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The warming of Arctic lakes is expected to result in increased methane flux from lakes into the atmosphere, with such fluxes tied to increasing lake productivity. Estimations of the magnitude of this flux, however, are limited by the heterogeneity of Arctic lakes. This research assesses a dataset of major ion and nutrient concentrations of ~175 Arctic lakes sampled between 2017 and 2024 for the purpose of assessing how two non-chemistry-based lake classifications (geomorphic classification by trained experts, and geological classification from surficial geological mapping of the surrounding area) reflect water chemistry, and therefore productivity. Canonical discriminant analyses of the two classification methods showed that both geomorphic and geologic classification differentiated between high-nutrient (therefore high productivity) lake classes. Geomorphic classification was more sensitive to nutrient-variables, likely reflecting sensitivity of these variables to biological processes affected by basin morphology. Geological classification was more sensitive to inorganic variables, reflecting the input of these constituents as a function of the sediments and groundwater flow paths surrounding catchments. The use of either non-sampling based method in determining lake chemistry would allow for lake inventory across the landscape, and more accurate extension of sampled data to unsampled lakes. This application of class-accurate chemical data would greatly increase the accuracy of lake flux calculations across the landscape, relative to treating lakes as monolithic.