The haar-CWT analysis of hyperspectral reflectance data to evaluating soil carbon and nitrogen

Abstract

The assessment of greenhouse gas emissions from soils requires an accurate knowledge on the fate of carbon and nitrogen in soils. Traditional analysis can not be used to evaluate carbon and nitrogen over large geographical areas. For this reason, hyperspectral remote sensing techniques for predicting soil organic carbon and nitrogen on a large scale have received much attention. This study mainly focused upon capturing the feature values of soil organic carbon (SOC) and total nitrogen (TN), and evaluating SOC and TN by applying wavelet analysis to reflectance spectra. Results of this study indicated that the maximum correlation coefficient between SOC, TN, and wavelet coefficient were more than 0.96 compared to the relationship between SOC, TN, and spectral reflectance (r=−0.79 for SOC, r=−0.40 for TN), especially for TN (the maximum negative correlation coefficient r=−0.964). For SOC+TN and SOC/TN, due to SOC contents accounted for a large proportion of soil composition compared to TN, their spectral feature were affected by SOC in soil sample. In addition, wavelet analysis also enhanced the features of SOC+TN and SOC/TN obviously. These results suggested that wavelet analysis was a better method for capturing the absorption features of soil composition using hyperstpectal remote sensing data.