| Cite this article: | 徐洪刚,陈震,程千,等.无人机多源光谱数据反演夏玉米LAI研究[J].灌溉排水学报,0,():-. |
| Xu Honggang,CHEN Zhen,CHENG Qian,et al.无人机多源光谱数据反演夏玉米LAI研究[J].灌溉排水学报,0,():-. |
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| UAV multi-source spectral data inversion for summer corn LAI |
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Xu Honggang, CHEN Zhen, CHENG Qian, Li Zongpeng, LI Peng, FAN Yongshen
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Farmland Irrigation Research Institute of Chinese Academy of Agricultural Sciences/ Henan Key laboratory of water-saving agriculture
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| Abstract: |
| 【Background】Leaf Area Index (LAI) can reflect the health status of crops, and appropriate LAI is of great significance to ensure the growth and yield of maize. The traditional LAI monitoring method is time-consuming and laborious, and it is difficult to achieve large-scale rapid monitoring. UAV remote sensing has become a research hotspot of LAI monitoring in recent years because of its advantages of flexibility, portability and high-precision image acquisition.【Objective】The objective is to invert the effect of summer maize LAI by spectrum.【Method】In sunny and windless weather, the DJI M210V2 UAV equipped with Micasense Red Edge MX and ZenmuseXT2 dual photothermal imaging sensors was used to obtain the multispectral and thermal infrared images of Summer Maize in different growth stages in the experimental area. Through Pix4D to complete the image mosaic operation, import ArcGIS to extract the thermal infrared canopy temperature (Tc) and multispectral vegetation index. Combined with the measured LAI data on the ground, the correlation between the spectral data and the measured LAI was analyzed. Then, the inversion model of summer maize LAI was established by using three algorithm models: Multiple Linear Regression (MLR), Support Vector Machine (SVM) and Random Forest (RF).【Result】(1) The results showed that the multispectral vegetation index and Tc were significantly correlated with summer maize LAI at P < 0.0001 level, and the correlation coefficients were all above 0.5. (2) The predicted LAI values and measured R2(0.707、0.834、0.849) of RF algorithm were higher than those of MLR algorithm and SVM algorithm at the jointing stage, trumpet stage, trumpet stage and silking stage, and the RMSE and NRMSE of RF algorithm were lower than those of MLR and SVM algorithm. (3) After fusion of Tc, the inversion accuracy of jointing stage model was significantly better than that of the other three growth stages. With the development of growth period, the promotion effect gradually decreased. However, the inversion accuracy of the RF model with the integration of thermal infrared Tc in each growth period was improved in varying degrees, indicating that the addition of Tc can improve the LAI inversion accuracy of summer maize.【Conclusion】Multispectral vegetation index and Tc have strong correlation with summer maize, and the accuracy of summer maize LAI inversion model constructed by RF algorithm is better than MLI and SVM algorithm. At the same time, the addition of Tc can effectively improve the accuracy of summer maize LAI inversion. This study provides the technology and method for the rapid and accurate remote sensing monitoring of summer maize LAI, which is of great significance to improve the accuracy of LAI low altitude remote sensing monitoring and realize the intelligent production management of agriculture. |
| Key words: Summer maize; Multispectral; Thermal infrared; Vegetation index; LAI; RF. |
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