Atmospheric Correction Research Articles

Evaluation of SDGSAT-1 MII data in total suspended matter estimation in clear to extremely turbid rivers

ABSTRACT The Multispectral Imager (MII) onboard the newly launched SDGSAT-1 holds significant promise for monitoring and understanding variations in water quality within nearshore water systems, with an impressive spatial resolution of 10 m, seven spectral bands in the visible and near-infrared domain and a wide swath of 300 km. This paper evaluates the MII data in estimating the concentration of total suspended matter (TSM) in rivers and estuaries from clear to extremely turbid conditions. For atmospheric correction, the ACOLITE model showed superior performance compared to the FLAASH and 6SV, with average unbiased relative error (AURE) ranging from 11.8% to 26.4% in the four visible bands of MII. Through comparison, a TSM model based on the visible band ratio (i.e., Rrs (660)/Rrs (560)) was proposed to ensure the application of MII data in both clear and turbid waters, with an overall AURE of 40.9%. The model was then applied to map the TSM variations in the Hai and Liao Rivers in northern China using the MII data, where reliable spatial and temporal patterns were observed. The study concludes by discussing the applicability of MII data in water quality mapping in nearshore waters, along with key considerations such as model applicability, atmospheric correction, and land adjacency.

Measuring and modelling directional effects in the frame of TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope)

Abstract. TRISHNA (Thermal infraRed Imaging Satellite for High-Resolution Natural resource Assessment) is a cross-purpose thermal infrared (TIR) Earth Observation (EO) mission designed to deliver images at high spatial (60 m) and temporal (3 days) resolutions. Its launch is foreseen in 2026 with a nominal mission duration of 5 years. This Indo-French polar-orbiting mission will overcome the limitations of thermal-optical observations from Landsat series and ASTER: low revisit, morning observations. TRISHNA scenes will be fully harnessed to pioneer the first cutting-edge high-resolution global maps of the land surface temperature (LST) and land surface emissivity (LSE) of natural and managed agroecosystems, man-made structures, water bodies, bare soils, rocks, snow, ice and sea. The quality of the preprocessing (radiometric calibration, atmospheric and directional corrections) is mandatory to satisfy the specifications with an expected precision of 1 K on LST in order to meet the targeted objectives. For such, it will be necessary to correct LST from directional effects with emphasis on the hot spot effect that can have an impact on LST up to several K. This is the goal of the TIRAMISU project to analyze TIR multiangular signatures over various biomes thanks to a pivoting system of cameras and a multi-model approach (1D, 3D, paramaterization). The modeling tools include 3 categories of models: SCOPE 1D, DART 3D and parametric BRDF models that are computationally efficient to perform inversion at global scale.

Characteristics of R2019 Processing of MODIS Sea Surface Temperature at High Latitudes

Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SSTskin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SSTskin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SSTskin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SSTskin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SSTskin data to study surface ocean processes and climate change in the Arctic.

An Empirical Algorithm for Estimating the Absorption of Colored Dissolved Organic Matter from Sentinel-2 (MSI) and Landsat-8 (OLI) Observations of Coastal Waters

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, acdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating acdom at the wavelength 412 nm (acdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, Rrs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite Rrs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate acdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m−1 from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived acdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of acdom (412) is provided for a coastal transect in the south of Vietnam.

Characterizing Chromophoric Dissolved Organic Matter Spatio-Temporal Variability in North Andean Patagonian Lakes Using Remote Sensing Information and Environmental Analysis

