## 2 Satellite Imagery Applications  
With an ever-expanding constellation of remote sensing satellite and communications platforms, many of today's geospatial technologies encompass considerable temporal and spatial depth. In recent years, we have seen temporal resolutions of multi-spectral satellite imagery increase from 18 days to daily (sub-daily near the poles), and spatially from 80 m to the sub-meter level [10; 11]. Online publicly-available databases have drastically reduced the costs of remote sensing data. At one time, a single Landsat image would cost the end-user $3600 USD [4], and could take months to receive [9]. Today, the entire Landsat archive is online and freely available, as are many other remote sensing datasets from the United States Government, European Space Agency, and others. In the past 40 years, there has also been a shift in the ownership of satellites from totally government-driven to a growing number of private-sector, for-profit remote sensing platforms such as Planet Labs [10] and DigitalGlobe [12]. This abundance of remote sensing data allows grazingland managers to address a series of probing research questions related to past, present, and future landscape conditions [13; 14]. The advanced very high-resolution radiometer (AVHRR) has been used in the past to estimate net primary production on grazinglands at a 1 km\({}^{2}\) resolution [15]. However, more recent advances in cloud computing availability has allowed for Landsat and MODIS (moderate resolution imaging spectroradiometer) data from 1984 to present to be used in the United States (US) to model grazingland primary productivity at a much finer scale [16; 17; 14; 18]. The Rangeland Analysis Platform ([[https://rangelands.app/](https://rangelands.app/)]([https://rangelands.app/](https://rangelands.app/)) accessed on 5 January 2022) is a new tool utilizing satellite imagery and big data to help private and public land managers in the US to observe current and past rangeland conditions to aid in research and decision-making processes [19; 20]. Potential applications of these datasets include: determination of appropriate stocking rates [21], identifying indicators of rangeland health, locating areas impacted by overgrazing, identifying state and transition phases [22; 23; 24], training new land managers on the history of management areas and allotments, and developing new management schemes by learning from past management practices [14; 17; 18; 25]. Advancements in privately-owned satellite services, such as RapidEye (5 m) and Planetscope (3 m) have provided grazingland scientists with exciting new opportunities to observe and study grazinglands [10; 26; 27]. Using high-resolution, multi-spectral imagery, normalized difference vegetation index (NDVI) data can be obtained with much more detail than with the Landsat platform. With daily global datasets available, grazingland production, woody plant cover, land use, and even species identification in some cases may be possible at high spatial and temporal resolutions [26; 27]. When combined with other geospatial data, management and restoration measures could be prioritized to areas where the potential of success is most prevalent [26]. However, this technology is currently subscription-based and may be out of reach for many producers and grazingland managers. Many academic institutions and federal research labs have limited free access for research purposes. As additional geospatial service providers emerge and contend for market share, pricing may become more competitive in the future. Another emerging use of satellite imagery is the mapping of forage quality for grazing animal diets [28; 29]. Coupled with GNSS tracking of livestock movements, we can begin to see how dispersion of forage quality and topography across the landscape may affect forage access, as well as movement and foraging behaviors of livestock [30; 31; 32; 33; 34].