Commercial smallsats are emerging as a key resource for the remote sensing community, with Planet Labs, Inc. operating the largest constellation. Global coverage is achieved through the combined efforts of many different sensors, but the unique spectral response of each sensor results in radiometric inconsistencies across the constellation. These inconsistencies have been an obstacle for researchers as the images do not match the quality the remote sensing community has come to expect from traditional platforms such as Landsat or Sentinel. Various approaches have been offered to correct cross-sensor radiometric inconsistencies in Planet image acquisitions, with many utilizing high radiometric quality platforms such as Landsat or Sentinel to normalize Planet surface reflectance via a linear transformation. These approaches have largely been applied in homogenous regions, and performance in heterogeneous landscapes is not well understood. The Planet surface reflectance images were transformed linearly using Sentinel-2 surface reflectance as a reference. Transformations were tested in six heterogeneous National Ecological Observatory Network sites where airborne hyperspectral data was available for validation. The probability density functions of the transformed Planet images and the corresponding Sentinel reference images were compared using the D statistic of the Kolmogorov-Smirnov test. The absolute spectral accuracy of the transformed Planet images was evaluated against airborne hyperspectral data. Results show that the transformations were effective in transforming the empirical cumulative distribution functions of the Planet images to be more similar to that of Sentinel regardless of initial offsets.