[Experiment Research and Analysis of Spectral Prediction on Soil Leaking Oil Content].

The spectral analysis method was applied experimentally to extract the spectral indices, measure and analyze the spectral characteristics and their difference of the mixture which are composed in soil in Central Shaanxi Plain and the diesel and motor oil respectively, aiming to provide solutions to practical difficulties in detecting, analyzing the spectral characteristics and difference between the soil leaking with equal content diesel and motor oil and predicting the leaking content of diesel in the soil. The spectral response curves of the soil leaking with different oil respectively and the soil leaking with diesel with different content were collected. Then the spectral prediction model for the leaking content of diesel in the soil was built based on the reflectance characteristics. The coefficient of the detection (R2) was introduced to evaluate the stability of the built model，and the parameter root mean squared error (RMSE) was introduced to estimate the precision and the predictability of the model built in this work. It is demonstrated that : (1) The reflectance of soil leaking with diesel is less than that of the equal content motor oil. And there is a double absorption trough of the reflectance curve of both soil leaking with diesel and motor oil at 1 740 and 2 328 nm. The spectral absorption indices and absorption depth of the soil leaking with diesel keep less than the equal content motor oil. (2) The built spectral prediction model for the leaking content of diesel in the soil demonstrates good stability with the coefficient of determination at R2=0.854, and performs favorable predictability (Root-Mean-Square Error, RMSE=0.016), which can benefit the effective prediction and quick estimation methods of the leaking content of diesel in the soil, enrich and progress the experimental method and theoretical research work of spectral prediction on soil leaking oil content and promote the application of remote sensing in safety production and environmental protection.