Search Results (1,524)

Ultrahigh resolution fine particulate matter (PM_2.5) mass concentration remote sensing products are crucial for atmospheric environmental monitoring, pollution source verification, health exposure risk assessment, and other fine-scale applications in urban environments. This study developed an ultrahigh resolution retrieval algorithm to estimate 30 m resolution PM_2.5 mass concentrations over urban areas from Landsat-8 and Sentinel-2A/B satellite measurements. The algorithm utilized aerosol optical depth (AOD) products retrieved from the Landsat-8 OLI and Sentinel-2 MSI measurements from 2017 to 2020, combined with multi-source auxiliary data to establish a PM_2.5-AOD relationship model across China. The results showed an overall high coefficient of determination (R²) of 0.82 and 0.76 for the model training accuracy based on samples and stations, respectively. The model prediction accuracy in Beijing and Wuhan reached R² values of 0.86 and 0.85. Applications in both cities demonstrated that ultrahigh resolution PM_2.5 has significant advantages in resolving fine-scale spatial patterns of urban air pollution and pinpointing pollution hotspots. Furthermore, an analysis of point source pollution at a typical heavy pollution emission enterprise confirmed that ultrahigh spatial resolution PM_2.5 can accurately identify the diffusion trend of point source pollution, providing fundamental data support for refined monitoring of urban air pollution and air pollution prevention and control. Full article

The high resolution of the Synthetic Aperture Radar (SAR) imagery, in addition to its capability to see through clouds and rain, makes it a crucial remote sensing technique. However, SAR images are very sensitive to radar parameters, the observation geometry and the scene’s characteristics. Moreover, for a complex scene of interest with targets located on a rough soil, a composite scattering between the target and the surface occurs and creates distortions on the SAR image. These characteristics can make the SAR images difficult to analyse and process. To better understand the complex EM phenomena and their signature in the SAR image, we propose a methodology to generate raw SAR signals and SAR images for scenes of interest with a target located on a rough surface. With this prospect, the entire radar acquisition chain is considered: the sensor parameters, the atmospheric attenuation, the interactions between the incident EM field and the scene, and the SAR image formation. Simulation results are presented for a rough dielectric soil and a canonical target considered as a Perfect Electric Conductor (PEC). These results highlight the importance of the composite scattering signature between the target and the soil. Its power is 21 dB higher that that of the target for the target–soil configuration considered. Finally, these simulations allow for the retrieval of characteristics present in actual SAR images and show the potential of the presented model in investigating EM phenomena and their signatures in SAR images. Full article

The WIVERN mission promises to deliver the first global observations of the three-dimensional wind field and the associated cloud and precipitation structure in a wide range of atmospheric phenomena, including isolated thunderstorms, tropical cyclones, mid-latitude frontal systems, and polar lows. A critical element in the development of the mission’s wind products is the differentiation between stratiform and convective regions. Convective regions are defined as those where vertical wind velocities exceed 1 m/s. This work introduces CONSTRAINN, a family of U-Net-based neural network models that utilise all of WIVERN observables—including vertical profiles of reflectivity and Doppler velocity, as well as brightness temperatures—to reconstruct convective wind activity within the Earth’s atmosphere. Results show that the retrieved convective/stratiform masks are well reconstructed, with an equitable threat score exceeding 0.6. Ablation experiments further reveal that Doppler velocity signals are the most informative for the reconstruction task. Full article

Inland waters, serving as crucial carbon sinks and pivotal conduits within the global carbon cycle, are essential targets for carbon assessment under global warming and carbon neutrality initiatives. However, the extensive spatial distribution and inherent sampling challenges pose fundamental difficulties for monitoring dissolved organic carbon (DOC) in these systems. Since 2010, remote sensing has catalyzed a technological revolution in inland water DOC monitoring, leveraging its advantages for rapid, cost-effective long-term observation. In this critical review, we systematically evaluate research progress over the past two decades to assess the performance of remote sensing products and existing methodologies in DOC retrieval. We provide a detailed examination of diverse remote sensing data sources, outlining their application characteristics and limitations. By tracing uncertainties in retrieval outcomes, we identify atmospheric correction, spatial heterogeneity, and model and data deficiencies as primary sources of uncertainty. Current retrieval approaches—direct, indirect, and machine learning (ML) methods—are thoroughly scrutinized for their features, effectiveness, and application contexts. While ML offers novel solutions, its application remains nascent, constrained by limited waterbody-specific samples and model constraints. Furthermore, we discuss current challenges and future directions, focusing on data optimization, feature engineering, and model refinement. We propose that future research should (1) employ integrated satellite–air–ground observations and develop tailored atmospheric correction for inland waters to reduce data noise; (2) develop deep learning architectures with branch networks to extract DOC’s intrinsic shortwave absorption and longwave anti-interference features; and (3) incorporate dynamic biogeochemical processes within study regions to refine retrieval frameworks using biogeochemical indicators. We also advocate for multi-algorithm collaborative prediction to overcome the spectral paradox and unphysical solutions arising from the single data-driven paradigm of traditional ML, thereby enhancing retrieval reliability and interpretability. Full article

