Which of the following is NOT a way to assess the accuracy of a raster?

The assessment of raster accuracy typically involves evaluating the data against established standards or ground truth. While options such as spectral analysis of cell values, spatial evaluation of location accuracy, and radiometric assessment of features are all methods that directly relate to the characteristics and precision of raster data, comparative historical data analysis does not directly assess the accuracy of the raster itself.

Spectral analysis focuses on the individual spectral characteristics recorded in raster cell values, which is crucial for determining the quality and reliability of the data. Spatial evaluation of location accuracy deals with the positional aspect, ensuring that features in the raster align properly with their real-world locations. Radiometric assessment examines the brightness values and spectral response to ensure that they faithfully represent the features on the ground.

On the other hand, comparative historical data analysis involves comparing the current raster dataset against previous datasets or existing external sources. While this can provide insights into trends over time or changes in land cover, it does not serve as a direct measure of accuracy for the raster data collected or produced in that moment. Thus, this option stands out as not being a direct method for assessing the accuracy of a raster dataset.