## 4 Results and Discussion
### Data and Sensors
Importantly, there are many studies applying OLI data within a short time frame, demonstrating the success of Landsat 8 and data continuity. Ancillary data are commonly used for preparing reference data or increasing the accuracy of results. As seen in Figure 12, land cover maps are the most common type of ancillary data used in Landsat change detection studies. Existing land cover maps are useful to determine the area and type of land cover within a given study area and are often a reliable source for collecting reference data. Historical land cover maps are usually in paper-form and should be digitized for integrating into a GIS or remote sensing framework. After digitization, historical land cover maps are one of the few reliable sources of reference data for evaluating the results of a time series. Google Earth provides access to high-resolution optical data from as early as 2000. Historical aerial photos are usually used for selecting training and validation data. Topographic data and digital elevation models (usually SRTM global 30 m data) are most often used for radiometric corrections of remote sensing imagery. Topographic characteristics, such as aspect and slope, are also extracted from DEM data and are used to improve classification accuracy. Field data, which is usually collected with GPS with a relatively high cost, is the most reliable source of reference data. Climate data are usually used to understand the relationship between land cover changes and climate. Figure 11: Usage of different Landsat sensors in studies over time. Various sources of Earth observation data can be combined with Landsat data to improve the spatial or temporal resolution of change detection products (see Figure 13). High-resolution satellites are usually used for deriving reference data and image interpretation. Increasingly, Sentinel-2, as a Landsat-like medium resolution EO sensor with higher spatial resolution, is combined with Landsat data for change detection (Figure 13) [72; 73; 74; 75]. With cross-calibration of Landsat 8, Sentinel-2A, and Sentinel-2B, a near-2.5 day global data coverage, an overall increased spatial resolution (from Sentinel), and historical radiometric consistency (from Landsat), can be achieved. However, MODIS data and products are currently the data most often fused with Landsat, totaling 55 studies. The long revisit cycles, frequent poor atmospheric conditions, and cloudy scenes common in Landsat have restricted its use in detecting rapid changes related to natural hazards and intraseasonal environment varieties. Comparatively, MODIS products have a daily revisit cycle but a coarse spatial resolution and therefore are not suitable for monitoring surface changes within heterogeneous areas. Therefore, combining Landsat and MODIS data is a feasible and more affordable approach to enhance change detection of land surface dynamics. By combining these datasets, both temporal frequency and spatial resolution are improved, contributing vital information for monitoring inter- and intra-annual vegetation phenology, for example. [PERSON] et al. (2006) developed the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to fuse MODIS and Landsat data to enhance both spatial and temporal resolution to better predict daily synthetic surface reflectance [76]. Later on, [PERSON] et al. (2010) used conversion coefficients to implement an enhanced STARFM (ESTARFM) algorithm to produce more accurate synthetic fine  
Figure 12: Ancillary data used in Landsat change detection studies. resolution reflectance products by fusing multisource Earth observation data, especially in mixed-pixel areas [77]. Similarly, the Spatial-Temporal Adaptive Algorithm for Mapping Reflectance Change (STAARCH) uses tasseled cap transformations of both Landsat and MODIS reflectance data to detect vegetated land surface changes. This algorithm requires at least two Landsat images: one at the beginning of the observation time frame and one at the end.