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Summer Nights in Berlin, Germany: Modeling Air Temperature Spatially With Remote Sensing, Crowdsourced Weather Data, and Machine Learning
Citation key Vulova2020
Author Vulova, S. and Meier, F. and Fenner, D. and Nouri, H. and Kleinschmit, B.
Pages 1-15
Year 2020
ISSN 1939-1404
DOI 10.1109/JSTARS.2020.3019696
Journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume 562
Abstract Urban areas tend to be warmer than their rural surroundings, well-known as the “urban heat island” effect. Higher nocturnal air temperature (Tair) is associated with adverse effects on human health, higher mortality rates, and higher energy consumption. Prediction of the spatial distribution of Tair is a step towards the “Smart City” concept, providing an early warning system for vulnerable populations. The study of the spatial distribution of urban Tair was thus far limited by the low spatial resolution of traditional data sources. Volunteered geographic information (VGI) provides alternative data with higher spatial density, with citizen weather stations monitoring Tair continuously in hundreds or thousands of locations within a single city. In this study, the aim was to predict the spatial distribution of nocturnal Tair in Berlin, Germany, one day in advance at a 30- m resolution using open-source remote sensing and geodata from Landsat and Urban Atlas, crowdsourced Tair data, and machine learning (ML) methods. Results were tested with a “leave-onedate- out” training scheme (testingcrowd) and reference Tair data (testingref). Three ML algorithms were compared - Random Forest (RF), Stochastic Gradient Boosting (GBM), and Model Averaged Neural Network (avNNet). The optimal model based on accuracy and computational speed is RF, with an average RMSE for testingcrowd of 1.16 °C (R2 = 0.512) and RMSE for testingref of 1.97 °C (R2 = 0.581). Overall, the most Important GIS predictors were morphometric parameters and albedo. The proposed method relies on open-source datasets and can, therefore, be adapted to many cities worldwide.
Bibtex Type of Publication Kleinschmit
Link to original publication [1] Download Bibtex entry [2]

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