Adaptability of the model for soil moisture measurement based on near-infrared spectroscopy

The precision of the soil moisture measurement using near-infrared spectral quantitative analysis model relies on the sample condition, so the model adaptability is extremely important. Three kinds of Hubei area soil were researched, and partial least square (PLS) and cross calibration method were employed to establish soil moisture analysis model. The results indicate that the decision coefficient R2 between predicted value by model and normal value was 0.9946, and the root mean square error of cross-validation (RMSECV) was 0.801%, the model predicted decision coefficient R2 was 0.9919, the root mean square error of prediction (RMSEP) was 0.912%. Principal component analysis(PCA) method was used to classify the raw soil samples and the processing soil samples. However, the results indicate that the quantitative analysis model has low prediction precision for raw sewage sample. After the slope /bias method was used to revise 12 raw sewage sample values predicted by the model, the average absolute error reduced from 0.78% to 0.38%. The results indicate that the method of slope/bias can enhance the adaptability of the model for near-infrared spectral quantitative analysis of soil moisture.