Chromophoric dissolved organic matter (CDOM) is crucial in aquatic ecosystems, influencing light penetration and biogeochemical processes. This study investigates the CDOM variability in seven oligotrophic lakes of North Andean Patagonia using Landsat 8 imagery. An empirical band ratio model was calibrated and validated for the estimation of CDOM concentrations in surface lake water as the absorption coefficient at 440 nm (acdom440, m−1). Of the five atmospheric corrections evaluated, the QUAC (Quick Atmospheric Correction) method demonstrated the highest accuracy for the remote estimation of CDOM. The application of separate models for deep and shallow lakes yielded superior results compared to a combined model, with R2 values of 0.76 and 0.82 and mean absolute percentage errors (MAPEs) of 14% and 22% for deep and shallow lakes, respectively. The spatio-temporal variability of CDOM was characterized over a five-year period using satellite-derived acdom440 values. CDOM concentrations varied widely, with very low values in deep lakes and moderate values in shallow lakes. Additionally, significant seasonal fluctuations were evident. Lower CDOM concentrations were observed during the summer to early autumn period, while higher concentrations were observed in the winter to spring period. A gradient boosting regression tree analysis revealed that inter-lake differences were primarily influenced by the lake perimeter to lake area ratio, mean lake depth, and watershed area to lake volume ratio. However, seasonal CDOM variation was largely influenced by Lake Nahuel Huapi water storage (a proxy for water level variability at a regional scale), followed by precipitation, air temperature, and wind. This research presents a robust method for estimating low to moderate CDOM concentrations, improving environmental monitoring of North Andean Patagonian Lake ecosystems. The results deepen the understanding of CDOM dynamics in low-impact lakes and its main environmental drivers, enhance the ability to estimate lacustrine carbon stocks on a regional scale, and help to predict the effects of climate change on this important variable.

Atmospheric Correction Effects on the NDSI: Snow Detection Characteristics across Land Cover Types

Atmospheric Correction Effects on the NDSI: Snow Detection Characteristics across Land Cover Types

Development of VIIRS-OLCI chlorophyll-a product for the coastal estuaries

Coastal waters require monitoring of chlorophyll-a concentration (Chl-a) in a wide range of Chl-a from a few mg/m3 to hundreds of mg/m3, which is of interest to the fisheries industry, evaluation of climate change effects, ecological modeling and detection of Harmful Algal Blooms (HABs). Monitoring can be carried out from the Visible Infrared Imaging Radiometer Suite (VIIRS) and Ocean and Land Colour Instrument (OLCI) Ocean Color (OC) satellite sensors, which are currently on orbit and are expected to be the main operational OC sensors at least for the next decade. A Neural Network (NN) algorithm, which uses VIIRS M3-M5 reflectance bands and an I1 imaging band, was developed to estimate Chl-a in the Chesapeake Bay, for the whole range of Chl-a from clear waters in the Lower Bay to extreme bloom conditions in the Upper Bay and the Potomac River, where Chl-a can be used for bloom detection. The NN algorithm demonstrated a significant improvement in the Chl-a retrieval capabilities in comparison with other algorithms, which utilize only reflectance bands. OLCI NIR/red 709/665 nm bands red edge 2010 algorithm denoted as RE10 was also explored with several atmospheric corrections from EUMETSAT, NOAA and NASA. Good consistency between the two types of algorithms is shown for the bloom conditions and the whole range of waters in the Chesapeake Bay (with RE10 switch to OC4 for lower Chl-a) and these algorithms are recommended for the combined VIIRS-OLCI product for the estimation of Chl-a and bloom monitoring. The algorithms were expanded to the waters in Long Island Sound, demonstrating good performance.

Interplay Between Atmospheric Correction and Fusion Techniques Enhances the Quality of Remote Sensing Image Fusion

Remote sensing image fusion technology integrates observational data from multiple satellite platforms to leverage the complementary advantages of the different types of remote sensing images. High-quality fused remote sensing images provide detailed information on surface radiation, climate, and environmental conditions, thereby supporting governmental policies on environmental changes. Improving the quality and quantitative accuracy of fused images is a crucial trend in remote sensing image fusion research. This study investigates the impact of atmospheric correction and five widely applied fusion techniques on remote sensing image fusion. By constructing four fusion frameworks, it evaluates how the choice of fusion method, the implementation of atmospheric correction, the synchronization of atmospheric parameters, and the timing of atmospheric correction influence the outcomes of remote sensing image fusion. Aerial flights using remote sensors were conducted to acquire atmospheric parameter distribution images that are strictly synchronous with the remote sensing images. Comprehensive and systematic evaluations of the fused remote sensing images were performed. Experiments show that for the remote sensing images used, selecting the appropriate fusion method can improve the spatial detail evaluation metrics of the fused images by up to 2.739 times, with the smallest deviation from true reflectance reaching 35.02%. Incorporating synchronous atmospheric parameter distribution images can enhance the spatial detail evaluation metrics by up to 2.03 times, with the smallest deviation from true reflectance reaching 5.4%. This indicates that choosing an appropriate fusion method and performing imaging-based synchronous atmospheric correction before fusion can maximize the enhancement of spatial details and spectral quantification in fused images.

Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network

The detection of complex formations, initially suspected to be oil spills, is investigated using atmospherically corrected multispectral satellite images and deep learning techniques. Several formations have been detected in an inland lake in Northern Greece. Four atmospheric corrections (ACOLITE, iCOR, Polymer, and C2RCC) that are specifically designed for water applications are examined and implemented on Sentinel-2 multispectral satellite images to eliminate the influence of the atmosphere. Out of the four algorithms, iCOR and ACOLITE are able to depict the formations sufficiently; however, the latter is chosen for further processing due to fewer uncertainties in the depiction of these formations as anomalies across the multispectral range. Furthermore, a number of formations are annotated at the pixel level for the 10 m bands (red, green, blue, and NIR), and a deep neural network (DNN) is trained and validated. Our results show that the four-band configuration provides the best model for the detection of these complex formations. Despite not being necessarily related to oil spills, studying these formations is crucial for environmental monitoring, pollution detection, and the advancement of remote sensing techniques.

Monitoring Salinity in Inner Mongolian Lakes Based on Sentinel-2 Images and Machine Learning

Salinity is an essential parameter for evaluating water quality and plays a crucial role in maintaining the stability of lake ecosystems, particularly in arid and semi-arid climates. Salinity responds to changes in climate and human activity, with significant impacts on water quality and ecosystem services. In this study, Sentinel-2A/B Multi-Spectral Instrument (MSI) images and quasi-synchronous field data were utilized to estimate lake salinity using machine learning approaches (i.e., XGB, CNN, DNN, and RFR). Atmospheric correction for MSI images was tested using six processors (ACOLITE, C2RCC, POLYMER, MUMM, iCOR, and Sen2Cor). The most accurate model and atmospheric correction method were found to be the extreme gradient boosting tree combined with the ACOLITE correction algorithm. These were used to develop a salinity model (N = 70, mean absolute percentage error = 9.95%) and applied to eight lakes in Inner Mongolia from 2016 to 2024. Seasonal and interannual variations were explored, along with an examination of potential drivers of salinity changes over time. Average salinities in the autumn and spring were higher than in the summer. The highest salinities were observed in the lake centers and tended to be consistent and homogeneous. Interannual trends in salinity were evident in several lakes, influenced by evaporation and precipitation. Climate factors were the primary drivers of interannual salinity trends in most lakes.

Monitoring the Amazon River plume from satellite observations

ABSTRACT Most of our knowledge about the Amazon River plume is limited from the field experiments or modeling due to various challenges for satellite remote sensing, i.e., constant cloud coverage, atmospheric correction, and difficulties to differentiate the Amazon River plume from normal Atlantic Ocean water. In this study, observations of ocean properties from the Visible Infrared Imaging Radiometer Suite (VIIRS) and sea surface salinity (SSS) from the Soil Moisture Active Passive (SMAP) missions in 2021 are used as an example to demonstrate the capability to monitor the Amazon River plume on daily basis and characterize the change of the ocean environment driven by the offshoots and migration of the plume in the western tropical Atlantic Ocean. The start, development, evolvement, dispersion, and dissipation of the Amazon River plume in 2021 in the western tropical Atlantic Ocean were effectively monitored. The extensive influence of the Amazon River runoff mostly occurred between June and November. The water diffuse attenuation coefficient at 490 nm (K d (490)) and SSS were significantly impacted by the Amazon River plume in the vast region of the western tropical Atlantic Ocean. The routes of the Amazon River plume that flowed into the Atlantic Ocean were highly variable. High turbidity, low salinity plume water with K d (490) reaching ~1.0 m−1 and SSS ~25 practical salinity unit (psu) was found in a region over 500 km from the Amazon River Estuary. The comparison of the Amazon River plume in 2020 and 2022 further shows the complication of the driving forcing and inter-annual variability of the Amazon River plume. The combination of gap-free satellite ocean color and SSS observations provided a new tool to monitor the short-term variability of the Amazon River plume and assess its seasonal and interannual impact on the ocean environment in the western tropical Atlantic Ocean.

