Study on Estimation of Water Status of Winter Wheat in Different Growth Stages Based on Hyperspectral Imaging

doi:10.6048/j.issn.1001-4330.2022.03.001

Abstract

Abstract:

【Objective】 Real-time and rapid acquisition of crop moisture status is extremely important for accurate management of crop water in farmland. 【Methods】 This study took typical drip irrigation winter wheat in Xinjiang as the research object, applied hyperspectral imaging technology to obtain winter wheat canopy spectrum information, and smoothed and conversed the data of the raw spectral reflectance. Simple linear regression (SLR), principal components regression (PCR) and partial least squares regression (PLSR) methods were used to construct plant moisture content estimation models for the raw spectrum and transformed spectrum of winter wheat canopy respectively. 【Results】 The results showed that the correlation between the raw spectral reflectance of winter wheat canopy and the plant moisture content was not high. Data transformation of the raw spectral reflectance significantly enhanced the correlation with the moisture content and the number of relevant bands. Among them, the correlation coefficient between reciprocal first-order differential transformation and winter wheat plant moisture content was the largest, which was -0.893,0. However, the band position corresponding to the maximum correlation coefficient of each transformation was not fixed. Among the three modeling methods, the PLSR method had the highest model accuracy, while the logarithmic transformed PLSR model had the highest estimation accuracy. The model $R_{p}^{2}$ , RMSE_p and RPD values were 0.880,8, 3.251,2%, and 2.934,3, respectively. There were differences in the estimation model accuracy of winter wheat at different growth stages. The estimation model accuracy of the jointing stage and the heading stage was lower, and the mid-grouting stage was the highest. The estimated model $R_{p}^{2}$ , RMSE_p and RPD values were 0.904,8, 1.381,1%, 3.454,7, respectively. 【Conclusion】 It is feasible to use hyperspectral imaging technology to estimate the moisture content of winter wheat plants, and the best estimation effect is in the mid-grouting stage.

Key words: winter wheat; moisture content; spectral transformation; growth stage; RPD

摘要：

【目的】研究实时、快速估测冬小麦不同生育时期水分状况并构建模型,为冬小麦水分精准管理提供科学依据。【方法】以新疆典型滴灌冬小麦为研究对象,应用高光谱成像技术获取冬小麦冠层光谱信息,并对原始光谱反射率进行平滑和数据变换,利用一元线性回归(Simple linear regression,SLR)、主成分回归(Principal components regression,PCR)和偏最小二乘回归(Partial least squares regression,PLSR)3种建模方法,对冬小麦冠层原始光谱及变换光谱分别构建植株水分含量估测模型。【结果】冬小麦冠层原始光谱反射率与植株水分含量相关性不高,对原始光谱反射率进行数据变换可以显著增强与水分含量的相关性和相关波段数,其中倒数一阶微分变换与冬小麦植株水分含量的相关系数最大,为-0.893 0,但不同变换最优相关系数所对应的波段位置并不固定。PLSR方法的模型精度最高,对数变换的PLSR模型估测精度最高,模型 $R_{p}^{2}$ 、RMSE_p、RPD值分别为0.880 8、3.251 2%、2.934 3;冬小麦不同生育时期估测模型精度存在差异,拔节期、抽穗期估测模型精度较低,灌浆中期最高,其估测模型 $R_{p}^{2}$ 、RMSE_p、RPD值分别为0.904 8、1.381 1%、3.454 7。【结论】利用高光谱成像技术对估测冬小麦植株水分含量是可行的,在灌浆中期的估测效果最佳。

关键词: 冬小麦, 水分含量, 光谱变换, 生育时期, 残差预测偏差

CLC Number:

S512

ZHU Zhen, LI Tiansheng, CUI Jing, CHEN Jianhua, SHI Xiaoyan, JIANG Menghao, WANG Haijiang. Study on Estimation of Water Status of Winter Wheat in Different Growth Stages Based on Hyperspectral Imaging[J]. Xinjiang Agricultural Sciences, 2022, 59(3): 521-532.

祝榛, 李天胜, 崔静, 陈建华, 史晓艳, 姜孟豪, 王海江. 基于高光谱成像估测冬小麦不同生育时期水分状况[J]. 新疆农业科学, 2022, 59(3): 521-532.

