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Acta Agronomica Sinica ›› 2022, Vol. 48 ›› Issue (7): 1746-1760.doi: 10.3724/SP.J.1006.2022.11053


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,*()   

  1. 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 Online:2022-07-12 Published:2021-11-26
  • Contact: Urs Christoph Schulthess,FENG Wei E-mail:15939266989@163.com;fengwei78@126.com;U.Schulthess@cgiar.org
  • 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)


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

Fig. 1

Location of the research areas and experiment designs"

Fig. 2

Terrain features of study fields"

Fig. 3

Canopy temperature distribution map measured and estimated crop canopy temperature at flowering stage"

Table 1

Spectral indices and temperature parameter in this study"

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]
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]
红波段比值植被指数Red ratio vegetation index (RVIred) $\frac{{{R}_{\text{nir}}}}{{{R}_{\text{red}}}}$ [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]

Table 2

Descriptive statistics of images remote sensing indices on slopes"

Growth stage
Slope (°)
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
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
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

Table 3

Correlation between growth parameters and slope at different growth stages"

Growth parameter
Booting stage
Flowering stage
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**

Table 4

Correlation between vegetation indices, temperature parameters and yield at different growth stages"

Booting stage
Flowering stage
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**

Table 5

Yield estimation of wheat based on single modal data"

Regression model
孕穗期Booting stage 开花期Flowering stage 灌浆期Filling stage
(kg hm-2)
(kg hm-2)
(kg hm-2)
(kg hm-2)
(kg hm-2)
(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

Fig. 4

Relationship between the measured and predicted yields of winter wheat a-c are the scatter plots between the predicted yield and the measured value based on the best RFR model established based on the vegetation indices in the booting stage, flowering stage, and the filling stage of winter wheat; d-f are the scatter plots between the predicted yield and the measured value based on the best RFR model established based on the temperature parameters in the booting stage, flowering stage, and the filling stage of winter wheat."

Table 6

Yield estimation of wheat based on multimodal data fusion"

Regression model
孕穗期Booting stage 开花期Flowering stage 灌浆期Filling stage
(kg hm-2)
(kg hm-2)
(kg hm-2)
(kg hm-2)
(kg hm-2)
(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

Fig. 5

Relationship between measured and predicted winter wheat yield of winter wheat a-c are the scatter plots between the predicted yield and the measured value based on the best RFR model established based on the vegetation indices and temperature parameters in the booting stage, flowering stage, and the filling stage of winter wheat; d-f are the scatter plots between the predicted yield and the measured value based on the best RFR model established based on the vegetation indices, temperature parameters and slope in the booting stage, flowering stage, and the filling stage of winter wheat."

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