Landsat 8 OLI atmospheric correction neural network for inland waters in tropical regions

Landsat 8 OLI atmospheric correction neural network for inland waters in tropical regions

Enhancing ocean color retrieval in sunglint regions: the efficacy of IPNOA over conventional atmospheric correction methods

Sunglint significantly impacts the extraction of ocean color information, particularly for sensors lacking tilt capabilities. Traditional atmospheric correction algorithms often fail to retrieve effective data in high-sunglint regions. The polynomial-based POLYMER method, applied to MERIS data, effectively addresses sunglint, although its accuracy decreases by about 15% in such conditions. To enhance data reliability in sunglint regions, we propose the Improved polynomial nonlinear optimization approach (IPNOA), a revision of the POLYMER atmospheric correction. IPNOA employs the QAA-RGR (quasi-analytical algorithm-red-green-bands-ratio) to refine the bio-optical ocean reflectance model. Additionally, due to the nonlinear optimization algorithm’s sensitivity to initial values, this study uses global 8-day average oceanic optical properties at 4 km resolution as the initial setting. The performance of IPNOA was initially evaluated using a synthetic dataset, with retrieved remote sensing reflectance (Rrs) closely matching the simulated Rrs across all wavelengths. The mean absolute percentage error (MAPE) remained below 1% for non-sunglint, moderate sunglint, and high sunglint conditions. Further analysis of in situ data revealed that IPNOA performs better, exceptionally at 412 nm, with a MAPE of 5.27% in sunglint regions. When processed by POLYMER, the dataset exhibited a MAPE of 68.47%. Finally, an analysis of global data from MODIS, VIIRS, and HY1C/D on July 15, 2022, showed good agreement among the three on a global scale. Above all, these results indicate that the IPNOA algorithm has strong potential for retrieving valid products in moderate, even high sunglint regions, offering practical benefits for expanding the spatial coverage of ocean color satellite data.

An Editorial for the Special Issue “Aerosol and Atmospheric Correction”

Aerosol is an important atmospheric component that severely influences the global climate and air quality of our planet [...]

Satellite-Based Detection of Algal Blooms in Large Alpine Lake Sevan: Can Satellite Data Overcome the Unavoidable Limitations in Field Observations?

Lake Sevan in Armenia is a unique, large, alpine lake given its surface, volume, and geographic location. The lake suffered from progressing eutrophication and, since 2018, massive cyanobacterial blooms repeatedly occurred. Although the lake is comparatively intensely monitored, the feasibility to reliably detect the algal bloom events appeared to be limited by the established in situ monitoring, mostly because algal bloom dynamics are far more dynamic than the realized monitoring frequency of monthly samplings. This mismatch of monitoring frequency and ecosystem dynamics is a notorious problem in lakes, where plankton dynamics often work at relatively short time scales. Satellite-based monitoring with higher overpass frequency, e.g., by Sentinel-3 OLCI with its daily overcasts, are expected to fill this gap. The goal of our study was therefore the establishment of a fast detection of algal blooms in Lake Sevan that operates at the time scale of days instead of months. We found that algal bloom detection in Lake Sevan failed, however, when it was only based on chlorophyll due to complications with optical water properties and atmospheric corrections. Instead, we obtained good results when true-color RGB images were analyzed or a specifically designed satellite-based HAB indicator was applied. These methods provide reliable and very fast bloom detection at a scale of days. At the same time, our results indicated that there are still considerable limitations for the use of remote sensing when it comes to a fully quantitative assessment of algal dynamics in Lake Sevan. The observations made so far indicate that algal blooms are a regular feature in Lake Sevan and occur almost always when water temperatures surpass approximately 20 °C. Our satellite-based method effectively allowed for bloom detection at short time scales and identified blooms over several years where classical sampling failed to do so, simply because of the unfortunate timing of sampling dates and blooming phases. The extension of classical in situ sampling by satellite-based methods is therefore a step towards a more reliable, faster, and more cost-effective detection of algal blooms in this valuable lake.

Reflectance spectrometry and multispectral data for mapping fractures and hydrothermal alterations in the northern edge of the eastern High Atlas, Morocco

