# Rangeland Productivity Partitioned to Sub-Pixel Plant Functional Types  
[PERSON]  
1W.A. Franke College of Forestry and Conservation, University of Montana, Misesoula, MT 59812, USA; [EMAIL_ADDRESS] (M.O.J.); [EMAIL_ADDRESS] (D.E.N.); [EMAIL_ADDRESS] (B.W.A.)  
2 Numerical Terradynamic Simulation Group, University of Montana, Misesoula, MT 59812, USA; [EMAIL_ADDRESS]  
3 Image Processing Laboratory, Universitat de Valencia, 46980 Paterna, Spain  
4 Google, Inc., Mountain View, CA 94043, USA; [EMAIL_ADDRESS]  
5 Correspondence: [PERSON]el.: +1-406-207-2405  
species across trophic levels. Although rangelands generally have lower NPP than other ecosystems (e.g., forests), it is often heterogeneous, providing forage at multiple scales and support more herbivores than any other terrestrial biome [9; 10]. The effective management and conservation of rangeland systems for the sustained supply of their goods and services requires accounting for the heterogeneity of rangeland productivity across multiple spatiotemporal scale--a non-trivial task [11]. Conventional rangeland monitoring procedures most often utilize direct measurements of vegetation on small-scale plots or transects, and, though effective, only capture dynamics at small, localized scales and represent snapshots in time and space with limited applicability at broader scales [12; 13]. Alternatively, satellite remote sensing-based approaches utilize spatially and temporally continuous satellite imagery to assess vegetation dynamics. Higher level models integrate remote sensing data with meteorological and land cover data to produce estimates of gross primary production (GPP) and NPP. These can be directly related to the amount of solar radiation absorbed by the plant canopy and the light use efficiency (LUE) of plant functional types [14]. The MOD17 model and product [15] utilizes remote sensing data from moderate resolution imaging spectroradiometer (MODIS) sensors to estimate global GPP and NPP, with a spatial resolution of 500 m, from the year 2000 to the present. Although valuable for broad-scale analyses, the product is limited in its application within rangelands as key vegetation groups and heterogeneity are simplified.  
A finer Landsat-derived 30 m resolution GPP and NPP product, based on the MOD17 model, improved the spatial resolution and increased the temporal extent (1984-present) of the original product, and is more applicable for monitoring rangelands at appropriate scales [16]. Despite these improvements, significant challenges for effective rangeland monitoring remain. Regardless of spatial resolution, pixel estimates are aggregations of sub-pixel dynamics [17]. At medium to fine resolutions, substantial heterogeneity of rangeland productivity can occur at sub-pixel scales (Figure 1). Similarly, the MOD17 model utilizes a categorical land cover classification to apply biome specific parameters to productivity estimates. Sub-pixel land cover dynamics or land cover changes are not accounted for [18]. To address these limitations, we incorporated a new continuous rangeland plant functional type (PFT) percent cover dataset, developed for western United States rangelands [19], into the MOD17 framework. This dataset provided annual estimates of the percent fractional cover of six rangeland PFTs: annual forbs and grasses (AFG), perennial forbs and grasses (PFG), shrubs (SHR), trees (TREE), bare ground (BG), and litter (LTR). Here we present the methodology for integrating the annual PFT dataset into the MOD17 workflow to produce 16-day estimates of GPP and annual estimates of NPP for each rangeland PFT at 30 m spatial resolution. In so doing, we refined the overall estimates of GPP and NPP and provided valuable insight into the spatiotemporal distribution of rangeland productivity across the western United States.