Predicting spatiotemporally-resolved mean air temperature over Sweden from satellite data using an ensemble model

归一化差异植被指数 广义加性模型 梯度升压 均方误差 仰角(弹道) 预测建模 土地覆盖 随机森林 经度 统计 数学 纬度 环境科学 计算机科学 地理 机器学习 气候变化 土地利用 地质学 大地测量学 工程类 土木工程 海洋学 几何学
作者
Zhihao Jin,Yiqun Ma,Lingzhi Chu,Yang Liu,Robert Dubrow,Kai Chen
出处
期刊:Environmental Research [Elsevier BV]
卷期号:204: 111960-111960 被引量:7
标识
DOI:10.1016/j.envres.2021.111960
摘要

Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reducing exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estimate daily mean Ta from satellite-based land surface temperature (Ts) over Sweden during 2001-2019 at a high spatial resolution of 1 × 1 km2. The ensemble model incorporated four base models, including a generalized additive model (GAM), a generalized additive mixed model (GAMM), and two machine learning models (random forest [RF] and extreme gradient boosting [XGBoost]), and allowed the weights for each model to vary over space, with the best-performing model for each grid cell assigned the highest weight. Various spatial predictors were included as adjustment variables in all the base models, including land cover type, normalized difference vegetation index (NDVI), and elevation. The ensemble model showed high performance with an overall R2 of 0.98 and a root mean square error of 1.38 °C in the ten-fold cross-validation, and outperformed each of the four base models. Although each base model performed well, the two machine learning models (RF [R2 = 0.97], XGBoost [R2 = 0.98]) had better performance than the two regression models (GAM [R2 = 0.95], GAMM [R2 = 0.96]). In the machine learning models, Ts was the dominant predictor of Ta, followed by day of year, NDVI, latitude, elevation, and longitude. The highly spatiotemporally-resolved Ta can improve temperature exposure assessment in future epidemiological studies.
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