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        <title>Frontiers in Remote Sensing | Atmospheric Remote Sensing section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/remote-sensing/sections/atmospheric-remote-sensing</link>
        <description>RSS Feed for Atmospheric Remote Sensing section in the Frontiers in Remote Sensing journal | New and Recent Articles</description>
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        <pubDate>2026-05-14T01:11:59.708+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1794848</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1794848</link>
        <title><![CDATA[Assessing climate anomalies in the strait of Hormuz using gradient boosting and remote sensing–based environmental parameters]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Priya Vijayan</author><author>Suraj Kumar Singh</author><author>Gowhar Meraj</author><author>Shruti Kanga</author>
        <description><![CDATA[Strait of Hormuz is a climatically sensitive marine transition zone in which interplay of complex air sea interactions, monsoonal forcing, and land ocean thermal contrasts produces a strong impact on the variability of environment in the region. The long term climate behavior of such confined coastal systems is a difficult issue to assess because of the nonlinear connections between the atmosphere and ocean, as well as vertical thermodynamic interactions and the sparsity of in situ measurements. To overcome these limitations this paper proposes Strait-ClimateAssessNet, a data-oriented assessment model of near-surface temperature anomalies on the Strait of Hormuz. The framework combines assimilated environmental variables based on ERA5 reanalysis data between 1971 and 2025 with diagnostics of climatological anomalies, vertical pressure, and lag representations which are maintained in time to maintain physical coherence in climate evolution. Through the combination of remotely sensed atmospheric and oceanic fields, the proposed method is able to capture the long term tendencies of warming as well as the interannual variability superimposed on it through the use of a single learning architecture. The model is executed in a reproducible Python-based setup with the use of the contemporary scientific computing libraries that guarantee transparency and scalability. Despite the improved anomaly reconstruction capability demonstrated by quantitative evaluation, root mean square error of 0.4327 °C and a mean absolute error of 0.3304 °C are more indicative of regional thermal dynamics when non-stationary, as opposed to stationary conditions. The findings emphasize the topography of coupled surface atmosphere processes in defining the variability in temperature in the Strait. In general, Strait-ClimateAssessNet represents a computationally-efficient and physically-consistent model of long-term climate assessment in dynamically constrained coastal zones which is useful in environmental monitoring, climate risk analysis, and regional climate-sensitive planning.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1710909</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1710909</link>
        <title><![CDATA[A new way to see the clouds: the hyper-angular rainbow polarimeter (HARP2) on the NASA PACE satellite mission]]></title>
        <pubdate>2026-01-08T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Rachel E. Smith</author><author>Brent A. McBride</author><author>Xiaoguang Xu</author><author>Anin Puthukkudy</author><author>Noah Sienkiewicz</author><author>J. Dominik Cieslak</author><author>Lorraine A. Remer</author><author>Roberto Fernandez-Borda</author><author>J. Vanderlei Martins</author>
        <description><![CDATA[The Hyper-Angular Rainbow Polarimeter 2 (HARP2) on the NASA Plankton Aerosol Cloud ocean Ecosystem (PACE) mission is a wide field of view imaging polarimeter instrument designed for highly accurate and resolved cloud observations. HARP2 is uniquely sensitive to the polarized cloudbow, a ring-like structure in polarized light that appears above liquid water clouds. The structure of the cloudbow encodes information about the droplet size distribution, which is a critical link between cloud microphysical and radiative properties. Matching a multi-angle measurement of the cloudbow to Mie scattering predictions allows for a retrieval of important cloud properties: droplet effective radius and variance. HARP2 is the first instrument of its kind suitable for this retrieval at 5 km spatial resolution. Its wide swath facilitates global coverage of polarimetric measurements in 2 days, making it a uniquely powerful tool for studying cloud microphysics. This paper briefly presents the HARP2 instrument, demonstrates its retrieval capabilities, and discusses future science that it makes possible.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1696519</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1696519</link>
        <title><![CDATA[Statistics of glinting clouds observed by DSCOVR and geostationary satellites]]></title>
        <pubdate>2026-01-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tamás Várnai</author><author>Alexander Marshak</author><author>Alexander Kostinski</author>
