| Comparison of multiple estimation models of soil organic matter based on laboratory hyperspectral reflectance in Shaanxi Province |
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| DOI：10.7606/j.issn.1000-7601.2021.04.14 |
| Key Words: soil organic matter spectral transformation SVR estimation accuracy Shaanxi Province |
| Author Name | Affiliation | | QIE Xin | College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi 712100, China | | QI Yanbing | College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi 712100, China | | LIU Jiaojiao | College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi 712100, China | | WANG Ke | College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi 712100, China | | CHEN Minhui | College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi 712100, China |
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| Abstract: |
| Soil organic matter estimation based on hyperspectral data is an important research topic of soil remote sensing and precision agriculture. However, different spectral processing and modeling methods make the estimation ability and accuracy of models vary significantly, which limits the universality of models. In order to establish the optimal model of soil organic matter estimation in Shaanxi Province, based on the spectral reflectance curve and soil organic matter content of 216 soil samples of 9 main soil types in Shaanxi Province, the spectral reflectance curve was transformed into four transformations: first order differential d(R), reciprocal logarithm log(1/R), reciprocal logarithm first order differential d\[log(1/R)\] and envelope line removal N(R). One variable linear regression (SLR), partial least square regression (PLSR) and support vector machine regression (SVR) were used to establish different models for estimating soil organic matter content. The results showed that the reflectance spectra curves and the absorption characteristic positions of different types of soil were basically the same, with the organic matter content and spectral reflectance showing a negative correlation trend. The accuracy of the nonlinear organic matter content estimation model based on SVR was the highest, PLSR was the second, SLR was the worst. Among them, the judgment coefficient R2 of SVR model and validation model based on d\[log(1/R)\] spectrum was the best, which were 0.9210 and 0.8874 respectively, the validation root mean square error RMSE was only 2.18, and RPD reached 2.8751. Therefore, SVR was the best model for estimating soil organic matter content. |
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