Moisture Influence Reducing on Soil Reflectance Using EPO for Organic Carbon Prediction

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

Keywords:

“SOC , EPO , PLSR , Soil Moisture”.