## 1 Introduction  
Satellite observations have been used for studying global vegetation growth and seasonal phenology, ranging from the use of vegetation greenness indices from optical-infrared (IR) sensors to monitor photosynthetic canopy cover [1; 2; 3; 4] to vegetation optical depth and backscatter retrievals from microwave sensors to monitor canopy biomass changes [5; 6]. In addition to remote sensing of canopy physical properties, bio-climatic indices related to light, temperature, and water-related environmental constraints to plant photosynthesis have also been used to predict vegetation growth and phenology metrics including growing season timing and length [7; 8; 9]. These climate indices, in conjunction with satellite observations of the vegetation Fraction of Photosynthetically Active Radiation (FPAR) and other environmental inputs, have been used in process based models for estimating vegetation Gross Primary Productivity (GPP) [10; 11; 12]. GPP is a fundamental indicator of ecosystem productivity and the primary conduit for atmosphere carbon (CO\({}_{2}\)) uptake and sequestration through plant photosynthesis. Remote sensing vegetation indices alone are unable to directly measure GPP, instead requiring the use of models driven by environmental constraint factors such as daily minimum temperature (Tmin), Vapor Pressure Deficit (VPD), and soil moisture (SM) for indirectly estimating and monitoring GPP spatial and temporal variability [10; 11]. Bioclimatic indices are an important component of Light Use Efficiency (LUE) based ecosystem productivity models [10; 13] and other land surface models [14]. These models use bioclimatic indices to characterize the effect of environmental factors influencing plant photosynthesis and the net ecosystem carbon flux. A well-known satellite LUE model product, the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17, uses only VPD and Tmin to represent moisture and temperature related controls on GPP. The Terrestrial Carbon Flux (TCF) model [11] underpins the NASA Soil Moisture Active Passive Level 4 Carbon product (SMAP-L4C) [15], which provides regular global daily assessments of terrestrial carbon fluxes, including GPP, and uses multi-sensor satellite observations and other operational data as primary inputs. The TCF model uses a LUE approach driven by VPD and SM inputs that define respective atmospheric moisture demand and soil water supply constraints to GPP, while Tmin is used to define low temperature constraints to growth. In these LUE models, the global representation of bioclimatic factors influencing productivity has largely been derived empirically using a limited number of globally distributed carbon (CO\({}_{2}\)) flux towers [7; 16; 17; 18]. The effectiveness of this approach is limited by the number of available tower sites needed to parameterize and verify model defined response characteristics for different biomes and plant functional types, which can be a major source of model uncertainty [19; 20]. The LUE approach for global operational modeling of GPP from NASA satellites was originally developed in the 1990s [21], while recent enhancements include improved calibration, finer spatial resolution observations, and representation of additional environmental controls on vegetation growth [22]. Attempts have also been made to represent spatial heterogeneity in plant functional traits influencing LUE and GPP within coarser biome type classifications [20; 23]. These model enhancements have benefitted from an increase in the number of tower monitoring sites, though the available tower network is still very sparse over much of the globe [24]. Recent developments in satellite remote sensing include global observations of solar-induced canopy fluorescence (SIF).