The Marginal Folds, in the north-western axis of Moroccan eastern High Atlas, are mainly composed of the Infra-Cenomanian terrains. These latter contain continental detrital deposits (red beds) and are characterized by a network of fractures. These marginal folds host significant concentrations of metallic minerals, such as Cu, Pb, Ag, and Zn, particularly at the Merija (Cu) and Bou Sellam (Pb-Zn) sites.Several techniques were applied to the Landsat-8 Oli images to improve the clarity and visibility of the linear features. Radiometric and atmospheric corrections, color compositions, directional filters, and principal component analysis were adopted. The lineaments resulting from automatic extraction, as well as their statistical analysis were validated by visual interpretation and field investigations. Statistical analysis revealed three dominants fracture systems: N-S, NE-SW, and E-W; the first two systems show a high density in the study area.The mineral alteration of the outcropping rocks is well described by ASTER multispectral imaging techniques. In this work, the effectiveness of this techniques for mineral alteration mapping was demonstrated on the Merija area. Several occurrences of mineral alteration were highlighted, using a combination of false color and band ratios, the principal component analysis (PCA), and the band ratios. This research was completed by reflectance spectrometry, covering the visible, near-infrared (VNIR), and short-wave infrared (SWIR) domains, to identify mineral alteration.Hydrothermal alteration in the Merija region includes clay, phyllite, carbonate, and phyllosilicate alteration, such as kaolinite, illite, montmorillonite, palygorskite, and vermiculite. Regarding to oxidation, as evidenced by the presence of goethite, jarosite, and hematite, it is widespread in most of the analyzed samples, resulting from the alteration of pyrite, followed by jarosite and goethite.

Forecasting Lakes' Chlorophyll Concentrations Using Satellite Images and Generative Adversarial Networks

AbstractSatellite data are extensively used for water quality monitoring purposes, offering a significantly reduced cost compared to in situ data sampling. Using past measurements to predict future conditions remains a challenging task, because of the complexity of the natural phenomena that are involved, with great potential in terms of water resources management. This paper proposes a model that can be used to forecast Chlorophyll‐ (Chl‐) values in water bodies, which are a common water quality indicator. The operation of the model lays on the fact that typically Chl‐ increases and decreases periodically. First, we apply C2RCC, which is a common atmospheric correction algorithm, to Sentinel‐2 images to get Chl‐ maps for 15 lakes for 12 consecutive months around Europe. Then, we use this data set (1,000 Sentinel‐2 images) to train a Generative Adversarial Network (GAN) to recognize spatiotemporal patterns. To accomplish this task, pix2pix algorithm is employed, matching consecutive past and current Chl‐ maps to future Chl‐ maps. This model has been applied to 3 water bodies around Europe that are not included in the 15‐lakes training data set and has been found to perform accurately, achieving high Pearson and Spearman correlations and low RMSE values. Overall, the model can be used to make Chl‐ maps' predictions with low computational cost and without using any in situ data and without the requirement of training for every water body.

Optical Characterization of Coastal Waters with Atmospheric Correction Errors: Insights from SGLI and AERONET-OC

This study identifies the characteristics of water regions with negative normalized water-leaving radiance (nLw(λ)) values in the satellite observations of the Second-generation Global Imager (SGLI) sensor aboard the Global Change Observation Mission–Climate (GCOM-C) satellite. SGLI Level-2 data, along with atmospheric and in-water optical properties measured by the sun photometers in the AErosol RObotic NETwork-Ocean Color (AERONET-OC) from 26 sites globally, are utilized in this study. The focus is particularly on Tokyo Bay and the Ariake Sea, semi-enclosed water regions in Japan where previous research has pointed out the occurrence of negative nLw(λ) values due to atmospheric correction with SGLI. The study examines the temporal changes in atmospheric and in-water optical properties in these two regions, and identifies the characteristics of regions prone to negative nLw(λ) values due to atmospheric correction by comparing the optical properties of these regions with those of 24 other AERONET-OC sites. The time series results of nLw(λ) and the single-scattering albedo (ω(λ)) obtained by the sun photometers at the two sites in Tokyo Bay and Ariake Sea, along with SGLI nLw(λ), indicate the occurrence of negative values in SGLI nLw(λ) in blue band regions, which are mainly attributed to the inflow of absorptive aerosols. However, these negative values are not entirely explained by ω(λ) at 443 nm alone. Additionally, a comparison of in situ nLw(λ) measurements in Tokyo Bay and the Ariake Sea with nLw(λ) values obtained from 24 other AERONET-OC sites, as well as the inherent optical properties (IOPs) estimated through the Quasi-Analytical Algorithm version 5 (QAA_v5), identified five sites—Gulf of Riga, Long Island Sound, Lake Vanern, the Tokyo Bay, and Ariake Sea—as regions where negative nLw(λ) values are more likely to occur. These regions also tend to have lower nLw(λ) values at shorter wavelengths. Furthermore, relatively high light absorption by phytoplankton and colored dissolved organic matter, plus non-algal particles, was confirmed in these regions. This occurs because atmospheric correction processing excessively subtracts aerosol light scattering due to the influence of aerosol absorption, increasing the probability of the occurrence of negative nLw(λ) values. Based on the analysis of atmospheric and in-water optical measurements derived from AERONET-OC in this study, it was found that negative nLw(λ) values due to atmospheric correction are more likely to occur in water regions characterized by both the presence of absorptive aerosols in the atmosphere and high light absorption by in-water substances.

