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作物学报

作物学报 ›› 2024, Vol. 50 ›› Issue (4): 1030-1042.doi: 10.3724/SP.J.1006.2024.33030

所属专题： 玉米：耕作栽培·生理生化

• 耕作栽培·生理生化 • 上一篇    下一篇

基于连续小波变换估测干旱胁迫下玉米籽粒产量

邹佳琪1(), 王仲林1,2, 谭先明1, 陈燎原1, 杨文钰1, 杨峰1,*()   

  1. 1四川农业大学农学院 / 农业农村部西南作物生理生态与耕作重点实验室 / 四川省作物带状复合种植工程技术研究中心, 四川成都 611130
    2四川农业大学水稻研究所, 四川成都 611130
  • 收稿日期:2023-05-13 接受日期:2023-10-23 出版日期:2024-04-12 网络出版日期:2023-11-15
  • 通讯作者: * 杨峰, E-mail: f.yang@sicau.edu.cn, Tel: 028-86290867
  • 作者简介:E-mail: 987732088@qq.com
  • 基金资助:
    国家重点研发计划项目(2022YFD2300902)

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 Published:2024-04-12 Published online: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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摘要：

利用高光谱遥感技术监测作物水分状况和籽粒产量, 对于调控作物生长、优化水分管理和改善产量形成具有重要意义。本研究玉米品种选用正红505, 于2018—2019年在四川雅安和仁寿的试验田设置4个水分处理(正常水分、轻度、中度和重度干旱), 分析玉米在拔节期(V6)、抽雄期(VT)和灌浆期(R2)的冠层含水量(canopy water content, CWC)与籽粒产量的定量关系, 利用植被指数和连续小波变换对光谱反射率数据进行处理, 采用线性回归方法构建CWC定量反演模型, 进一步探索以CWC为桥梁建立的玉米籽粒产量的预测模型效果。结果表明, (1) 利用小波特征构建的CWC估测模型的预测效果高于植被指数, V6、VT和R2期分别以小波特征gaus3770,64、rbio3.31635,2和rbio3.3838,2构建的线性回归模型检验精度较高, R2分别为0.770、0.291和0.233。(2) CWC与玉米籽粒产量间建立的线性回归模型均达极显著水平(P<0.01), V6、VT和R2期的R2分别为0.596、0.366和0.439。(3) 基于光谱反射率构建的产量预测模型以V6期小波特征gaus3770,64的验证效果最好(R2 = 0.577, RMSE = 1.625 t hm-2), 可作为预测玉米籽粒产量的最佳时期。因此, 本研究提出的“光谱反射率—冠层含水量—产量”建模方法能够实现对玉米籽粒产量的精确估测, 为未来大面积监测玉米生产力提供了理论依据。

关键词: 玉米, 籽粒产量, 冠层含水量, 植被指数, 小波特征

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

表1

3个试验年份、地点、试验载体、品种及处理"

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%)

图1

剔除波段后正常水分处理下玉米光谱反射率 A: 拔节期; B: 抽雄期; C: 灌浆期。"

图2

CWT的流程图 CWT: 冠层含水量。"

表2

本文采用的植被指数"

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]

表3

玉米冠层含水量和籽粒产量的描述性统计"

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

图3

玉米冠层含水量与VIs之间相关系数(r)矩阵图 A: 拔节期; B: 抽雄期; C: 灌浆期。DVI：差值植被指数；RVI：比值植被指数；NDVI：归一化差值植被指数。"

表4

不同生育期玉米冠层含水量与VIs的相关性"

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**

图4

玉米冠层含水量与小波特征之间相关系数(r)矩阵图 A: 拔节期; B: 抽雄期; C: 灌浆期。"

表5

玉米冠层含水量与特定尺度小波特征之间的相关性"

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**

表6

不同生育期冠层含水量的预测模型"

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

图5

冠层含水量实测值与预测值的关系 A: 拔节期; B: 抽雄期; C: 灌浆期；CWC: 冠层含水量。"

图6

利用冠层含水量预测玉米籽粒产量的模型 A: 拔节期; B: 抽雄期; C: 灌浆期。"

图7

基于光谱反射率数据的玉米籽粒产量估测模型的检验 A: 拔节期; B: 抽雄期; C: 灌浆期。"

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