        <description><![CDATA[This study examines how frequently the specular reflection of sunlight—that is, sun glint—reveals the presence of ice crystals that maintain a steady horizontal orientation. The study analyzes data from the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) spacecraft and from collocated images taken by geostationary satellites. The analysis of spatio-temporal variations in glint frequency over vegetated land surfaces reveals that (a) year-to-year variations are modest with no clear trends; (b) glints typically occur 7%–8% more frequently than previously estimated; (c) glints are most frequently observed during the May-August period, and over Asia. The results also show that glint frequency drops for very high (>12–13 km) clouds but otherwise displays little sensitivity to geostationary satellite-provided cloud parameters, namely altitude, optical thickness, and particle size. This is because glints come from horizontal crystals near cloud tops whereas geostationary satellites characterize the entire cloudy column. This suggests that glint-free passive satellite observations are not well-suited for estimating the likelihood of horizontal ice crystals and underlines the importance of analyzing direct sun glint observations from satellite instruments such as EPIC.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1705235</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1705235</link>
        <title><![CDATA[Constraining orientation statistics of ice crystals in clouds with observations from deep space]]></title>
        <pubdate>2026-01-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alexander Kostinski</author><author>Alexander Marshak</author><author>Tamás Várnai</author>
        <description><![CDATA[Ice crystals in clouds are often modeled as chaotically oriented despite frequent in situ and remote observations of horizontally oriented crystals. Zenith-pointing ground-based and nadir-pointing space-borne lidars often encounter intense specular reflections (glints), attributed to horizontally oriented particles (HOPs). When the size and shape of these ice crystals are just right, they appear to fall in precisely horizontal orientation with remarkable accuracy. Here, we attempt to constrain the relative contributions, wobbling amplitudes, and sizes of HOPs. Although there is an extensive literature on the topic, our discussion renders orientation randomness more precise and includes several additional considerations: (i) deep space optics of the Earth polychromatic imaging camera (EPIC)/Deep Space Climate Observatory (DSCOVR) observations of angular sizes for cloud glints are brought to bear on the problem; (ii) exponential decay of glint reflectance with angles is observed; (iii) a dimensionless moment of inertia constraint is considered to further constrain sizes; (iv) the dependence of air kinematic viscosity ν is introduced into the argument in tandem with the one on the Reynolds number.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1676851</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1676851</link>
        <title><![CDATA[MAIAC-based climatology of atmospheric iron-oxide dust species from DSCOVR EPIC observations]]></title>
        <pubdate>2025-10-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sujung Go</author><author>Alexei Lyapustin</author><author>Myungje Choi</author><author>Sergey Korkin</author><author>Yujie Wang</author><author>Edward J. Hyer</author>
        <description><![CDATA[This study investigates the vertical distribution and seasonal climatology of absorbing iron-oxide minerals, specifically hematite and goethite, in atmospheric dust using the updated MAIAC EPIC version 3 algorithm. Leveraging data from July 2015 to December 2023, the key innovation is the improved Level 2 product which now incorporates Aerosol Layer Height (ALH), enabling the first-ever long-term characterization of their vertical and seasonal distribution globally. Our analysis reveals distinct seasonal and spatial patterns across major dust-emitting regions. Critically, we find that the spectral absorption properties, such as the imaginary refractive index (k) and Single Scattering Albedo (SSA), are strongly proportional to the mass fraction of iron oxides, highlighting the potential of SSA at UV wavelengths as a valuable tool for global monitoring. Furthermore, we confirm that hematite exhibits significant seasonal vertical variability; its concentration is high near the surface in winter but extends up to 2–4 km into the free troposphere in summer, a finding consistent with independent CALIOP observations. The resulting comprehensive climatology provides novel observational constraints for Earth System Models (ESMs), particularly for accurately modeling dust-radiative forcing. The developed methodology is readily applicable to other UV-NIR satellite platforms, such as PACE, GEMS, and TEMPO, demonstrating its broad utility for future atmospheric composition monitoring efforts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1689824</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1689824</link>
        <title><![CDATA[Application of radon transform to multi-angle measurements made by the research scanning polarimeter: a new approach to cloud tomography. Part II: examples of retrievals from CAMP2Ex dataset]]></title>
