Use Of Satellite Observations Research Articles

Satellites capture socioeconomic disruptions during the 2022 full-scale war in Ukraine

Since February 2022, the full-scale war in Ukraine has been strongly affecting society and economy in Ukraine and beyond. Satellite observations are crucial tools to objectively monitor and assess the impacts of the war. We combine satellite-based tropospheric nitrogen dioxide (NO2) and carbon dioxide (CO2) observations to detect and characterize changes in human activities, as both are linked to fossil fuel combustion processes. We show significantly reduced NO2 levels over the major Ukrainian cities, power plants and industrial areas: the NO2 concentrations in the second quarter of 2022 were 15–46% lower than the same quarter during the reference period 2018–2021, which is well below the typical year-to-year variability (5–15%). In the Ukrainian capital Kyiv, the NO2 tropospheric column monthly average in April 2022 was almost 60% smaller than 2019 and 2021, and about 40% smaller than 2020 (the period mostly affected by the COVID-19 restrictions). Such a decrease is consistent with the essential reduction in population and corresponding emissions from the transport and commercial/residential sectors over the major Ukrainian cities. The NO2 reductions observed in the industrial regions of eastern Ukraine reflect the decline in the Ukrainian industrial production during the war (40–50% lower than in 2021), especially from the metallurgic and chemical industry, which also led to a decrease in power demand and corresponding electricity production by thermal power plants (which was 35% lower in 2022 compared to 2021). Satellite observations of land properties and thermal anomalies indicate an anomalous distribution of fire detections along the front line, which are attributable to shelling or other intentional fires, rather than the typical homogeneously distributed fires related to crop harvesting. The results provide timely insights into the impacts of the ongoing war on the Ukrainian society and illustrate how the synergic use of satellite observations from multiple platforms can be useful in monitoring significant societal changes. Satellite-based observations can mitigate the lack of monitoring capability during war and conflicts and enable the fast assessment of sudden changes in air pollutants and other relevant parameters.

Estimation of Anthropogenic CH4 and CO2 Emissions in Taiyuan‐Jinzhong Region: One of the World's Largest Emission Hotspots

AbstractCoal mining ranks as the largest anthropogenic CH4 source in China with emission factors (EFs) varying up to 30‐fold among inventories when applied to different provinces. The lack of independent evaluation of coal mining CH4 EFs in China is one of the main uncertainties in estimating national total CH4 emissions. Shanxi province, which supplies 25% of the national coal production, is the largest coal mining CH4 emission region in China and even among the world's largest coal production regions. This area is also a significant anthropogenic CO2 source because of high‐density power and industrial activities. Given the large uncertainties in CH4 and CO2 inventories from provincial to city scales, questions remain whether state‐of‐the‐art inventories have accurately estimated these emission hotspots. Here, we evaluate CH4 and CO2 emissions from one of the world's largest coal production regions near Taiyuan City, the capital of Shanxi province, China. CH4 and CO2 concentrations were measured from March 2018 to February 2019 from a 30‐m tower. These data were used within an inverse modeling framework to simulate both CH4 and CO2 concentrations and to evaluate EFs for this region. Results show generally good agreement between observed and simulated CH4 concentrations. However, the CO2 simulations were much lower compared to the observations. Given the minor role of NEE‐induced CO2 enhancements, we believe that the large difference is attributed to the underestimation of anthropogenic CO2 emissions. In general, the derived posteriori anthropogenic CH4 emissions were 85.2(±18.1)% of a priori emissions, where fugitive CH4 from coal mining accounted for ∼92.7% of total anthropogenic emissions. The derived coal mining EF was 23.2(±4.9) m3 CH4/ton coal, close to the default value of high CH4‐content coal, but twofold the province average that were reported by previous observation‐based studies in Shanxi province, indicating large spatial inhomogeneity in the coal mining CH4 EF. The posteriori CO2 emissions were 1.6‐fold of the a priori emissions, highlighting underestimation of CO2 emissions in industrial cities and some potential large emission sources that are missing from state‐of‐the‐art inventories. Finally, we also emphasize the use of satellite observations and denser tower‐based networks are essential in resolving the spatial inhomogeneity of greenhouse gas emissions.