Figures/Tables 7

Table 1 Irrigation volume of different irrigation treatments (mm)

处理 Treatment	灌溉定额 Irrigation quota										总计 Total
处理 Treatment	1	2	3	4	5	6	7	8	9	10	总计 Total
W₁	60.00	90.00	0	0	0	0	0	0	0	0	150.00
W₂	60.00	90.00	18.75	18.75	18.75	18.75	18.75	18.75	18.75	18.75	300.00
W₃	60.00	90.00	37.50	37.50	37.50	37.50	37.50	37.50	37.50	37.50	450.00
W₄	60.00	90.00	56.25	56.25	56.25	56.25	56.25	56.25	56.25	56.25	600.00
W₅	60.00	90.00	75.00	75.00	75.00	75.00	75.00	75.00	75.00	75.00	750.00

Table 2 Descriptive statistical analysis of plant moisture content in different growth stage of winter wheat(%)

生育时期 Growth stages	样本类型 Sample type	样本数 Numbers	最大值 Max	最小值 Min	平均值 Average	标准差 SD
拔节期 Jointing stage	建模集	22	86.89	78.69	83.78	2.15
	预测集	8	86.88	80.16	83.42	2.74
	检验样本	25	84.10	78.62	81.95	1.31
抽穗期 Heading stage	建模集	22	81.70	71.64	78.66	2.28
	预测集	8	81.66	73.56	78.17	3.27
	检验样本	25	81.35	76.39	79.24	1.47
扬花期 Flowering stage	建模集	22	78.86	70.75	74.48	2.02
	预测集	8	78.10	70.81	74.30	2.32
	检验样本	25	80.54	69.80	76.14	2.78
灌浆前期 Pre-grouting stage	建模集	22	73.38	55.37	65.85	3.82
	预测集	8	73.32	58.18	65.20	5.42
	检验样本	25	72.25	60.51	66.42	3.26
灌浆中期 Mid-grouting stage	建模集	22	66.86	52.45	59.85	3.93
	预测集	8	66.72	53.73	59.52	4.77
	检验样本	25	65.95	52.10	59.53	4.30
全生育期 Whole growth stage	建模集	112	86.89	53.73	72.47	9.15
	预测集	38	86.62	52.45	72.26	9.54
	检验样本	125	84.10	52.10	73.51	8.42

Fig.1 Variation characteristics of canopy spectral reflectance of winter wheat under different water condition

Fig.2 Correlation analysis between plant water content of winter wheat and different transformed spectral reflectance

