## 2 Landsat Overview
### Landsat Archive and Data Characteristics  
Before 2008, the Landsat data policy affected the widespread use of Landsat data, particularly during the commercial management (EOSAT) years, due to the relatively high cost of a single Landsat image (as high as $4400) and the existence of copyright rules that limited sharing [22]. This resulted in low usage of Landsat data and an interest in using as few images as possible for given study (see Figure 2). From economic benefit studies, this limited the potential societal benefits of Landsat and the return on Landsat program investments [55]. The 2008 change in Landsat data policy [21] is evident in both numbers of images downloaded and the number of scientific publications. For some time, the imagery provided by the Landsat global archive was sparse (an estimated 4 million images archived at International Cooperators and 1.9 million at USGS as of March 2006 [56]. This was due to limitations of Landsat-1 to Landsat-5, such as the absence of onboard data storage capacity and data transition [57], and the lack of systematic data gathering from international ground stations (IGS) to USGS [56]. However, changes in the data policy since 2008 and the need for a truly complete global archive with deep temporal records resulted in the Landsat Global Archive Consolidation (LPGS) and improvements of onboard and data transition capacity [24]. Figure 2: Annual publications that included “ Landsat” in title, abstract, or keywords within the Scopus database (purple line), Landsat scene downloads from USGS archive (blue line), and Landsat image price (black line) (Source: USGS [22]) showing the impact of Landsat data policy through time on the usage of Landsat data. As a result of the Landsat Global Archive Consolidation (LGAC), the USGS Landsat archive contains around 9.2 million Landsat Level-1 images as of 31 December 2020 (see Figure 3). Additionally, with the new data acquisition policy, \(\sim\)1200 new observations are added to the USGS archive each day from Landsat-7 and -8, and multiple million images are downloaded per year [9]. Since implementation of free data policy in 2008, many new applications have been developed. These new algorithms have increased the total number of downloaded images from USGS, which now surpasses the 100 million mark. Several applications of EO data rely on pixel-level radiometric and geometric registration and radiometric consistency within a time series, between sensors, and band-to-band. These requirements lead to the processing of the Landsat Collection 1, released in May 2017 [25], reducing preprocessing requirements and increasing consistency, while implementing the latest in methods and science. Collection 2, released December 2020, further improved geometric accuracy (through updated elevation data), improved radiometric calibration, additional higher level products, more quality assessment bands, updated metadata files, and a cloud optimized file format. Given Global Navigation Satellite System (GNSS), star tracker cameras, and inertial navigation system, and Landsat 8 OLI Harmonized Global Reference Images (GLIs) with Sentinel-2, the Landsat Collection 2 has achieved georectification with a less than 12 m root mean square error (RMSE) and enhanced usage of ground control points (GCPs) to produce more level-1 terrain precision (L1 TP) products. The Collection 2 reflectance-based calibration approach has \(\sim\)3% accuracy, which is updated through time and is used to propagate radiometric consistency to past generations. Although some algorithms and applications do not require atmospheric correction and can perform well using Landsat TOA data alone (e.g., single-date image classification), multitemporal classification and geographically large-scale change detection studies require atmospheric correction [58]. Surface reflectance, as a physical value, is consistent through space and time. As a result, the application of atmospheric correction makes change detection results more reliable.