Zhongting Wang

The DaQi-1 (DQ-1) Wide-Swath Imager (WSI) combines an approximately 2300 km swath with 15 visible-to-shortwave-infrared channels at 75–600 m resolution, providing broad regional coverage and flexible spectral support. We developed an ExtraTrees estimator from WSI signals and ERA5 planetary boundary-layer height, winds, and relative humidity for Beijing–Tianjin–Hebei in 2024. H5 footprints placed verified daylight overpasses between 04:45 and 05:38 UTC, making 05:00 UTC the nearest available ERA5 field for all 96 dates. Multispectral screening retained 12,768 North China records; the strict benchmark used 4210 records from 65 stations. The pre-specified date split selected B3 (0.470 μm), but ten repeated balanced date-block allocations found no stable single-band winner (B3 mean R² = 0.3373 ± 0.0311; highest mean, B14 = 0.3506 ± 0.0274). In a controlled date-grouped ablation, B3 plus meteorology and physics-guided terms achieved R² = 0.1516; adding coordinates increased R² to 0.1640, whereas adding calendar encodings increased it to 0.3919. This full contextual configuration was used only for within-2024 mapping. The primary coordinate- and calendar-free joint date–station benchmark yielded R² = 0.1643 for ExtraTrees with WSI plus meteorology, compared with 0.1085 for meteorology alone and −0.2349 for WSI alone. Ninety-seven scenes produced 11 monthly composites. CHAP comparison yielded pooled pixel–month r = 0.799 and city–month r = 0.830, but May and July agreement was near zero and October differed by −16.01 μg m⁻³. Bias above 200 μg m⁻³ was −158.27 μg m⁻³ under date blocking. The results support a conditional WSI contribution within a meteorology-dominated estimator; they do not establish a stable optimal band, an independent radiometric retrieval, or reliable severe-event exposure estimates.

Accurate and timely monitoring of atmospheric CO₂ concentrations is a crucial prerequisite for achieving carbon peaking and carbon neutrality goals. However, existing satellite observations are often limited by cloud cover, sensor constraints, and orbital characteristics, resulting in data gaps that hinder the direct analysis of CO₂ spatiotemporal dynamics. To address this, we develop an operational processing pipeline that leverages an integrated deep learning framework to reconstruct monthly, full-coverage XCO₂ over China at 0.05° spatial resolution for the period 2019–2024. This pipeline integrates multi-source satellite data (GOSAT, OCO-2, GF-5B) with auxiliary variables including vegetation indices, nighttime light remote sensing, NO₂ column concentrations, and ERA5 meteorological parameters. By eliminating reliance on assimilated model products, the pipeline generates monthly XCO₂ maps with a processing latency of 1–2 months after satellite observation.Validation against TCCON ground-based observations demonstrates strong reconstruction performance (R² = 0.88, RMSE = 1.50 ppm). When compared to CarbonTracker and CAMS datasets, the reconstructed dataset shows mean biases of −0.28 ppm and −0.46 ppm, and standard deviations of 0.53 ppm and 0.72 ppm, respectively, indicating that it accurately captures the spatial distribution and interannual trends of CO₂ concentrations. Comparative experiments further reveal that incorporating GF-5B data improves the overall model performance. Regional analyses show that the model effectively identifies high-emission hotspot areas. Interannual analysis indicates that the average XCO₂ in China increased from 410.30 ppm in 2019 to 422.73 ppm in 2024, corresponding to an annual growth rate between 0.4% and 0.9%.

The Directional Polarimetric Camera (DPC) aboard the Chinese GaoFen-5 02 satellite is designed to monitor aerosols and particulate matter (PM). In this study, we retrieved the aerosol optical depth (AOD) over the Jing–Jin–Ji (JJJ) region using multi-angle data from the DPC, employing a combination of dark dense vegetation (DDV) and multi-angle retrieval methods. The added value of our method included novel hybrid methodology and good practical performance. The retrieval process involves three main steps: (1) deriving AOD from DPC data collected at the nadir angle using linear parameters of land surface reflectance between the blue and red bands from the MOD09 surface product; (2) after performing atmospheric correction with the retrieved AOD, calculating the variance of the normalized reflectance at all observation angles; and (3) leveraging the calculated variance to obtain the final AOD values. AOD images over the JJJ region were successfully retrieved from DPC data collected between January and June 2022. To validate the retrieval method, we compared our results with aerosol products from the AErosol RObotic NETwork (AERONET) Beijing-RADI site, as well as aerosol data from MODerate-resolution Imaging Spectroradiometer (MODIS) and the generalized retrieval of atmosphere and surface properties (GRASP)/models over the same site. In terms of validation metrics, the correlation coefficient (R²) and root mean square error (RMSE) indicated that our method achieved high accuracy, with an R² value greater than 0.9 and an RMSE below 0.1, closely aligning with the performance of GRASP.