Huge CH4, NO2 and CO Emissions from Coal Mines in the Kuznetsk Basin (Russia) Detected by Sentinel-5P

The coal industry is the largest global emitter of carbon dioxide (CO2). However, recent data suggests that coal mine methane (CH4) emissions worldwide are higher than those of the oil and gas industry. Furthermore, the coal industry is less active in reducing methane emissions than the oil and gas sectors due to lower profitability. Although uncertainties remain in quantifying methane emissions from mines, the use of satellite observations is revolutionizing the process of monitoring and improving the accuracy of emission accounting. The methodology presented here allows us to determine background CH4 concentrations and improve our ability to detect emission events using Sentinel-5P data. Knowing the background concentrations for the area of interest provides us the opportunity to track seasonal and annual variations and trends, as well as quickly detect periodic or accidental emissions from unregulated sources, etc. The methodology and systematic research applied in this paper for the period of May 2018 to the end of 2022 enables us to detect hundreds of large-scale emissions of CH4, NO2, and CO from the coal mines in the Kuznetsk Basin (the Kemerovo region), the largest coal mining area in Russia. We estimated that the amount of these emissions is significantly higher than the emissions reported by various authors for other coal mining regions such as Poland and Australia. We found that in cases of high methane quantity there is a positive correlation between NO2 and CO emissions in time and location. The source of emissions in the study area is homogeneous, which allows it to be used as a benchmark for building models to estimate and track emissions in heterogeneous areas.

Machine Learning for Detection of Macroalgal Blooms in the Mar Menor Coastal Lagoon Using Sentinel-2

The Mar Menor coastal lagoon in southeastern Spain has experienced a decline in water quality due to increased nutrient input, leading to the eutrophication of the lagoon and the occurrence of microalgal and macroalgal blooms. This study analyzes the macroalgal bloom that occurred in the lagoon during the spring-summer of 2022. A set of machine learning techniques are applied to Sentinel-2 satellite imagery in order to obtain indicators of the presence of macroalgae in specific locations within the lagoon. This is supported by in situ observations of the blooming process in different areas of the Mar Menor. Our methodology successfully identifies the macroalgal bloom locations (accuracies above 98%, and Matthew’s Correlation Coefficients above 78% in all cases), and provides a probabilistic approach to understand the likelihood of occurrence of this event in given pixels. The analysis also identifies the key parameters contributing to the classification of pixels as algae, which could be used to develop future algorithms for detecting macroalgal blooms. This information can be used by environmental managers to implement early warning and mitigation strategies to prevent water quality deterioration in the lagoon. The usefulness of satellite observations for ecological and crisis management at local and regional scales is also highlighted.

Exploring the use of satellite observations of soil moisture, solar-induced chlorophyll fluorescence and vegetation optical depth to monitor droughts across India

Exploring the use of satellite observations of soil moisture, solar-induced chlorophyll fluorescence and vegetation optical depth to monitor droughts across India

Improved soil moisture estimation: Synergistic use of satellite observations and land surface models over CONUS based on machine learning

Three widely used primary soil moisture (SM) data sources, namely, in-situ measurements, satellite observations, and land surface models (LSM), possess different characteristics. This study combined three SM data sources using machine learning (ML): random forest, artificial neural networks, and support vector regression, and simple averaging ensemble approaches to produce improved daily SM data over the contiguous United States (CONUS). For each ML model, three schemes were tested using different independent variables, namely, satellite-derived, LSM-derived, and both. Triple collocation analysis (TCA) was adopted to address the scale mismatch problem between in-situ and coarse gridded SM data. The proposed approach was evaluated using the International Soil Moisture Network (ISMN), Soil Moisture Active Passive Core Validation Sites (SMAP CVS), and TCA. In the ISMN-based evaluation, the proposed ML-based ensemble generally produced better evaluation metrics and showed robust skills over topographically complex and densely vegetated regions where existing SM products showed poor skills. The SMAP CVS-based evaluation demonstrated that the ML ensemble approach yielded a better performance than the existing SM datasets, resulting in a correlation coefficient of 0.78, unbiased root mean squared difference of 0.035 m3/m3, and bias of 0.006 m3/m3. In addition, the TCA results additionally confirmed that the ML-based ensemble had better spatiotemporal quality than the other SM products. The data-driven approach proposed in this study has three major novelties: (1) the proposed ML-based method synergistically merges various data sources to improve SM; (2) the performance of the proposed ML-based SM was robust to topography and vegetation; and (3) the average ensemble of three ML models additionally improves performances. The SM time-series data generated by the proposed approach are expectedly suitable variables for environmental and climate applications over CONUS. The research findings suggest that ML algorithms can be effectively used for modeling dynamic soil moisture.

An assessment of flood event along Lower Niger using Sentinel-1 imagery.