        <pubdate>2025-10-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mikhail D. Alexandrov</author><author>Bastiaan Van Diedenhoven</author><author>Brian Cairns</author><author>Andrzej P. Wasilewski</author>
        <description><![CDATA[In Part II of the series we present results of application of our recently developed tomographic technique to real measurements made by the Research Scanning Polarimeter (RSP). This instrument served as a prototype for the Aerosol Polarimetery Sensor launched on-board the NASA Glory satellite. The retrieval algorithms developed for the Research Scanning Polarimeter were adopted for analysis of the measurements by the space-borne polarimeters on the recently launched NASA's Plankton, Aerosol, Cloud Ocean Ecosystem (PACE) satellite. The RSP is an airborne along-track scanner with uniquely high angular resolution and high frequency of measurements. Besides characterization of liquid-water cloud droplet sizes the RSP observations also provide for derivation of 2D fields of extinction coefficient inside the cloud using a tomographic technique described in Part I of the series. This technique utilizes the family of cloud shapes derived using “cutout” method, which can be interpreted as level curves of an abstract “reflectance density”. The latter is then used for derivation of the directional cloud optical thickness (dCOT) tomogram, a collection of dCOTs is parameterized by the angles and offsets of the view rays (chords). After this, the inverse Radon Transform (the mathematical basis of the X-ray computed tomography) is applied to the dCOT tomogram yielding 2D spatial distribution of the extinction coefficient. The later can be converted into droplet number concentration using the droplet size profiles derived from the RSP’s polarized reflectance measurements. After successful tests on synthetic data, this technique was applied to real RSP measurements from NASA’s Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) conducted in the vicinity of the Philippines during the Southwest Monsoon (August–September 2019). We have investigated the interiors of a number of clouds observed during CAMP2Ex focusing on Cu and CuCg (Tcu) cases, two of which are presented in this paper. Our retrievals were compared with the correlative measurements by lidar (HSRL-2) and cloud radar (APR-3) that were deployed on the same airborne platform (NASA’s P-3B) during this field experiment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1668676</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1668676</link>
        <title><![CDATA[Exploring the utility of remote sensing technology in vegetation below ground biomass (BGB) estimation: a critical review of methods and challenges]]></title>
        <pubdate>2025-10-14T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Celuxolo Michal Dlamini</author><author>John Odindi</author><author>Trylee Nyasha Matongera</author><author>Onisimo Mutanga</author>
        <description><![CDATA[Understanding vegetation Below Ground Biomass (BGB) dynamics is essential to ensure long-term ecological functions such as carbon sequestration and optimizing critical tuber crops productivity. Whereas the utility of remote sensing in assessing vegetation Above Ground Biomass (AGB) is well documented in literature, studies using this technology to estimate BGB have become elusive due to technical challenges of direct underground sensing. Therefore, this study aims to critically review the methods and challenges in adopting remote sensing technology for estimating vegetation BGB, while proposing a consolidated approach for improving the accuracy of subsurface biomass assessment. The review indicates that although remote sensors do not directly measure underground, variations in BGB can be inferred through deriving canopy vegetation indices, where machine learning algorithms and empirical relationships play a crucial role in extrapolating these indices to predict subsurface biomass. While optical multispectral and hyperspectral sensors provide critical canopy biophysical information, offering invaluable insights about BGB status, these cameras are constrained by atmospheric interference and inability to penetrate dense vegetation. Active remote sensing cameras such as LiDAR do not provide biophysical information, however, they stand out for their ability to penetrate atmospheric conditions, dense vegetation, and provide topographic information, that can improve BGB estimation. Amongst the challenges highlighted, the review raises concerns about the reliability of using the remote sensing of vegetation AGB status and canopy spectral reflectance for estimating BGB, considering the influence of seasonality in crown cover fluctuations. Nevertheless, advances in Unmanned Aerial Vehicle (UAV) platforms coupled with smart optical and active sensors remain promising for accurately assessing vegetation BGB while overcoming various limitations such as low spatial resolution, long revisit cycles, and atmospheric influence. This review has consolidated methods for estimating vegetation and crop BGB, allowing researchers to evaluate their choice of technique based on the tradeoffs between sensors spectral characteristics, spatial coverages, and practicality.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1657038</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1657038</link>
