融合植被遥感数据的北京市次日花粉浓度预测

中国国土绿化状况公报指出,2010—2020年中国许多城市的绿化面积增加、绿化质量提高,可随之而来的影响人体健康的致敏性花粉风险也逐渐提高。本文利用遥感手段获得北京市乔木和草地生长区域平均植被叶面积指数（LAI）时间序列作为植被物候信息,并将其作为花粉浓度预测因子之一,结合日气象数据,使用具有外部输入的非线性自回归神经网络模型（NARXnet）,进行北京市次日花粉浓度的预测。结果显示：① 通过逐步回归计算,对于春季数据,日均气温3日平滑,积温,叶面积指数（LAI）和叶面积指数一阶导为次日花粉浓度预测的关键变量;对于秋季数据,日均气温、平均风速、最低日气温、日均气温3日平滑、积温和叶面积指数（LAI）为次日花粉浓度预测的关键变量;② 加入遥感物候信息可显著地提高NARXnet模型的春秋时段的花粉浓度的预测精度。使用本文提出的结合叶面积指数的NARX模型后,预测模型的总体精度为71%。由此,本研究认为在原有气象因子的基础上,辅之以用遥感技术手段获取的大面积植被物候信息,如叶面积指数动态,可作为预测次日花粉浓度的一种有效手段。

Next-day Prediction of Pollen Concentration in Beijing by Integrating Remote Sensing Derived Leaf Area Index

There has been an increase in the area and quality of vegetation coverage in many cities of China, which leads to a concomitant increase in the risk of allergenic pollen that affects human health. However, there is still limitation in the accuracy and regional applicability of pollen forecasting services, partly because pollen concentration predictors are more focused on meteorological observations rather than phenological observation of plants. For the seasonal trend of allergenic pollen concentration, phenological observations of vegetation may be an important indicator as well as meteorological factors, because the characteristics of vegetation phenology are directly correlated with pollen release. In this study, the time series Leaf Area Index (LAI) for tree and grass covers that reflects vegetation growth processes was derived from remote sensing techniques and represented as one of the predictors of pollen concentration in air. By combining the derived LAI information with the daily meteorological data, Nonlinear Autoregressive Neural Networks with External Input (NARXnet) combined with stepwise regression were employed to predict the pollen concentration in air of the next day in Beijing. The results show that (1) the three-day moving average of daily temperature, the cumulative temperature, LAI, and the first-order derivatives of LAI were key predictors of the next-day pollen concentration for the spring season, while the mean daily temperature, mean wind speed, the minimum daily air temperature, the three-day moving average of daily temperature, the cumulative temperature, and the LAI were key predictors of the next-day pollen concentration for the fall season; (2) in Beijing, the inclusion of remotely sensed phenological information could significantly improve the prediction accuracy of the pollen concentration for both the spring and autumn seasons from NARXnet model. According to the results, we conclude that, in combination with the meteorological factors, vegetation phenology information such as LAI obtained from remote sensing is an effective predictor of the next-day pollen concentration.