Recent evidence supports using visible-near infrared reflectance spectroscopy (VNIRS) for sensing soil quality; advantages include low-cost, nondestructive, rapid analysis that retains high analytical accuracy for numerous soil performance measures. Research has primarily targeted agricultural applications (precision agriculture, performance diagnostics), but implications for assessing ecological systems are equally significant. Our objective was to extend chemometrics for sensing soil quality to wetlands. Hydric soils posed two challenges. First, wetland soils exhibit a wider range of organic matter concentrations, particularly in riparian areas where levels range from <1% in sedimentation zones to >90% in backwater floodplains; this may mute spectral responses from other soil fractions. Second, spectral inference of cation concentrations in terrestrial soils is for oxidized species; under reducing conditions in wetlands, oxidation state variability is observed, which strongly affects chroma. Riparian soils (n = 273) from western Florida exhibiting substantial target parameter variability were compiled. After minimal pre-processing, soils were scanned under artificial illumination using a laboratory spectrometer. A multivariate data mining technique (regression trees) was used to relate post-processed reflectance spectra to laboratory observations (pH, organic content, cation concentrations, total N, C, and P, extracellular enzyme activity). High validation accuracy was generally observed (r2(validation) > 0.8, RPD > 2.0, where RPD is the ratio of the standard deviation of an attribute to the observed standard error of validation); where accuracy was lower, categorical models (classification trees) successfully screened samples based on diagnostic functional thresholds (validation odds ratio > 10). Graphical models verified significant association between predictions and observations for all parameters, conditioning on biogeochemical covariates. Visible-near infrared reflectance spectroscopy offers both cost and statistical power advantages; hydric conditions do not appear to constrain application.