关键词: 风传花粉, 植被遥感, 浓度预测, 非线性自回归神经网络, 时间序列

Abstract:

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.

Key words: windborne pollen, vegetation remote sensing, pollen concentration prediction, NARXnet, time series