Predicting Yield Reduction Rates of Frost-Damaged Winter Wheat After Jointing Using Sentinel-2 Broad-Waveband Spectral Indices

Ai-ping ZHAO; Jun-cheng MA; Yong-feng WU; Xin HU; De-chao REN; Chong-rui LI

doi:10.3964/j.issn.1000-0593(2022)07-2225-08

Spectroscopy and Spectral Analysis, Volume. 42, Issue 7, 2225(2022)

Predicting Yield Reduction Rates of Frost-Damaged Winter Wheat After Jointing Using Sentinel-2 Broad-Waveband Spectral Indices

Ai-ping ZHAO^1、*, Jun-cheng MA^1、1;, Yong-feng WU^{1、1; *;}, Xin HU^2、2;, De-chao REN^2、2;, and Chong-rui LI^1、1;

Author Affiliations

¹1. Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China

²2. Wheat Research Institute, Shangqiu Academy of Agriculture and Forestry Sciences, Shangqiu 476000, China

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Figures & Tables(9)

Fig. 1. Spatial distribution of winter wheat planting area and the distribution of random sampling points

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Fig. 2. Reflectance curves of winter wheat canopy during the dimple occurrence (a) and the stigma elongation (b) under low-temperature treatments
Note: CK is the control, without low-temperature treatment, normal growth; Wavebands of 1 340~1 450 and 1 800~2 050 nm are removed due to strong absorption of water vapor

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Fig. 3. Scatter plots showing the measured and predicted yield reduction rates of winter wheat based on the constructed spectral indices B8a-B12 (a), B8-B12 (b) in this study and the published spectral index EVI (c)

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Fig. 4. Spatial distribution of the winter wheat predicted yield reduction rates by the constructed spectral index B8a-B12 in this study

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Table 1. Published spectral indices and corresponding expressions selected in this study

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Table 1. Published spectral indices and corresponding expressions selected in this study

类型	光谱指数	符号	表达式	文献
宽波段光谱指数	归一化植被指数	NDVI	(R_NIR-R_R)/(R_NIR+R_R)	[8]
	绿波段归一化植被指数	GNDVI	(R_NIR-R_G)/(R_NIR+R_G)	[8]
	比值植被指数	RVI	R_NIR/R_R	[8]
	差值植被指数	DVI	R_NIR-R_R	[8]
	增强植被指数	EVI	2.5(R_NIR-R_R)/(R_NIR+6R_R-7.5R_B+1)	[8]
	可见大气阻抗指数	VARI_green	(R_G-R_R)/(R_G+R_R-R_B)	[5]
窄波段光谱指数	比值植被指数	RVI_800_450	R₈₀₀/R₄₅₀	[8]
	三角绿色指数	TGI	-0.5(190(R₆₇₀-R₅₅₀)-120(R₆₇₀-R₄₈₀))	[2]
	归一化差异水分指数	NDWI₁₆₄₀	(R₈₅₈-R₁₆₄₀)/(R₈₅₈+R₁₆₄₀)	[5]
	归一化差异水分指数	NDWI₂₁₃₀	(R₈₅₈-R₂₁₃₀)/(R₈₅₈+R₂₁₃₀)	[5]
	归一化多波段干旱指	NMDI	[R₈₆₀-(R₁₆₄₀-R₂₁₃₀)]/[R₈₆₀+(R₁₆₄₀-R₂₁₃₀)]	[15]
	归一化差异红指数	NDII	(R₈₅₀-R₁₆₅₀)/(R₈₅₀+R₁₆₅₀)	[5]
	水分胁迫	MSI	R₁₆₀₀/R₈₂₀	[16]
	Datt指数	Datt-I	(R₈₅₀-R₇₁₀)/(R₈₅₀-R₆₈₀)	[16]
	水波段指数	WBI	R₉₅₀/R₉₀₀	[16]
	改进叶绿素吸收率指数	MCARI	[(R₇₀₀-R₆₇₀)-0.2(R₇₀₀-R₅₅₀)]×(R₇₀₀/R₆₇₀)	[2]
	改进三角植被指数	MTVI2	(1.5(1.2(R₈₀₀-R₅₅₀)-2.5(R₆₇₀-R₅₅₀))/((2R₈₀₀+1)²- (6R₈₀₀-5 $R_{670}^{1 / 2}$ )-0.5)^1/2)	[17]
	改进叶绿素吸收率指数/ 改进三角植被指数	MCARI/MTVI2	[(R₇₀₀-R₆₇₀)-0.2(R₇₀₀-R₅₅₀)]×(R₇₀₀/R₆₇₀)/MTVI2	[17]
	生理反射	PRI	(R₅₅₀-R₅₃₁)/(R₅₅₀+R₅₃₁)	[2]

Table 2. Essential parameters of the selected Sentinel-2 wavebands in this study

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Table 2. Essential parameters of the selected Sentinel-2 wavebands in this study

