Soil organic carbon prediction using remotely sensed data at Lighvan watershed, Northwest of Iran

• Main Authors: Mehdi Rahmati, Mohammad Reza Neyshabouri, Majid Mohammady Oskouei, Ahmad Fakheri Fard, Abbas Ahmadi
• Format: Article
• Language: English
• Published: Azarian Journals 2016-04-01
• Series: Azarian Journal of Agriculture
• Subjects: Landsat data; Lighvan watershed; Remote sensing; Soil organic carbon
• Online Access: http://azarianjournals.ir/wp-content/uploads/aja16042201.pdf

The current research was directed at Lighvan watershed, northwest of Iran to investigate ETM+ data applicability for the Soil Organic Carbon (SOC) prediction. So, Ground Measurements (GM’s) of SOC was carried out in 225 points of study area and ETM+ data were downloaded from NASA’s website. Different linear and nonlinear regressions were applied to predict SOC using GM’s from whole study area and bare soil only to train the models. Results showed that ETM+ data was impractical for remote sensing of SOC within whole study area due to vegetation effects. Contrary, ETM+ data showed satisfactory accuracy for SOC prediction in bare soils with mean evaluating error (ER) of 18. 34 percent for evaluation stage. A first and second order polynomial between measured SOC and the reflectance of band 1 to 7 of the ETM+ data showed the highest accuracy for SOC prediction with ER of 14 % and R2 of 0.665 for the evaluation stage. Although, ETM+ data application for remote sensing of SOC were restricted by vegetation, it seems that EMT+ data showed enough accuracy for predicting SOC through bare soils.