Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment

Linze Li; Dalai Hao; Xuecao Li; Min Chen; Yuyu Zhou; Dawn Jurgens; Ghassam Asrar; Amir Sapkota

doi:10.1016/j.envres.2021.111937

Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment

Environ Res. 2022 Mar;204(Pt A):111937. doi: 10.1016/j.envres.2021.111937. Epub 2021 Aug 28.

Authors

Linze Li¹, Dalai Hao², Xuecao Li³, Min Chen⁴, Yuyu Zhou⁵, Dawn Jurgens⁶, Ghassam Asrar⁷, Amir Sapkota⁸

Affiliations

¹ School of Remote Sensing and Information Engineering, Wuhan University, Hubei, 430079, China; Maryland Institute for Applied Environmental Health, University of Maryland School of Public Health, College Park, MD, 20740, USA.
² University of Chinese Academy of Sciences, Beijing, 100049, China; Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, 20740, USA; State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China.
³ College of Land Science and Technology, China Agricultural University, Beijing, 100083, China.
⁴ Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, 20740, USA; Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, USA.
⁵ Department of Geology and Atmosphere Sciences, Iowa State University, IA, 50014, USA.
⁶ Aerobiology Research Laboratories, Ottawa, Canada.
⁷ Universities Space Research Association, Columbia, MD, 21046, USA.
⁸ Maryland Institute for Applied Environmental Health, University of Maryland School of Public Health, College Park, MD, 20740, USA. Electronic address: amirsap@umd.edu.

PMID: 34464616
DOI: 10.1016/j.envres.2021.111937

Abstract

Ongoing climate variability and change is impacting pollen exposure dynamics among sensitive populations. However, pollen data that can provide beneficial information to allergy experts and patients alike remains elusive. The lack of high spatial resolution pollen data has resulted in a growing interest in using phenology information that is derived using satellite observations to infer key pollen events including start of pollen season (SPS), timing of peak pollen season (PPS), and length of pollen season (LPS). However, it remains unclear if the agreement between satellite-based phenology information (e.g. start of season: SOS) and the in-situ pollen dynamics vary based on the type of satellite product itself or the processing methods used. To address this, we investigated the relationship between vegetation phenology indicator (SOS) derived from two separate sensor/satellite observations (MODIS, Landsat), and two different processing methods (double logistic regression (DLM) vs hybrid piecewise logistic regression (HPLM)) with in-situ pollen season dynamics (SPS, PPS, LPS) for three dominant allergenic tree pollen species (birch, oak, and poplar) that dominate the springtime allergy season in North America. Our results showed that irrespective of the data processing method (i.e. DLM vs HPLM), the MODIS-based SOS to be more closely aligned with the in-situ SPS, and PPS while upscaled Landsat based SOS had a better precision. The data products obtained using DLM processing methods tended to perform better than the HPLM based methods. We further showed that MODIS based phenology information along with temperature and latitude can be used to infer in-situ pollen dynamic for tree pollen during spring time. Our findings suggest that satellite-based phenology information may be useful in the development of early warning systems for allergic diseases.

Keywords: Allergy; Climate change; Phenology; Pollen; Remote sensing; Spring onset.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Climate Change
Climate*
Pollen*
Satellite Imagery
Seasons
Temperature