Abstract:To explore the capability of GF-1 satellite to predict soil organic matter (SOM), Luoding City of Yunfu City, Guangdong Province was taken as the study area, and 9 multi-spectral remote sensing variables retrieved from GF-1 and 9 terrain variables derived from DEM were used as predictors to establish two kinds of artificial neural network models (Model A: terrain; Model B: terrain & remote sensing) for predicting soil organic matter (SOM) at five soil depths (L1: 0-20 cm, L2: 20-40 cm, L3: 40-60 cm, L4: 60-80 cm, and L5: 80-100 cm). The results showed that the accuracies of SOM full-variable B model at five depths was higher than those of A model with topographic variables only. Especially for the L1 and L2 layers of soil, the accuracy was obviously improved. The R² of the L1 and L2 layers of SOM were increased by 13% and 10% respectively. However, the accuracies of deep soils (L3, L4, L5) were only improved by 4%, 5% and 4%, respectively, and RMSE and ROA±10% also showed a similar trend. The results show that GF-1 remote sensing image can be used as a new data source to predict SOM.