Canopy temperature (T_c) measurements are increasingly being used to compute crop thermal indices for water stress estimation and improved irrigation management. Conventionally monitoring crop thermal response requires maintenance of a well-watered crop from which non-stressed canopy temperature (T_cns) is measured as a reference for thermal index computation. This study alternatively evaluated the performance of 36 weather data driven model combinations to predict peak time (12:00–17:00 h) T_cns in maize grown in semi-arid climates at the West Central Research, Extension, and Education Center (WCREEC) in North Platte, NE, and at the Limited Irrigation Research Farm (LIRF) in Greeley, CO. Data-driven models considered were multilinear regression (MLR), forward feed neural network (NN), recurrent neural network (RNN), multivariate adoptive regression splines (MARS), random forest (RF), and k-nearest neighbor (KNN). For each of these models, the following weather data combinations were tested: average air temperature (T_a), average relative humidity (RH), wind speed (U₂), and solar radiation (R_s) (combination 1); RH, U₂, R_s (combination 2), T_a, RH, R_s (combination 3); T_a, RH (combination 4); RH, R_s (combination 5); and T_a, R_s (combination 6). Ranking the performance of weather data × model combinations across both climate sites showed that MARS model with combination 1 was a better predictor of T_cns with R² of 0.866 and RMSE value of 0.966 °C at WCREEC and R² of 0.910 and RMSE value of 0.693 °C at LIRF. The performance of site specific (localized) and generalized model combinations was compared and indicated that cross site prediction of T_cns was primarily determined by weather data combinations, rather than model specificity.

冠层温度 ( T _c ) 测量越来越多地用于计算作物热指数，以估计水分胁迫和改进灌溉管理。传统上监测作物热反应需要保持水分充足的作物，从中测量非胁迫冠层温度 ( T _cns ) 作为热指数计算的参考。本研究评估了 36 种天气数据驱动模型组合预测高峰时间 (12:00–17:00 h) T _cns的性能位于内布拉斯加州北普拉特的西部中央研究、推广和教育中心 (WCREEC) 以及位于科罗拉多州格里利的有限灌溉研究农场 (LIRF) 在半干旱气候下种植的玉米。考虑的数据驱动模型是多线性的回归 (MLR)、前馈神经网络 (NN)、递归神经网络 (RNN)、多元自适应回归样条 (MARS)、随机森林 (RF) 和 k 最近邻 (KNN)。对于这些模型中的每一个，测试了以下天气数据组合：平均气温 ( T _a )、平均相对湿度 (RH)、风速 ( U ₂ ) 和太阳辐射 ( R _s )（组合 1）；RH , U ₂ , R _s(组合2)，T _a，RH，R _s (组合3)；T _a , RH (组合 4); RH , R _s (组合5)；和T _a , R _s（组合6）。对两个气候站点的天气数据×模型组合的性能进行排名表明，具有组合 1 的 MARS 模型是T _cns的更好预测因子，WCREEC和R ^2的R ²为 0.866，RMSE 值为 0.966 °CLIRF 的 0.910 和 RMSE 值为 0.693 °C。比较了站点特定（本地化）和广义模型组合的性能，表明T _cns的跨站点预测主要取决于天气数据组合，而不是模型特异性。