Snow water equivalent (SWE) can be estimated using Differential SAR Interferometry (DInSAR), which captures changes in snow depth and density between two SAR acquisitions. However, challenges arise due to SAR signal penetration into the snowpack and the intrinsic limitations of DInSAR measurements. This study addresses these issues and explores the use of multi-band SAR data to derive SWE maps in alpine regions characterized by steep terrain, small spatial extent, and a potentially heterogeneous snowpack. We first conducted a performance analysis to assess SWE estimation precision and the maximum unambiguous SWE variation, considering incidence angle, wavelength, and coherence. Based on these results, we selected C-band Sentinel-1 and L-band SAOCOM data acquired over alpine areas and applied tailored DInSAR processing. Atmospheric artifacts were corrected using zenith total delay maps from the GACOS service. Additionally, sensitivity maps were generated for each interferometric pair to identify pixels suitable for reliable SWE estimation. A comparative analysis of the C- and L-band results revealed several critical issues, including significant atmospheric artifacts, phase decorrelation, and phase unwrapping errors, which impact SWE retrieval accuracy. A comparison between our Sentinel-1-based SWE estimations and independent measurements over an instrumented site shows results fairly in line with previous works exploiting C-band data, with an RSME in the order of a few tens of mm. Full article

Most existing cloud base height (CBH) retrieval algorithms are only applicable for daytime satellite observations due to their dependence on visible observations. This study presents a novel algorithm to retrieve day and night CBH using infrared observations of the geostationary Advanced Himawari Imager (AHI). The algorithm is featured by integrating deep learning techniques with a physical model. The algorithm first utilizes a convolutional neural network-based model to extract cloud top height (CTH) and cloud water path (CWP) from the AHI infrared observations. Then, a physical model is introduced to relate cloud geometric thickness (CGT) to CWP by constructing a look-up table of effective cloud water content (ECWC). Thus, the CBH can be obtained by subtracting CGT from CTH. The results demonstrate good agreement between our AHI CBH retrievals and the spaceborne active remote sensing measurements, with a mean bias of −0.14 ± 1.26 km for CloudSat-CALIPSO observations at daytime and −0.35 ± 1.84 km for EarthCARE measurements at nighttime. Additional validation against ground-based millimeter wave cloud radar (MMCR) measurements further confirms the effectiveness and reliability of the proposed algorithm across varying atmospheric conditions and temporal scales. Full article

The knowledge of the radiative properties of clouds and the atmospheric state is of fundamental importance in modelling phenomena in numerical weather predictions and climate models. In this study, we show the results of the retrieval of cloud properties, along with the atmospheric state and the surface temperature, from far-infrared spectral radiances, in the 100–1000 cm⁻¹ range, measured by the Far-Infrared Radiation Mobile Observation System-Balloon version (FIRMOS-B) spectroradiometer from a stratospheric balloon launched from Timmins (Canada) in August 2022 within the HEMERA 3 programme. The retrieval study is performed with the Optimal Estimation inversion approach, using three different forward models and retrieval codes to compare the results. Cloud optical depth, particle effective size, and cloud top height are retrieved with good accuracy, despite the relatively high measurement noise of the FIRMOS-B observations used for this study. The retrieved atmospheric profiles, computed simultaneously with cloud parameters, are in good agreement with both co-located radiosonde measurements and ERA-5 profiles, under all-sky conditions. The findings are very promising for the development of an optimised retrieval procedure to analyse the high-precision FIR spectral measurements, which will be delivered by the ESA FORUM mission. Full article

