Interpretation Of Satellite Data Research Articles

Rockfall Mapping and Monitoring Across the Kalymnos Sport Rock Climbing Sites, Based on Ultra-High-Resolution Remote Sensing Data and Integrated Simulations

This manuscript presents a multidisciplinary study that proposes a methodology for delineating and categorizing vulnerability at rockfall risk areas to avoid human injuries and infrastructure damage caused by rockfalls. The presented workflow includes (i) classical geological mapping, (ii) the interpretation of high-resolution satellite data for observing the spatial distribution of fallen boulders, (iii) analytical hierarchy processing of spatial information within a Geographical Information System (GIS) platform, (iv) close-range remote sensing campaigns with Unmanned Aerial Systems (UASs), and (v) integrated simulation of rockfall events. This methodology was applied to Kalymnos Island, which belongs to the Dodecanese Islands complex of the southeastern Aegean Sea in Greece. It is characterized by unique geomorphological features, including extensive vertical limestone cliffs that span the island. These cliffs make it one of the world’s most densely concentrated areas for sport climbing. The results highlighted the areas that the local authorities need to focus on and suggested measures for increasing the safety of climbers and infrastructure.

G.I.S. SEARCH ENGINE

Abstract– This paper presents the development of a Geographic Information System (GIS) search engine leveraging Django, OpenLayers, MySQL, and GDAL. The application enables users to perform spatial searches and visualize geographic data interactively. This research details the system architecture, implementation process, and evaluation of performance and usability. The scope of the project encompasses the development of advanced tools for data integration, real-time processing, visualization, and the dissemination of insights derived from multiple satellite sources. By providing an infrastructure that supports the seamless flow and interpretation of satellite data, SDIS aims to enhance decision-making processes and foster innovative applications across different sectors. Keywords: GIS, Search Engine, QGIS, Spatial data, OpenLayers, GDAL

G.I.S. SEARCH ENGINE

Abstract– This paper presents the development of a Geographic Information System (GIS) search engine leveraging Django, OpenLayers, MySQL, and GDAL. The application enables users to perform spatial searches and visualize geographic data interactively. This research details the system architecture, implementation process, and evaluation of performance and usability. The scope of the project encompasses the development of advanced tools for data integration, real-time processing, visualization, and the dissemination of insights derived from multiple satellite sources. By providing an infrastructure that supports the seamless flow and interpretation of satellite data, SDIS aims to enhance decision-making processes and foster innovative applications across different sectors. Keywords: GIS, Search Engine, QGIS, Spatial data, OpenLayers, GDAL

G.I.S. SEARCH ENGINE

Abstract– This paper presents the development of a Geographic Information System (GIS) search engine leveraging Django, OpenLayers, MySQL, and GDAL. The application enables users to perform spatial searches and visualize geographic data interactively. This research details the system architecture, implementation process, and evaluation of performance and usability. The scope of the project encompasses the development of advanced tools for data integration, real-time processing, visualization, and the dissemination of insights derived from multiple satellite sources. By providing an infrastructure that supports the seamless flow and interpretation of satellite data, SDIS aims to enhance decision-making processes and foster innovative applications across different sectors. Keywords: GIS, Search Engine, QGIS, Spatial data, OpenLayers, GDAL

Assessment of C-Band Polarimetric Radar for the Detection of Diesel Fuel in Newly Formed Sea Ice

There is a heightened risk of an oil spill occurring in the Arctic, as climate change driven sea ice loss permits an increase in Arctic marine transportation. The ability to detect an oil spill and monitor its progression is key to enacting an effective response. Microwave scatterometer systems may be used detect changes in sea ice thermodynamic and physical properties, so we examined the potential of C-band polarimetric radar for detecting diesel fuel beneath a thin sea ice layer. Sea ice physical properties, including thickness, temperature, and salinity, were measured before and after diesel addition beneath the ice. Time-series polarimetric C-band scatterometer measurements monitored the sea ice evolution and diesel migration to the sea ice surface. We characterized the temporal evolution of the diesel-contaminated seawater and sea ice by monitoring the normalized radar cross section (NRCS) and polarimetric parameters (conformity coefficient (μ), copolarization correlation coefficient (ρco)) at 20° and 25° incidence angles. We delineated three stages, with distinct NRCS and polarimetric results, which could be connected to the thermophysical state and the presence of diesel on the surface. Stage 1 described the initial formation of sea ice, while in Stage 2, we injected 20L of diesel beneath the sea ice. No immediate response was noted in the radar measurements. With the emergence of diesel on the sea ice surface, denoted by Stage 3, the NRCS dropped substantially. The largest response was for VV and HH polarizations at 20° incidence angle. Physical sampling indicated that diesel emerged to the surface of the sea ice and trended towards the tub edge and the polarimetric scatterometer was sensitive to these physical changes. This study contributes to a greater understanding of how C-band frequencies can be used to monitor oil products in the Arctic and act as a baseline for the interpretation of satellite data. Additionally, these findings will assist in the development of standards for oil and diesel fuel detection in the Canadian Arctic in association with the Canadian Standards Association Group.

