Study of sample temperature compensation in the measurement of soil moisture content

Since the near-infrared (NIR) spectrum is susceptible to sample temperature fluctuations, we investigate the influence of sample temperature on the predictive power of calibration model for soil moisture content (MC) and propose the multi-source information fusion technology based on back propagation neural network (BPNN) to compensate for sample temperature effect. With the discrete wavelet transform (DWT) as the pre-processing method and the least squares support vector machine (LS-SVM) regression as the modeling method, a model at 20 °C to predict MC of the soil samples at other temperatures was established. The results show that except for 20 °C, the root mean square error of prediction (RMSEP) are large. We analyze the predicted results with the dual-factor analysis of variance without duplication and the result shows that the effect of sample temperature on the prediction model for soil MC is significant. A temperature compensation model was then established with combining of soil MC and sample temperature based on BPNN. The predicted results showed that the prediction precision of the model was improved significantly.