Abstract
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.
Highlights
IntroductionLandsat satellites have been collecting images since 1972 and are administered by the United States Geological Survey (USGS) in conjunction with the National Aeronautics and Space Administration (NASA) (Straub et al, 2019)
1.1 Landsat Overview and Remote Sensing ImportanceLandsat satellites have been collecting images since 1972 and are administered by the United States Geological Survey (USGS) in conjunction with the National Aeronautics and Space Administration (NASA) (Straub et al, 2019)
Adjustments for effects of the sun, topography, the atmosphere, and the sensor are included in absolute preprocessing, with absolute pre-processing aiding in values being able to be evaluated with other values of the same amount of pre-processing that were acquired from different sensors, times, or locations (Young et al, 2017)
Summary
Landsat satellites have been collecting images since 1972 and are administered by the United States Geological Survey (USGS) in conjunction with the National Aeronautics and Space Administration (NASA) (Straub et al, 2019). These satellite images are provided at no-cost (USGS, 2018) and are utilized for a wide variety of applications including improving water use in vineyards (Ecker, 2020), wildfire and clear-cut mapping (Schroeder et al, 2011), invasive species mapping (Evangelista et al, 2009), and evaluating flooded areas (Sivanpillai et al, 2020, Wang et al, 2002). Relative pre-processing is performed to bring about radiometric scale equivalence between matching bands of a reference image and an evaluated image (Young et al, 2017)
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