Interpretation and origin determination of oil seepages using marine seismic data: a case study of the Western Continental Margin of India

This article discusses the experience of a joint using remote sensing data, marine seismic data and open geoscientific data to search for natural mineral oil seepages. The study was carried out as part of an assessment of oil prospects in the offshore sedimentary basins of the Arabian Sea on the west coast of the Indian Peninsula. The methodology is based on visual interpretation of natural and anthropogenic oil seepages using Sentinel-2 multispectral imageries. Modern offshore seismic data is used to establish the mineral origin of oil seepages by the presence of reservoirs and fault systems as possible sources of hydrocarbons and channels for their migration to the surface, respectively. The final map of detected oil seepages location is the result of research. Areas of natural mineral oil seepages accumulation confined to the western continental margin of India have been identified. Recommendations for exploration activities in the Kutch, Saurashtra and Kerala-Konkan offshore sedimentary basins were given. The acquired experience of visual interpretation of multispectral satellite data confirms efficiency of marine seismic data application for delineation of natural oil seepages of mineral origin in the areas of intensive navigation. The proposed methodology of research can be used as an indirect assessment of oil prospects in the Western Continental Margin of India and other areas of the world ocean.

Morphometric Analysis of Irang River Watershed, Manipur, India using Remote Sensing and GIS

Abstract: Remote sensing (RS) and GIS is considered to be a very effective tool for interpretation of high resolution satellite data for understanding and managing the nature of a drainage basin. The morphometric analysis of the drainage basin and channel network plays a significant role in comprehension of the geohydrological nature of drainage basin and expresses the prevailing climate, geological setting, geomorphology and structural antecedents of the catchment area

Glacial lakes of Sikkim Himalaya: their dynamics, trends, and likely fate—a timeseries analysis through cloud-based geocomputing, and machine learning

In the background of ongoing climate change, it is important to monitor the spatial and temporal changes of glacial lakes (GLs) since they influence snowmelt runoff, stream discharge, water resources, and glacial lake outburst flood (GLOF). However, accurate identification and mapping of GLs in the background of snow-clad mountains through visual interpretation of satellite data is a tedious and challenging assignment when multiyear time-series analysis is considered. To overcome this challenge, automated extraction of GLs in satellite images has been carried out in this study with the help of machine learning (ML). The novelty of this study is identification and tracking of GLs over three decades using ML and geospatial analysis using pixel-based image classification. For this, Random Forest Classifier (RFC) and Artificial Neural Network (ANN) were employed. The methodology is demonstrated here for the identification and mapping of GLs in the Sikkim Himalaya from 1987 to 2020 and for forecasting the possible fate of these GLs through time-series modelling. The geospatial time-series analysis using Google Earth Engine, ML classifiers, and GIS framework, has captured the dynamics of GLs in Sikkim and has revealed the spatial and temporal patterns in GLs’ dimensions as well as GLOF risk.

Retrieval of Missing Remotely Sensed Tropospheric NO2 Data Using Tensor Completion

Missing values in remotely sensed satellite data present a significant challenge for accurate analysis and interpretation of environmental data. Factors such as dead pixel values or cloud coverage can lead to significant gaps in datasets, degrading its overall quality and value. Tensor completion utilizes high-dimensional arrays known as tensors and their low-rank decompositions to recover missing values. This paper demonstrates the application and value of applying Tensor Completion to enhance remotely sensed Nitrogen-Dioxide satellite data and proposes an algorithm for its use. An efficient and accurate recovery method is proposed by leveraging relationships within the tensor and by employing suitable tensor decomposition methods. The proposed algorithm will enhance the analysis, interpretation, and utilization of satellite data. The algorithm is validated on real-world satellite datasets and its superiority demonstrated against existing alternate data recovery methods such as IDW and Kriging.