Cities house more than half of the world’s population and are responsible for more than 70% of the world anthropogenic CO₂ emissions. Therefore, quantifications of emissions from major cities, which are only less than a hundred intense emitting spots across the globe, should allow us to monitor changes in global fossil fuel CO₂ emissions in an independent, objective way. The study adopted a high-spatiotemporal-resolution regional assimilation method using satellite observation data and atmospheric transport model WRF-Chem/DART to assimilate CO₂ concentration and fluxes in Lisbon, a major city in Portugal. It is based on Zhang’s assimilation method, combined OCO-2 XCO₂ retrieval data, ODIAC 1 km anthropogenic CO₂ emissions and Ensemble Adjustment Kalman Filter Assimilation. By employing three two-way nested domains in WRF-Chem, we refined the spatial resolution of the CO₂ concentrations and fluxes over Lisbon to 3 km. The spatiotemporal distribution characteristics and main driving factors of CO₂ concentrations and fluxes in Lisbon and its surrounding cities and countries were analyzed in March 2020, during the period affected by COVID-19 pandemic. The results showed that the monthly average CO₂ and XCO₂ concentrations in Lisbon were 420.66 ppm and 413.88 ppm, respectively, and the total flux was 0.50 Tg CO₂. From a wider perspective, the findings provide a scientific foundation for urban carbon emission management and policy-making. Full article

The downwelling light sensor (DLS) is the industry-standard solution for generating UAV-based digital orthophoto maps (DOMs). Current mainstream DLS correction methods primarily rely on angle compensation. However, due to the temporal mismatch between the DLS sampling intervals and the exposure times of multispectral cameras, as well as external disturbances such as strong wind gusts and abrupt changes in flight attitude, DLS data often become unreliable, particularly at UAV turning points. Building upon traditional angle compensation methods, this study proposes an improved correction approach—FIM-DC (Fitting and Interpolation Model-based Data Correction)—specifically designed for data collection under clear-sky conditions and stable atmospheric illumination, with the goal of significantly enhancing the accuracy of reflectance retrieval. The method addresses three key issues: (1) field tests conducted in the Qingpu region show that FIM-DC markedly reduces the standard deviation of reflectance at tie points across multiple spectral bands and flight sessions, with the most substantial reduction from 15.07% to 0.58%; (2) it effectively mitigates inconsistencies in reflectance within image mosaics caused by anomalous DLS readings, thereby improving the uniformity of DOMs; and (3) FIM-DC accurately corrects the spectral curves of six land cover types in anomalous images, making them consistent with those from non-anomalous images. In summary, this study demonstrates that integrating FIM-DC into DLS data correction workflows for UAV-based multispectral imagery significantly enhances reflectance calculation accuracy and provides a robust solution for improving image quality under stable illumination conditions. Full article

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH₄) is essential for quantifying methane (CH₄) emissions, yet uncharacterized spatially varying biases in XCH₄ observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH₄ emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH₄ observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results. Full article

Water vapor plays a crucial role in maintaining global energy balance and water cycle, and it is closely linked to various meteorological disasters. Precipitable water vapor (PWV), as an indicator of variations in atmospheric water vapor content, has become a key parameter for meteorological and climate monitoring. However, due to limitations in observation costs and technology, traditional atmospheric monitoring techniques often struggle to accurately capture the distribution and variations in space–time water vapor. With the continuous advancement of Global Navigation Satellite System (GNSS) technology, ground-based GNSS monitoring technology has shown rapid development momentum in the field of meteorology and is considered an emerging monitoring tool with great potential. Hence, based on the GNSS observation data from July 2023, this study retrieves PWV using the Global Pressure and Temperature 3 (GPT3) model and evaluates its application performance in the “7·31” extremely torrential rain event in Beijing in 2023. Research has found the following: (1) Tropospheric parameters, including the PWV, zenith tropospheric delay (ZTD), and zenith wet delay (ZWD), exhibit high consistency and are significantly affected by weather conditions, particularly exhibiting an increasing-then-decreasing trend during rainfall events. (2) Through comparisons with the PWV values through the integration based on fifth-generation European Centre for Medium-Range Weather Forecasts (ERA-5) reanalysis data, it was found that results obtained using the GPT3 model exhibit high accuracy, with GNSS PWV achieving a standard deviation (STD) of 0.795 mm and a root mean square error (RMSE) of 3.886 mm. (3) During the rainfall period, GNSS PWV remains at a high level (>50 mm), and a strong correlation exists between GNSS PWV and peak hourly precipitation. Furthermore, PWV demonstrates the highest relative contribution in predicting extreme precipitation, highlighting its potential value for monitoring and predicting rainfall events. Full article

