Increased frequency and severity of chilling damage events pose potential risks to crop performance and productivity due to climate change. Accurate and real-time access to chilling damage is important for crop growth and yield stability based on field's actual environment. To precisely identify regional chilling events and evaluate the impacts on crops, this study presents a model to estimate field air temperature in view of field crop situations. Land surface temperature, enhanced vegetation index, solar-induced chlorophyll fluorescence and solar declination were involved in the model. With field simultaneous continuous monitoring and multisource fused remote sensing data, the model was calibrated and validated in Jiefangzha Irrigation Area (JIA) and Changchun City (CC) in North China, accompanied by the determination coefficient ≥0.756, root mean square error ≤0.782°C, relative error ≤0.041 and consistency index ≥0.902. Meanwhile, sensitivities of the model factors were determined through path analysis, where the factors performed according to the order solar-induced chlorophyll fluorescence >solar declination >land surface temperature > enhanced vegetation index. Using the validated model, chilling damage to maize was further detected in JIA and CC from 2010 to 2020. Results showed that the severity of chilling damage was greater in CC than in JIA, along with the sterile-type occurring three events in JIA and seven in CC, while the delayed-type only twice in JIA in 2012 and 2016, but five times in CC in 2013, 2014, 2016, 2017 and 2019, respectively, being consistent with local statistics. In response to chilling damage, enhanced vegetation index and solar-induced chlorophyll fluorescence demonstrated the negative chilling effects on greenness and light use efficiency for fluorescence. Serious yield losses were caused, with yield-reducing by 5.00% (Dehui, 2013), 19.00% (Jiutai, 2014), 21.65% (Suburban district, 2016), 8.83% (Shuangyang, 2017) and 2.19% (Jiutai, 2019) in CC. The linear relationship between yield and growing degree days was a bit weakened by chilling damage, with the determination coefficient varying from 0.614 to 0.531. The increasing rate of yield with growing degree days decreased from 20.365 kg/(°C·d) in non-chilling damage years to 9.670 kg/(°C·d) in chilling damage years. These findings indicate that the presented model is especially adaptive for agricultural field environments, enabling rapid precision detection of chilling damage on crops at regional scales. It will provide references for gauging the impact of chilling damage on crops, finding efficient solutions to the stress and ensuring sustainable development of agriculture.

中文翻译：

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考虑地表温度、作物生长状况和太阳辐射变化的玉米冷害区域检测与评估

由于气候变化，冷害事件的频率和严重程度增加对作物性能和生产力构成潜在风险。根据田间实际环境，准确、实时掌握冷害情况对于作物生长和产量稳定具有重要意义。为了准确识别区域寒冷事件并评估对作物的影响，本研究提出了一种根据大田作物情况估计田间气温的模型。该模型涉及地表温度、增强植被指数、太阳引起的叶绿素荧光和太阳赤纬。利用现场同时连续监测和多源融合遥感数据，在华北解放闸灌区（JIA）和长春市（CC）对模型进行了校准和验证，判定系数≥0.756，均方根误差≤0.782° C、相对误差≤0.041，一致性指数≥0.902。同时，通过路径分析确定了模型因子的敏感性，各因子按照太阳诱导叶绿素荧光>太阳赤纬>地表温度>增强植被指数的顺序排列。利用验证的模型，进一步检测了2010年至2020年JIA和CC玉米的冷害。结果表明，CC的冷害严重程度高于JIA，JIA发生了3起不育型事件，7个发生了不育型事件。 CC 中出现了 2 次，而 JIA 中延迟型仅在 2012 年和 2016 年出现过两次，而 CC 中则分别在 2013 年、2014 年、2016 年、2017 年和 2019 年出现了 5 次，与当地统计数据一致。为了应对冷害，增强的植被指数和太阳诱导的叶绿素荧光证明了冷效应对绿度和荧光光利用效率的负面影响。产量损失严重，减产5.00%（德惠，2013）、19.00%（九台，2014）、21.65%（郊区，2016）、8.83%（双阳，2017）、2.19%（九台，2019） ）在抄送。产量与生长度日之间的线性关系因冷害而有所减弱，决定系数在0.614~0.531之间。产量随生长度日数的增加率由非冷害年的20.365 kg/(℃·d)下降到冷害年的9.670 kg/(℃·d)。这些发现表明，所提出的模型特别适合农业田间环境，能够快速精确地检测区域范围内农作物的冷害。为衡量农作物冷害影响、寻找高效应对措施、保障农业可持续发展提供参考。