Key Factors for Improving the Resolution of Mapped Sea Surface Height from Multi-Satellite Altimeters in the South China Sea

A satellite altimeter measures sea surface height (SSH) along the nadir track. Multiple satellite altimeters have been in orbit, and the measurements been merged for mapping mesoscale eddies of ~100 km in size in the oceans. The capability of the mapped SSH for resolving mesoscale eddies depends on mapping algorithms. A two-dimensional variational (2DVAR) algorithm was implemented to generate mapped SSH at a grid size of 1/12° in the South China Sea. A range of comparisons were performed between the mapped SSH and the commonly used AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic satellite data) mapped SSH data product at a grid size of 1/8° and 1/4°. The effective resolution, which represents the spatial scale that the data can resolve, was examined. The effective resolution of the mapped SSH using the 2DVAR algorithm is approximately 100 km, while it is 250 km with the 1/8° and 1/4° AVISO data products. The difference in the effective resolution results from the difference in the background state and thus the background error. The result suggests that the effective resolution of the mapped data could be increased by choosing a background state so that the associated errors could have a smaller decorrelation length scale.

Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

Chromite is a strategic mineral resource for many countries, and chromite deposit occurrences are widespread in the ultramafic rocks of the Yarlung Zangbo ophiolite belt, particularly in the harzburgite unit of the mantle section. Conducting field surveys in complex and poorly accessible terrain is challenging, expensive, and time-consuming. Remote sensing is an advanced method of achieving modern geological work and is a powerful technical means of geological research and mineral exploration. In order to delineate outcrops of chromite-bearing mantle peridotite, the present research study integrates seven image-enhancement techniques, including optimal band combination, decorrelation stretching, band ratio, independent component analysis, principal component analysis, minimum noise fraction, and false color composite, for the interpretation of Landsat8 OLI and WorldView-2 satellite data. This integrated approach allows the effective discrimination of chromite-containing peridotite outcrops in the Luobusa area, Tibet. The interpretation results derived from these integrated image-processing techniques were systematically verified in the field and formed the basis of the feature selection process of different lithologies supported by the support vector machine algorithm. Furthermore, the distribution range of the ferric contamination anomaly is detected through the de-interference abnormal principal component thresholding technique, which shows a high spatial matching relationship with mantle peridotite. This is the first study to utilize Landsat8 OLI and WorldView-2 remote sensing satellite data to explore the largest chromite deposit in China, which enriches the research methods for the chromite deposits in the Luobusa area. Accordingly, the results of this investigation indicate that the integration of information extracted from image-processing algorithms using remote sensing data could be a broadly applicable tool for prospecting chromite ore deposits associated with ophiolitic complexes in mountainous and inaccessible regions such as Tibet’s ophiolitic zones.

Active faults studies in Delhi and national capital region (NCR): Inferences from satellite data and field investigations

In recent years, the National Capital Region (NCR) of Delhi has experienced several earthquakes ranging in magnitude from 1.0 to 6.7. According to the last 50 years of earthquake data, the majority of earthquakes in the NCR have occurred near the Mahendragarh Dehradun Fault (MDF) and the Sohna Fault (SF). The region is bounded by a number of subsurface Ridges, Faults, and Lineaments, which are also influenced by the active plate boundary of the Indian and Eurasian plates. Active fault mapping is critical for the precise identification and marking of active fault traces in the NCR area for a precise seismic hazard assessment. We used high resolution Cartosat-1 stereopair data obtained from NRSC, Hyderabad, and Anaglyph (A 3D representation of the surface) and DEM prepared with ENVI software to map the active faults. We identified 12 sites in the NCR region based on satellite data interpretation, primarily along the MDF and Sohna Fault and their extensions. The presence of tectono-geomorphic markers along the MDF and Sohna Fault, such as warped surfaces indicative of fault scarps, stream offsets, gully erosion, and sag ponds, suggests active tectonic movement along these faults, most likely in the recent geological past. We believe the MDF is a right-lateral strike-slip fault with a compressional component on the western side and an extensional component on the eastern side. It acts as a segment boundary between compressional and extensional boundaries. We also identified the right lateral Nuh-Jhirka fault (NJF), which can be the Sohna Fault’s southern extension from Nuh to Jhirka. The western limb of the Delhi Mega fold has also seen a few right-lateral strike-slip movements that have extended up to the eastern bank of the Yamuna River, where the river reflects the base-level change and tight meandering on its upward side and a straight pattern on its downward side. This fault is known as the Delhi Fault (DF). The findings are preliminary, and further research would be required to create a detailed active fault map of the Delhi-NCR region to conduct a precise Seismic Hazard Assessment (SHA) of the region.

