# Rangeland Productivity Partitioned to Sub-Pixel Plant Functional Types
## 3 Results and Discussion  
Integrating the annual PFT dataset with the 16-day annual NDVI composites enabled us to disaggregate pixel level NDVI values to the sub-pixel PFTs that compose the pixel. The process capitalized on established methodologies, whereby disaggregated NDVI values were estimated through weighted ridge regression [30]. By knowing the variation of fractional cover estimates along with pixel NDVI values, an overdetermined set of linear equations was built to solve for the NDVI value for each PFT across ecologically homogenous regions. The resulting NDVI values for every PFT within each pixel represented an estimation of the pure pixel NDVI for the given PFT. These values captured the unique phenological characteristics of each PFT within a given pixel through time and enabled refined estimations of GPP and NPP for the PFTs that composed each pixel. We evaluated this large-scale approach by examining four randomly selected points across the western United States representing unique phenological dynamics. We compared the partitioned results to the original 30 m Landsat GPP/NPP estimates. The productivity of the example sites was driven by a variety of climatic conditions found across western US rangelands. In central Montana (Figure 4a), a site characteristic of northern semi-arid prairies, rangeland productivity was largely driven by winter and spring precipitation. The National Land Cover Dataset (NLCD) classifies this site as grassland, and the subsequent pixel NDVI through time (Figure 4a1) reflects the expected phenological characteristics of a grassland site for this region. Although dominated by grasses, the site is composed of a mix of AFG (23%), PFG (61%), SHR (2%), and BG/LTR (14%) according to the annual PFT dataset. Disaggregating the NDVI for each of these components (Figure 4a1) revealed the individual and unique phenological profiles. AFG NDVI both increased and decreased rapidly early in the growing season, while PFG NDVI increased rapidly during the growing season but decreased more slowly as the growing season progressed. Similar dynamics were seen in a second grassland dominated site in western Nebraska (Figure 4b), characteristic of the central Great Plains where rangeland productivity is more constant through the spring and summer. While classified as a grassland by the NLCD, this site is also a mix of AFG (8%), PFG (60%), SHR (5%), and BG/LTR (17%), each contributing uniquely to the overall NDVI through time (Figure 4b1). The contribution to the overall productivity of both sites (Figure 4a2,a3,b2,b3) from the AFG and SHR components, however, was minimal due to the combination of low fractional covers, short growing period for AFGs and the low NDVI of the shrub components. As these sites are heavily dominated by grasslands, the cumulative values of GPP and NPP for the PFTs did not depart substantially from the original 30 m Landsat GPP/NPP product (Figure 4a3,b3). Two example sites in arid regions, classified as shrublands by the NLCD, clearly demonstrate the added value of the partitioning methods for estimating GPP and NPP. A site from Oregon (Figure 4c) is characteristic of the Great Basin where productivity is driven by winter and spring precipitation. This site, while classified as a shrubland dominated system by the NLCD, is a relatively even mix of SHR (17%), PFG (15%), and AFG (13%), and dominated by more than 50% BG/LTR. The disaggregated NDVI (Figure 4c1) again revealed unique phenological characteristics of each PFT. Additionally, GPP and NPP are shown to be driven by the productivity of PFGs and AFGs rather than SHRs (Figure 4c1,c2). Similar dynamics were demonstrated for a site in southern Arizona (Figure 4d), where productivity is largely driven by precipitation in the late summer. Although classified as a shrubland by the NLCD, this area is largely dominated by BG/LTR (>50%), with relatively even SHR (12%), PFG (9%), and AFG (7%) components, with the PFG and AFG components contributing disproportionately to the overall GPP and NPP estimates (Figure 4d2,d3).