        <title><![CDATA[Hourly, daily, and monthly variabilities of spectral reflectance and shortwave flux from EPIC observations]]></title>
        <pubdate>2025-10-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Guoyong Wen</author><author>Alexander Marshak</author><author>Wenying Su</author><author>Elizabeth Weatherhead</author>
        <description><![CDATA[The Deep Space Climate Observatory (DSCOVR), launched in 2015, is the first Earth-observing mission to a Sun-Earth first Lagrange point (L1) orbit, about 1.5 million km from Earth on the Sun-Earth line. The goal of the mission is to provide continuous solar wind measurements for accurate space weather forecasting and observe the sunlit side of the Earth for enhancing climate science. The Earth Polychromatic Imaging Camera (EPIC) is one of the two Earth-observing instruments on DSCOVR. It takes images of nearly the entire sunlit side of the Earth in 10 spectral channels at a relatively high temporal resolution to monitor the changing planet. EPIC’s view contains polar regions that are barely visible from geostationary satellite (GEOs), providing observations of the global reflected spectral radiation. Among other capabilities of EPIC, such as observing atmospheric and surface properties, the well calibrated reflected global spectral radiation observed by EPIC and EPIC-based broadband shortwave (SW) radiance and flux can be used to monitor the changing planet of the Earth. However, to assess the long-term change of the Earth in terms of its spectral brightness and reflected SW radiation, the natural variability of global spectral reflectance and SW radiation must be quantitatively determined. This work provides quantitative estimates of the variability of global spectral reflectance and SW radiance and flux on different time scales. The main finds of this work are: (1) the hourly variability of global average reflectance in red and NIR bands is much larger than the variation in UV and blue bands, and the 24-h variability in boreal summer is significantly larger than in winter; (2) the presence of Antarctica and the Arctic is primarily responsible for seasonal variation in spectral reflectance and SW radiance and flux; (3) the global average SW radiance is highly anisotropic, particularly over land, and assumption of Lambertian reflection will overestimate the SW flux by 20%–30%. Furthermore, the responsible physical mechanisms are provided.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1646764</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1646764</link>
        <title><![CDATA[EPIC and NISTAR radiometric stability assessment using ERA5 reanalysis data]]></title>
        <pubdate>2025-10-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alexander Cede</author><author>Ragi Rajagopalan</author><author>Yinan Yu</author><author>Jay Herman</author><author>Liang-Kang Huang</author><author>Karin Blank</author><author>Alexander Marshak</author><author>Allan Smith</author><author>Steven Lorentz</author>
        <description><![CDATA[A technique to determine the radiometric stability of the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR), the two Earth-viewing instruments operating aboard the Deep Space Climate Observatory (DSCOVR) satellite, which is orbiting the Sun at the Lagrange-1 point, L1, approximately 1.5 million kilometers away from Earth, has been developed and applied. Apart from the satellite’s own measurements, it only uses output from the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate data center (ERA5). This method can be applied to all channels (and not just a subset) and can be repeated periodically to track the instruments’ stability. The method includes the removal of climatological diurnal and seasonal cycles, a multivariate regression fitting with selected ERA5 model output parameters, and referencing the data to the EPIC 551-nm channel, which has been determined to show no drift over the entire mission lifetime together with the NISTAR photodiode channel (200–1,100 nm). The obtained sensitivity changes were very small, ranging from a maximum total degradation of 3% over 10 years in the short UV (<340 nm) to no detectable changes for some channels. For the EPIC UV channels, the derived results were confirmed through a comparison of the EPIC data with radiances from the Ozone Mapping and Profiler Suite (OMPS). We attribute this excellent instrument performance mostly to the L1 orbit, which is not only an ideal location for Earth observation, but is also extremely beneficial (quiet) with respect to instrument performance. At L1, there are only minor temperature variations and much smaller exposure to charged particles from the Sun compared to satellites orbiting the Earth, which are fully or partly inside the Earth’s radiation belts. In this sense, L1 can be considered “observational and instrumental heaven.” The technique described here could only be applied because DSCOVR has two different instruments (EPIC and NISTAR) observing the same Earth flux input. This suggests that it is extremely useful (maybe even essential) to combine imaging instruments (like EPIC) with integrating instruments (like NISTAR) in remote sensing applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1654779</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1654779</link>
