In the present study, artificial neural networks (ANNs) were employed to develop models to predict soil organic carbon density (SOCD) at different depths of soil layers. Selected environmental variables such as vegetation indices, soil particle size distribution, land use type, besides primary and secondary terrain attributes were considered as the input variables. According to the results, the ANN models explained 77% and 72% of the variability in SOCD at soil layer depths of 0-20 cm and 20-40 cm, respectively, at the site studied. Sensitivity analyses showed that the most considerable positive contribution of variables for predicting SOCD included by land use type, normalized difference vegetation index (NDVI)> normalized difference water index (NDWI)> Silt> Clay> Elevation in 0-20 cm soil layer. On the other hand, for 20-40 cm soil layer, land use type following by NDVI> NDWI> Clay> Silt were identified as the most powerful predictive factors. In Deylaman region in both soil layers, Sand had a considerable negative effect on SOCD and most of the terrain attributes had no significant impact on the SOCD prediction. Therefore, these results provide valuable information for sustainable management and decision making on a landscape scale for governors and other users
کلید واژگان :Artificial neural network; Remote sensing index; SOCD
ارزش ریالی : 600000 ریال
با پرداخت الکترونیک