Alexandre ten Caten

TL;DR: In this article, the authors compared the influence of preprocessing techniques on prediction performance, assess the modeling performance of a wide range of multivariate methods, and evaluate the potential of Vis-NIR spectroscopy to predict organic carbon.

TL;DR: In this paper, the authors describe peat mapping experiences from 12 countries or regions and review 90 recent studies on peatland mapping, finding that interest in mapping peat information derived from satellite imageries and other digital mapping technologies is growing.

TL;DR: The Brazilian Soil Spectral Library (BSSL) as mentioned in this paper was developed in a joint partnership with the Brazilian pedometrics community to standardize and evaluate spectra within the 350-2500nm range of Brazilian soils.

TL;DR: In this article, a local spectral library of soils (n = 841 samples) was used in the Planalto region of the State of Rio Grande do Sul, Brazil, to characterize and identify differences among spectra obtained for subtropical soils samples, evaluate different pre-processing techniques and multivariate methods to propose SOC prediction models from the spectral data and evaluate the performance of SOC prediction from the stratification of a local library.

TL;DR: In this article, the authors used spectral features as covariables to predict soil organic carbon (SOC), clay, sand, and silt content using reduced spectral features selected by two spectral preprocessing techniques.