基于无人机平台多模态数据融合的小麦产量估算研究

doi:10.3724/SP.J.1006.2022.11053

作物学报 ›› 2022, Vol. 48 ›› Issue (7): 1746-1760.doi: 10.3724/SP.J.1006.2022.11053

基于无人机平台多模态数据融合的小麦产量估算研究

张少华¹(), 段剑钊¹^,², 贺利¹, 井宇航¹, 郭天财¹, 王永华¹, 冯伟¹^,^*()

¹河南农业大学农学院 / 省部共建小麦玉米作物学国家重点实验室, 河南郑州 450046
²International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico

收稿日期:2021-06-02 接受日期:2021-10-19 出版日期:2022-07-12 网络出版日期:2021-11-26
通讯作者: 冯伟
作者简介:E-mail: 15939266989@163.com
基金资助:
国家“十三五”重点研发计划粮食丰产增效科技创新项目(2018YFD0300701);财政部和农业农村部国家现代农业产业技术体系建设专项(CARS-03);河南省科技攻关项目(212102110041)

Wheat yield estimation from UAV platform based on multi-modal remote sensing data fusion

ZHANG Shao-Hua¹(), DUAN Jian-Zhao¹^,², HE Li¹, JING Yu-Hang¹, Urs Christoph Schulthess²^,^*(), Azam Lashkari¹^,², GUO Tian-Cai¹, WANG Yong-Hua¹, FENG Wei¹^,^*()

¹Agronomy College of Henan Agriculture University / State Key Laboratory of Wheat and Maize Crop Science, Zhengzhou 450002, Henan, China
²International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico

Received:2021-06-02 Accepted:2021-10-19 Published:2022-07-12 Published online:2021-11-26
Contact: Urs Christoph Schulthess,FENG Wei
Supported by:
National “13th Five-Year” Key Research and Development Program of China(2018YFD0300701);China Agriculture Research System of MOF and MARA(CARS-03);Key Technologies Research & Development Program of Henan Province, China(212102110041)

摘要/Abstract

摘要：

作物产量估测关系到人民生活质量和国家粮食安全问题, 在田块尺度下及时准确估算产量, 对于农事操作管理、收获、销售及种植计划制定均具有重要意义。选择地势起伏及空间差异较大的农田为研究区, 利用低空无人机遥感平台搭载多光谱相机、热红外相机和RGB相机, 同步获取小麦关键生育时期的无人机遥感影像, 并提取光谱反射率、热红外温度和数字高程信息。首先统计不同地形特征下遥感参数和生长指标的空间变异情况, 分析植被指数和温度参数与小麦产量的相关性, 然后利用多元线性回归(multiple linear regression, MLR)、偏最小二乘回归(partial least squares regression, PLSR)、支持向量机回归(support vector machine regression, SVR)和随机森林回归(random forest regression, RFR) 4种机器学习方法以单模态数据和多模态遥感信息融合2种方式进行建模, 比较单模态数据和多模态数据融合的产量估测能力。结果表明, 坡度是影响作物生长和产量的重要因子, 3个生育期内, 不同坡度等级下遥感参数差异明显, 土壤含水量、植株含水量和地上部生物量与坡度的相关性均达显著水平, 植被指数和温度参数与产量的相关性均达显著水平。依据与产量的相关性, 筛选7个植被指数(NDVI、GNDVI、EVI2、OSAVI、SAVI、NDRE、WDRVI)和2个温度参数(NRCT、CTD)作为模型输入变量, 对于单模态数据而言, 对产量的估算效应为植被指数 > 温度参数, 以灌浆期植被指数的RFR模型效果最好(R²=0.724, RMSE=614.72 kg hm^-2, MAE=478.08 kg hm^-2); 对于双模态数据融合来说, 在植被指数基础上融入冠层温度参数表现最好, 开花期RFR模型效果进一步提高(R²=0.865, RMSE=440.73 kg hm^-2, MAE=374.86 kg hm^-2); 在双模态数据基础上引入坡度信息进行三模态数据融合, 其产量估算效果明显优于单模态和双模态数据融合, 其中以开花期植被指数、温度参数和坡度信息融合的RFR估算效果最好(R²=0.893, RMSE=420.06 kg hm^-2, MAE=352.69 kg hm^-2), 模型验证效果较好(R²= 0.892, RMSE=423.55 kg hm^-2, MAE=334.43 kg hm^-2)。可见, 在本试验条件下通过引入地形因子, 结合随机森林回归算法将多模态数据有效融合, 可充分发挥不同遥感信息源之间互补协同作用, 有效提高了产量估算模型的精度与稳定性, 为作物生长监测及产量估算提供思路参考和方法支持。

