Our work is focused on the development of non-invasive methods to estimate skin constitutive elements. Such methods can play an important clinical and scientific role in detecting the early onset of skin tumors. Given current statistics by the American Academy of Dermatology suggesting that more than 10 people die each hour worldwide due to skin related conditions, this has potentially high impact on the delivery of skin cancer diagnostics, and patient mortality and morbidity. It can also serve as a valuable tool for research in cosmetology and pharmaceuticals in general. We combine a physics-based model of human skin with machine learning and hyperspectral imaging to non-invasively estimate physiological skin parameters, including melanosomes, collagen, oxygen saturation, blood volume, and skin thickness. While some prior work has been done in this regard, no validation against ground truth has occurred whatsoever. In this specific study we develop a protocol to validate our methodology for estimating one of these skin parameters, skin thickness, using a dataset of 48 hyperspectral signatures obtained in vivo, and cross-validate our depth estimates with a gold standard obtained via Ultrasound. Relative to this gold standard, we find promising mean absolute errors of less than 0.1 mm for skin thickness estimation.