| Study on the spectral prediction model of soil moisture content based on SPA-MLR method |
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| DOI：10.7606/j.issn.1000-7601.2018.03.39 |
| Key Words: spectral transform soil moisture content successive projection algorithm multiple linear regression |
| Author Name | Affiliation | | JIA Xue-qin | College of Agronomy, Shanxi Agricultural University, Taigu, Shanxi 030801 | | FENG Mei-chen | College of Agronomy, Shanxi Agricultural University, Taigu, Shanxi 030801 | | YANG Wu-de | College of Agronomy, Shanxi Agricultural University, Taigu, Shanxi 030801 | | WANG Chao | College of Agronomy, Shanxi Agricultural University, Taigu, Shanxi 030801 | | SUN Hui | College of Agronomy, Shanxi Agricultural University, Taigu, Shanxi 030801 | | WU Gai-hong | College of Agronomy, Shanxi Agricultural University, Taigu, Shanxi 030801 | | ZHANG Song | College of Agronomy, Shanxi Agricultural University, Taigu, Shanxi 030801 |
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| Abstract: |
| Based on hyperspectral data of artificially deployed soil with different moisture content, 11 conventional transformation methods were used to transform the original spectral reflectance, successive projection algorithm (SPA) was used to extract the sensitive wavelengths, and then the multiple linear regression (MLR) model was established. different models were evaluated and compared in order to select the best hyperspectral model for monitoring soil moisture and achieve hyperspectral monitoring of soil moisture content. The results showed that the spectral reflectance increased first and then decreased with the increase of soil moisture content; and the characteristic bands of SPA extraction ranged from 3 to 5, and there were differences in the characteristic bands extracted by different spectral transformations. The establishment of the MLR regression model using the characteristic wavebands shows that the original spectrum can improve the hyperspectral monitoring accuracy of soil moisture after a certain mathematical transformation. the SPA[CD*2]MLR model based on spectral reflectivity of the first-order differential logarithmic transform (T8) were the best, the calibration model showed that R2=0.957，RMSE=2.16，RPD=4.74 and validation model showed that R2=0.903，RMSE=3.41，RPD=2.95. Therefore, the SPA-MLR model based on the logarithmic first differential transformation of reflectance can realize the hyperspectral monitor of soil moisture content, and the study would provide technical support for rapid monitoring of the soil moisture content. |
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