Abstract Tropical rice production is at risk from rising temperature. Understanding regional and seasonal heterogeneity of optimum temperatures for rice production is important for model simulation to predict rice yield change under climate change. However, studies or tools for widely observation of crop responses to temperature over broad spatial scales with long time spans are limited. In this study, we detected optimum temperature range for rice gross primary production ( T opt GPP ) in the lower Gangetic plains and delta region using the near-infrared reflectance of vegetation (NIRV), which is a new photosynthetic proxy, to improve ORYZA model performance in high-temperature season and assessed how tropical rice would respond to temperature increase in the study area. According to satellite observations of NIRV from 2001 to 2015, current ambient air temperature has exceeded the mean T opt GPP of Boro rice (24.8 ± 1.8 °C) and Aman rice (26.7 ± 1.2 °C) in the lower Gangetic plains and delta region, suggesting a downtrend of rice production under future warming. The detection results show that rice has lower T opt GPP in the regions with more drought stress and lower background temperature under water-limited conditions. Furthermore, the model modified by NIRv- T opt GPP shows better performance in potential yields, especially in high-temperature seasons on the region scale. Without CO2 fertilization effect, each degree-Celsius increase is expected to reduce rice potential yields by 4.9 ± 1.6% based on the default T opt GPP range in ORYZA model and by 7.0 ± 1.2% based on the detected NIRv- T opt GPP range in the study area. This study implies that global grid-based model simulation may underestimate sensitivity of tropical rice yield to temperature rise due to the neglect of regional and seasonal heterogeneity of T opt GPP . NIRV makes it possible to determine local optimal temperatures for crop production, and to improve grid-based modelling across various agricultural systems in different growing seasons at the regional scale.

中文翻译：

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热带季风区卫星 NIRv 水稻生产力最适温度模拟水稻产量

摘要 热带水稻生产正面临气温升高的风险。了解水稻生产最适温度的区域和季节异质性对于模型模拟预测气候变化下水稻产量变化非常重要。然而，在广泛的空间尺度和长的时间跨度上广泛观察作物对温度的反应的研究或工具是有限的。在这项研究中，我们使用植被的近红外反射率 (NIRV)（一种新的光合作用代理）检测了恒河下游平原和三角洲地区水稻总初级生产的最佳温度范围（ T opt GPP ），以改进 ORYZA 模型高温季节的表现，并评估热带水稻对研究区温度升高的反应。根据 NIRV 2001 年至 2015 年的卫星观测，当前环境气温已超过恒河下游和三角洲地区波罗水稻（24.8±1.8°C）和阿曼水稻（26.7±1.2°C）的平均 T opt GPP，表明未来变暖下水稻产量将呈下降趋势。检测结果表明，在缺水条件下，水稻在干旱胁迫较多、背景温度较低的区域具有较低的T opt GPP。此外，由 NIRv-T opt GPP 修改的模型在潜在产量方面表现出更好的表现，尤其是在区域尺度上的高温季节。在没有 CO2 施肥效应的情况下，基于 ORYZA 模型中默认的 T opt GPP 范围，每升高摄氏度预计会使水稻潜在产量降低 4.9 ± 1.6%，并根据检测到的 NIRv-T opt GPP 范围降低 7.0 ± 1.2%研究区。该研究表明，由于忽略了 T opt GPP 的区域和季节性异质性，基于全球网格的模型模拟可能会低估热带水稻产量对温度升高的敏感性。NIRV 可以确定作物生产的局部最佳温度，并在区域范围内改进不同生长季节的各种农业系统的基于网格的建模。