基于星-机光谱融合的棉花叶片SPAD值反演

doi:10.3864/j.issn.0578-1752.2022.24.004

Abstract

Abstract:

【Objective】The aim of this study was to improve the inversion accuracy of chlorophyll content in cotton leaves, and to grasp its spatial distribution characteristics in Xiajin county, Shandong province. 【Method】Taking Xiajin county, Dezhou city, Shandong province as the study area and Dalizhuang cotton field in Xiajin county as the test area, the relative value of chlorophyll content (SPAD value) in cotton leaves in the experimental area was measured by SPAD (soil and plant analyzer development), and obtained the near earth multispectral image of unmanned aerial vehicle (UAV) and Sentinel-2A MSI (MSI) satellite image in the study area in the same period; Then, based on the spectral reflectance of UAV and MSI satellite images, the optimal spectral parameters were constructed and selected, and the inversion model of SPAD value was established by multiple linear regression (MLR); Finally, the quadratic polynomial fitting method was used to fuse the optimal spectral parameters corresponding to UAV and Sentinel-2A MSI. By comparing and analyzing the model effects before and after fusion, the inversion model was optimized, and the SPAD value inversion of the study area was realized. 【Result】(REG-R)/(REG+R), R/G, Cl(red edge) and NDVI could be the optimal spectral parameters of SPAD value. The precision of cotton leaf SPAD inversion model based on UAV near ground image was better than that based on satellite image; After quadratic polynomial fitting, the calibration R² was increased by 0.015-0.057, and RMSE was decreased by 0.457-0.638, while the validation R² was increased by 0.040-0.085, RMSE was decreased by 0.387-0.397, and RPD was increased by 0.020-0.139. The fused spectral parameters based on Sentinel-2A MSI image were input to the inversion model based on UAV data (Fused MSI-Mod_UAV), the high inversion accuracy of SPAD value in cotton leaves could be obtained, with the model calibration R² up to 0.672, RMSE of 3.982, validation R² up to 0.713, RMSE of 3.859, and RPD of 1.685. Based on the above model, two inversion prediction maps of different scales were obtained. The SPAD value of cotton leaves in the test area showed the distribution trend of high in the south and low in the north, and the study area showed the distribution trend of low in the middle and high around, which were consistent with the field situation and showed the model had a good prediction effect. 【Conclusion】Therefore, the fusion of UAV and satellite image data by using quadratic polynomial fitting method could better realize the quantitative inversion of regional high-precision crop growth indicators. The research results could enrich the theory and technology of multi-source remote sensing fusion, and provide the technical reference and data support for cotton growth monitoring and precision production.

Key words: SPAD value, UAV, Sentinel-2A MSI, inversion model, quadratic polynomial fitting method

WANG ShuTing, KONG YuGuang, ZHANG Zan, CHEN HongYan, LIU Peng. SPAD Value Inversion of Cotton Leaves Based on Satellite-UAV Spectral Fusion[J].Scientia Agricultura Sinica, 2022, 55(24): 4823-4839.

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波段名称 Band name	无人机 UAV			卫星 Sentinel-2A MSI
波段名称 Band name	波段 Band	中心波长 Central wavelength (nm)	带宽 Bandwidth (nm)	波段 Band	中心波长 Central wavelength (nm)	带宽 Bandwidth (nm)
G	Green	550	40	B3-Green	560	45
R	Red	660	40	B4-Red	665	38
REG	Red Edge	735	10	B6-Vegetation Red Edge	740	18
NIR	Near IR	790	40	B7- Vegetation Red Edge	783	28

植被指数 Vegetation index	名称 Name	计算公式 Calculation formula	参考文献 Reference
RVI	比值植被指数 Ratio vegetation index	NIR/R	[22]
DVI	差值植被指数 Difference vegetation index	NIR-R
GNDVI	绿色归一化植被指数 Green normalized difference vegetation index	(NIR-G)/(NIR+R)
NDRE	红边植被指数 Normalized difference red edge	(NIR-REG)/(NIR+REG)	[29]
CL(red edge)	红边叶绿素指数Ⅱ Red edge chlorophyII index	REG/R-1	[31]
MCARI	改进叶绿素吸收比值指数 Modified chlorophyII absorption ratio index	[(REG-R)-0.2×(REG-G)] ×(REG/R)
TCARI	转化叶绿素吸收比值指数 Transformed chlorophyII absorption ratio index	3×[(REG-R)-0.2×(REG-G)×(REG/R)]
OSAVI	优化型土壤调节植被指数 Optimized soil adjusted vegetation index	1.16×(NIR-R)/(NIR+R+0.16)
NDVI	归一化植被指数 Normalized difference vegetation index	(NIR-R)/(NIR+R)
CIRE	红边叶绿素指数Ⅰ Red edge chlorophyII index	NIR/REG-1	[36]
MSR	改进简单比值植被指数 Modified simple ratio	(NIR/R-1)(NIR/R+1)^0.5	[37]
RDVI	重归一化植被指数 Renormalized difference vegetation index	(NIR-R)/(NIR+R)^0.5	[37]
(REG-R)/(REG+R)	—	(REG-R)/(REG+R)	[38]
R/G	—	R/G
sqrt(R2+G^2)	—	sqrt(R^2+G^2)
G×R	—	G×R
G×R×REG	—	G×R×REG
REG-R	—	REG-R
REG-NIR	—	REG-NIR

