EVALUATION OF CONVERTING LANDSAT DN TO TA AND SR VALUES ON SELECT SPECTRAL INDICES
- 1Dept. of Ecosystem Science and Management, University of Wyoming, Laramie, WY 82071, USA
- 2Wyoming GIS Center, University of Wyoming, Laramie, WY 82071, USA
Keywords: Pre-processing, Digital number, ToA, Surface reflectance, Spectral indices, Landsat
Abstract. The complete archive of images collected across all Landsat missions has been reprocessed and categorized by the U.S. Geological Survey (USGS) into a three-tiered architecture: Real-time, Tier-1, and Tier-2. This tiered architecture ensures data compatibility and is convenient for acquiring high quality scenes for pixel-by-pixel change analyses. However, it is important to evaluate the effects of converting older Landsat images from digital numbers (DN) to top-of-the-atmosphere (TA) and surface reflectance (SR) values that are equivalent to more recent Landsat data. This study evaluated the effects of this conversion on spectral indices derived from Tier-1 (the highest quality) Landsat 5 and 8 scenes collected in 30 m spatial resolution. Spectral brightness and reflectance of mixed conifers, Northern Mixed Grass Prairie, deep water, shallow water, and edge water were extracted as DNs, TA, and SR values, respectively. Spectral indices were estimated and compared to determine if the analysis of these land cover classes or their conditions would differ depending on which preprocessed image type was used (DN, TA, or SR). Results from this study will be informative for others making use of indices with images from multiple Landsat satellites as well as for engineers planning to reprocess images for future Landsat collections. This time-series study showed that there was a significant difference between index values derived from three levels of pre-processing. Average index values of vegetation cover classes were consistently significantly different between levels of pre-processing whereas average water index values showed inconsistent significant differences between pre-processing levels.