Abstract:The content of soil organic matter (SOM) is an important indicator for evaluating soil fertility and ecological quality. However, the chemical analysis methods prescribed by national standards for detecting SOM content are difficult to meet the demands of rapid detection. To provide a foundation for the development of a rapid SOM content detector, a total of 760 soil samples were collected from apple orchards in ten provinces in northern China with complex soil types and significant spatial differences. According to geographic and climatic characteristics, the samples were grouped into four regions: North China, Northeast, Northwest Arid, and Northwest Frontier. Soil reflectance spectra from 400~2450nm and SOM contents measured following the national standard method were used to examine the effects of SOM levels and regional differences on spectra and the linear correlations between reflectance and SOM. Partial least squares regression (PLSR), support vector regression (SVR), and least-squares support vector machine (LS-SVM) models were then constructed to predict SOM. The Northeast region had the highest mean SOM content (25.443 g/kg), while the Northwest Frontier had the lowest (13.286 g/kg). Soil reflectance showed an overall negative correlation with SOM. LS-SVM achieved the best prediction performance for North China and Northeast samples, with the residual prediction deviation (RPD) values of 2.814 and 2.475. PLSR performed best for the Northwest Arid and Northwest Frontier regions (RPD value of 2.888 and 3.572). For mixed samples from all four regions, LS-SVM provided the highest accuracy (RPD value of 2.864). These results indicated that building a universal SOM prediction model for apple orchard soils in the ten northern provinces of China was feasible, while building region-specific models was able to improve prediction accuracy for most regions.
