Saham Mirzaei - PhD student, Department of Remote Sensing and GIS, University of Tehran, Iran
Ali Darvishi Boloorani - Associate professor, Department of Remote Sensing and GIS, University of Tehran
Hossein Ali Bahrami - Professor, Department of Soil Science, Tarbiat Modares University, Iran
Seyed Kazem Alavipanah - Professor, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Iran
Alijafar Mousivand - Assistant professor, Department of Remote Sensing & GIS , Tarbiat Modares University

Soil moisture hampers the estimation of soil variables from remote and proximal sensing data, reducing the strength of the relevant spectral absorption features. In the present study, ۹۵ soil samples which have different texture were rewetted to ۷ different moisture levels (air-dry, ۶, ۱۲, ۱۸, ۲۴, ۳۰ and ۳۶%) and reflectance measured by spectroradiometer (ASD-Fieldspec ۳.). External Parameter Orthogonalization (EPO) used to minimize the influence of soil moisture on Soil Organic Carbon (SOC) estimation. Partial Least Squares Regression (PLSR) model was applied to SOC prediction after removing effect of moisture by EPO. The result shows that removing the effects of moisture from the soil reflectance by EPO algorithm lead to improve precision of SOC prediction by PLSR model. Therefore, the EPO could be useful method for field level spectrometry for SOC estimation without getting affected by moisture

“SOC, EPO, PLSR, Soil Moisture”.