Table 3 Estimation model of plant moisture content of winter wheat

光谱类型 Spectral types	SLR						PCR						PLSR
	波段数量 Numbers	建模集 Calibration sample		预测集 Prediction sample		RPD	波段数量 Numbers	建模集 Calibration sample		预测集 Prediction sample		RPD	波段数量 Numbers	建模集 Calibration sample		预测集 Prediction sample		RPD
	波段数量 Numbers	$R_{c}^{2}$	RMSE_c (%)	$R_{p}^{2}$	RMSE_p (%)	RPD	波段数量 Numbers	$R_{c}^{2}$	RMSE_c (%)	$R_{p}^{2}$	RMSE_p (%)	RPD	波段数量 Numbers	$R_{c}^{2}$	RMSE_c (%)	$R_{p}^{2}$	RMSE_p (%)	RPD
T₀	1	0.530 5	6.244 7	0.670 2	5.512 3	1.730 7	319	0.807 7	3.996 9	0.765 4	4.568 1	2.088 4	319	0.872 2	3.258 0	0.800 8	3.986 4	2.393 2
T₁	1	0.530 3	6.246 0	0.677 7	5.461 1	1.746 9	319	0.749 6	4.112 0	0.769 9	4.517 5	2.111 8	319	0.878 5	3.176 2	0.843 4	3.727 0	2.559 7
T₂	1	0.585 6	5.867 2	0.755 3	4.910 6	1.942 7	319	0.780 2	4.273 0	0.788 3	4.332 5	2.202 0	319	0.853 2	3.492 4	0.861 1	3.509 8	2.718 1
T₃	1	0.597 3	5.783 4	0.757 8	4.954 8	1.925 4	319	0.843 4	3.606 9	0.873 5	3.348 8	2.848 8	319	0.863 2	3.371 2	0.880 8	3.251 2	2.934 3
T₄	1	0.510 2	6.378 5	0.579 6	6.376 8	1.496 0	319	0.471 9	6.622 8	0.508 7	6.600 8	1.445 3	319	0.499 2	6.449 4	0.505 4	6.623 0	1.440 4
T₅	1	0.651 7	5.378 6	0.675 5	5.375 9	1.774 6	319	0.809 9	3.974 1	0.794 1	4.272 8	2.232 7	319	0.854 2	3.480 2	0.820 4	3.990 6	2.390 6
T₆	1	0.398 1	7.070 6	0.469 9	7.035 2	1.356 0	319	0.795 4	4.122 1	0.665 4	5.447 3	1.751 3	319	0.892 7	2.985 1	0.014	4.196 6	2.273 3
T₇	1	0.638 0	5.483 3	0.799 6	4.369 2	2.183 5	319	0.831 3	3.743 4	0.820 2	3.993 4	2.388 9	319	0.882 1	3.129 6	0.877 1	3.301 9	2.889 3
T₈	1	0.424 0	6.917 0	0.465 9	7.029 0	1.357 2	319	0.803 6	4.038 7	0.773 5	4.481 5	2.128 8	319	0.922 1	2.544 1	0.820 9	3.984 8	2.394 1
T₉	1	0.660 4	5.311 4	0.704 0	5.412 5	1.762 6	319	0.509 1	6.385 5	0.571 8	6.162 4	1.548 1	319	0.321 4	7.507 5	0.471 8	6.844 3	1.393 9
T₁₀	1	0.548 6	6.123 6	0.571 0	6.445 8	1.480 0	319	0.385 3	8.936 2	0.446 0	7.851 3	1.215 1	319	0.632 6	5.523 9	0.489 7	6.635 0	1.437 8
T₁₁	1	0.775 1	4.321 9	0.800 4	4.305 4	2.215 8	319	0.681 8	5.141 1	0.775 3	4.463 7	2.137 2	319	0.857 6	3.439 2	0.859 1	3.546 9	2.689 7
T₁₂	1	0.694 4	5.038 5	0.767 7	4.823 1	1.978 0	319	0.779 2	4.650 0	0.756 2	4.649 7	2.051 8	319	0.781 5	4.259 7	0.657 6	5.510 5	1.731 3