River effects on sea-level rise in the Río de la Plata estuary during the past century

Abstract. Identifying the causes for historical sea-level changes in coastal tide-gauge records is important for constraining oceanographic, geologic, and climatic processes. The Río de la Plata estuary in South America features the longest tide-gauge records in the South Atlantic. Despite the relevance of these data for large-scale circulation and climate studies, the mechanisms underlying relative sea-level changes in this region during the past century have not been firmly established. I study annual data from tide gauges in the Río de la Plata and stream gauges along the Río Paraná and Río Uruguay to establish relationships between river streamflow and sea level over 1931–2014. Regression analysis suggests that streamflow explains 59 %±17 % of the total sea-level variance at Buenos Aires, Argentina, and 28 %±21 % at Montevideo, Uruguay (95 % confidence intervals). A long-term streamflow increase effected sea-level trends of 0.71±0.35 mm yr−1 at Buenos Aires and 0.48±0.38 mm yr−1 at Montevideo. More generally, sea level at Buenos Aires and Montevideo respectively rises by (7.3±1.8)×10-6 m and (4.7±2.6)×10-6 m per 1 m3 s−1 streamflow increase. These observational results are consistent with simple theories for the coastal sea-level response to streamflow forcing, suggesting a causal relationship between streamflow and sea level mediated by ocean dynamics. Findings advance understanding of local, regional, and global sea-level changes; clarify sea-level physics; inform future projections of coastal sea level and the interpretation of satellite data and proxy reconstructions; and highlight future research directions. Specifically, local and regional river effects should be accounted for in basin-scale and global mean sea-level budgets as well as reconstructions based on sparse tide-gauge records.

Interpretation of multispectral satellite data as a tool for detecting archaeological artifacts (Navkur Plain and Karamleis Plain, Iraq)

Contemporary studies of geographical space, including archaeological research, incorporate multiple spatial digital data. Such data provide an opportunity to extend research to large areas, and to objectify studies on the basis of quantitative data thus obtained and gaining access to the hard-to-reach study area. Examples of such data are satellite images at various spatial resolutions and in a wide spectrum of electromagnetic radiation (visible, infrared, and microwave). The authors made an attempt to use satellite images to analyze the areas of probable location of the Battle of Gaugamela (the Navkur Plain and the Karamleis Plain in Iraq). The photointerpretation was performed, enhanced by the multivariate processing of the multispectral image. The aim of the work was indicating the most likely places where the camp and the battle were located based on the visual interpretation of an array of satellite data. The adopted methodology of precise allocation of interpretative values to remote sensing materials for every detected artifact provided an opportunity to accumulate an extensive amount of information. It also provided the basis for a synthetic analysis regarding the methods of image processing on the one hand and the dates of recording on the other. It turned out that the season in which the photos are recorded is very important—although the best data for analysis turned out to be the autumn data (38% of all recognized artifacts), the use of data from three seasons increased the total number of indicated artifacts by as much as about 50% (the so-called unique detections). In addition, advanced image processing (such as principal component analysis and decorrelation stretch) turned out to be important, as it increased the number of areal artifacts by 31% compared to the interpretation of only photos in natural (true) color composite and false color composite (with near-infrared). The conducted analyses have confirmed the usefulness of high-resolution satellite data for archaeological applications, and the detected and described anomalies visible in satellite images are excellent material for selecting sites for detailed field research.

