Predicting the onset of Betula pendula flowering in Poznań (Poland) using remote sensing thermal data.

Abstract Due to the urban heat island effect, the time of plant pollination might markedly vary within the area of a city. However, existing pollen forecasts do not reflect the spatial variations in the pollen release time within a heterogeneous urban environment. The main objective of this study was to model the spatial pattern of flowering onset (and thus the moment of pollen release) in silver birch (Betula pendula Roth.) in Poznań (Western Poland) using land surface temperature (LST) data and in situ phenological observations. The onset of silver birch flowering was observed at 34 urban and rural sites (973 trees) in Poznań from 2012 to 2014. Forty-four thermal variables were retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) data. To predict the spatio-temporal distribution of B. pendula flowering onset dates in a city, the ordinary and partial least squares, support vector machine and random forest regression models were applied. The models' performance was examined by an internal repeated k -fold cross-validation and external validation with archival phenological data (2010). Birch flowering began significantly earlier in the urban sites compared to the rural sites (from −1.4 days in 2013, to −4.1 days in 2012). The maximum March LST difference between the urban and rural sites reached 2.4 °C in 2013 and 4.5 °C in 2012. The random forest model performed best at validation stage, i.e. the root mean square error between the predicted and observed onset dates was 1.461 days, and the determination coefficient was 0.829. A calibrated model for predicting the timing of flowering in a heterogeneous city area is an important step in developing a fine-scale forecasting system that can directly estimate pollen exposure in places where allergy sufferers live. Importantly, by incorporating only pre-flowering thermal data into the model, location-specific allergy forecasts can be delivered to the public before the actual flowering time. Graphical abstract Unlabelled Image Highlights • Birch flowering onset was modelled by machine learning at fine spatial scale (1 km). • In situ flowering data were combined with land surface temperature (LST) data. • LSTs were obtained using Landsat and MODIS remote sensing satellite data. • The random forest model showed the highest quality (R2 = 0.829, RMSE = 1.461 days). • Developed model accurately predicts the flowering onset in heterogeneous city areas. [ABSTRACT FROM AUTHOR]