一款无人机高光谱传感器的验证及其在玉米叶面积指数反演中的应用

doi:10.3864/j.issn.0578-1752.2018.08.004

Abstract

Abstract: 【Objective】 The objective of this study is to validate the unmanned aerial vehicle (UAV) hyperspectral sensor S185, and then to design a new method for maize leaf area index (LAI) estimation based on its collected image. 【Method】 Taking maize in northeastern China as study material, nitrogen fertilizer experiment was conducted in Gongzhuling city in Jilin province. In the experiment, five nitrogen treatments and three replications were applied. The UAV flight experiment, the ground spectrum and LAI were measured at V5-V6, V11, R1-R2 growth stage (Ritchie growth stage) of maize. At last, 45 groups of data were collected. To validate images from hyperspectral sensor S185, the spectra from S185 image and from ground spectral device were extracted and compared in the same scale. On the one hand, the correlation analysis was taken to analysis the relationship between S185 data and ground measured spectra, which were from the same target; On the other hand, 15 commonly used spectral indices were selected, and then they were calculated from S185 date and from the ground spectra, respectively, and at last, the relationship between them during the whole maize growth stage was analyzed to show their change trend consistency. 30 groups of data were randomly selected from the collected 45 groups of data, artificial neural network method (ANN) was used to establish LAI prediction model by using S185 images, and then the remainder 15 groups of data were used to validate the performance of ANN model. In addition, based on the same calibration and validation dataset, LAI prediction models were designed by using each selected spectral index individually in order to compare with ANN LAI prediction model. 【Result】 In each maize growth stage, S185 spectra had a high relationship with corresponding spectra measured by ground spectral device for the same target, with correlation coefficients higher than 0.99; in the whole maize growth stage, the calculated spectral indices from the S185 images had a high relationship with the corresponding value calculated from ground measured spectra, with correlation coefficient higher than 0.88; During the design of the ANN prediction model for LAI, the model had an R2 value of 0.96, a RMSE value of 0.42 and a RMSE% value of 13.15% during calibration and had an R2 value of 0.95, an RMSE value of 0.54 and an RMSE% value of 16.74% during external validation. The model performed better than models designed by spectral indices. 【Conclusion】 The results showed S185 can be mounted on UAV to measure maize canopy hyperspectra image accurately, and ANN method can be used to design LAI prediction model based on UAV.

Key words: unmanned aerial vehicle, hyperspectra, leaf area index, maize, S185

CHEN PengFei, LI Gang, SHI YaJiao, XU ZhiTao, YANG FenTuan, CAO QingJun. Validation of an Unmanned Aerial Vehicle Hyperspectral Sensor and Its Application in Maize Leaf Area Index Estimation[J].Scientia Agricultura Sinica, 2018, 51(8): 1464-1474.

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