Background The C-factor estimation models cannot be used universally used in northern and southern China due to the influence of spatial heterogeneity. A review of current literature suggests that there is a lack of research on the vertical structure of forest water and soil conservation in southern China. Therefore, the objective of this research was to explore the best estimation model of C factor at different scales based on the Southern Structured Vegetation Index (V_s). Nanjing is a typical subtropical hilly area with abundant vegetation.

Methods This study investigated a total of 87 typical quadrats (six land use types) from 16 mountains in 8 districts of Nanjing. The quadrats were arranged according to the principle of uniform spatial distribution. The data including the longitude and latitude, elevation, slope, slope position, land use, litter layer thickness, tree species, shrub, grassland and tree height of the sample plot recorded in detail. The remote sensing index was extracted from the corresponding remote sensing images. The V_s of different vegetation types was fitted with the C value to obtain the C factor prediction model. At the regional scale, the best remote sensing index was selected by V_s, then the C factor was retrieved by remote sensing.

Results 1) C-factor estimation based on V_s effectively improved the accuracy of the model. 2) Factor C showed an increased sensitivity to yellowness index and as a result the protective effect of senile vegetation and dead leaf cover on surface soil cannot be ignored. 3) Inversion of C factor based on the optimal remote sensing index, model R²=0.598, M_E>0.5, and thus this model is recommended for estimation of C factor in large scale.

Conclusions In the southern region, the C-factor of slope scale estimated by V_s is more accurate. At the regional scale, the remote sensing index based on V_s screening can effectively invert the vertical information of vegetation to the remote sensing index. This is the first reported study focusing on the structured vegetation index in southern China.