## 1 Introduction  
Rangelands include vast expanses of grassland, savanna, shrubland, desert, and tundra on which the indigenous vegetation is predominantly grasses, forbs, or shrubs and which are often managed in terms of the abundance and distribution of native and domestic herbivores. Rangelands cover about 50% of the Earth's land surface [1], provide 70% of the forage for ruminant livestock [2], and are often located in remote, rural areas. Aboveground net herbaceous production (ANHP) estimates are critical to many rangeland management and monitoring efforts. They are used to estimate potential carrying capacity for herbivores [3], understand the effects of management decisions and natural drivers on ecosystem structure and function [4; 5], predict fire risk and behavior [6; 7], inform grazing management decisions [8], and calibrate and validate ecological models (e.g., carbon cycling, wildlife habitat; [9]). Given the vast extent and remote nature of most rangelands, spatially explicit maps of ANHP are desirable to provide information for locations where intensive ground data collection is cost-prohibitive or impractical. Satellite imagery has proven valuable for estimating production parameters such as gross primary production (GPP), net primary production (NPP), aboveground net primary production (ANPP), and ANHP (which is the herbaceous-only fraction of ANPP). Satellite-based estimates of plant production tend to rely on radiation use efficiency logic [3; 10]. In short, this logic assumes that plant production is a function of the amount of incoming photosynthetically active radiation (PAR) absorbed by the plant canopy and the proportion of that radiation that is converted into plant biomass. One of the simplest approaches to estimating plant-absorbed photosynthetically active radiation (APAR) from satellites is to use the normalized difference vegetation index (NDVI), which can be calculated from the red and near-infrared reflectance bands obtained by many land surface monitoring satellites. NDVI has been shown to be related to leaf area index (LAI) [11] and the fraction of photosynthetically active radiation (fPAR) absorbed by plants [11]. By measuring or estimating incoming PAR, fPAR can be translated into APAR, which, in turn, can be used to estimate plant production by applying processed-based or empirical models [3; 12]. Spatially coarse estimates of GPP from satellites have been available for decades [13]. The most widely used comes from the Moderate Resolution Imaging Spectroradiometer (MODIS) and uses the processed-based MOD17 algorithm [14; 15], originally designed for global coverage at 0.5-1 km spatial resolution. Empirical models have also been tested using MODIS-derived APAR (250 m) with strong relationships at the padock scale between cumulative APAR and ANHP estimated from ground-based clipping [3]. ANHP estimates at finer spatial scales are needed to monitor and manage heterogeneous rangeland landscapes. Fine-scale ANHP estimates are particularly important to rangeland managers since herbaceous production is directly related to available forage for livestock and provides habitat for wildlife species. Recent improvements in computational capacity and satellite coverage, combined with clever downscaling and data fusion techniques, have allowed for advancements in fine-scale estimates of ANHP using both processed-based and empirical approaches. For example, the Rangeland Analysis Platform (RAP) builds on the MOD17 algorithm. RAP estimates APAR from Landsat NDVI (30 m) for rangelands across the United States every 16 days, then uses a process-based model to convert APAR to GPP, partition GPP by vegetation type, and ultimately estimate the portion of GPP allocated to aboveground herbaceous biomass [16]. This process is used to provide, among other things, an estimate of ANHP every 16 days at 30 m resolution [17]. In another approach, [PERSON] et al.