光谱波段	光谱范围/nm	波段宽度/nm	空间分辨率/m
B1	433~453	20	60
B2	458~523	65	10
B3	543~578	35	10
B4	650~680	30	10
B5	698~713	15	20
B6	733~748	15	20
B7	773~793	20	20
B8	785~900	115	10
B8a	855~875	20	20
B9	930~950	20	60
B11	1 565~1 655	90	20
B12	2 100~2 280	180	20

Table 3. Correlation analysis between published spectral indices and yield reduction rates of winter wheat

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Table 3. Correlation analysis between published spectral indices and yield reduction rates of winter wheat

光谱指数	重采样前		重采样后
光谱指数	相关系数	p值	相关系数	p值
MCARI	0.090	0.450	-0.260	0.028
MTVI2	-0.862	0.000	-0.850	0.000
TGI	-0.383	0.001	-0.031	0.794
WBI	0.861	0.000	0.633	0.000
NDWI₁₆₄₀	-0.716	0.000	-0.690	0.000
NDWI₂₁₃₀	-0.848	0.000	-0.844	0.000
NMDI	-0.417	0.000	0.794	0.000
RVI_800_450	-0.695	0.000	-0.626	0.000
EVI	-0.846	0.000	-0.835	0.000
VARI_green	-0.682	0.000	-0.719	0.000
NDVI	-0.774	0.000	-0.727	0.000
GNDVI	-0.796	0.000	-0.709	0.000
RNDVI	-0.796	0.000	-0.709	0.000
RVI	-0.634	0.000	-0.673	0.000
DVI	-0.720	0.000	-0.814	0.000
NDII	-0.847	0.000	-0.740	0.000
MSI	0.688	0.000	0.729	0.000
Datt-I	-0.511	0.000	-0.641	0.000
PRI	-0.474	0.000	-0.349	0.003

Table 4. Accuracy of linear regression equations of published and constructed spectral indices in this study on predicting the yield reduction rates of winter wheat

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Table 4. Accuracy of linear regression equations of published and constructed spectral indices in this study on predicting the yield reduction rates of winter wheat

构建形式	光谱指数	校正集			验证集
构建形式	光谱指数	线性回归方程	R²	RMSE/%	R²	RMSE/%
	EVI	y=-221.28 x+170.56	0.733	15.594	0.631	15.513
已有	NDWI₂₁₃₀	y=-174.45 x+155.59	0.681	17.040	0.769	10.375
	MTVI2	y=-189.26x+144.55	0.749	15.094	0.671	14.128
	(B8a-B9)/(B8a+B9)	y=-1 343.38x+58.07	0.700	17.866	0.752	12.014
归一化	(B7-B11)/(B7+B11)	y=-157.82x+95.42	0.694	18.040	0.851	8.700
	(B8a-B12)/(B8a+B12)	y=-181.6x+160.38	0.689	18.201	0.778	10.338
	B8-B12	y=-380.01x+149.82	0.776	15.437	0.717	14.253
简单差值	B8a-B12	y=-389.68x+152.4	0.752	16.235	0.681	15.200
	B8a-B9	y=-1 812.36x+58.71	0.725	17.094	0.741	12.792
	B11/B7	y=132.17x-26.33	0.709	17.590	0.833	9.391
简单比值	B11/B6	y=122.93x-32.67	0.680	18.454	0.796	10.083
	B9/B8a	y=682.82x-625.14	0.702	17.821	0.754	11.919

Table 5. Accuracy of the linear regression based on the Sentinel-2 broad-waveband spectral indices in predicting the yield reduction rates of winter wheat

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Table 5. Accuracy of the linear regression based on the Sentinel-2 broad-waveband spectral indices in predicting the yield reduction rates of winter wheat

构建形式	光谱指数	R²	RMSE/%	P values
	EVI	0.486	9.030	0.000
已有	NDWI₂₁₃₀	0.324	10.348	0.000
	MTVI2	0.367	10.015	0.000
	(B8a-B9)/(B8a+B9)	0.006	12.554	0.671
归一化	(B7-B11)/(B7+B11)	0.375	9.951	0.000
	(B8a-B12)/(B8a+B12)	0.357	10.096	0.001
	B8-B12	0.492	8.971	0.000
简单差值	B8a-B12	0.543	8.510	0.000
	B8a-B9	0.012	12.514	0.537
	B11/B7	0.379	9.922	0.000
简单比值	B11/B6	0.344	10.195	0.000
	B9/B8a	0.006	12.548	0.647

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Ai-ping ZHAO, Jun-cheng MA, Yong-feng WU, Xin HU, De-chao REN, Chong-rui LI. Predicting Yield Reduction Rates of Frost-Damaged Winter Wheat After Jointing Using Sentinel-2 Broad-Waveband Spectral Indices[J]. Spectroscopy and Spectral Analysis, 2022, 42(7): 2225

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Paper Information

Category: Orginal Article

Received: May. 18, 2021

Accepted: --

Published Online: Nov. 16, 2022

The Author Email: ZHAO Ai-ping (apzhao98@163.com)

DOI:10.3964/j.issn.1000-0593(2022)07-2225-08

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