基于高光谱技术的土壤水分无损检测

doi:10.3964/j.issn.1000-0593(2018)08-2563-08

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Abstract This article summarizes a near-infrared hyperspectral imaging technique was investigated for non-destructive determination of soil moisture content. A total of 208 soil samples were collected by hyperspectral imaging system. The differences of soil water content and spectral change, and the spectra of different water contents were compared. Different spectral preprocessing methods were analyzed and the characteristic wavelengths were extracted by different methods. MLR, PCR and PLSR modeling were used to optimize the best model. The results show that the reflectivity of the spectral curve decreases with the increase of soil water content, and the reflectivity of the spectral curve increases with the increase of soil moisture content when it increases beyond the field water holding capacity. With the increase of soil moisture content, the spectral reflectance of soil showed a decrease at first before increasing. When the soil moisture content is 30%, the reflectivity of soil spectrum increases. It is mainly because the soil moisture content exceeds the amount of soil surface water layer,form a double structure the soil can accommodate . The method of different pretreatment is analyzed, and the pretreatment method of normalization of unit vector is proposed. The number of characteristic wavelengths extracted by UVE, CARS, β coefficient, SPA were 49, 30, 5, 7, respectively. In order to reduce the data redundancy, the characteristic wavelengths of UVE and CARS were further extracted by SPA method. The number of characteristic wavelengths of UVE+SPA and CARS+SPA were 5, 8. On the basis of this, the MLS, PCR and PLSR methods were used to model the characteristic wavelengths of the range of 400~1 000 nm. The MLR model of the characteristic wavelengths extracted by β coefficient was obtained by comparing the different modeling results.The optimal characteristic wavelength is 411, 440, 622, 713, 790 nm. The prediction coefficient R_p=0.979 is the best model, and the RMSEP is 0.763.Therefore, the soil moisture content can be quantitatively analyzed in different bands in the future.

Key words：Hyperspectral imaging; Soil; Moisture content; Non-destruction

Received: 2017-07-10 Accepted: 2017-12-17

ZTFLH:

TV93

Corresponding Authors: HE Jian-guo E-mail: hejg@nxu.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)08-2563-08 OR https://www.gpxygpfx.com/EN/Y2018/V38/I08/2563

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