Flood incidence, especially in global south countries, is one of the most challenging natural disasters in the light of changing climates, especially in Africa. This is because African countries have a large sub-section of vulnerable people who either live within flood-prone areas or depend on flood-prone areas for their means of livelihood such as we have in Nigeria. Recent flood disasters in Nigeria have been of major concern to people, communities, and institutions. Several studies have been conducted on flood events and their impacts in Nigeria. However, most of these studies are on public perception, flood modeling (rainfall-runoff), and the provision of binary maps with few studies engaging in the use of satellite observations, especially the use of Synthetic Aperture Radar, SAR, to enhance flood early warning designs, especially in Sub-Saharan Africa. This study is aimed at assessing the 2018 flood event in Lokoja, Kogi State, Nigeria, using the Sentinel-1 imagery. The study confirmed that a total of 69 buildings out of 611 buildings were affected by the flood disaster with about 24,902 people displaced by this singular flood event. The study shows that backscattering from microwave sensors provides very useful information for highlighting inundated areas that could prove useful in forecasting, monitoring, and precision-based flood early warning designs before, during, and after flood events.

Use of satellite observations for weather prediction

Use of satellite observations for weather prediction

On the use of satellite observations to fill gaps in the Halley station total ozone record

Abstract. Measurements by the Dobson ozone spectrophotometer at the British Antarctic Survey's (BAS) Halley research station form a record of Antarctic total column ozone that dates back to 1956. Due to its location, length, and completeness, the record has been, and continues to be, uniquely important for studies of long-term changes in Antarctic ozone. However, a crack in the ice shelf on which it resides forced the station to abruptly close in February of 2017, leading to a gap of two ozone hole seasons in its historic record. We develop and test a method for filling in the record of Halley total ozone by combining and adjusting overpass data from a range of different satellite instruments. Comparisons to the Dobson suggest that our method reproduces monthly ground-based total ozone values with an average difference of 1.1 ± 6.2 DU for the satellites used to fill in the 2017–2018 gap. We show that our approach more closely reproduces the Dobson measurements than simply using the raw satellite average or data from a single satellite instrument. The method also provides a check on the consistency of the provisional data from the automated Dobson used at Halley after 2018 with earlier manual Dobson data and suggests that there were likely inconsistencies between the two. The filled Halley dataset provides further support that the Antarctic ozone hole is healing, not only during September but also in January.

Carbon monoxide emissions assessment by using satellite and modeling data: Central Mexico case study

This paper quantifies and reduces the differences in emissions from the 2008 inventory with respect to the real ones through the use of satellite observations and modeling. Carbon monoxide column comparisons from the Infrared Atmospheric Sounding Interferometer (IASI) satellite data were made against columns obtained from the WRF-Chem model, during February 2011. The analysis was carried out at the satellite passage local time (approximately 10:00 LT) over Mexico City. The 2008 National Emissions Inventory generated by the Mexican Ministry of Environment and Natural Resources was utilized. An inversion method was applied to the modeled and observed column data. With the above, scaling factors were obtained for five regions and the concentration from the model domain boundaries, which were used to update the emissions. These were used in modeling and the result was compared with surface measurements. For Mexico City and the Metropolitan Area, a scaling factor equal to 0.43 was obtained when using the 2008 emissions inventory; for Toluca, Morelos and Puebla, a less than one factor was estimated, while for Hidalgo and the concentration from model boundaries it was close to two. The model performance was improved by an increment in the agreement index and a reduction on the mean square error when the updated CO emissions were used.

Observations of Mesoscale Eddies in Satellite SSS and Inferred Eddy Salt Transport

Observations of sea surface salinity (SSS) from NASA’s Soil Moisture Active-Passive (SMAP) and ESA’s Soil Moisture and Ocean Salinity (SMOS) satellite missions are used to characterize and quantify the contribution of mesoscale eddies to the ocean transport of salt. Given large errors in satellite retrievals and, consequently, SSS maps, we evaluate two products from the two missions and also use two different methods to assess the eddy transport of salt. Comparing the two missions, we find that the estimates of the eddy transport of salt agree very well, particularly in the tropics and subtropics. The transport is divergent in the subtropical gyres (eddies pump salt out of the gyres) and convergent in the tropics. The estimates from the two satellites start to differ regionally at higher latitudes, particularly in the Southern Ocean and along the Antarctic Circumpolar Current (ACC), resulting, presumably, from a considerable increase in the level of noise in satellite retrievals (because of poor sensitivity of the satellite radiometer to SSS in cold water), or they can be due to insufficient spatial resolution. Overall, our study demonstrates that the possibility of characterizing and quantifying the eddy transport of salt in the ocean surface mixed layer can rely on the use of satellite observations of SSS. Yet, new technologies are required to improve the resolution capabilities of future satellite missions in order to observe mesoscale and sub-mesoscale variability, improve the signal-to-noise ratio, and extend these capabilities to the polar oceans.

