###### Abstract
Efforts have been made by the Landsat Global Archive Consolidation (LGAC) to ensure that all images gathered at international receiving stations are captured, processed, stored, and made available via the collection of Landsat data into the USGS Landsat data archive. Today, the Landsat archive is more complete than ever [24]. Much of the globally acquired Landsat data was collected by numerous instruments under various atmospheric conditions and angles, meaning that geometric and radiometric calibration are necessary if Landsat data are used in a time series analysis or within a stack. This ultimately limited the use of these data by those lacking certain knowledge or software capabilities. However, with the availability of Landsat Collection 1, and recently Collection 2, and other preprocessed products [25; 26] with automated cloud detection algorithms [27], there is now broad capacity for the use of Landsat data in time series analysis and change detection applications. For several years, land cover change detection using remote sensing data largely focused on capturing abrupt changes between two time intervals, where one land cover type is replaced by another [28]. Over time, however, improvements to instruments and data availability and advancements in algorithms and computation methods allowed for the development of different change-detecting techniques, such as time series analysis and temporal trajectory-based change detection [29]. As a result, change detection and land cover classification are no longer implemented as separate and independent activities as was typical in past studies, allowing for the extraction of more thorough and meaningful change-related information. Additionally, recent advances in cloud computing platforms, such as Google Earth Engine (GEE) [30], Amazon Web Services [31], NASA Earth Exchange [32], which host petabyte-scale data archive and offer high performance cloud computing capability, make implementation of state-of-the-art algorithms over large areas feasible [33]. Improvements in space-borne instrument technology and the growing importance of earth observation (EO) satellites for environmental monitoring have increased the number of active civilian remote sensing satellites [20]. For example, high spatial resolution satellite constellations with frequent data acquisition rates have been launched, such as the constellation of more than 180 shoe-sized \"Doves\" satellites by Planet with a daily 3.7 m spatial resolution [34]. Despite their high spatial and temporal resolutions, many of these optical sensor programs lack radiometric consistency, consistent spectral bandpasses, and orbital stability [5]. As such, the use of a reference sensor for cross-calibration of surface reflectance products is essential for the integration of data between sensors and to provide measurement integrity through space and time [35]. The Landsat program addresses this issue as it offers radiometric consistency within its historical archive and thus, is one of the few instruments that can act as a reference sensor for other optical satellite programs [9]. Synergistic international partnerships between medium spatial resolution satellites can result in a virtual constellations [36], such as between Landsat and Sentinel-2 [37], reducing the effective revisit rate, such as a latitude-dependent <3 days revisit time for Landsat-8, Sentinel-2A, and Sentinel-2B [38]. Additional improvements via combining Landsat and Sentinel-2 are found in improved geometric fidelity [35]. Given the large number of applications and high rate of new algorithm developments, there are several change detection review papers currently published [5, 28, 29, 39, 40]. Despite the important position of Landsat data in change detection studies and applications, there is only a single review article focusing specifically on Landsat change detection [41].