A metrology system (100, 300) for substrate processing, comprising: an optical metrology station including: a plurality of optical sensors to perform measurements from a plurality of measurement locations on a substrate: a plurality of fiber cables connected to the plurality of optical sensors; and a spectrometer selectively connected to the plurality of fiber cables to generate spectra at the plurality of measurement locations; a mass metrology station to measure at least one of a mass or mass change of the substrate; and a controller including: a modelling module to generate thickness values at the plurality of measurement locations based on the spectra from the plurality of measurement locations and a learned model used to convert the spectra into the thickness values, wherein the learned model is generated using machine learning; and a spatial modelling module to generate a spatial thickness distribution model of a surface of the substrate based on a known density of a layer added or removed to or from the substrate, and based on the thickness values at the plurality of measurement locations from the modelling module and the at least one of the mass or the mass change from the mass metrology station, the mass or mass change being used by the spatial modelling module as a constraint or boundary condition for regression analysis for the spatial distribution model, whereby the resulting spatial thickness distribution model of the substrate surface is used to determine the thickness of the top surface of the substrate at locations other than the measurement locations. an optical metrology station including: a plurality of optical sensors to perform measurements from a plurality of measurement locations on a substrate: a plurality of fiber cables connected to the plurality of optical sensors; and a spectrometer selectively connected to the plurality of fiber cables to generate spectra at the plurality of measurement locations; a plurality of optical sensors to perform measurements from a plurality of measurement locations on a substrate: a plurality of fiber cables connected to the plurality of optical sensors; and a spectrometer selectively connected to the plurality of fiber cables to generate spectra at the plurality of measurement locations; a mass metrology station to measure at least one of a mass or mass change of the substrate; and a controller including: a modelling module to generate thickness values at the plurality of measurement locations based on the spectra from the plurality of measurement locations and a learned model used to convert the spectra into the thickness values, wherein the learned model is generated using machine learning; and a spatial modelling module to generate a spatial thickness distribution model of a surface of the substrate based on a known density of a layer added or removed to or from the substrate, and based on the thickness values at the plurality of measurement locations from the modelling module and the at least one of the mass or the mass change from the mass metrology station, the mass or mass change being used by the spatial modelling module as a constraint or boundary condition for regression a modelling module to generate thickness values at the plurality of measurement locations based on the spectra from the plurality of measurement locations and a learned model used to convert the spectra into the thickness values, wherein the learned model is generated using machine learning; and a spatial modelling module to generate a spatial thickness distribution model of a surface of the substrate based on a known density of a layer added or removed to or from the substrate, and based on the thickness values at the plurality of measurement locations from the modelling module and the at least one of the mass or the mass change from the mass metrology station, the mass or mass change being used by the spatial modelling module as a constraint or boundary condition for regression analysis for the spatial distribution model, whereby the resulting spatial thickness distribution model of the substrate surface is used to determine the thickness of the top surface of the substrate at locations other than the measurement locations.