Dust aerosols significantly affect climate and air quality in Northwest China (30–50° N, 70–110° E), where frequent dust storms complicate accurate aerosol classification when using CALIPSO satellite data. This study introduces an Enhanced 1D U-Net model to enhance dust aerosol retrieval, incorporating Inception modules for multi-scale feature extraction, Transformer blocks for global contextual modeling, CBAM attention mechanisms for improved feature selection, and residual connections for training stability. Using CALIPSO Level 1B and Level 2 Vertical Feature Mask (VFM) data from 2015 to 2020, the model processed backscatter coefficients, polarization characteristics, and color ratios at 532 nm and 1064 nm to classify aerosol types. The model achieved a precision of 94.11%, recall of 99.88%, and F1 score of 96.91% for dust aerosols, outperforming baseline models. Dust aerosols were predominantly detected between 0.44 and 4 km, consistent with observations from CALIPSO. These results highlight the model’s potential to improve climate modeling and air quality monitoring, providing a scalable framework for future atmospheric research. Full article

Air-quality standards (AQS) are key regulatory tools to protect public health by setting pollutant thresholds. However, most are based on sea-level data. High-altitude (HA) environments differ in atmospheric conditions, influencing pollutant behavior and human vulnerability. These differences have prompted proposals for altitude-specific AQS adjustments. This systematic review identifies models and criteria supporting such adaptations and examines regulatory air-quality frameworks in countries with substantial populations living at very high altitudes (VHA). This review follows PRISMA-P guidelines, focusing on studies examining AQS adjustment approaches based on altitude. The Population/Concept/Context (PCC) framework was used to define search terms: population (AQS), concept (air pollutants), and context (altitude), with equivalents. The literature was retrieved from PubMed, Scopus, Web of Science, and Gale OneFile: Environmental Studies and Policy. A total of 2974 articles were identified, with 2093 remaining after duplicate removal. Following title and abstract screening, 2081 papers were excluded, leaving 12 for full-text evaluation. Ultimately, six studies met the eligibility criteria. Three studies focused on adjustment models based on atmospheric conditions, such as temperature and pressure changes, while the other three examined human physiological responses, particularly the increased inhaled air volume. China, Peru, and Bolivia have the largest populations living above 3500 m a.s.l., yet none of these countries have specific air-quality regulations tailored to HA conditions. The review underscores the necessity for tailored AQS in HA environments, highlighting specific criteria related to both atmospheric conditions and human physiological responses. Full article

Background and Objectives: Air pollutants have been shown to affect hypertensive disorders and placental hypoxia due to vasoconstriction, inflammation, and oxidative stress. The objective of this study was to evaluate whether high levels of maternal exposure to heavy metals during the second trimester of pregnancy are associated with an increased risk of preeclampsia and eclampsia, using national health insurance claim data from South Korea. Methods: Data on mothers and their newborns from 2016 to 2020, provided by the National Health Insurance Service, were used (n = 1,274,671). Exposure data for ambient air pollutants (PM2.5, CO, SO₂, NO₂, and O₃) and heavy metals (Pb, Cd, Cr, Cu, Mn, Fe, Ni, and As) during the second trimester of pregnancy were retrieved from the Korea Environment Corporation. Atmospheric condition data based on the mother’s registration area were matched. A logistic regression model was adjusted for maternal age, infant sex, season of conception, and household income. Results: In total, 16,920 cases of preeclampsia and 592 cases of eclampsia were identified. In the multivariate model, copper exposure remained significantly associated with an increased risk of preeclampsia (odds ratio: 1.011; 95% confidence interval: 1.001–1.023), and higher ozone exposure during pregnancy was associated with an elevated risk of eclampsia. Conclusions: Increased copper exposure during the second trimester of pregnancy was associated with a high incidence of preeclampsia. Full article

Stratospheric ozone shields life on Earth from harmful ultraviolet radiation and plays a crucial role in climate formation, while tropospheric ozone is a pollutant and greenhouse gas. Satellite methods based on measurements of outgoing thermal radiation are the only methods that provide information on global ozone distribution, independent of solar illumination. Since about 90% of atmospheric ozone is concentrated in the stratosphere, ozone total column measurements can be used as stratospheric ozone measurements. We present techniques for deriving information on total ozone columns (TOCs) and tropospheric ozone columns (TrOCs) from spectra of outgoing thermal radiation measured by the IKFS-2 instrument aboard the Meteor-M No. 2 satellite. The techniques are based on principal component analysis and the artificial neural network approach, providing high accuracy in TOC (less than 3%) and TrOC (within 2–4 DU) retrieval in accordance with the WMO requirements for the quality of satellite measurements. Full article