样本类型 Sample type	样本数 Number of samples	最大值 Maximum value	最小值 Minimum value	平均值 Average value	标准差 Standard deviation
建模集 Calibration set	64	59.5	20.2	43.5	7.8
验证集 Validation set	31	68.2	17.1	43.3	8.3
全部 All the samples	95	68.2	17.1	43.3	8.6
研究区 Study area	58	63.1	21.5	46.9	8.1

序号 Number	光谱参量 Spectral parameter	相关系数 Correlation coefficient
序号 Number	光谱参量 Spectral parameter	无人机 UAV	卫星 Sentinel-2A MSI
1	NDVI	0.688**	0.559**
2	RVI	0.655**	0.518*
3	DVI	0.300	0.558*
4	MSR	0.683**	0.534
5	RDVI	0.530*	0.553*
6	GNDVI	-0.613	0.542
7	CIRE	-0.547*	0.411
8	OSAVI	0.531*	0.545*
9	CL(red edge)	0.756**	0.549*
10	MCARI	0.687**	0.517*
11	TCARI	-0.490	-0.496
12	NDRE	-0.196	0.412
13	(REG-R)/(REG+R)	0.800**	0.557**
14	R/G	-0.768**	-0.547**
15	sqrt(R^2+G^2)	-0.653**	-0.529*
16	G×R	-0.626**	-0.541*
17	G×R×REG	-0.405	-0.510
18	REG-R	0.680**	0.548**
19	REG-NIR	0.023	-0.505*

数据源 Data source	反演模型 Inversion model	建模精度 Calibration accuracy
数据源 Data source	反演模型 Inversion model	决定系数 R2	均方根误差RMSE	决定系数 R2	均方根误差RMSE	相对分析误差 RPD
无人机 UAV	Y=37.362+1.093×NDVI+1.26×CL(red edge)- 4.946×R/G+8.286×(REG-R)/(REG+R)	0.709	3.782	0.753	3.589	2.045
卫星 Sentinel-2A MSI	Y=-39.914+152.318×NDVI-2.267×CL(red edge)- 4.990×R/G-25.234×(REG-R)/(REG+R)	0.452	5.823	0.447	5.909	1.521

SPAD Value Inversion of Cotton Leaves Based on Satellite-UAV Spectral Fusion

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区域 Region	棉花种植区 Cotton-growing area	非棉花种植区 Non cotton-growing area	样本总数 Total number of samples
棉花种植区 Cotton-growing area	1007	60	1067
非棉花种植区 Non cotton-growing area	12	1360	1372
样本总数 Total number of samples	1019	1420	2439

最优光谱参量 Optimal spectral parameter	光谱转换公式 Spectral conversion formula	融合前 Unfused		融合后 Fused
最优光谱参量 Optimal spectral parameter	光谱转换公式 Spectral conversion formula	拟合度R2 Fitting degree	相关系数 Correlation coefficient	拟合度R2 Fitting degree	相关系数 Correlation coefficient
NDVI	NDVI′= 0.325×NDVI²+0.2682×NDVI+0.4053	0.704	0.559**	0.721	0.580**
CL(red edge)	CL(red edge)′=-0.2687×CL(red edge)²+3.0079×CL (red edge) -0.821	0.670	0.549**	0.714	0.611**
R/G	R/G′=-0.0682×R/G²+0.4316×R/G+0.3128	0.697	-0.547**	0.712	-0.587**
(REG-R)/(REG+R)	(REG-R)/(REG+R)′= 0.4694×(REG-R)/(REG+R)²+ 0.1082×(REG-R)/(REG+R)+0.453	0.695	0.557**	0.718	0.618**

模型 Model	公式 Formula	建模精度 Calibration accuracy		验证精度 Verification accuracy
模型 Model	公式 Formula	决定系数 R2	均方根误差RMSE	决定系数 R2	均方根误差RMSE	相对分析误差 RPD
MSI-Mod_MSI	Y=-39.914+152.318×NDVI-2.267×CL(red edge)- 4.990×R/G-25.234×(REG-R)/(REG+R)	0.452	5.823	0.447	5.909	1.521
Fused MSI-Mod_MSI	Y=-39.914+152.318×NDVI′-2.267×CL(red edge)′- 4.990×R/G′-25.234×(REG-R)/(REG+R)′	0.467	5.366	0.487	5.522	1.541
MSI-Mod_UAV	Y=37.362+1.093×NDVI+1.26×CL(red edge)- 4.946×R/G+8.286×(REG-R)/(REG+R)	0.615	4.620	0.628	4.256	1.546
Fused MSI-Mod_UAV	Y=37.362+1.093×NDVI′+1.26×CL(red edge)′- 4.946×R/G′+8.286×(REG-R)/(REG+R)′	0.672	3.982	0.713	3.859	1.685
AR Fused MSI-Mod_UAV	Y=37.362+1.066×NDVI+5.388×CL(red edge)- 3.645×R/G+18.652×(REG-R)/(REG+R)	0.638	4.259	0.664	4.015	1.598

等级 Level	反演值 Inversion value		样点实测值 Measured value of sample point		样点预测值 Predicted values of sample points
等级 Level	像元数 Number of pixels	所占比例 Proportion (%)	个数 Number	所占比例 Proportion (%)	个数 Number	所占比例 Proportion (%)
<30	664632	6.747	6	6.316	4	4.210
30-40	1829536	18.574	17	17.895	17	17.895
40-50	6483804	65.825	55	57.895	62	65.263
>50	872169	8.854	17	17.894	12	12.632

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