Table 3 Estimation model of plant moisture content of winter wheat

光谱类型 Spectral types	SLR						PCR						PLSR
	波段数量 Numbers	建模集 Calibration sample		预测集 Prediction sample		RPD	波段数量 Numbers	建模集 Calibration sample		预测集 Prediction sample		RPD	波段数量 Numbers	建模集 Calibration sample		预测集 Prediction sample		RPD
	波段数量 Numbers	$R_{c}^{2}$	RMSE_c (%)	$R_{p}^{2}$	RMSE_p (%)	RPD	波段数量 Numbers	$R_{c}^{2}$	RMSE_c (%)	$R_{p}^{2}$	RMSE_p (%)	RPD	波段数量 Numbers	$R_{c}^{2}$	RMSE_c (%)	$R_{p}^{2}$	RMSE_p (%)	RPD
T₀	1	0.530 5	6.244 7	0.670 2	5.512 3	1.730 7	319	0.807 7	3.996 9	0.765 4	4.568 1	2.088 4	319	0.872 2	3.258 0	0.800 8	3.986 4	2.393 2
T₁	1	0.530 3	6.246 0	0.677 7	5.461 1	1.746 9	319	0.749 6	4.112 0	0.769 9	4.517 5	2.111 8	319	0.878 5	3.176 2	0.843 4	3.727 0	2.559 7
T₂	1	0.585 6	5.867 2	0.755 3	4.910 6	1.942 7	319	0.780 2	4.273 0	0.788 3	4.332 5	2.202 0	319	0.853 2	3.492 4	0.861 1	3.509 8	2.718 1
T₃	1	0.597 3	5.783 4	0.757 8	4.954 8	1.925 4	319	0.843 4	3.606 9	0.873 5	3.348 8	2.848 8	319	0.863 2	3.371 2	0.880 8	3.251 2	2.934 3
T₄	1	0.510 2	6.378 5	0.579 6	6.376 8	1.496 0	319	0.471 9	6.622 8	0.508 7	6.600 8	1.445 3	319	0.499 2	6.449 4	0.505 4	6.623 0	1.440 4
T₅	1	0.651 7	5.378 6	0.675 5	5.375 9	1.774 6	319	0.809 9	3.974 1	0.794 1	4.272 8	2.232 7	319	0.854 2	3.480 2	0.820 4	3.990 6	2.390 6
T₆	1	0.398 1	7.070 6	0.469 9	7.035 2	1.356 0	319	0.795 4	4.122 1	0.665 4	5.447 3	1.751 3	319	0.892 7	2.985 1	0.014	4.196 6	2.273 3
T₇	1	0.638 0	5.483 3	0.799 6	4.369 2	2.183 5	319	0.831 3	3.743 4	0.820 2	3.993 4	2.388 9	319	0.882 1	3.129 6	0.877 1	3.301 9	2.889 3
T₈	1	0.424 0	6.917 0	0.465 9	7.029 0	1.357 2	319	0.803 6	4.038 7	0.773 5	4.481 5	2.128 8	319	0.922 1	2.544 1	0.820 9	3.984 8	2.394 1
T₉	1	0.660 4	5.311 4	0.704 0	5.412 5	1.762 6	319	0.509 1	6.385 5	0.571 8	6.162 4	1.548 1	319	0.321 4	7.507 5	0.471 8	6.844 3	1.393 9
T₁₀	1	0.548 6	6.123 6	0.571 0	6.445 8	1.480 0	319	0.385 3	8.936 2	0.446 0	7.851 3	1.215 1	319	0.632 6	5.523 9	0.489 7	6.635 0	1.437 8
T₁₁	1	0.775 1	4.321 9	0.800 4	4.305 4	2.215 8	319	0.681 8	5.141 1	0.775 3	4.463 7	2.137 2	319	0.857 6	3.439 2	0.859 1	3.546 9	2.689 7
T₁₂	1	0.694 4	5.038 5	0.767 7	4.823 1	1.978 0	319	0.779 2	4.650 0	0.756 2	4.649 7	2.051 8	319	0.781 5	4.259 7	0.657 6	5.510 5	1.731 3

Table 4 PLSR estimation model of winter wheat plant moisture content in different growth stages based on SPA

生育时期 Growth stages	波段数量 Numbers	建模集 Calibration sample		预测集 Prediction sample		RPD
生育时期 Growth stages	波段数量 Numbers	$R_{c}^{2}$	RMSE_c (%)	$R_{p}^{2}$	RMSE_p (%)	RPD
拔节期 Jointing stage	6	0.348 2	1.520 3	0.281 8	2.476 7	1.104 7
抽穗期 Heading stage	5	0.476 3	1.801 4	0.318 1	2.912 2	1.123 8
扬花期 Flowering stage	10	0.849 3	0.597 1	0.837 9	0.867 5	2.679 6
灌浆前期 Pre-grouting stage	5	0.852 2	2.499 1	0.853 0	1.993 1	2.720 8
灌浆中期 Mid-grouting stage	13	0.908 7	1.490 5	0.904 8	1.381 1	3.454 7
全生育期 Whole growth stage	9	0.856 1	3.457 4	0.892 5	3.088 0	3.089 4

Table 4 PLSR estimation model of winter wheat plant moisture content in different growth stages based on SPA

生育时期 Growth stages	波段数量 Numbers	建模集 Calibration sample		预测集 Prediction sample		RPD
生育时期 Growth stages	波段数量 Numbers	$R_{c}^{2}$	RMSE_c (%)	$R_{p}^{2}$	RMSE_p (%)	RPD
拔节期 Jointing stage	6	0.348 2	1.520 3	0.281 8	2.476 7	1.104 7
抽穗期 Heading stage	5	0.476 3	1.801 4	0.318 1	2.912 2	1.123 8
扬花期 Flowering stage	10	0.849 3	0.597 1	0.837 9	0.867 5	2.679 6
灌浆前期 Pre-grouting stage	5	0.852 2	2.499 1	0.853 0	1.993 1	2.720 8
灌浆中期 Mid-grouting stage	13	0.908 7	1.490 5	0.904 8	1.381 1	3.454 7
全生育期 Whole growth stage	9	0.856 1	3.457 4	0.892 5	3.088 0	3.089 4

Fig.3 Estimation model checking of plant moisture content in different growth stages

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