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Acta Agronomica Sinica ›› 2024, Vol. 50 ›› Issue (4): 1030-1042.doi: 10.3724/SP.J.1006.2024.33030

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• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

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

ZOU Jia-Qi1(), WANG Zhong-Lin1,2, TAN Xian-Ming1, CHEN Liao-Yuan1, YANG Wen-Yu1, YANG Feng1,*()   

  1. 1College of Agronomy, Sichuan Agricultural University / Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture and Rural Affairs / Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu 611130, Sichuan, China
    2Rice Research Institute, Sichuan Agricultural University, Chengdu 611130, Sichuan, China
  • Received:2023-05-13 Accepted:2023-10-23 Online:2024-04-12 Published:2023-11-15
  • Contact: * E-mail: f.yang@sicau.edu.cn, Tel: 028-86290867
  • Supported by:
    National Key Research and Development Program of China(2022YFD2300902)

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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 gaus3770,64, rbio3.31635,2 and rbio3.3838,2 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 gaus3770,64 (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

Table 1

Years, sites, experimental carrier, cultivars, treatments for three experiments"

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%)

Fig. 1

Spectral reflectance of maize under normal moisture treatment after band removal A: jointing stage; B: tasseling stage; C: filling stage."

Fig. 2

Flowchart of the CWT CWT: canopy water content."

Table 2

Vegetation indices used in this study"

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]

Table 3

Descriptive statistics of maize canopy water content and grain yield"

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

Fig. 3

Correlation coefficient (r) matrix diagram between canopy water content and VIs of maize A: jointing stage; B: tasseling stage; C: filling stage. DVI: difference vegetation index; RVI: ratio vegetation index; NDVI: normalized difference vegetation index."

Table 4

Correlation between canopy water content and VIs of maize in different growth stages"

生育期Grown stages WI MSI DVI RVI NDVI
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**

Fig. 4

Correlation coefficient (r) matrix diagram between canopy water content and wavelet features of maize A: jointing stage; B: tasseling stage; C: filling stage."

Table 5

Correlation between canopy water content and wavelet features of maize at specific scale"

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 波长
W		 尺度Scale 波长
W		 尺度Scale 波长
W		 尺度Scale
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**

Table 6

Prediction models of canopy water content at different growth stages"

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 DVI750,751 y = -0.065-1.410 x 0.776** 0.054
RVI2150,1189 y = 0.543-0.837 x 0.814** 0.050
NDVI1471,1209 y = -0.105-0.805 x 0.877** 0.040
db3718,64 y = 0.097-0.017x 0.857** 0.044
bior1.5722,1 y = 0.087+3226.741x 0.868** 0.042
rbio3.3795,128 y = 0.181-0.020 x 0.870** 0.035
gaus3770,64 y = 0.097-0.018 x 0.862** 0.043
VT DVI1667,1583 y = 1.19-0.142 x 0.628** 0.131
RVI807,761 y = -15.049+15.193 x 0.688** 0.120
NDVI761,807 y = 0.127-31.888 x 0.689** 0.120
db31524,1 y = 1.194-3.761 x 0.648** 0.128
bior1.51530,1 y = 0.987-485.085 x 0.614** 0.134
rbio3.31635,2 y = 1.189-1.572 x 0.617** 0.133
gaus31641,2 y = 1.231-1.215 x 0.624** 0.132
R2 DVI2409,1065 y = 0.827-0.005 x 0.256** 0.130
RVI855,856 y = 582.227-581.635 x 0.290** 0.127
NDVI855,856 y = 0.592-1162.731 x 0.290** 0.127
db3840,2 y = 0.938-77.240 x 0.355** 0.121
bior1.5466,1 y = 0.817+45351.913 x 0.299** 0.126
rbio3.3838,2 y = 0.932+43.040 x 0.343** 0.122
gaus3466,1 y = 0.89+338.957 x 0.311** 0.125

Fig. 5

Relationship between measured and predicted canopy water content A: jointing stage; B: tasseling stage; C: filling stage; CWC: canopy water content."

Fig. 6

Prediction model of maize grain yield using canopy water content A: jointing stage; B: tasseling stage; C: filling stage."

Fig. 7

Testing of maize grain yield estimation model based on spectral reflectance data A: jointing stage; B: tasseling stage; C: filling stage."

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