Vertical artifacts in high-resolution WorldView-2 and WorldView-3 satellite imagery of aquatic systems

ABSTRACT Satellite image artefacts are features that appear in an image but not in the original imaged object and can negatively impact the interpretation of satellite data. Vertical artefacts are linear features oriented in the along-track direction of an image system and can present as either banding or striping; banding are features with a consistent width, and striping are features with inconsistent widths. This study used high-resolution data from DigitalGlobeʻs (now Maxar) WorldView-3 satellite collected at Lake Okeechobee, Florida (FL), on 30 August 2017. This study investigated the impact of vertical artefacts on both at-sensor radiance and a spectral index for an aquatic target as WorldView-3 was primarily designed as a land sensor. At-sensor radiance measured by six of WorldView-3ʻs eight spectral bands exhibited banding, more specifically referred to as non-uniformity, at a width corresponding to the multispectral detector sub-arrays that comprise the WorldView-3 focal plane. At-sensor radiance measured by the remaining two spectral bands, red and near-infrared (NIR) #1, exhibited striping. Striping in these spectral bands can be attributed to their time delay integration (TDI) settings at the time of image acquisition, which were optimized for land. The impact of vertical striping on a spectral index leveraging the red, red edge, and NIR spectral bands—referred to here as the NIR maximum chlorophyll index (MCINIR)—was investigated. Temporally similar imagery from the European Space Agencyʻs Sentinel-3 and Sentinel-2 satellites were used as baseline references of expected chlorophyll values across Lake Okeechobee as neither Sentinel-3 nor Sentinel-2 imagery showed striping. Striping was highly prominent in the MCINIR product generated using WorldView-3 imagery, as noise in the at-sensor radiance exceeded any signal of chlorophyll in the image. Adjusting the image acquisition parameters for future tasking of WorldView-3 or the functionally similar WorldView-2 satellite may alleviate these artefacts. To test this, an additional WorldView-3 image was acquired at Lake Okeechobee, FL, on 26 May 2021 in which the TDI settings and scan line rate were adjusted to improve the signal-to-noise ratio. While some evidence of non-uniformity remained, striping was no longer noticeable in the MCINIR product. Future image tasking over aquatic targets should employ these updated image acquisition parameters. Since the red and NIR #1 spectral bands are critical for inland and coastal water applications, archived images not collected using these updated settings may be limited in their potential for analysis of aquatic variables that require these two spectral bands to derive.

Unwrapping reworked crust at the Columbia supercontinent margin within central southern Amazon Craton using multi-source geophysics and geochronology data synergy

The geometry and evolution of pre-existing basement in accretionary belts bordering supercontinents are often unclear. Integrative interpretation of long-wavelength potential field satellite data can image deep crust structure, improving our understanding of lithospheric processes that formed these margins bottom-up. Here, we present a multidisciplinary interpretation of the lithospheric architecture of the central southern Amazon Craton, a fragment of an accretionary belt at the southwestern Columbia supercontinent margin. Satellite-borne gravity and magnetic data, airborne magnetic data, passive seismic (Vp/Vs ratio, crustal thickness) and seismic tomography data reveals that basement terranes from the interior of the craton extend into the accretionary margin of Columbia. We demonstrate a vertically heterogeneous structure with an underlying strongly reworked pre-Columbia tectonic wedge that sustained prolonged modification during the supercontinent assembly as corroborated by Nd isotope and geochronology data. Nd isotope data suggest that the protracted orogenic wedge was influenced by subduction angle shifts over time, including addition of substantial juvenile material during slab retreat events. This interplay promoted Craton growth at the supercontinent margin while keeping a subtle record of the pre-existing framework. Our findings point to the possible misrepresentation of basement extension and geometry of supercontinent margins elsewhere.

Land Use/Land Cover (LULC) Dynamics in a Semi-Arid Watershed in Eastern Rajasthan, India Using Geospatial Tools

Land use/land cover (LULC) change analysis has become a unique approach in determining the extent of degradation of natural resources within a given period of time. Remote sensing and GIS techniques have proved to be efficient tools for mapping and analyzing LULC changes over the last few decades. LULC change analysis has been carried out in Ruparel watershed which is situated in Alwar district, Eastern Rajasthan, India, based on visual image interpretation and change detection analysis of multi-temporal satellite data pertaining to IRS-P6 LISS III data of 2004 (Path-Row 95:52), IRS-P6 LISS III of 2014 (Path-Row 95:52) and IRS-R2A LISS III data of 2021. Visual image interpretation led to the delineation of 13 LULC classes using ArcGIS 10.5 software and include categories such as cultivated land, fallow land dense forest, open forest, degraded forest, open scrub, gullied/ravenous land, settlement/built-up land, River/waterbody, dry waterbody/dry river, plantation, barren/rocky/stony waste, and stone quarry. Results of the analysis depict significant LULC changes that have taken place in the area from 2004 to 2021. LULC categories such as cultivated land and settlement/built-up land have reported major changes in terms of their increase with 56.42 km2 (4.63%) and 31.9 km2 (2.63%) respectively primarily because of an increase in population. Likewise, the dense forest has reported a decrease of 33.78 km2 (2.78%) in its area and has been converted into degraded forest i.e., 32.04 km2 (2.64%) and open forest 2.85 km2 (0.24%) due to increased human exploitation of forest resources and mining activities taking place within the forested area. The study area needs the immediate attention of policymakers and stakeholders as the study area being part of the National Capital Region (NCR) will see excessive in-migration of the population in coming years which will further deplete the precious resources in the area.

