## 1 Introduction
Spectral reflectance enables non-destructive sampling over a wide range of spatial and temporal scales. Remote sensing data from multiple satellite platforms have been available since the 1970s [18]. Many publicly funded satellites monitor the Earth, including the NASA Terra and Aqua satellites, which carry the MODIS (Moderate Resolution Imaging Spectroradiometer) sensors. These satellite sensors provide 250 m spatial resolution from 36 spectral bands, with daily coverage [18; 22]. MODIS products are created from quality-controlled multiband surface radiance and reflectance, and are freely available through websites developedby U.S. government agencies. The availability of quality-controlled satellite data has led to their widespread use and increased practicality for management and science. Remote sensing data are also readily available for purchase from commercial satellite vendors. For example, the Systeme Pour l'Observation de la Terre SPOT 5 satellite produces 10 m spatial resolution multispectral imagery (SPOT 4 can also produce 10 m spatial resolution by co-registering a 10 m panchromatic and 20 m colour image) from four bands, two in the visible wavelengths, one in the near infrared and one in the shortwave infrared [23]. The increased spatial resolution limits the possible temporal resolution such that SPOT samples a position on the Earth every 26 days, although the sensor can be pointed off nadir at targets not directly below the satellite, increasing the temporal resolution [18]. Commercial satellites offer several potential advantages over MODIS; the finer spatial resolution of commercial satellites offer the potential of providing data products that more closely match the scales of small management units [24]. However, images from commercial systems are often prohibitively expensive for practical application for range management [18]. Commercial satellites also require tasking requests far in advance, and data gaps can occur with excessive cloud cover or otherwise reduced image quality during the tasking period [3]. Despite these limitations, private satellites represent alternatives to government-funded programs and are also being explored for rangeland monitoring. Past studies have demonstrated the ability of remote sensing to accurately estimate plant biomass or yield in grasslands [18; 20; 25; 26]. Satellite remote sensing also allows repetitive sampling, i.e., the creation of time series for evaluating growing season phenology and interannual variability in production. These methods enable productivity monitoring in near real time, relatively inexpensively, and with multiple scales of coverage. Many vegetation indices have been established from remotely sensed data. These indices provide proxies for vegetation biophysical properties, and can be used to diagnose rangeland conditions and trends [27; 28]. The most commonly used vegetation index is the Normalized Difference Vegetation Index (NDVI) [21; 29]. This index compares the low reflectance of photosynthetic materials in the red wavelengths to the high reflectance in the near-infrared to produce an estimation of plant biophysical parameters, including biomass, leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (\(f_{\text{APAR}}\)). Numerous studies have demonstrated that NDVI strongly correlates with green biomass and leaf area index, particularly for grassland ecosystems [19; 25; 26; 30; 31]. In grassland ecosystems, NDVI also provides a strong indicator of photosynthetic carbon uptake, typically measured via gas exchange or eddy covariance [16; 26; 31; 32; 33], indicating its potential as a useful metric for carbon sequestration. Furthermore, in prairies, NDVI often scales with metrics of species richness and evenness [16; 17], so may also provide a useful indicator of biodiversity or other ecosystem co-benefits. Due to the potential economic benefits of accurate prairie ecosystem monitoring, there is renewed commercial interest in evaluating cost-effective methods of assessing mixed prairie biomass.