
	验证集 Validation set	V6	46	0.836	0.056	0.347	0.227	65.4
		VT	33	0.952	0.174	0.493	0.168	34.0
		R2	36	0.993	0.275	0.642	0.197	30.6
籽粒产量 Grain yield (t hm^-2)	建模集 Calibration set	V6	12	5.051	3.353	4.060	0.547	13.5
		VT	12	6.024	3.199	4.772	0.851	17.8
		R2	10	6.200	3.420	5.403	0.828	15.3
	验证集 Validation set	V6	12	8.761	3.940	5.964	1.249	20.9
		VT	9	5.764	3.425	4.782	0.926	19.4
		R2	9	11.101	6.402	9.022	1.413	15.7

生育期Grown stages	WI		MSI		DVI		RVI		NDVI
生育期Grown stages	波长 W	相关系数 r	波长 W	相关系数 r	波长 W	相关系数 r	波长 W	相关系数 r	波长 W	相关系数 r
V6 n = 44	900, 970	0.691^**	1599, 819	-0.766^**	750, 751	0.881^**	2150, 1189	0.933^**	1471, 1209	0.937^**
VT n = 48	900, 970	0.684^**	1599, 819	-0.395^**	1667, 1583	0.793^**	807, 761	-0.831^**	761, 807	0.830^**
R2 n = 38	900, 970	0.117	1599, 819	0.142	2409, 1065	0.506^**	855, 856	0.538^**	855, 856	0.538^**

生育期Grown stage	db3			bior1.5			rbio3.3			gaus3
	特征位置 Feature location		相关系数 r	特征位置 Feature location		相关系数 r	特征位置 Feature location		相关系数 r	特征位置 Feature location		相关系数 r
	波长 W	尺度Scale	相关系数 r	波长 W	尺度Scale	相关系数 r	波长 W	尺度Scale	相关系数 r	波长 W	尺度Scale	相关系数 r
V6 n = 44	718	64	-0.926^**	722	1	0.932^**	795	128	-0.933^**	770	64	-0.929^**
VT n = 48	1524	1	-0.805^**	1530	1	-0.783^**	1635	2	-0.786^**	1641	2	-0.790^**
R2 n = 38	840	2	-0.596^**	466	1	0.548^**	838	2	0.585^**	466	1	0.557^**

Estimation of maize grain yield under drought stress based on continuous wavelet transform

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生育期 Growth stage	参数 Parameter	回归模型 Regression model	决定系数 R²	均方根误差 RMSE (kg m^-2)
V6	DVI_750,751	y = -0.065-1.410 x	0.776^**	0.054
	RVI_2150,1189	y = 0.543-0.837 x	0.814^**	0.050
	NDVI_1471,1209	y = -0.105-0.805 x	0.877^**	0.040
	db3_718,64	y = 0.097-0.017x	0.857^**	0.044
	bior1.5_722,1	y = 0.087+3226.741x	0.868^**	0.042
	rbio3.3_795,128	y = 0.181-0.020 x	0.870^**	0.035
	gaus3_770,64	y = 0.097-0.018 x	0.862^**	0.043
VT	DVI_1667,1583	y = 1.19-0.142 x	0.628^**	0.131
	RVI_807,761	y = -15.049+15.193 x	0.688^**	0.120
	NDVI_761,807	y = 0.127-31.888 x	0.689^**	0.120
	db3_1524,1	y = 1.194-3.761 x	0.648^**	0.128
	bior1.5_1530,1	y = 0.987-485.085 x	0.614^**	0.134
	rbio3.3_1635,2	y = 1.189-1.572 x	0.617^**	0.133
	gaus3_1641,2	y = 1.231-1.215 x	0.624^**	0.132
R2	DVI_2409,1065	y = 0.827-0.005 x	0.256^**	0.130
	RVI_855,856	y = 582.227-581.635 x	0.290^**	0.127
	NDVI_855,856	y = 0.592-1162.731 x	0.290^**	0.127
	db3_840,2	y = 0.938-77.240 x	0.355^**	0.121
	bior1.5_466,1	y = 0.817+45351.913 x	0.299^**	0.126
	rbio3.3_838,2	y = 0.932+43.040 x	0.343^**	0.122
	gaus3_466,1	y = 0.89+338.957 x	0.311^**	0.125

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