농업과학연구 (Korean Journal of Agricultural Science)

  • 제47권1호
  • /
  • Pages.59-66
  • /
  • 2020
  • /
  • 2466-2402(pISSN)
  • /
  • 2466-2410(eISSN)
충남대학교 농업과학연구소 (Institute of Agricultural Science, CNU)
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Estimation of ambient PM10 and PM2.5 concentrations in Seoul, South Korea, using empirical models based on MODIS and Landsat 8 OLI imagery

  • 투고 : 2019.09.30
  • 심사 : 2019.12.16
  • 발행 : 2020.03.01
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초록

Particulate matter (PM) is regarded as a major threat to public health and safety in urban areas. Despite a variety of efforts to systemically monitor the distribution of PM, the limited amount of sampling sites may not provide sufficient coverage over the areas where the monitoring stations are not located in close proximity. This study examined the capacity of using remotely sensed data to estimate the PM10 and PM2.5 concentrations in Seoul, South Korea. Multiple linear regression models were developed using the multispectral band data from the Moderate-resolution imaging spectro-radiometer equipped on Terra (MODIS) and Operational Land Imager equipped on Landsat 8 (Landsat 8) and meteorological parameters. Compared to MODIS-derived models (r2 = 0.25 for PM10, r2 = 0.30 for PM2.5), the Landsat 8-derived models showed improved model reliabilities (r2 = 0.17 to 0.57 for PM10, r2 = 0.47 to 0.71 for PM2.5). Landsat 8 model-derived PM concentration and ground-truth PM measurements were cross-validated to each other to examine the capability of the models for estimating the PM concentration. The modeled PM concentrations showed a stronger correlation to PM10 (r = 0.41 to 0.75) than to PM2.5 (r = 0.14 to 0.82). Overall, the results indicate that Landsat 8-derived models were more suitable in estimating the PM concentrations. Despite the day-to-day fluctuation in the model reliability, several models showed strong correspondences of the modeled PM concentrations to the PM measurements.

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참고문헌

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피인용 문헌

  1. 부스팅 기반 기계학습기법을 이용한 지상 미세먼지 농도 산출 vol.37, pp.2, 2020, https://doi.org/10.7780/kjrs.2021.37.2.11