## 1 Introduction  
Gross primary production (GPP) in maize and soybean crops is an important measure for quantifying large scale carbon fluxes and plant productivity. GPP is defined as the total amount of carbon dioxide fixed by photosynthesis in a given area over a unit of time. GPP is a useful metric in determining the patterns and dynamics of the terrestrial carbon cycle [1] and it is essential in the study of ecosystem respiration and biomass accumulation [2]. The need to quantify the North American carbon sink necessitates precise carbon dioxide flux measurements [3] and while in situ data sources are available for this quantification, they represent field level data collection at specific locations. Extending GPP from field to regional scales can identify larger patterns and dynamics and helps quantify long-term carbon trends. Landscape level models created to derive an accurate estimation of GPP at regional scales for managed and unmanaged ecosystems at coarse temporal scales have varying degrees of accuracy; light use efficiency is at the core of these models. For example, [PERSON] et al. [4] reported a 20% error in estimating annual NPP while [PERSON] et al. [5] reported an 18% error. [PERSON] et al. [6] found errors of 20-30% in estimates of annual GPP. [PERSON] et al. [7] reported errors in seasonally integrated GPP estimates of 3-20% while [PERSON] et al. [1] reported root mean square errors (RMSE) of 2.97 g C m\({}^{-2}\) d\({}^{-1}\). Although [PERSON] et al. [8] reported good agreement between estimated and observed GPP values when using only remotely sensed data, the best estimates of GPP were obtained when meteorological data were included with vegetation indices. [PERSON] et al. [9] provide a global GPP dataset for 2000-2016 (as an alternate to MOD17 GPP) based on a modified vegetation photosynthesis model (VPM GPP V20) driven by MODIS and climate data. The model utilizes \(\varepsilon\), scalars for temperature and moisture, photosynthetically active radiation (PAR), and the energy absorbed chlorophyll. [PERSON] et al. [10] tested seven LUE-based models and found that models that incorporate environmental factors, including diffuse radiation, do not significantly underestimate GPP for cloudy days, unlike models excluding diffuse radiation. [PERSON] et al. [11] explained about 67% of tower-measured GPP accounting for the effect of diffuse radiation on \(\varepsilon\). Critically, many of the models referenced above do not estimate GPP at a daily temporal resolution. [PERSON] et al. [12] provide a daily estimate of GPP in the MOD17A2/A3 algorithm using global daily estimates of PAR (\"a challenging problem\") and a biome lookup table to estimate light use efficiency. Based on LUE-based models for estimating GPP on a regional scale, the use of satellite imagery and modeled weather data, including diffuse radiation, is essential as monetary, personnel, time, and equipment constraints hinder collection of daily in situ data at a broad scale. The overall accuracy of models using satellite and modeled weather data input may be lower than those that utilize in situ field-level input. This is due in part to generalization of model parameters for application to heterogeneous regions, spatial and temporal restrictions of satellite data, and the accuracy of modeled weather input data. However, the ability to study large regions may offset loss of accuracy, depending upon research scope and objectives [1,4]. A daily light use efficiency model, known as the cloud-adjusted light use efficiency model (LUEc), introduced by [PERSON] et al. [13], combines aspects of the Vegetation Photosynthesis Model (VPM) [7] and the LUE model [3]. The LUEc provides more reliable estimates of GPP by taking into account the effects of environmental and canopy factors (e.g., light quality, water availability, temperature, and phenology) on photosynthesis. This is achieved, using daily meteorological measurements and in situ green leaf area index (gLAI). [PERSON] et al.