Carbon dioxide (CO₂) is a key driver of global climate change. Since the Industrial Revolution, the rapid rise in atmospheric CO₂ levels has significantly intensified global warming and climate-related issues. To accurately and promptly monitor changes in CO₂ concentrations and to support the development of climate policies, this study proposes a method based on random forest models to generate a continuous monthly dataset of CO₂ column concentration (XCO₂) across the entire Chinese region from 2004 to 2023. The study integrates XCO₂ satellite observations from SCIAMACHY, GOSAT, OCO-2, and GF-5B, alongside nighttime light remote sensing data, meteorological parameters, vegetation indices, and CO₂ profile data. Using the random forest algorithm, a complex relationship model was established between XCO₂ concentrations and various environmental variables. The goal of this model is to provide XCO₂ estimates with enhanced spatial coverage and accuracy. The XCO₂ concentrations predicted by the model show a high level of consistency with satellite observations, achieving a correlation coefficient (R-value) of 0.9959 and a root mean square error (RMSE) of 1.1631 ppm. This indicates that the model offers strong predictive accuracy and generalization ability. Additionally, ground-based validation further confirmed the model’s effectiveness, with a correlation coefficient (R-value) of 0.956 when compared with TCCON site observation data.

The Wide-Swath Imaging (WSI) sensor is a Chinese satellite launched in 2022, capable of providing data at resolutions ranging from 75 to 600 m for monitoring aerosols, fire points, and dust, among other uses. In this study, we developed a Dark Dense Vegetation method to retrieve the Aerosol Optical Depth (AOD) quickly from WSI 600 m data. First, after splitting into three types according to the Normalized Difference Vegetation Index (NDVI), we calculated the empirical parameters of land reflectance between the red (0.65 μm) and blue (0.47 μm) channels using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products over the Beijing area. Second, the decrease in the NDVI was simulated and analyzed under different AODs and solar zenith angles, and we introduced an iterative inversion approach to account for it. The simulation retrievals demonstrated that the iterative inversion produced accurate results after less than four iterations. Thirdly, we utilized the atmospherically corrected NDVI for dark target identification and output the AOD result. Finally, retrieval experiments were conducted using WSI 600 m data collected over Beijing in 2023. The retrieved AOD images highlighted two air pollution events occurring during 3–8 March and 27–31 October 2023. The inversion results in 2023 showed a strong correlation with Aerosol Robotic Network station data (the correlation coefficient was greater than 0.9). Our method exhibited greater accuracy than the MODIS aerosol product, though it was less accurate than the Multi-Angle Implementation of Atmospheric Correction product.

The use of satellite data to estimate PM_2.5 is an appropriate approach for long-term, substantial monitoring and assessment. To estimate PM_2.5, the majority of the algorithms now in use utilize the top-of-atmosphere (TOA) reflectance or aerosol optical depth (AOD) derived from scalar satellite data. However, there is relatively little research on the retrieval of PM_2.5 using multi-angle polarized data. With its directional polarimetric camera (DPC), the Chinese new-generation satellite Gaofen 5B (henceforth referred to as GF-5B) offers a unique opportunity to close this gap in multi-angle polarized observation data. In this research, we utilized TOA data from the DPC payload and applied the gradient boosting machine method to simulate the impact of the observation angle, wavelength, and polarization information on the accuracy of PM_2.5 retrieval. We identified the optimal conditions for the effective estimation of PM_2.5. The quantitative results indicated that, under these optimal conditions, the PM_2.5 concentrations retrieved by GF-5B showed a strong correlation with the ground-based data, achieving an R² of 0.9272 and an RMSE of 7.38 µg·m⁻³. By contrast, Himawari-8’s retrieval accuracy under similar data conditions consisted of an R² of 0.9099 and RMSE of 7.42 µg·m⁻³, indicating that GF-5B offers higher accuracy. Furthermore, the retrieval results in this study demonstrated an R² of 0.81 when compared to the CHAP dataset, confirming the feasibility and effectiveness of the use of GF-5B for PM_2.5 retrieval and providing support for PM_2.5 estimation through multi-angle polarized data.

