Abstract Earth observation data are increasingly used to provide consistent eco-physiological information over large areas through time. Production efficiency models (PEMs) estimate Gross Primary Production (GPP) as a function of the fraction of photosynthetically active radiation absorbed by the canopy, which is derived from Earth observation. GPP can be summed over the growing season and adjusted by a crop-specific harvest index to estimate yield. Although PEMs have many advantages over other crop yield models, they are not widely used, because performance is relatively poor. Here, a new PEM is presented that addresses deficiencies for macro-scale application: Production Efficiency Model Optimized for Crops (PEMOC). It was developed by optimizing functions from the literature with GPP estimated by eddy covariance flux towers in the United States. The model was evaluated using newly developed Earth observation products and county-level yield statistics for major crops. PEMOC generally performed better at the field and county level than another commonly used PEM, the Moderate Resolution Imaging Spectroradiometer GPP (MOD17). PEMOC and MOD17 estimates of GPP had an R2 and root mean squared error (RMSE) over the growing season of 0.71–0.89 (9.87–17.47 g CO2 d−1) and 0.59–0.83 (6.86–22.20 g CO2 d−1) with flux tower GPP. PEMOC produced R2s and RMSE of 0.70 (0.52), 0.60 (0.61), and 0.62 (0.59), while MOD17 produced R2s and RMSE of 0.65 (0.57), 0.53 (0.66), and 0.65 (0.57) with corn, soybean, and winter wheat crop yield anomalies. The sample size of rice was small, so yields were compared directly. PEMOC and MOD17 produced R2s and RMSE of 0.53 (3.42 t ha−1) and 0.40 (4.89 t ha−1). The most sizeable model improvements were seen for C3 and C4 crops during emergence/senescence and peak season, respectively. These improvements were attributed to C3 and C4 partitioning, optimized temperature and moisture constraints, and an evapotranspiration-based soil moisture index.

中文翻译：

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优化农业生态系统中宏观 GPP 和产量估算的遥感生产效率模型

摘要 地球观测数据越来越多地用于提供大面积随时间一致的生态生理信息。生产效率模型 (PEM) 将初级生产总值 (GPP) 估计为冠层吸收的光合有效辐射分数的函数，该分数来自地球观测。GPP 可以在整个生长季节求和，并通过特定作物的收获指数进行调整以估算产量。尽管 PEM 与其他作物产量模型相比具有许多优势，但由于性能相对较差，因此并未得到广泛应用。在这里，提出了一种新的 PEM，可以解决宏观应用的缺陷：针对作物优化的生产效率模型 (PEMOC)。它是通过使用美国涡流协方差通量塔估计的 GPP 优化文献中的函数而开发的。该模型使用新开发的地球观测产品和主要作物的县级产量统计数据进行评估。PEMOC 在现场和县级通常比另一个常用的 PEM，中分辨率成像光谱仪 GPP (MOD17) 表现得更好。PEMOC 和 MOD17 对 GPP 的估计在生长季节具有 0.71–0.89 (9.87–17.47 g CO2 d-1) 和 0.59–0.83 (6.86–22.20 g CO2 d-1) 的 R2 和均方根误差 (RMSE)助焊剂塔 GPP。PEMOC 产生的 R2s 和 RMSE 分别为 0.70 (0.52)、0.60 (0.61) 和 0.62 (0.59)，而 MOD17 产生的 R2s 和 RMSE 分别为 0.65 (0.57)、0.53 (0.66) 和 0.65 (0.5,7) 和 0.65 (0.5,7) 的大豆冬小麦作物产量异常。水稻样本量小，直接比较产量。PEMOC 和 MOD17 产生的 R2 和 RMSE 分别为 0.53（3.42 t ha-1）和 0.40（4.89 t ha-1）。C3 和 C4 作物分别在出苗/衰老和旺季期间看到了最显着的模型改进。这些改进归因于 C3 和 C4 分配、优化的温度和湿度限制以及基于蒸发蒸腾的土壤湿度指数。