Potential use of satellite observations to detect suspended sediment in delta region: a case study of the Red river delta, Vietnam

Potential use of satellite observations to detect suspended sediment in delta region: a case study of the Red river delta, Vietnam

Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

A comparison of ocean model data and satellite observations of features affecting the growth of the North Equatorial Counter Current during the strong 1997–1998 El Niño

Abstract. Descriptions of the ocean's role in the El Niño usually focus on equatorial Kelvin waves and the ability of such waves to change the mean thermocline depth and the sea surface temperature (SST) in the central and eastern Pacific. In contrast, starting from a study of the transport of water with temperatures greater than 28 ∘C, sufficient to trigger deep atmospheric convection, Webb (2018) found that, during the strong El Niños of 1983–1984 and 1997–1998, advection by the North Equatorial Counter Current (NECC) had a much greater impact on sea surface temperatures than processes occurring near the Equator. Webb's analysis, which supports the scheme proposed by Wyrtki (1973, 1974), made use of archived data from a high-resolution ocean model. Previously the model had been checked in a preliminary comparison against SST observations in the equatorial Pacific, but, given the contentious nature of the new analysis, the model's behaviour in key areas needs to be checked further against observations. In this paper this is done for the 1987–1988 El Niño, making use of satellite observations of SST and sea level. SST is used to check the movement of warm water near the Equator and at the latitudes of the NECC. Sea level is used to check the model results at the Equator and at 6∘ N in the North Equatorial Trough. Sea level differences between these latitudes affect the transport of the NECC, the increased transport at the start of each strong El Niño being associated with a drop in sea level at 6∘ N in the western Pacific. Later rises in sea level at the Equator increase the transport of the NECC in mid-ocean. The variability of sea level at 6∘ N is also used to compare the strength of tropical instability waves in the model and in the observations. The model showed that in a normal year these act to dilute the temperature in the core of the NECC. However their strength declined during the development of the strong El Niños, allowing the NECC to carry warm water much further than normal across the Pacific. The results of this paper should not be taken as providing proof of the hypotheses of Wyrtki (1973, 1974) or Webb (2018) but instead as a failure of a targeted study, using satellite observations, to disprove the hypotheses.

Decadal Changes of Meridional Eddy Heat Transport in the Subpolar North Atlantic Derived From Satellite and In Situ Observations

AbstractThe interannual variability of the meridional eddy heat transport in the North Atlantic is investigated based on the combined use of satellite observations of sea level anomaly (SLA) and sea surface temperature (SST) over the 25 year period from 1993 to 2017. The focus is on 47°N, a latitude close to the boundary between the subpolar and the subtropical gyres. The vertical structure of the eddy heat transport is reconstructed in 10° longitude × 5° latitude boxes from a composite analysis of Argo profile data and observations of mesoscale eddies in altimetric SLA. The transport is surface intensified with almost 95% happening above the pycnocline. It can vary immensely both zonally and vertically, in some cases reversing sign below the pycnocline. The basin‐wide integrated eddy heat transport accounts for around 6% of the total meridional heat transport. During the observed period, the eddy heat transport increased on average by 0.6 TW/yr, accelerating to 1.3 TW/yr after 2005. The observed trends and variations in the eddy heat transport can be related to the large‐scale temperature gradients in the region and in response to the North Atlantic Oscillation. They provide a considerable contribution to the variability of the total oceanic heat transport, suggesting a potentially important forcing mechanism in the ocean component of the climate system.

Improvement of the Rapid-Development Thunderstorm (RDT) Algorithm for Use with the GK2A Satellite

New technologies for the classification of convective cloud lifecycles and the prediction of their movements are needed to detect severe convective weather and to support objective cloud guidance. Satellites enable earlier detection of severe weather over larger coverage areas than ground-based observations or radar. The use of satellite observations for nowcasting is thus likely. In this study, convective initiation (CI) data are paired with a modified rapid-development thunderstorm (RDT) algorithm for the analysis of new data from the Geostationary Korea Multi-Purpose Satellite-2A (GEO-KOMPSAT-2A, GK2A). The RDT algorithm is further modified to accommodate the additional GK2A satellite channels, and new satellite data are used to continuously analyze thunderstorms associated with severe weather in Korea. The logistic regression (LR) machine learning approach is used to optimize the criteria of interest fields and weighting coefficients of the RDT algorithm for convective detection. In addition, auxiliary data (cloud type, convective rainfall rate, and cloud top temperature/height) calculated from RDT sub-module is replaced with GK2A derived products. The fully modified RDT algorithm (K-RDT) is quantitatively verified using lightning data from summer convection cases. The probability of detection (POD) for convective clouds is increased by 30–40%, and the threat score (TS) for average lightning activity is improved by 10–30%. The channel properties of Japan Himawari-8 satellite are similar to those of the GK2A satellite. Due to the lack of GK2A satellite data during the development period, CI data from the Himawari-8 satellite are used as proxies.