Light-absorbing black carbon and brown carbon components of smoke aerosol from DSCOVR EPIC measurements over North America and central Africa

Abstract. Wildfires and agricultural burning generate seemingly increasing smoke aerosol emissions, impacting societal and natural ecosystems. To understand smoke's effects on climate and public health, we analyzed the spatiotemporal distribution of smoke aerosols, focusing on two major light-absorbing components, namely black carbon (BC) and brown carbon (BrC) aerosols. Using NASA's Earth Polychromatic Imaging Camera (EPIC) instrument aboard NOAA's Deep Space Climate Observatory (DSCOVR) spacecraft, we inferred BC and BrC volume fractions and particle mass concentrations based on spectral absorption provided by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm with 1–2 h temporal resolution and ∼ 10 km spatial resolution over North America and central Africa. Our analyses of regional smoke properties reveal distinct characteristics for aerosol optical depth (AOD) at 443 nm, spectral single-scattering albedo (SSA), aerosol layer height (ALH), and BC and BrC amounts. Smoke aerosols in North America showed extremely high AOD up to 6, with elevated ALH (6–7 km) and significant BrC components up to 250 mg m−2 along the transport paths, whereas the smoke aerosols in central Africa exhibited stronger light absorption (i.e., lower SSA) and lower AOD, resulting in higher-BC mass concentrations and similar BrC mass concentrations than the cases in North America. Seasonal burning source locations in central Africa, following the seasonal shift in the Intertropical Convergence Zone and diurnal variations in smoke amounts, were also captured. A comparison of retrieved AOD443, SSA443, SSA680, and ALH with collocated AERONET and CALIOP measurements shows agreement with RMSE values of 0.2, 0.03–0.04, 0.02–0.04, and 0.8–1.3 km, respectively. An analysis of the spatiotemporal average reveals distinct geographical characteristics in smoke properties closely linked to burning types and meteorological conditions. Forest wildfires over western North America generated smoke with a small-BC volume fraction of 0.011 and a high ALH with large variability (2.2 ± 1.2 km), whereas smoke from wildfires and agricultural burning over Mexico region shows more absorption and low ALH. Smoke from savanna fires over central Africa had the most absorption, with a high-BC volume fraction (0.015) and low ALH with a small variation (1.8 ± 0.6 km) among the analyzed regions. Tropical forest smoke was less absorbing and had a high variance in ALH. We also quantify the estimation uncertainties related to the assumptions of BC and BrC refractive indices. The MAIAC EPIC smoke properties with BC and BrC volume and mass fractions and assessment of the layer height provide observational constraints for radiative forcing modeling and air quality and health studies.

Characterization of the aerosol contribution to the top-of-atmosphere radiance for satellite ocean color retrievals

A method is proposed for characterization of the aerosol contribution to the top-of-atmosphere (TOA) radiance. The method is based on solving the problem of radiative transfer in the atmosphere-ocean system and expanding the solution in powers of the aerosol optical thickness τ a . We show that the linear term of the expansion is analytically expressed in terms of the bidirectional transmittance/reflectance of the aerosol-free Rayleigh atmosphere. A procedure is also proposed for successively extracting the terms of higher order in τ a from the data of the TOA radiance computation with the DISORT code. As analysis shows, the radiance expansion in τ a is not purely polynomial. Beginning from the quadratic term, the coefficients of the series expansion in powers of τ a become dependent logarithmically on τ a . The approach proposed enables us to reproduce analytically the τ a -dependence of the TOA radiance with controlled accuracy. We determine the expansion coefficients up to the cubic term inclusive and validate our results on the aerosol model embedded in NASA’s SeaDAS algorithm for aerosol loadings, representative for the Barents and Kara seas. In the visible and near-infrared spectral ranges, accounting for the terms up to a quadratic one is found to be sufficient for the atmospheric correction of satellite ocean color data typical for the Arctic region.