Jalbuena Rey, Yee Jurng-Jae
Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, Republic of Korea.
Sensors (Basel). 2024 Nov 25;24(23):7507. doi: 10.3390/s24237507.
The near-surface air temperature (NSAT) is crucial for understanding thermal and urban environments. Traditional estimation methods using general remote sensing images often focus on the types of spatial data or machine learning models used, neglecting the importance of seasonal and temporal variations, limiting their accuracy. This study introduces a novel ensemble model that incorporates both seasonal and temporal information integrated with satellite-derived land surface temperature (LST) data to enhance NSAT estimation, along with a rigorous feature importance analysis to identify the most impactful parameters. Data from 2022, collected from 147 South Korean weather stations, were used to develop and evaluate the models. Thirteen initial variables, including the LST and other auxiliary data, were considered. Random forest regression was employed to build separate models for each season. This novel approach of separating data by season allowed optimized feature selection tailored to each season, improving the model efficiency and capturing finer seasonal and daily temperature variations. These seasonal models were then combined to form an ensemble model. The seasonal models demonstrated varying accuracy, with the R values indicating a strong correlation between the predicted and actual NSAT, particularly high in spring and fall and lower in summer and winter. The ensemble model showed improved performance, achieving an MAE of 0.534, an RMSE of 0.391, an R of 0.996, and a cross-validated R of 0.968. These findings highlight the effectiveness of incorporating seasonal and temporal information into NSAT estimation models, offering significant improvements over traditional approaches. The developed models support precise temperature monitoring and forecasting, aiding environmental and urban management.
近地表气温（NSAT）对于理解热环境和城市环境至关重要。使用一般遥感图像的传统估算方法通常侧重于所使用的空间数据类型或机器学习模型，而忽略了季节和时间变化的重要性，从而限制了其准确性。本研究引入了一种新颖的集成模型，该模型将季节和时间信息与卫星衍生的地表温度（LST）数据相结合，以增强NSAT估算，同时进行严格的特征重要性分析，以识别最具影响力的参数。利用2022年从147个韩国气象站收集的数据来开发和评估模型。考虑了13个初始变量，包括LST和其他辅助数据。采用随机森林回归为每个季节建立单独的模型。这种按季节分离数据的新颖方法允许针对每个季节进行优化的特征选择，提高了模型效率，并捕捉到更精细的季节和每日温度变化。然后将这些季节模型组合成一个集成模型。季节模型表现出不同的准确性，R值表明预测的NSAT与实际NSAT之间存在很强的相关性，在春季和秋季尤其高，在夏季和冬季较低。集成模型表现出更好的性能，MAE为0.534，RMSE为0.391，R为0.996，交叉验证R为0.968。这些发现突出了将季节和时间信息纳入NSAT估算模型的有效性，与传统方法相比有显著改进。所开发的模型支持精确的温度监测和预测，有助于环境和城市管理。