        <title><![CDATA[Climatology and variability of smoke aerosols from MAIAC EPIC observations over North America (2016–2024)]]></title>
        <pubdate>2025-09-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Myungje Choi</author><author>Alexei Lyapustin</author><author>Yujie Wang</author><author>Edward J. Hyer</author><author>Thomas F. Eck</author><author>Sergey Korkin</author>
        <description><![CDATA[This study presents a comprehensive analysis of the monthly, seasonal, and interannual variability of smoke aerosol properties over North America from 2016 to 2024, using data retrieved from the MAIAC algorithm applied to NASA’s EPIC instrument aboard the DSCOVR spacecraft. The MAIAC EPIC data provide high-frequency, multi-year retrievals of key smoke properties, including aerosol optical depth (AOD), spectral absorption, aerosol layer height (ALH), and inferred black carbon (BC) and brown carbon (BrC) concentrations. The analysis reveals strong seasonal and regional variations, with peak smoke activity occurring in spring over Mexico and in summer over Canada and the western United States. Canadian and Alaskan smoke plumes frequently reach higher altitudes and exhibit elevated AOD, while smoke in Mexico tends to remain at lower altitudes with notably higher BC concentrations, likely influenced by smaller and lower-intensity fires and mixed biomass burning sources (agriculture and forest). The eastern United States, as a downwind region, shows increasing smoke influences, characterized by elevated ALH and rising levels of AOD and absorbing aerosols. Most study regions show a significant increase in smoke AOD (up to 5% per year in Canada), absorbing AOD, and BrC concentrations, highlighting the growing impact of wildfires on atmospheric composition and their potential implications for climate, air quality, and solar energy resources. These findings underscore the utility of MAIAC EPIC observations for monitoring multi-year smoke aerosol changes and for assessing their environmental consequences.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1638095</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1638095</link>
        <title><![CDATA[The relationship between directional area scattering factor and foliage clumping based on DSCOVR EPIC data over Australian TERN sites ]]></title>
        <pubdate>2025-09-01T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Jan Pisek</author><author>Catherine Akinyi Odera</author><author>Angela Erb</author><author>Alexander Marshak</author><author>Yuri Knyazikhin</author>
        <description><![CDATA[The Directional Area Scattering Factor (DASF) quantifies the fraction of visible canopy leaf area in a given direction and has demonstrated utility in characterizing vegetation structure. While traditionally applied to dense canopies under the assumption of a non-reflective background, its broader applicability remains under investigation. This brief research report presents the first, direct empirical assessment of the relationship between DASF and the clumping index (CI), which describes the non-random grouping of foliage within canopy structures. Using data from the Earth Polychromatic Imaging Camera (EPIC) onboard the DSCOVR satellite, we evaluate DASF and CI across a variety of Australian Terrestrial Ecosystem Research Network (TERN) sites representing diverse vegetation densities and structures. Complementary in situ digital hemispherical photography (DHP) and CI estimates are used to validate satellite observations. Our findings provide empirical support for previously modeled relationships between DASF and CI. The retrieval accuracy in sparse canopies is challenged by increased background influence, requiring either refined observation conditions or advanced correction techniques. Our results confirm the potential of DASF as a scalable structural vegetation metric, aiding the development and interpretation of remote sensing vegetation indices and supporting improvements in canopy structural parameter retrieval from spaceborne platforms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1623828</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1623828</link>
        <title><![CDATA[Validation of DSCOVR-EPIC total column O3 retrievals using ground-based Pandora as well as OMPS, OMI, and TEMPO satellite data]]></title>
        <pubdate>2025-08-29T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Jay Herman</author><author>Jianping Mao</author><author>Liang Huang</author><author>Alexander Cede</author>
