Near-infrared spectroscopy for determination of soil organic C,microbial biomass C and C and N fractions in a heterogeneous sample of German arable surface soils |
| |
Authors: | Deborah Linsler Anja Sawallisch Heinrich Höper Harald Schmidt Michael Vohland Bernard Ludwig |
| |
Institution: | 1. Department of Environmental Chemistry, University of Kassel, Witzenhausen, Germanylinsler@uni-kassel.de;3. Department of Environmental Chemistry, University of Kassel, Witzenhausen, Germany;4. Landesamt für Bergbau, Energie und Geologie, Hannover, Germany;5. Foundation Ecology &6. Agriculture (S?L), Bad Dürkheim, Germany;7. Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, Leipzig, Germany |
| |
Abstract: | Visual and near-infrared spectroscopy (vis-NIRS) is an established method to estimate soil properties. However, only limited information is available to estimate C and N fractions in a heterogeneous sample. The objectives of our study were to determine estimation accuracies of vis-NIRS using two software for soil organic carbon (SOC), total nitrogen (Nt), pH, texture, and C and N fractions (light (LF), mineral (MF), labile, intermediate and passive fractions) in a heterogeneous sample (consisting of 51 units with different mineralogy) and to compare these results with those obtained by mid-infrared spectroscopy (MIRS). Analyzing vis-NIRS spectra and the mentioned properties showed a possibility to distinguish between high and low values for SOC (residual prediction deviation (RPD) = 1.90) and Nt (RPD = 1.93). Sand and clay could be estimated, whereas pH and silt could not. No useful estimation was possible for N-LF, passive C, intermediate C or intermediate N. C-LF, C-MF and N-MF could be differentiated between high and low values, whereas for passive N the estimation was approximate quantitative. MIRS reached one or two times higher estimation categories than vis-NIRS for SOC, Nt, pH and texture, suggesting that MIRS has a higher potential to estimate soil properties in a heterogeneous sample. |
| |
Keywords: | Heterogeneous sample carbon fractions nitrogen fractions cross-validation |
|
|