Gross primary production (GPP) is a useful metric for determining trends in the terrestrial carbon cycle. To estimate daily GPP, the cloud-adjusted light use efficiency model (LUEc) was developed by adapting a light use efficiency (LUE, ε) model to include in situ meteorological data and biophysical parameters. The LUEc uses four scalars to quantify the impacts of temperature, water stress, and phenology on ε. This study continues the original investigation in using the LUEc, originally limited to three AmeriFlux sites (US-Ne1, US-Ne2, and US-Ne3) by applying gridded meteorological data sets and remotely sensed green leaf area index (gLAI) to estimate daily GPP over a larger spatial extent. This was achieved by including data from four additional AmeriFlux locations in the U.S. Corn Belt for a total of seven locations. Results show an increase in error (RMSE = 3.5 g C m⁻² d⁻¹) over the original study in which in situ data were used (RMSE = 2.6 g C m⁻² d⁻¹). This is attributed to poor representation of gridded weather inputs (vapor pressure and incoming solar radiation) and application of gLAI algorithms to sites in Iowa, Minnesota, and Illinois, calibrated using data from Nebraska sites only, as well as uncertainty due to climatic variation. Despite these constraints, the study showed good correlation between measured and LUEc-modeled GPP (R² = 0.80 and RMSE of 3.5 g C m⁻² d⁻¹). The decrease in model accuracy is somewhat offset by the ability to function with gridded weather datasets and remotely sensed biophysical data. The level of acceptable error is dependent upon the scope and objectives of the research at hand; nevertheless, the approach holds promise in developing regional daily estimates of GPP.

中文翻译：

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利用卫星和网格化天气数据对美国中西部玉米和大豆农田的初级生产总值进行建模

初级生产总值（GPP）是确定陆地碳循环趋势的有用指标。为了估计每日GPP，通过调整光利用效率（LUE，ε）模型以包括原地气象数据和生物物理参数来开发云调整的光利用效率模型（LUEc）。LUEc使用四个标量来量化温度，水分胁迫和物候对ε的影响。这项研究通过使用网格气象数据集和遥感绿叶面积指数（gLAI）进行每日估计，继续了使用LUEc的原始研究，该LUEc最初限于三个AmeriFlux站点（US-Ne1，US-Ne2和US-Ne3）。 GPP在更大的空间范围内。这是通过将来自美国玉米带的另外四个AmeriFlux地点（总共七个地点）的数据包括在内而实现的。^-2 d ^-1）超过使用原位数据的原始研究（RMSE = 2.6 g C m ^-2 d ^-1）。这归因于栅格化的天气输入（蒸气压和太阳辐射的辐射）表现不佳，以及仅使用内布拉斯加州站点的数据对gLAI算法应用于爱荷华州，明尼苏达州和伊利诺伊州的站点进行了校准，以及由于气候变化而导致的不确定性。尽管有这些限制，但研究显示，实测和LUEc模型的GPP之间具有良好的相关性（R ² = 0.80和RMSE为3.5 g C m ^-2 d ^-1）。模型精度的下降在一定程度上可以抵消栅格化的天气数据集和遥感生物物理数据的作用。可接受的误差水平取决于手头研究的范围和目标。但是，该方法在开发GPP的每日区域估计方面具有希望。