        <description><![CDATA[The Earth polychromatic imaging camera (EPIC) onboard the deep space climate observatory (DSCOVR) began obtaining fully illuminated Earth images across 10 wavelength bands on 6 July 2015. The ultraviolet bands 317, 325, 340, and 388 nm are used to retrieve the total column ozone (TCO) values at different local times during the day. On 28 June 2019, the spacecraft experienced a gyroscope failure; after recovery, the EPIC TCO values retrieved from 2021 to 2024 still agree well with those obtained from the ground-based Pandora spectrometer instruments in terms of both the hourly and weekly average basis. The hourly EPIC TCO values show more variability than the matched Pandora TCO values but generally deviate within 2% while tracking the shape of the Pandora daily variations in most cases. At 13:30 hours, the TCO data from the ozone and mapping profiler suite (OMPS) and ozone monitoring instrument (OMI) are also observed to frequently agree with the time-matched Pandora and EPIC TCO values. In addition, comparisons were made with the version-3 (V03) hourly TCO retrievals from the US tropospheric monitoring of pollution (TEMPO) geostationary satellite over two North American sites, namely, Toronto (Canada) and Dearborn (Michigan, United States). The long-term weekly lowess average EPIC and Pandora TCO values agree with deviations of less than 2%, as does the 3-week lowess average of the OMPS TCO value. An analysis of the TCO values from Pandora and 1 year of TEMPO V03 suggests that the noon TCO values are 2%–5% higher than the morning and afternoon values.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1634922</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1634922</link>
        <title><![CDATA[Ten years of tropospheric ozone from DSCOVR EPIC: science and applications]]></title>
        <pubdate>2025-08-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jerry R. Ziemke</author><author>Natalya A. Kramarova</author><author>Stacey M. Frith</author><author>Kai-Liang Huang</author><author>Kanghyun Baek</author><author>Jay R. Herman</author>
        <description><![CDATA[The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) spacecraft has enabled near-global measurements of total ozone, SO2, aerosols, surface reflectivity, surface UV, and cloud pressure from June 2015 to the present at high spatiotemporal resolution. The EPIC instrument measures these geophysical parameters synoptically over the entire sunlit disk of the Earth every 1–2 h each day at a resolution down to ∼18 km × 18 km at the nadir sub-satellite point. No current satellite instruments other than EPIC make measurements every 1–2 h over the sunlit disk of the Earth while still obtaining near-global coverage each day. We present scientific results from 10 years of tropospheric column ozone (TCO) data derived from combined EPIC and Modern-Era Retrospective analysis for Research and Applications-2 (MERRA-2) ozone data. We use the EPIC TCO to characterize variabilities in tropospheric ozone from daily to decadal timescales. We also use EPIC TCO with hourly sampling to evaluate the geostationary measurements of total and tropospheric ozone from the Geostationary Environmental Monitoring Spectrometer (GEMS) instrument. The EPIC TCO hourly data gridded at 1o × 1o horizontal resolution for June 2015–present are made available to the general public from the NASA Langley Atmospheric Science Data Center (ASDC) data portal.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1632157</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1632157</link>
        <title><![CDATA[Decadal observations of global daytime cloud properties from DSCOVR–EPIC]]></title>
        <pubdate>2025-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yuekui Yang</author><author>Surendra Bhatta</author><author>Alfonso Delgado-Bonal</author>
        <description><![CDATA[This study presents a decadal analysis of global daytime cloud properties using observations from the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) satellite from July 2015 to December 2024. We focus on cloud fraction and cloud effective height (CEH) from the EPIC standard Level-2 (L2) cloud products. Consistent with other satellite observations, the EPIC-derived decadal global cloud fraction shows high cloudiness over tropical convergence zones and midlatitude storm tracks and reduced cloud cover over subtropical regions associated with the descending branches of the Hadley circulation. Seasonal analysis shows greater variability over land, while cloud fraction remains consistently higher over oceans. Trend analysis using the Mann–Kendall and Theil–Sen methods identifies a statistically significant decreasing trend in the cloud fraction over land (−0.0329 per decade, p = 0.014), primarily confined to the Northern Hemisphere. No significant trend is found over ocean or in the CEH. Spatial trend maps highlight that the regional cloud fraction decreases over the western tropical Pacific and central Africa and increases over parts of the midlatitude oceans. These results demonstrate EPIC’s capability in tracking global and regional cloud variability and contribute to our understanding of cloud–climate interactions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1565245</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1565245</link>
        <title><![CDATA[Average optical path length estimation in a slab of arbitrary finite thickness]]></title>
        <pubdate>2025-06-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tim Kindervatter</author><author>Wei Li</author><author>Nan Chen</author><author>Yuping Huang</author><author>Yongxiang Hu</author><author>Snorre Stamnes</author><author>Xiaomei Lu</author><author>Børge Hamre</author><author>Jakob J. Stamnes</author><author>Tomonori Tanikawa</author><author>Jennifer Lee</author><author>Carl Weimer</author><author>Xubin Zeng</author><author>Charles K. Gatebe</author><author>Knut Stamnes</author>