Evaluating the use of satellite observations to supplement ground-level air quality data in selected cities in low- and middle-income countries

Using data from ground-level measurements, this work evaluates the performance of one chemical transport model (CTM)-based approach using MERRA2 model output and one statistical approach using a generalized additive model to translate remotely-sensed Aerosol Optical Depth (AOD) measurements from the MODIS combined Deep Blue and Dark Target algorithm to surface-level PM2.5 concentrations for nine cities in low and middle-income countries that include a range of environmental conditions (e.g., mountainous, dusty, or coastal conditions) and only have between 1 and 10 ground level monitoring sites available. This evaluation shows that the CTM-based and statistical approaches tested here generally had a low correlation with the true daily-average PM2.5 values within these nine cities, in contrast to previous studies that had showed stronger correlations over other regions. In addition, the uncertainty in the satellite-based estimates of the daily-average PM2.5 concentration at a given location in a city tended to be very large (21–77% for the statistical methods, and 48–85% for the CTM-based methods). Many cities also had significant limitations in the availability of satellite observations of aerosols throughout the year due to their coastal location, persistent clouds, or persistent seasonal snow cover. The satellite-based methods tested in this work appear to work best for low altitude, inland cities like Hanoi and Delhi, but still have significant errors (43–60%) in predictions of daily-average PM2.5 concentrations at sites within these cities. The CTM-based satellite approach tested here tended to underestimate PM2.5 in high-altitude cities (except for Addis Ababa, Ethiopia, where they overestimate). However, this work suggests that under some conditions, adding satellite data to ground-level monitoring (GLM) network data via co-kriging may reduce the number of GLM sites needed to characterize PM2.5 concentrations within a city, but this needs to be determined on a case-by-case basis. This evaluation is then used to make recommendations to countries with sparse GLM networks on how to incorporate the use of satellite observations in their PM2.5 monitoring and under what conditions satellite approaches are likely to be unsuccessful.

RMetS National Meeting ‐ Progress in Weather Forecasting ‐ the user's perspective

RMetS National Meeting ‐ Progress in Weather Forecasting ‐ the user's perspective

Detecting intercepted snow on mountain needleleaf forest canopies using satellite remote sensing

Snow interception in cold regions needleleaf forest canopies is a crucial process that controls local snow accumulation and redistribution over >20% of the Earth's land surface. Various ground-based methods exist to measure intercepted snow load, however all are based on single-tree measurements and are difficult to implement. No research has focussed on detecting large areal intercepted snow loads and no studies have assessed the use of satellite observations. In this study, four remote sensing indices (NDSI, NDVI, albedo, and land surface temperature (LST)) were retrieved from Landsat images to study their sensitivity to canopy intercepted snow and the possibility of using them to detect the presence of intercepted snow. The results indicate that presence of intercepted snow on canopy increased NDSI and albedo, but decreased NDVI. Intercepted snow presence also decreased the areal variability of NDSI and NDVI while increasing that of albedo. For these three indices, the differences between snow-free and snowcovered canopies were correlated to topography and forest canopy cover. Of these indices, NDSI changed the greatest. Intercepted snow noticeably decreased the LST difference between forest and open areas in springtime while the influence in wintertime was relatively smaller. An intercepted snow detection approach that uses both NDSI and NDVI to classify pixels into either snowcovered canopy or other (snow-free canopy and non-forest areas) is proposed here. A case study applying this approach compared remote sensing detection to simulations by the snow interception and sublimation model implemented in the Cold Regions Hydrological Modelling platform (CRHM). This used local meteorological observations from the pine, spruce and fir forest covered Marmot Creek Research Basin in the Canadian Rockies. The remote sensing detection of intercepted snow agreed well with CRHM simulations for continuous forests (83%) and less well for sparse forests (72%) and clearings with small trees (70%). Therefore, the approach is suitable for intercepted snow detection over continuous evergreen canopies. This technique provides a new capability for large-scale snow interception model validation and data assimilation to cold regions hydrological forecasting models.

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

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).