Near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is important for gross primary production (GPP) estimation. While NIRvP is a useful indicator of canopy structure and solar radiation, its association with heat or moisture stress is not fully understood. Thus, this research aimed to explore the impact of air temperature (Ta) and vapor pressure deficit (VPD) on the NIRvP-GPP relationship. Using Moderate Resolution Imaging Spectroradiometer (MODIS) observations, eddy-covariance measurements, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) data, we found that NIRvP cannot fully explain the response of plant photosynthesis to Ta and VPD at both seasonal and daily scales. Therefore, we incorporated a polynomial function of Ta and an exponential function of VPD to correct its seasonal response to stress and calibrated the GPP residual via a linear function of Ta and VPD time-varying derivatives to account for its daily response to stress. Leave-one-site-out cross-validation suggested that the improvements relative to its original version were especially noteworthy under stress conditions while less significant when there was no water or heat stress across grasslands and croplands. When compared to six other GPP models, the enhanced NIRvP model consistently outperformed them or performed comparably with the best model in terms of bias, RSME, and coefficient of determinant against measurements in grasslands and croplands. Moreover, we found that parameterizing the fraction of photosynthetically active radiation term using NIRv notably improved the performance of the classic MOD17 and vegetation photosynthesis model, with an average RMSE reduction of 13 % across grasslands and croplands. Overall, this study highlights the need to consider environmental stressors for improved NIRvP-based GPP and shed light on future improvements of LUE models.

中文翻译：

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结合环境胁迫可以提高美国大平原牧场和中西部农田 NIRvP 光合作用的估计


植被的近红外反射率乘以入射阳光 （NIRvP） 对于总初级生产力 （GPP） 估计非常重要。虽然 NIRvP 是冠层结构和太阳辐射的有用指标，但它与热或湿胁迫的关系尚不完全清楚。因此，本研究旨在探讨空气温度 （Ta） 和蒸气压亏缺 （VPD） 对 NIRvP-GPP 关系的影响。使用中分辨率成像光谱仪 （MODIS） 观测、涡度协方差测量和独立斜率模型上的参数-高程回归 （PRISM） 数据，我们发现 NIRvP 不能完全解释植物光合作用在季节和日尺度上对 Ta 和 VPD 的响应。因此，我们结合了 Ta 的多项式函数和 VPD 的指数函数来校正其对压力的季节性响应，并通过 Ta 的线性函数和 VPD 时变导数校准 GPP 残差，以解释其对压力的日常响应。Leave-one-site-out 交叉验证表明，相对于其原始版本的改进在胁迫条件下特别值得注意，而当草原和农田没有水或热胁迫时，则不太显着。与其他六个 GPP 模型相比，增强的 NIRvP 模型在草原和农田测量的偏差、RSME 和决定因素系数方面始终优于它们或表现与最佳模型相当。此外，我们发现使用 NIRv 参数化光合有效辐射项的分数显着提高了经典 MOD17 和植被光合作用模型的性能，草原和农田的 RMSE 平均降低了 13%。 总体而言，本研究强调了考虑环境压力源以改进基于 NIRvP 的 GPP 的必要性，并阐明了 LUE 模型的未来改进。