Analysis of soil nutrient content based on near infrared reflectance spectroscopy in Beijing region

To study the distribution of soil nutrients, Fourier transform infrared spectroscopy techniques were used to predict total nitrogen, organic matter, total potassium and pH values of soil. With 72 soil samples collected from the experimental field in the suburbs of Beijing, the models were constructed using partial least-squares (PLS) regression based on the spectral data and measured soil nutrient. The model accuracy was evaluated using determination coefficient (R2), adjusted standard deviation (RMSECV), standard deviation of prediction (RMSEP), and residual prediction deviation (RPD). The results showed that, good consistency can be found between the prediction models and the spectral data of total nitrogen, total potassium, organic matter and pH value, the results and the measured data has, the highest coefficient of determination is R2=0.9544. Nutrient prediction model established by PLS could predict total nitrogen, organic matter, and total potassium and pH values accurately.