Mapping soil organic carbon content using spectroscopic and environmental data: A case study in acidic soils from NW Spain.
In this study we present a methodology to estimate and map the content of soil organic carbon (SOC) in topsoils using spectroscopic (FTIR-ATR) and environmental raster data. We determined the SOC content in 221 topsoil samples in Galicia (NW Spain) using the Walkley-Black method. FTIR-ATR spectroscopic data was measured upon the same set of samples. The Random Forest (RF) technique was used to link the measured SOC concentrations to the FTIR-ATR measurements in order to identify the relevant absorbance bands explaining most of the variability in SOC. We then used linear regression (MLR) to predict SOC concentrations from the selected FTIR-ATR bands as independent proxy. This model showed a good predictive performance (r-squared=0.88; RSME=2.14; ME=0.05; RPD=3.14), indicating that SOC can be effectively estimated from the identified spectral bands. Finally, we used Partial Least Squares (PLS) to model the spatial distribution of the predictor bands using a number of environmental raster maps (climate, land use and geology) as covariates. This new raster was used within the MLR model previously created to generalize the predictions of SOC in the whole study area. This approach shows that FTIR data can be used to map SOC while minimizing analytical costs and time efforts.
Departamento de Edafoloxía e Química Agrícola, Facultade de Bioloxía, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain.
Marcela
M
Rial
Departamento de Edafoloxía e Química Agrícola, Facultade de Bioloxía, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain.
Antonio Martínez
AM
Cortizas
Departamento de Edafoloxía e Química Agrícola, Facultade de Bioloxía, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain. Electronic address: luis.lado@usc.es.
Luis
L
Rodríguez-Lado
FTIR–ATR; Partial Least Squares Regression; Random Forest; Spatial statistics