        <description><![CDATA[A method for determining the average photon path length in a slab of multiple scattering material is presented. Radiances can be obtained from the radiative transfer equation and subsequently differentiated to obtain the average photon path length. These radiances can be obtained via multiple methods including Monte Carlo simulations, analytic two-stream approximations, and multi-stream numerical solutions such as the AccuRT computational tool. Average path lengths obtained via numerical differentiation of these radiances are found to agree closely with path length estimates predicted by existing methods found in the literature. The average photon path length is further considered for a slab of finite physical thickness. It was found that for a slab consisting of non-absorbing material there is a linear relationship between the slab thickness and the average photon path length, but that for materials with nonzero absorption, this linear relationship breaks down as the slab thickness increases. Average path lengths may be converted to time spans to determine the amount of time a photon spends in a multiple scattering medium, which may be used to quantify the impact of multiple scattering on pulse stretching in lidar/radar applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1591276</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1591276</link>
        <title><![CDATA[Neural network-based snow depth retrieval from AMSR-2 brightness temperatures using ICESat-2 measurement as ground truth]]></title>
        <pubdate>2025-06-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sunny Sun-Mack</author><author>Yongxiang Hu</author><author>Xiaomei Lu</author><author>Yan Chen</author><author>Ali Omar</author>
        <description><![CDATA[IndroductionEstimating snow depth over Arctic sea ice is essential for understanding climate processes and supporting operational forecasting. Previous work has demonstrated the use of lidar backscattering pathlength moments from Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) for snow depth retrieval. However, passive microwave sensors like the Advanced Microwave Scanning Radiometer 2 (AMSR-2) offer the potential for more frequent and spatially extensive observations.MethodsWe developed a neural network (NN) algorithm to estimate snow depth over Arctic sea ice using multi-channel brightness temperatures from AMSR-2, combined with humidity profiles and surface temperatures from the Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System for Instrument Teams (GEOS-IT) product. The NN was trained with temporally and spatially matched ICESat-2 snow depth data from the 2018–2019 winter season. The trained NN was then applied to AMSR-2 clear-sky wide-swath observations for the 2018–2019 and 2019–2020 Arctic winters, generating daily snow depth estimates across Arctic sea ice.ResultsValidation against independent ICESat-2 data showed strong performance: the NN-based AMSR-2 snow depth retrievals had a near-zero bias and a root mean square error (RMSE) of 10 cm. Further validation using (a) instantaneous matchups, (b) daily geolocation comparisons, and (c) monthly Arctic-wide averages confirmed consistent results. Instantaneous comparisons yielded a 9 cm RMSE with minimal bias, daily comparisons showed a 3 cm underestimation and 9 cm RMSE, and monthly averages exhibited a 1 cm bias and 10 cm RMSE.DiscussionThese results confirm the reliability of the neural network-based method for snow depth retrieval from AMSR-2. The approach enables daily, long-term monitoring of snow depth over Arctic sea ice, offering significant benefits for climate research and operational applications such as snowstorm and blizzard monitoring.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1546565</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1546565</link>
        <title><![CDATA[A comparative study on quantitative precipitation estimation based on GPM satellite and X-band phased-array weather radar]]></title>
        <pubdate>2025-03-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yongyan Su</author><author>Di Wang</author><author>Wenyu Kong</author><author>Bo Zhao</author><author>Yan Liu</author><author>Xuejiao Chen</author><author>Debin Su</author>
        <description><![CDATA[This study investigates the performance of the Global Precipitation Measurement (GPM) satellite in comparison to the X-band Phased Array Weather Radar (XPAR) regarding precipitation measurement accuracy, focusing on radar echoes. A comparative analysis was conducted on two significant precipitation events that occurred in 2023 in Xiong’ an New Area, Hebei Province, China, utilizing data from XPAR, GPM, and ground-based observations. The results reveal that XPAR outperforms the GPM satellite in quantitative precipitation estimation, with a correlation coefficient of approximately 0.88 between XPAR data and ground observations, compared to 0.66 for GPM. Furthermore, the root mean square error (RMSE) and mean absolute error (MAE) for XPAR against ground observations were 1.2g mm and 0.64 mm, respectively, while for GPM, these values were significantly higher at 6.98 mm and 1.91 mm. findings highlight the superior capability of XPAR in accurately estimating precipitation, which is crucial for enhancing the detection and early warning of heavy rainfall events.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1533803</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1533803</link>
