Possibilities of near infrared reflectance spectroscopy for the prediction of organic carbon concentrations in grassland soils

Abstract

SU MMARYFor the determination of soil organic carbon (OC) concentrations, the availability of a fast, low-cost analysis method is required. The aim of the present study was to evaluate the possibilities of near infrared reflectance spectroscopy (NIRS) to build a spectral database and to develop calibrations for the prediction of organic carbon concentrations in grassland soils. NIRS spectra of 1626 soil samples from different grasslands (both agricultural and natural) were collected between 1100 and 2500 nm. NIRS calibrations were developed with modified partial least square regression and tested with independent validation samples. The best equations were obtained with the first derivative of the spectra without scatter corrections. For the global calibration, containing the samples of all origins, the standard errors of calibration (SEC) and of prediction (SEP) were respectively 3 . 70 g OC/kg dry soil (R 2 =0 . 89) and 3 . 95 g OC/kg dry soil (R 2 =0 . 88). The ratio of the standard deviation of the reference validation data to the SEP (RPD), indicating the performance of the calibration, was 2 . 9. Dividing the samples into groups according to their practice (agricultural or natural grassland), improved SEP by 5 . 8 and 7 . 7 %, respectively. Dividing the samples into texture groups (clay, silt, sand) improved SEP for agricultural grassland by, on average, 7 . 4 % and for natural grassland by 16 . 2 %.

Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy

“…This strategy allowed, depending on the multivariate calibration type, spectral range and attribute used to split the dataset, to improve considerably the accuracy of the models. The same kind of results was obtained by Van Waes et al (2005) under laboratory conditions. They showed that dividing samples into groups according to agricultural practices or texture improved the prediction of soil organic carbon in grassland soils by 7-16%.…”

Linking spatial patterns of soil organic carbon to topography — A case study from south-eastern Spain

“…Typically soil reflectance decreases with increasing SOC in the wavelength range 0.4-2.5 μm (Baumgardner et al, 1985) and different modelling techniques are used to predict SOC from continuous reflectance spectra or derivatives Banin, 1994, 1995;Reeves et al, 2002;Udelhoven et al, 2003;van Waes et al, 2005;Brown et al, 2006;Viscarra Rossel et al, 2006). The transfer of prediction models to airborne or satellite remote sensing data has only rarely been conducted (Hill and Schütt, 2000;Gomez et al, 2008;Jarmer et al, 2010), although the approach has promise to obtain area-wide estimates of SOC distribution and support regional carbon inventories.…”

Identifying optimal spectral bands to assess soil properties with VNIR radiometry in semi-arid soils

“…Spectral derivative analysis can be used to determine the position and magnitude of absorption bands within a spectrum (Mather, 2004). Derivative analysis has been previously applied to several soil biological-physical-chemical properties estimations (Kariuki et al, 2004;Brown et al, 2005;Van Waes et al, 2005;Mutuo et al, 2006). A future and promising research area in soil radiometry is the robust identification of absorption coefficients for specific soil biogeochemical components needed for the development of soil radiative transfer models (Ustin et al, 2005), and derivative analysis is highly suitable to help in the identification of such absorption features.…”