Assessment of Total, Stable and Labile Carbon Using Visible, Near-Infrared Diffuse Reflectance Spectroscopy.
Gustavo M. Vasques1, Sabine Grunwald2, and James O. Sickman1. (1) Soil and Water Science Dept, Univ of Florida, McCary Hall 2169, PO Box 110290, Gainesville, FL 32611, (2) Soil and Water Sci. Dept, Univ of Florida, 2169 McCarty Hall, PO Box 110290, Gainesville, FL 32611
To assess the soil carbon pools and carbon sequestration potential at the landscape scale rapid, cost-effective and reliable methods are in need. Visible, near-infrared diffuse spectroscopy is a rapid and cost-effective method that provides inferences on multiple soil properties. The objectives of our study were to relate different carbon attributes (total, recalcitrant, hydrolysable and hot water extractable) to soil spectral signatures derived using visible, near-infrared diffuse spectroscopy. Long-term sequestration of carbon in soils typically involves movement of fixed carbon into the stable recalcitrant pool from the smaller labile pool which has greater relative exchange with the atmosphere. Our approach targets multiple aspects of soil carbon and provides a comprehensive assessment of biogeochemically active carbon pools of a wide variety of soils. We used 550 soil samples collected at 4 different depths (0-30, 30-60, 60-120 and 120-180 cm) in the Santa Fe River Watershed in north-east Florida representing typical soils and land uses. Dominant soils were Ultisols (36.7%), Spodosols (25.8%), and Entisols (14.7%) and less prominent are Histosols (2.0%), Inceptisols (1.1%), and Alfisols (1.0%). Land use consisted of pine plantation (32.2%), wetlands (16.2%), upland forest (14.7%), improved pasture (14.0%), urban (8.8%), forest regeneration (6.0%), crops (5.0%), rangeland (3.7%) and a variety of high intensity land uses such as tree groves, dairies, and feeding operations. Soil samples were analyzed for total carbon, hydrolyzable labile C with 6 N HCl, and labile C by hot water extraction. Visible, near infrared diffuse reflectance spectroscopy was used to scan the same soil samples. Chemometric modelling was used to relate the laboratory measurements to spectral data using Partial Least Squares and tree-based modelling. Results indicated that robust models for all soil properties could be developed with slightly better results derived using tree-based modelling. Our chemometric models have the potential to more rapidly assess total, labile and stable carbon pools at landscape scale.