Clouds play an important role in the radiative energy balance of the Earth–atmosphere system. Compared with traditional optical satellite sensors, polarimetric sensors combine multi-angle, multi-polarization, and multispectral information, displaying the advantages of high spatial and temporal resolutions and global coverage. Such remote sensing measurements improve the accuracy of cloud properties retrieval. Due to the observation characteristics of passive satellites, even a tiny variation in position will result in a great change in the observation geometry. A large number of studies have shown that the scattering angle is very crucial for the polarization characteristics retrieval of reflected light. In this study, we analyze the dependence of the remote sensing retrieval implement of different cloud characteristics on the observed scattering angle coverage, considering both ice and water clouds. Three satellite sensors – POLarization and Directionality of the Earth’s Reflectance-3/Polarization and Anisotropy of Reflectance for Atmospheric Sciences coupled with Observations from a Lidar (POLDER-3/PARASOL), Directional Polarimetric Camera/ GaoFen-5 spacecraft (DPC/GF-5), and DPC/GF-5(02) – were selected to compare their scattering angle coverages and the number of angular measurements at equatorial, middle, and high latitudes. The requirements for angular polarized and nonpolarized observations varied depending on the retrieval of cloud properties. The impact of orbital characteristics and viewing settings was investigated for cloud detection, cloud phase classification, and cloud microphysical properties retrieval. Finally, an analytical model to comprehensively evaluate the effective angular measurements according to the orbital characteristics and viewing settings was developed to facilitate the future design of similar sensors for cloud remote sensing.

Large volumes of atmospheric pollutants injected into the troposphere and stratosphere from volcanic eruptions can exert significant influence on global climate. Through utilizing multi-satellite observations, we present a large-scale insight into the long-range transport and transformation of sulfur dioxide (SO₂) emissions from the Hunga Tonga-Hunga Ha’apai eruption on 15 January 2022. We found that the transport of volcanic emissions, along with the transformation from SO2 to sulfate aerosols, lasted for two months after the Tongan eruption. The emitted volume of SO₂ from the volcano eruption was approximately 183 kilotons (kt). Both satellite observation and numerical simulation results show that the SO₂ and volcanic ash plumes moved westward at a rate of one thousand kilometers per day across the Pacific and Atlantic Ocean regions and that SO₂ transformation in the atmosphere lasted for half a month. The transport and enhancement of aerosols is related to the conversion of SO₂ to sulfate. CALIPSO lidar observations show that SO₂ reached an altitude of 25–30 km and transformed into sulfate in the stratosphere after 29 January. Sulfate aerosols in the stratosphere deceased gradually with transport and fell back to the background level after two months. Our study shows that satellite observations give a good characterization of volcanic emissions, transport, and SO₂-sulfate conversion, which can provide an essential constraint for climate modeling.

A directional polarimetric camera (DPC) is a key payload on board China’s Gaofen 5B (hereafter denoted as GF-5B) satellite, a hyperspectral observation instrument for monitoring aerosols. On the basis of the dark dense vegetation (DDV) algorithm, this study applied DDV algorithm to DPC measurements. First, the reflectance of vegetation in three channels (0.443, 0.49, and 0.675 μm) was analyzed, and inversion channels were identified. Subsequently, the decrease in normalized difference vegetation index associated with various view angles was simulated, and the optimal view angle for extracting dark pixels was determined. Finally, the top-of-atmosphere reflectance at different view angles was simulated to determine the optimal view angle for aerosol inversion. The inversion experiments were conducted by using DPC data collected over North China from November 2021 to January 2022. The results revealed that DDV algorithm could monitor pollution from 30 December 2021 to 4 January 2022, and the inversion results were strongly correlated with Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product and AERONET station data (R > 0.85).

Carbon dioxide and methane are the two most important greenhouse gases and are closely related to global warming and extreme weather events. To master their spatial and temporal variations, the CO₂ and CH₄ concentration data monitored by the GOSAT satellite in 2020 and 2021 were used to map and analyse the annual, seasonal and monthly changes in CO₂ and CH₄ concentrations in the world and major countries/regions. The results demonstrate that (1) in 2021, the average annual CO₂ concentration over the global land area was 412.74 ppm, an increase in 0.64% compared with the same period last year, and there were spatial differences in the distribution of CO₂ concentration, with high values mostly concentrated in the middle latitudes of the Northern Hemisphere; (2) compared with 2020, the CO₂ concentration in China, the United States, India, the European Union and other countries/regions increased significantly; (3) in 2020 and 2021, the quarterly CO₂ trend of the global and major countries/regions was the same, which was higher in the first (January, February, March) and second (April, May, June) quarters, significantly lower in the third (July, August, September) quarter, and gradually increased in the fourth (October, November, December) quarter. Further work on long time series and validation needs to be conducted.