Fourier Transform Near Infra-Red Reflectance Spectroscopy (FT-NIRS) is a cheap, rapid and non-destructive method for analysing organic sediment components. Here we examine the robustness of a within lake FT-NIRS calibration using a dataset of almost 400 core samples from Lake Suigetsu, Japan, as a means to rapidly reconstruct % total organic carbon (TOC). We evaluate the best spectra pre-treatment, examine different statistical approaches, and provide recommendations for the optimum number of calibration samples required for accurate predictions. Results show that the most robust method is based on first order derivatives of all spectra modelled with partial least squares regression. We construct a TOC model training set using 247 samples and a validation test set using 135 samples (for test set R2 = 0.951, RMSE = 0.280) to determine TOC and illustrate the use of the model in a ultra-high resolution (e.g. 1mm/annual) study of a long sediment core from a climatically sensitive archive.