Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data

Jang, Eunna; Im, Jungho; Park, Geun-Ha; Park, Young-Gyu

doi:10.3390/rs9080821

Cited by 23 publications

(14 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…S. Park et al: Estimation of ground-level particulate matter concentrations 2013; Jerrett et al, 2017). Consequently, the monitoring and assessment of exposure to PM 10 and PM 2.5 are crucial for effective management of public health risks.…”

mentioning

confidence: 99%

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Park

Shin

et al. 2019

Atmos. Chem. Phys.

Self Cite

View full text Add to dashboard Cite

Abstract. Long-term exposure to particulate matter (PM) with aerodynamic diameters < 10 (PM10) and 2.5 µm (PM2.5) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e., NO, NH3, SO2, primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 concentrations with a total of 32 parameters for 2015–2016. The results show that the RF-based models produced good performance resulting in R2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 µg m−3 for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ).

show abstract

mentioning

confidence: 99%

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Park

Shin

et al. 2019

Atmos. Chem. Phys.

Self Cite

View full text Add to dashboard Cite

show abstract

“…RF is widely used in various remote sensing applications for both classification and regression [35][36][37][38][39]. RF is based on Classification and Regression Tree (CART) methodology [40], which is a rule-based decision tree.…”

Section: Random Forestmentioning

confidence: 99%

Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

Jang

Kang

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy~99.16%, probability of detection (POD) 93.08%, probability of false detection (POFD)~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD~93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD~50-60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires.

show abstract

“…By developing many independent trees from different sets of training samples and input variables, RF tries to provide relatively unbiased results [45][46][47]. RF has proven useful in various remote sensing tasks for both classification and regression [48][49][50][51]. RF also provides information on how input variables contribute to a given task.…”

Section: Machine Learningmentioning

confidence: 99%

Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches

Sim

Park

et al. 2018

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Even though deicing or airframe coating technologies continue to develop, aircraft icing is still one of the critical threats to aviation. While the detection of potential icing clouds has been conducted using geostationary satellite data in the US and Europe, there is not yet a robust model that detects potential icing areas in East Asia. In this study, we proposed machine-learning-based icing detection models using data from two geostationary satellites-the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) and the Himawari-8 Advanced Himawari Imager (AHI)-over Northeast Asia. Two machine learning techniques-random forest (RF) and multinomial log-linear (MLL) models-were evaluated with quality-controlled pilot reports (PIREPs) as the reference data. The machine-learning-based models were compared to the existing models through five-fold cross-validation. The RF model for COMS MI produced the best performance, resulting in a mean probability of detection (POD) of 81.8%, a mean overall accuracy (OA) of 82.1%, and mean true skill statistics (TSS) of 64.0%. One of the existing models, flight icing threat (FIT), produced relatively poor performance, providing a mean POD of 36.4%, a mean OA of 61.0, and a mean TSS of 9.7%. The Himawari-8 based models also produced performance comparable to the COMS models. However, it should be noted that very limited PIREP reference data were available especially for the Himawari-8 models, which requires further evaluation in the future with more reference data. The spatio-temporal patterns of the icing areas detected using the developed models were also visually examined using time-series satellite data.

show abstract

Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data

Cited by 23 publications

References 67 publications

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches

Contact Info

Product

Resources

About