Abstract
Accurate soil organic matter (SOM) estimation could provide critical information to understand soil organic carbon sequestration, soil fertility, and the global carbon cycle. A nearest-neighbourhood autoregressive moving average (NN-ARMA) modelling technique along with linear regression has been used to predict localized soil SOM variation based on topographical characteristics and vegetation indices in semi-arid region of Saudi Arabia. Seven topographic variables derived using DEM, and twelve vegetation indices obtained from Landsat 8 used in the model. The best NN-ARMA model showed seven significant variables explaining 96.4% of the total variation of SOM, whereas the best linear regression model could explain 78.8% of the total variation of SOM. The results showed that NN-ARMA model gave better results compared to the linear regression model. Our research gave a better understanding of the possibility of accurate estimation of SOM using the NN-ARMA approach using topographical characteristics and vegetation indices easily acquired by RS sensors.