## 1 Introduction  
Semi-arid regions and drylands together cover more than 40% of the globe. In spite of the low fractional cover of vegetation and minimal annual precipitation, the contribution of semi-arid biomes to the global uptake of carbon dioxide from the atmosphere can be significant on annual time scales [1; 2]. The inter-annual variability of precipitation in these ecosystems can result in both rapid carbon stock accumulation, and subsequent turnover due to frequent disturbance (e.g., drought, insect outbreak, fire), but how these relationships alter the net storage of carbon is poorly understood (e.g., [1]). The climate in the southwestern USA has recently experienced both increased regional air temperature, and decreased precipitation, both trends which are projected to continue in the coming decades [3; 4; 5; 6; 7]. The combined effects of these changes in climate have increased drought severity, exacerbated ecosystem stress, and ultimately triggered widespread forest mortality across the region [8; 9; 10]. Given the rapid state transitions of vegetation in these climatically sensitive biomes, and the projected changes in climate and associated mortality for this region [11], accurate monitoring of carbon uptake dynamics in these semi-arid ecosystems is an essential part of constraining uncertainties associated with regional carbon balance. Monitoring ecosystem carbon uptake over large geographic extents requires the use of remote sensing. Typically, remote sensing driven models of ecosystem function predict Gross Primary Productivity (GPP), the total atmospheric carbon taken up via photosynthesis (e.g., [12; 13]). Satellite remote sensing provides the means to predict GPP by employing light use efficiency-based algorithms [14; 15] that rely on vegetation indices (VIs), such as the normalized difference vegetation index (NDVI, [16]). This approach has been used at a variety of sites and scales, both by employing empirical (e.g., [17; 18]) and light use efficiency/process driven techniques (e.g., [19]), generally using high temporal resolution, coarse spatial resolution (\(\geqslant\)250 m) remote sensing data from sensors, such as the Moderate Resolution Imaging Spectrometer (MODIS) (e.g., [12]). To this end, the success of most space borne estimates of GPP hinge on a robust relationship between satellite estimated light interception and photosynthesis, via light use efficiency models, which generally perform well relative to _in situ_ measurements from flux tower networks [20]. However, in semi-arid regions, vegetation function is constrained by water availability for the majority of the year, with periods of drought and heat interrupted briefly by spring snow melt or episodic pulses of precipitation. Consequently, in these biomes, changes in NDVI are constrained primarily by the availability of water, rather than light or temperature [21]. This results in NDVI and GPP often being decoupled in semi-arid regions due to low soil moisture, particularly where evergreen plants are present [22]. Other characteristics unique to semi-arid ecosystems that challenge the use of NDVI for characterizing changes in GPP in these biomes are highly variable precipitation patterns which trigger high inter-annual variability in GPP due to seasonal water limitations, but low variability in LAI and/or chlorophyll concentration (subsequently low variability in NDVI). Further, the low LAI (<1.5 mean LAI across landscapes, e.g., [23]) and spatially heterogeneous plant canopies typical of these systems can result in further uncertainties due to high reflectance by the soil background, thus confounding spectral signals relating to plant function (e.g., [24; 25; 26]). One of the most universal responses to leaf stress is increasing visible reflectance [27], due to a combination of stressors which ultimately reduce chlorophyll a + b, and consequently reduce the absorption of incident light [27; 28].