Spectrally-based soil carbon models to support the National Forest Inventory of Rio de Janeiro, Brazil

• Visible-near infrared spectroscopy expedites soil carbon content assessment • Cubist and partial least squares regression produced reasonable predictions • Preprocessing soil spectral curves improved soil carbon content predictions • Model ensemble did not improve soil carbon content predictions • National Forest Inventory soil samples can be analyzed by soil spectroscopy Methods for assessing soil carbon must be fast and accurate to support soil health and security evaluation, measurement, reporting and verification of carbon stocks. Visible-near infrared (Vis-NIR) spectroscopy minimizes the costs and time required for assessing soil carbon. Soil samples were obtained at 189 sites from the National Forest Inventory (NFI) of Rio de Janeiro state (∼​​43,782 km²), Brazil, between 2013 and 2016, at 0-20 and 30-50 cm, with a total of 373 recovered samples. The objective was to compare different preprocessing transformations of spectral curves, and calibration methods to predict soil carbon contents from soil Vis-NIR spectral curves in NFI samples from Rio de Janeiro state. Soil carbon contents were measured by dry combustion in an CHNS elemental analyzer, and soil spectral curves were obtained in the laboratory. Cubist combined with log(1/reflectance) preprocessing emerged as the optimal combination to predict soil carbon content (root mean square error of cross-validation of 5.1 g kg -1 ), whereas elastic net obtained good soil carbon content predictions consistently in both cross-validation and external validation. Partial least squares regression, random forest, support vector machines, and model ensemble produced poorer results. The results agree with previous studies comparing calibration methods for soil carbon content prediction, and stress the importance of preprocessing soil spectral curves, as well as testing different methods to produce robust results. Soil Vis-NIR spectroscopy can be used to assess soil carbon contents in Rio de Janeiro, supporting expedited and accurate soil carbon stock, and stock change assessments in future phases of the NFI.