关键词: 冬小麦, 无人机, 产量估算, 地形因子, 多模态数据

Abstract:

Crop yield estimations are important for national food security, people, and the environment. Timely and accurate estimation of crop yield at the field scale is of great significance for crop management, harvest and trade. It ultimately enables farmers to optimize inputs and economic return. We selected an irrigated wheat field in a region near Kaifeng, Henan province, for this study. The terrain in that region is undulating and spatial differences. We used a low-altitude unmanned aerial vehicle (UAV) remote sensing platform equipped with a multi-spectral camera, thermal infrared camera, and RGB camera to simultaneously obtain different remote sensing parameters during the key growth stages of wheat. Based on the extracted spectral reflectivity, thermal infrared temperature, and digital elevation information, we calculated the spatial variability of remote sensing parameters, and growth indices under different terrain characteristics. We also analyzed the correlations between vegetation indices, temperature parameters and wheat yield. By means of four machine learning methods, including multiple linear regression method (MLR), partial least squares regression method (PLSR), support vector machine regression method (SVR), and random forest regression method (RFR), we compared the yield estimation capability of single-modal data versus multimodal data fusion frameworks. The results showed that slope was an important factor affecting crop growth and yield. We observed significant differences in remote sensing parameters under different slope grades. Soil water content, water content of plants, and above-ground biomass at the three growth stages were significantly correlated with slope. Most of the vegetation indices and temperature parameters of three growth stages were significantly correlated with yield as well. Based on the strength of their correlation with yield, seven vegetation indices (NDVI, GNDVI, EVI2, OSAVI, SAVI, NDRE, and WDRVI) and two temperature parameters (NRCT, CTD) were selected as the final input variables for the model. For the single-modal data framework, the model constructed with the vegetation indices was better than the yield model constructed with the temperature parameters, and the highest accuracy was obtained with a RFR model based on vegetation indices at filling stage (R²= 0.724, RMSE = 614.72 kg hm^-2, MAE = 478.08 kg hm^-2). For the double modal data fusion approach, the highest accuracy resulted at flowering stage, using the temperature parameters combined with the vegetation indices of RFR model (R²=0.865, RMSE=440.73 kg hm^-2, MAE=374.86 kg hm^-2). Even higher accuracies were obtained, using the multimodal data fusion approach with a RFR model based on vegetation indices, temperature parameters and slope information at flowering stage (R²= 0.893, RMSE = 420.06 kg hm^-2, MAE = 352.69 kg hm^-2), and the highest validation model (R²= 0.892, RMSE = 423.55 kg hm^-2, MAE = 334.43 kg hm^-2) for fusion of the flowering stage. The results revealed that by using a multimodal data fusion framework of terrain factors combined with RFR, we can fully exploit the complementary and synergistic roles of different remote sensing information sources. This effectively improves the accuracy and stability of the yield estimation model, and provides a reference and support for crop growth monitoring and yield estimation.

Key words: winter wheat, unmanned aerial vehicle (UAV), yield estimation, terrain factor, multimodal data

张少华, 段剑钊, 贺利, 井宇航, 郭天财, 王永华, 冯伟. 基于无人机平台多模态数据融合的小麦产量估算研究[J]. 作物学报, 2022, 48(7): 1746-1760.

ZHANG Shao-Hua, DUAN Jian-Zhao, HE Li, JING Yu-Hang, Urs Christoph Schulthess, Azam Lashkari, GUO Tian-Cai, WANG Yong-Hua, FENG Wei. Wheat yield estimation from UAV platform based on multi-modal remote sensing data fusion[J]. Acta Agronomica Sinica, 2022, 48(7): 1746-1760.

