Comparison and analysis of estimation models of soil organic matter content established by hyperspectral on ground
Yongmin WANG1, Xican LI2, Linya TIAN1, Bin JIA3, Hui YANG4
1.School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China 2.College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China 3.CCFED the Third Construction Engineering Co., Zunyi 563000, China 4.School of Transportation, Southeast University, Nanjing 210000, China
Using the data obtained by hyperspectral techniques to estimate the content of soil organic matter is a hotspot in recent years. For the purpose of determining the effective estimation modeling method, specific data such as reflectance obtained by hyperspectral on ground and organic matter content were used in this paper. Wavelet analysis was used to remove the noise, and continuum removal was used to extract the parameters and compress the data. Combining a variety of different data transformation methods and utilizing BP neural networks, multiple linear regression (MLR) and least squares regression (LSR), many different estimation models of soil were established. It is found that the neural network method is superior to the regression model among various data transformation methods after comparing different estimation models established by the three modeling methods. The optimal estimation model is the model established by the combination of logarithmic square transformation and neural network. The R2 of the model is 0.933 and the RMSE is 0.069. The authors creatively carried out the data transformation at the modeling factor level and established the good estimation model. It is shown that the learning mechanism of BP + LS model is suitable for hyperspectral estimation of soil organic matter and works well. The methods, models and conclusions of this paper have some reference significance for the hyperspectral estimation of soil organic matter.
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2019, Vol. 31 