        <title><![CDATA[Scaled RTLS BRDF model extended to high zenith angles]]></title>
        <pubdate>2025-01-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alexei Lyapustin</author><author>Yujie Wang</author><author>Sergey Korkin</author><author>Crystal Schaaf</author><author>Weile Wang</author><author>Zhuosen Wang</author>
        <description><![CDATA[The Ross-Thick Li-Sparse (RTLS) model provides a good description of the surface bidirectional reflectance distribution function (BRDF) for zenith angles (ZA) up to ∼60°–70°. At higher zenith angles, the behaviour of the RTLS model is not well constrained. This becomes a limiting factor for the processing of geostationary satellite data covering the full range of solar and view zenith angles. Here, we propose a scaled sRTLS model extending the zenith angle range to ∼80°–84° and demonstrate an improved performance based on examples from the processing of GOES-16 ABI data using MAIAC algorithm.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1553347</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1553347</link>
        <title><![CDATA[Editorial: Towards 2030: a remote sensing perspective on achieving sustainable development goal 13 – climate action]]></title>
        <pubdate>2025-01-09T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Tero Mielonen</author><author>Alexander Marshak</author><author>Yongxiang Hu</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1474560</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1474560</link>
        <title><![CDATA[DISCO-2 – an ambitious earth observing student CubeSat for arctic climate research]]></title>
        <pubdate>2024-10-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Andreas Kjær Dideriksen</author><author>Mads Fredslund Andersen</author><author>Julian Priest</author><author>Nikolaj Forskov Eriksen</author><author>Mads Toudal Frandsen</author><author>Claus Melvad</author><author>Tobias Frejo Rasmussen</author><author>Noah Harry Kjeldgård Nielsen</author><author>Cecillie Thorup Strømsnes</author><author>Mads Juul Ahlebæk</author><author>Sigrid Samsing</author><author>Thomas Buris Larsen</author><author>Jeppe Don</author><author>Lasse Alexander Nissen Pedersen</author><author>Rune Hylsberg Jacobsen</author><author>Søren Rysgaard</author><author>Jung Min Kim</author><author>Robert Bayer</author><author>Caroline Christensen</author><author>Emil Fredsted Christiansen</author><author>Izabella Katharina Gosvig-Leach</author><author>Rasmus Gramstrup</author><author>Bóas Hermansson</author><author>Jesper Hesselvig</author><author>Jonas Mariager Jakobsen</author><author>Daniel Gjesse Kjellberg</author><author>Magnus Sejer Lind</author><author>Jeppe Lindhard</author><author>Mads Mikkelsen</author><author>Oliver Millinge</author><author>Tara Møller Moltesen</author><author>Sebastian Dahl Negendahl</author><author>Alexander Björn Kerff Nielsen</author><author>Gustav Balslev Nielsen</author><author>Maja Chieng Frisenberg Pedersen</author><author>Alexander Stegler Schøler Platz</author><author>Paul Rosero</author><author>Sofia Savic</author><author>Ívar Óli Sigurðsson</author><author>Gustav Skjernov</author><author>Nikolaj Sørensen</author><author>Samuel Grund Sørensen</author><author>Astrid Guldberg Theil</author><author>Jacob Bay Thomsen</author><author>Nicolaj Valsted</author><author>Anna Vestergaard</author><author>Christoffer Karoff</author>
        <description><![CDATA[The severe impact of global warming, especially in the arctic region, have a multitude of consequences spanning from sea-level rises and freshening of the ocean, to significant changes to the animal life, biodiversity and species distribution. As the arctic regions are inherently remote and can be both hazardous and difficult to reach, research to improve our understanding of the climate change impact is often limited to short term field-campaigns. Here we present the Danish DISCO-2 student CubeSat mission, designed to meet the growing need for an Earth-observing platform. This mission leverages the rapid advancements in CubeSat technology over the past decades to overcome the limitations of traditional fieldwork campaigns. DISCO-2 will assist on-going arctic climate research with a payload of optical and thermal cameras in combination with novel in-orbit data analysis capabilities. It will further be capable of performing photogrammetric observations to determine ice volumes from deteriorating glaciers and provide surface temperatures, enabling studies of heat transfer between glaciers and arctic fjords. As a student satellite, the payload capabilities will also be offered to novel student research ideas throughout the mission life time. The modularity and wide range of of-the-shelf-components for CubeSats has facilitated an immense opportunity to tailor this earth observing CubeSat to accommodate specific scientific goals and further provided students at the participating universities with an unparalleled possibility to go from an initial research idea to a running CubeSat mission.]]></description>
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