图/表 11

图1

图2

图3

表1

本文采用的光谱指数和温度参数"

参数 Index	公式 Formula	参考文献 Reference
归一化植被指数Normalized difference vegetation index (NDVI)	$\frac{\left( {{R}_{\text{nir}}}-{{R}_{\text{red}}} \right)}{\left( {{R}_{\text{nir}}}+{{R}_{\text{red}}} \right)}$	[25]
归一化绿度植被指数Green normalized difference vegetation index (GNDVI)	$\frac{\left( {{R}_{\text{nir}}}-{{R}_{\text{green}}} \right)}{\left( {{R}_{\text{nir}}}+{{R}_{\text{green}}} \right)}$	[26]
双波段增强植被指数Two-band enhanced vegetation index (EVI2)	$2.5\times \frac{\left( {{R}_{\text{nir}}}-{{R}_{\text{red}}} \right)}{\left( {{R}_{\text{nir}}}+2.4\times {{R}_{\text{red}}}+1 \right)}$	[27]
优化土壤调整植被指数Optimized soil adjusted vegetation index (OSAVI)	$1.16\times \frac{\left( {{R}_{\text{nir}}}-{{R}_{\text{red}}} \right)}{\left( {{R}_{\text{nir}}}+{{R}_{\text{red}}}+0.16 \right)}$	[28]
土壤调整植被指数Soil adjusted vegetation index (SAVI)	$1.5\times \frac{\left( {{R}_{\text{nir}}}-{{R}_{\text{red}}} \right)}{\left( {{R}_{\text{nir}}}+{{R}_{\text{red}}}+0.5 \right)}$	[29]
红边归一化植被指数Red edge normalized index (NDRE)	$\frac{\left( {{R}_{\text{nir}}}-{{R}_{\text{re}}} \right)}{\left( {{R}_{\text{nir}}}+{{R}_{\text{re}}} \right)}$	[30]
宽动态植被指数Wide dynamic range vegetation index (WDRVI)	$\frac{\left( 0.1\times {{R}_{\text{nir}}}-{{R}_{\text{red}}} \right)}{\left( 0.1\times {{R}_{\text{nir}}}+{{R}_{\text{red}}} \right)}$	[31]
改善简单比率植被指数Modified simple ratio index (MSR)	$\frac{\left( \frac{{{R}_{\text{nir}}}}{{{R}_{\text{red}}}-1} \right)}{\left( \sqrt{\frac{{{R}_{\text{nir}}}}{{{R}_{\text{red}}}}}+1 \right)}$	[32]
改良叶绿素吸收率指数 Modified chlorophyll absorption ratio index (MCARI)	$\frac{\left[ {{R}_{\text{re}}}-{{R}_{\text{red}}}-0.2\left( {{R}_{\text{re}}}-{{R}_{\text{green}}} \right) \right]}{\left( \frac{{{R}_{\text{re}}}}{{{R}_{\text{red}}}} \right)}$	[33]
参数 Index	公式 Formulas	参考文献 References
红波段比值植被指数Red ratio vegetation index (RVI_red)	$\frac{{{R}_{\text{nir}}}}{{{R}_{\text{red}}}}$	[34]
标准化相对冠层温度Normalized relative canopy temperature (NRCT)	$\frac{\left( {{T}_{c}}-{{T}_{\text{min}}} \right)}{\left( {{T}_{\max }}+{{T}_{\text{min}}} \right)}$	[35]
冠-气温差Canopy temperature depression (CTD)	${{T}_{c}}-{{T}_{\alpha }}$	[36]

表1

表2

表3

表4

表5

图4

表6

图5

参考文献 58

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生育期 Growth stage	坡度 Slope (°)	GNDVI				CT (℃)
生育期 Growth stage	坡度 Slope (°)	最大值 Max.	最小值 Min.	均值 Mean	标准差 SD	最大值 Max.	最小值 Min.	均值 Mean	标准差 SD
孕穗期 Booting stage	2-4	0.805	0.731	0.778	0.051	26.62	25.46	26.24	0.44
	4-6	0.723	0.602	0.637	0.049	28.24	27.56	27.42	0.34
	6-8	0.671	0.526	0.594	0.087	28.51	27.84	28.26	0.61
开花期 Flowering stage	2-4	0.837	0.758	0.809	0.047	28.64	27.14	27.84	0.46
	4-6	0.772	0.652	0.725	0.054	29.85	28.37	29.02	0.51
	6-8	0.713	0.608	0.634	0.069	30.64	29.02	29.79	0.58
灌浆期 Filling stage	2-4	0.796	0.719	0.747	0.053	31.05	29.46	30.54	0.52
	4-6	0.705	0.564	0.598	0.064	31.46	29.85	31.12	0.56
	6-8	0.669	0.518	0.573	0.081	33.97	30.31	32.85	0.78

生长参数 Growth parameter	孕穗期 Booting stage	开花期 Flowering stage	灌浆期 Filling stage
土壤含水量 Soil moisture content (%)	-0.605^**	-0.673^**	-0.731^**
植株含水量 Plant water content (%)	-0.450^**	-0.503^**	-0.521^**
地上部生物量 Above-ground biomass (kg hm^-2)	-0.332^*	-0.406^**	-0.434^**

