Digital Mapping of Soil Organic Carbon (Case Study: Marivan, Kurdistan Province)

Soil organic carbon is one of the most important soil properties which its spatial variability is essential to crop management, land degradation and environmental studies. Investigation of variability of soil organic carbon using traditional methods is expensive and time consuming. Therefore, one of the ways to overcomethis challenge is using digital soil mapping whichcan predict soil characteristics using auxiliary data and data mining methods. Previous studies have shown that digital elevation model (DEM) and remotely sensed data are the most commonly useful ancillary data for soil organic carbon prediction. Artificial neural network (ANN) is a common technique of digital mapping. The region of Marivan in Kurdistan province is one of the forested areas inIran. In recent decades, due to population growth and the increased need for food, thisforested area has been threatened and some parts are now cultivated. Therefore, accurate mapping of soil organic carbon so as to improve land management and prevent land degradation is necessary. The purpose of this research wasusing ANN model and auxiliary data to mapsoil organic carbon. The study area is located in Kurdistan Province, Marivan (cover 20000 ha). Soil moisture and temperature regimes are Xeric and Mesic, respectively. Elevation also varies between 1280 and 1980 m. The main land use typesarecropland, forestland and wetland. The major physiographic units are piedmont plain, mountain and hills with flat to steep slopes. Using stratified random soil sampling method, 137 soil samples (for the depth of 0-30 cm) were collectedand soil organic carbon were measured. In the current study, auxiliary data were terrain attributes and ETM+ data of Landsat 7. Terrain parameters (including 15 factors), bands 1, 2, 3, 4, 5, 6, 7, brightness index (BI) and normalized difference vegetative index (NDVI) were computed and extracted using SAGA and ArcGIS software, respectively. ANN model was applied to establish a relationship between soil organic carbon and auxiliary data. Finally, soil organic carbon weremappedusing ANN and validated based oncross validation method. Three different statistics was used for evaluating the performance of model in predicting soil organic carbon, namely the coefficient of determination (R2), mean error (ME) and root mean square error (RMSE). Based on sensitive analysis of ANN model, auxiliary variables includingwetness index, index of valley bottom flatness (MrVBF), LS factor, NDVI index, and B3were the most important factors
for prediction of soil organic carbon. The quantities of R2, ME and RMSE calculated for ANN model were0.80,0.01 and 0.67, respectively.Soil organic carbon content ranged from0.26 to 8.45 % and the highest contentwasobserved in forestland with hill and mountain physiography and wetland around the lake. It is noteworthy that the differences fordifferent land uses were not statistically significant. Auxiliary data including wetness index, index of valley bottom flatness, LS factor, and B3 in different land uses had statistically significant difference (p<0.05) indicatinga closerelationship between auxiliary data and soil organic carbon. MrVBF and wetness index were lower and higher in forestland and wetland, respectively. Conversely, LS factor was higher and lower in forestland and wetland, respectively. Band 3 was lower in cropland and wetland compared to in forestland. NDVI index was also insignificantly higher in forestland compared to in cropland and wetland. In this research,ANN model was used to investigate the spatial variability of soil organic carbon in Marivan, Kurdistan province. The highest content of Soil organic carbon was foundin forestland and wetland. NDVI index was the most important auxiliary data to predict soil organic carbon within ourstudy area. According to the values of statistics, ANN accurately estimated the soil organic carbon. Therefore, employingpedometric techniques such as ANN model, auxiliary data of terrain attributes and satellite images to digitally mapsoil properties and updateold maps is recommendable. Further studies are needed to compare these results withdirect measurements of soil organic carbon.