Forecasting Pollen Aerobiology with Modis EVI, Land Cover, and Phenology Using Machine Learning Tools

Alfredo Huete, Ngoc Nguyen Tran, Ha Nguyen, Qiaoyun Xie, Constance Katelaris

Research output: Chapter in Book/Conference paperConference paperpeer-review

4 Citations (Scopus)

Abstract

Grass pollens are a major source of aeroallergens globally, inducing allergic asthma and hay fever in up to 500 million people worldwide. Pollen forecasting research and methods are site-dependent and tend to be empirically derived composites of expert knowledge and weather data. In this study we utilize satellite-based information of landscape conditions and phenology to better discern and predict grass pollen evolution. We employed machine learning approaches to formulate and better understand relationships between landscape phenology and seasonal flowering-induced pollen concentrations. We show that machine learning approaches significantly improved pollen prediction capabilities and provided key information to better attribute changes in pollen counts driven by shifting ecological landscapes from climate change drivers.
Original languageEnglish
Title of host publicationIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages5429-5432
Number of pages4
ISBN (Electronic)9781538691540
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan
Duration: 28 Jul 20192 Aug 2019

Conference

Conference2019 IEEE International Geoscience and Remote Sensing Symposium
Country/TerritoryJapan
CityYokohama
Period28/07/192/08/19

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