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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.

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CO concentration (XCO) shows the spatial and temporal variation in Iran. The major purpose of this investigation is the assessment of the spatial distribution of carbon dioxide concentration in the different seasons of 2013 based on the Thermal And Near Infrared Sensor for Carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) level 2 GOSAT data by implementing the ordinary kriging (OK) method. In this study, the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) data from the MODerate resolution Imaging Spectroradiometer (MODIS), and metrological parameters (temperature and precipitation) were used for the analysis of the spatial distribution of CO over Iran in 2013. The spatial distribution maps of XCO show the highest concentration of this gas in the south and south-east and the lowest concentration in the north and north-west. These results indicate that the concentration of carbon dioxide decreased with the increase of LST and temperature and a decrease of NDVI and humidity in the study area. Therefore, the existence of vegetation has an effective role in capturing carbon from the atmosphere by photosynthesis phenomena, and sustainable land management can be effective for carbon absorption from the atmosphere and mitigation of climate change in arid and semi-arid regions.

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