指数 Indices	孕穗期 Booting stage	开花期 Flowering stage	灌浆期 Filling stage
NDVI	0.587^**	0.718^**	0.799^**
GNDVI	0.616^**	0.756^**	0.821^**
EVI2	0.663^**	0.732^**	0.806^**
OSAVI	0.653^**	0.741^**	0.820^**
SAVI	0.668^**	0.735^**	0.809^**
NDRE	0.666^**	0.770^**	0.743^**
WDRVI	0.567^**	0.705^**	0.796^**
MSR	0.390^**	0.689^**	0.688^**
MCARI	0.475^**	0.614^**	0.714^**
RVI	0.280^*	0.664^**	0.752^**
NRCT	‒0.487^**	‒0.758^**	‒0.590^**
CTD	0.443^**	0.669^**	0.467^**

参数 Index	回归模型 Regression model	孕穗期Booting stage			开花期Flowering stage			灌浆期Filling stage
参数 Index	回归模型 Regression model	R²	RMSE (kg hm^-2)	MAE (kg hm^-2)	R²	RMSE (kg hm^-2)	MAE (kg hm^-2)	R²	RMSE (kg hm^-2)	MAE (kg hm^-2)
VI	MLR	0.496	760.76	648.32	0.663	659.23	567.27	0.672	638.43	502.93
	PLSR	0.508	765.69	635.14	0.626	662.28	574.84	0.648	644.11	523.51
	SVR	0.521	755.52	597.46	0.673	648.37	532.46	0.680	618.41	486.64
	RFR	0.564	700.62	576.70	0.711	645.85	527.65	0.724	614.72	478.08
TH	MLR	0.340	902.84	728.61	0.637	676.12	608.38	0.391	859.12	654.23
	PLSR	0.349	883.15	694.48	0.609	680.17	615.89	0.368	880.24	676.27
	SVR	0.361	876.73	671.52	0.659	668.87	581.64	0.463	807.35	594.65
	RFR	0.417	839.74	650.37	0.707	642.19	530.89	0.540	788.59	563.06

参数 Index	回归模型 Regression model	孕穗期Booting stage			开花期Flowering stage			灌浆期Filling stage
参数 Index	回归模型 Regression model	R²	RMSE (kg hm^-2)	MAE (kg hm^-2)	R²	RMSE (kg hm^-2)	MAE (kg hm^-2)	R²	RMSE (kg hm^-2)	MAE (kg hm^-2)
VI+TF	MLR	0.544	766.67	642.58	0.677	615.09	546.26	0.727	625.12	497.92
	PLSR	0.513	722.35	615.79	0.635	654.25	559.85	0.711	650.38	513.67
	SVR	0.604	693.45	567.62	0.729	598.46	514.24	0.769	584.27	481.37
	RFR	0.633	671.14	541.46	0.746	576.69	495.46	0.786	561.47	459.64
VI+TH	MLR	0.644	743.29	634.84	0.801	521.62	439.67	0.780	596.92	482.47
	PLSR	0.609	749.22	629.75	0.790	542.45	456.87	0.752	606.89	489.67
	SVR	0.663	709.49	574.57	0.822	495.88	423.15	0.795	573.61	474.35
	RFR	0.699	643.36	535.74	0.865	440.73	374.86	0.831	480.73	401.91
TF+TH	MLR	0.371	863.76	705.27	0.652	671.64	571.29	0.417	816.05	639.37
	PLSR	0.403	854.87	685.23	0.622	694.52	586.13	0.432	832.48	656.74
	SVR	0.495	838.46	664.67	0.708	658.24	546.82	0.562	727.13	587.68
	RFR	0.561	797.06	642.13	0.727	640.49	518.79	0.594	697.58	559.98
VI+TF+TH	MLR	0.660	656.07	527.41	0.817	460.94	375.15	0.787	507.92	447.26
	PLSR	0.654	637.50	514.79	0.805	505.66	412.35	0.753	585.85	479.84
	SVR	0.702	600.37	487.37	0.824	451.26	379.65	0.813	476.67	421.31
	RFR	0.729	611.19	470.94	0.893	420.06	352.69	0.856	457.56	380.71

基于无人机平台多模态数据融合的小麦产量估算研究

Wheat yield estimation from UAV platform based on multi-modal remote sensing data fusion

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