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Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ??m, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis (PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements. ?? 2021 Korean Society of Remote Sensing. All rights reserved

Classification and mapping of paddy rice by combining Landsat and SAR time series data

Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach

?????? ????????? ???????????? ????????? ????????? ????????? ????????? ?????? SO2 ???????????? ??????

Sulfur dioxide (SO2) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO2 can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO2 crucial. In South Korea, SO2 monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO2 concentrations. Thus, this research estimated spatially continuous ground-level SO2 concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO2 estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO2 concentrations based on the proposed model agreed with the general seasonality of SO2 and the temporal patterns of emission sources

Improved Monitoring and Nowcasting of Particulate Matter based on Machine Learning through the Synergistic Use of Satellite and Model Products over East Asia

Department of Urban and Environmental Engineering (Environmental Science and Engineering)clos

Monitoring and short-term forecasting for Ground-level Particulate Matter Using Satellite Observations and Numerical Model Output

Air pollution seriousness is on the rise over the word. Particulate matter (PM) is one of the atmospheric aerosols which have influenced on the adverse human health. Especially, PM with aerodynamic diameter less than 10 micrometers and 2.5 micrometers (i.e. PM10 and PM2.5, respectively) is available to inhales into the body without filtering, and it leads to cause significant health problems such as cardiovascular and respiratory-related diseases. The accurate PM concentration monitoring and forecasting is a prerequisite for providing early warning. In this study, the several variables from multi satellite sensors (i.e. GOCI, GPM, SRTM, MODIS), numerical models (i.e. RDAPS), emission model (i.e. SMOKE), and in-situ measurements were used to establish short-term forecasting model of hourly PM10 and PM2.5 concentrations. one to three hours before in-situ PM measurements were interpolated using kriging method, and these values also used as an input variable. The proposed forecasting model was conducted based on two different machine learning approach, i.e. random forest (RF), support vector machine regression (SVR), over South Korea during 2015 to 2016 time period. The early results showed underestimation at high PM concentration part due to low concentrations of PM10 and PM2.5 relatively predominate in our study area. Thus, over- and sub- sampling technique was addressed to make the dataset balance. Oversampling approach is applied to high concentration samples with 3x3 or 5x5 windows for construction of training dataset. Log transformation on the target variables was additionally conducted to improve the model accuracy. The real-time training, which can be implemented on-line in real time, was conducted to make the short-term forecasting model for hourly PM10 and PM2.5 concentration, and this real-time forecasting model was evaluated by the leave-one-out validation based on the station. The results indicate that the RF model has better performances in compare with SVR model. In addition, when using only 1 hour ago PM in-situ observation, the model accuracy was higher than when using only 3 hours ago PM in-situ observation

Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of < 10 mu m (PM10) and < 2.5 mu m (PM2.5) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM10 and PM2.5 were R-2 = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM10 and PM2.5 were R-2 = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results

Estimation of Ground-level Nitrogen Dioxide and Ozone Concentrations Using Satellite Data and Numerical Model Output

Long exposure to high concentrations of nitrogen dioxide (NO2) and ozone (O3) at ground level could be harmful to human health. Air pollutant concentrations including NO2 and O3 have been measured at monitoring stations, which has a major limitation that it is difficult to provide spatially continuous air quality information. In this study, machine learning based models were developed to estimate ground-level NO2 and O3 concentrations using satellite-based remote sensing data and numerical model output over East Asia to overcome such a limitation. NO2 and O3 vertical column density products from the Aura Ozone Monitoring Instrument (OMI) play an important role in monitoring of the spatial and temporal patterns of the gases, although one third to one half of the OMI products have been missing due to row anomalies. In this study, missing pixels of OMI products were filled using an interpolation approach to generate spatio-temporally continuous distribution of NO2 and O3 concentrations. In addition to satellite-derived data, model-based meteorological parameters and emission information during 2015-2016 were used to estimate surface air quality concentrations over East Asia. Random forest (RF) was used to develop the estimation models for NO2 and O3 concentrations. Over South Korea, the RF-based models showed good performance resulting in R2 values of 0.78 and 0.73, and RMSEs of 8.88 ppb and 10.50 ppb for NO2 and O3, respectively. The NO2 vertical column density was identified most important variable in both models. The model-based meteorological variables such as max wind speed, planetary boundary layer height (PBLH), frictional velocity, and solar radiation were also considered significant for estimation. Spatial distribution of ground-level NO2 and O3 concentrations were also examined over South Korea. Relatively high concentrations were shown around large cities including Seoul metropolitan area