ПРОГНОЗУВАННЯ ПОКЛАДІВ ВУГЛЕВОДНІВ З ВИКОРИСТАННЯМ КОМПЛЕКСУ АТМОГЕОХІМІЧНИХ І ДИСТАНЦІЙНИХ МЕТОДІВ

Using the technology of structural, thermic, and atmogeochemical investigations a number of the studies within the Piatyhorivka area of the DnieperDonets Depression were performed. Among the studies there were the geostructural and morphostructural analyses, the structural interpretation of satellite data, field works on the emanation and thermometric surveys, laboratory chromatographic analyses of gas samples collected from the undersoil layer, integrated interpretation of atmogeochemical and remote sensing data, predicting oil-gas occurrence for the Piatyhorivka area. It is found that this area is located in the complicated structural and tectonic environment. Within its boundaries the relative "autonomic" northern and southern zones of the anomalous gas elimination are detected and five local plots of the difficult configuration in a plane projected for the further exploration of oil and gas deposits are identified.

Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations

In this study, Sentinel-2 data were used for the retrieval of three key biophysical parameters of crops: leaf area index (LAI), leaf chlorophyll content (LCC), and leaf water content (LWC) for dominant crop types in the Czech Republic, including winter wheat (Triticum aestivum), spring barley (Hordeum vulgare), winter rapeseed (Brassica napus subsp. napus), alfalfa (Medicago sativa), sugar beet (Beta vulgaris), and corn (Zea mays subsp. Mays) in different stages of crop development. Artificial neural networks were applied in combination with an approach using look-up tables that is based on PROSAIL simulations to retrieve the biophysical properties tailored for each crop type. Crop-specific PROSAIL model optimization and validation were based upon a large dataset of in situ measurements collected in 2017 and 2018 in lowland of Central Bohemia region. For LCC and LAI, respectively, low relative root mean square error (rRMSE; 25%, 37%) was achieved. Additionally, a relatively strong correlation with in situ measurements (r = 0.80) was obtained for LAI. On the contrary, the results of the LWC parameter retrieval proved to be unsatisfactory. We have developed a generic tool for biophysical monitoring of agricultural crops based on the interpretation of Sentinel-2 satellite data by inversion of the radiation transfer model. The resulting crop condition maps can serve as precision agriculture inputs for selective fertilizer and irrigation application as well as for yield potential assessment.

Satellite Monitoring for Air Quality and Health.

Data from satellite instruments provide estimates of gas and particle levels relevant to human health, even pollutants invisible to the human eye. However, the successful interpretation of satellite data requires an understanding of how satellites relate to other data sources, as well as factors affecting their application to health challenges. Drawing from the expertise and experience of the 2016-2020 NASA HAQAST (Health and Air Quality Applied Sciences Team), we present a review of satellite data for air quality and health applications. We include a discussion of satellite data for epidemiological studies and health impact assessments, as well as the use of satellite data to evaluate air quality trends, support air quality regulation, characterize smoke from wildfires, and quantify emission sources. The primary advantage of satellite data compared to in situ measurements, e.g., from air quality monitoring stations, is their spatial coverage. Satellite data can reveal where pollution levels are highest around the world, how levels have changed over daily to decadal periods, and where pollutants are transported from urban to global scales. To date, air quality and health applications have primarily utilized satellite observations and satellite-derived products relevant to near-surface particulate matter <2.5 μm in diameter (PM2.5) and nitrogen dioxide (NO2). Health and air quality communities have grown increasingly engaged in the use of satellite data, and this trend is expected to continue. From health researchers to air quality managers, and from global applications to community impacts, satellite data are transforming the way air pollution exposure is evaluated.