prediction of Soil Organic Carbon (SOC) in Semi-Arid Rangeland Using Multivariate Statistical Analysis based upon Remotely Sensing Data (Case study: Neyshabur Rangeland, Khorasan-Razavi Province, Iran)

Document Type : Research and Full Length Article


1 Department of Soil science, collage of agriculture, Lorestan University, Khoramabad, Iran

2 Department of soil science, collage of agriculture, Lorestan University, khoramabad, Iran

3 Department of soil science, collage of agriculture, Lorestan University, Khoramabad, Iran


Estimation of soil organic carbon (SOC) is an important factor to manage natural resources. This study focused on three objectives: i) to compare the performance of the multivariate linear regression (MLR), principal component analysis (PCA), and the Euclidian distance from soil line (D), models to estimate SOC, using remote sensing data (ii) to map SOC using the most suitable technique. A total of 102 soil samples (0-10 cm depth) was collected from the study area located in the South-West of Neyshabur, Khorasan-Razavi Province, Iran. The Remote sensing data were used to develop the models including Landsat7 ETM+, visible, near-infrared, middle-infrared, and thermal infrared bands. Models were developed between dependent variables of SOC and independent variables of spectral data, and first principal component (PC1), Euclidian distance from soil line (D), and then the developed models were validated by additional samples (30 points). The results illustrated that the MLR, PCA, and soil line models explain 62, 45, and 53 % of the total variability of SOC and RMSE values of 0.09, 0.21, and 0.05, respectively. Therefore, the MLR technique could provide superior predictive performance when compared with PCA and soil line techniques. The MLR technique was applied for SOC mapping in a study area. Our results illustrated that the soil organic carbon spatial information derived using the MLR technique had much greater spatial detail and higher quality compared to one derived from the conventional soil map


Main Subjects

Articles in Press, Accepted Manuscript
Available Online from 25 February 2022
  • Receive Date: 26 August 2021
  • Revise Date: 21 February 2022
  • Accept Date: 25 February 2022
  • First Publish Date: 25 February 2022