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As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview of the various types of satellites, including the various instrument parameters, and describes the different types of satellite retrieval algorithms. In addition, the currently popular methane point source quantification method is presented. Based on existing research, we delineate the classification of methane remote sensing satellites into two overarching categories: area flux mappers and point source imagers. Area flux mappers primarily concentrate on the assessment of global or large-scale methane concentrations, with a further subclassification into active remote sensing satellites (e.g., MERLIN) and passive remote sensing satellites (e.g., TROPOMI, GOSAT), contingent upon the remote sensing methodology employed. Such satellites are mainly based on physical models and the carbon dioxide proxy method for the retrieval of methane. Point source imagers, in contrast, can detect methane point source plumes using their ultra-high spatial resolution. Subcategories within this classification include multispectral imagers (e.g., Sentinel-2, Landsat-8) and hyperspectral imagers (e.g., PRISMA, GF-5), contingent upon their spectral resolution disparities. Area flux mappers are mostly distinguished by their use of physical algorithms, while point source imagers are dominated by data-driven methods. Furthermore, methane plume emissions can be accurately quantified through the utilization of an integrated mass enhancement model. Finally, a prediction of the future trajectory of methane remote sensing satellites is presented, in consideration of the current landscape. This paper aims to provide basic theoretical support for subsequent scientific research. [ABSTRACT FROM AUTHOR]

The effects of periodic hot-press drying on drying behavior and mechanical damage to Chinese fir lumber were investigated by taking the heating platens' temperature (TP) and compression ratio (Rc) as experimental factors. The temperature and pressure inside lumber were analyzed during drying process. The results were as follows. The drying rate of lumber was significantly increased with increasing TP and Rc. Scanning electron microscope (SEM) micrographs showed that bordered pit membranes, cross-field pits, middle lamella between adjacent cells, and tracheid walls were damaged after drying, and the damage became more severe with higher TP and Rc. Detachments between ray parenchyma cells and tracheids were observed at 170 °C. Nitrogen-adsorption measurement results demonstrated that more cell wall pores in the 2.5~6.2 nm pore diameter range were generated at higher TP, resulting in an enlarged specific surface area and pore volume of cell walls. These structural changes contributed to accelerating moisture migration and decreasing the drying time. Furthermore, fluctuating pressure inside lumber was the main driving force leading to moisture migration and cell tissue damage in lumber during drying. The influence of TP on internal temperature (TM) and pressure (PM) was greater than Rc. With the increase in TP from 130 to 170 °C at the Rc of 10%, the maximum TM and PM were increased by 30.90% and 39.84%, respectively. However, TP should not be too high to prevent the formation of macro-cracks caused by high pressure, which may significantly affect wood's mechanical properties. These results provide theoretical support for periodic hot-press drying processes' improvement and high-value utilization of Chinese fir. [ABSTRACT FROM AUTHOR]

Accurate global monitoring of carbon dioxide (CO2) is essential for understanding climate change and informing policy decisions. This study compares column-averaged dry-air mole fractions of CO2 (XCO2) between ACOS_L2_Lite_FP V9r for Japan's Greenhouse Gases Observing Satellite (GOSAT), OCO-2_L2_Lite_FP V10r for the USA's Orbiting Carbon Observatory-2 (OCO-2), and IAPCAS V2.0 for China's Carbon Dioxide Observation Satellite (TANSAT) collectively referred to as GOT, with data from the Total Carbon Column Observing Network (TCCON). Our findings are as follows: (1) Significant data quantity differences exist between OCO-2 and the other satellites, with OCO-2 boasting a data volume 100 times greater. GOT shows the highest data volume between 30–45°N and 20–30°S, but data availability is notably lower near the equator. (2) XCO2 from GOT exhibits similar seasonal variations, with lower concentrations during June, July, and August (JJA) (402.72–403.74 ppm) and higher concentrations during December, January, and February (DJF) (405.74–407.14 ppm). XCO2 levels are higher in the Northern Hemisphere during March, April, and May (MAM) and DJF, while slightly lower during JJA and September, October, and November (SON). (3) The differences in XCO2 (ΔXCO2) reveal that ΔXCO2 between OCO-2 and TANSAT are minor (−0.47 ± 0.28 ppm), whereas the most significant difference is observed between GOSAT and TANSAT (−1.13 ± 0.15 ppm). Minimal differences are seen in SON (with the biggest difference between GOSAT and TANSAT: −0.84 ± 0.12 ppm), while notable differences occur in DJF (with the biggest difference between GOSAT and TANSAT: −1.43 ± 0.17 ppm). Regarding latitudinal variations, distinctions between OCO-2 and TANSAT are most pronounced in JJA and SON. (4) Compared to TCCON, XCO2 from GOT exhibits relatively high determination coefficients (R2 > 0.8), with GOSAT having the highest root mean square error (RMSE = 1.226 ppm, <1.5 ppm), indicating a strong relationship between ground-based observed and retrieved values. This research contributes significantly to our understanding of the spatial characteristics of global XCO2. Furthermore, it offers insights that can inform the analysis of differences in the inversion of carbon sources and sinks within assimilation systems when incorporating XCO2 data from satellite observations. [ABSTRACT FROM AUTHOR]

The East Asian summer monsoon (EASM) was extremely strong in 2018, which substantially affected surface ozone (O3) in China. Taking 2018 and the average synthesis of 2003 and 2010 to represent the strong and weak EASM cases, respectively, GEOS-Chem with constant anthropogenic emission was employed to investigate the impact of the EASM on surface O3 in the east of China. Simulations show that surface O3 decreased in the northeast and the eastern coast of China and increased in most of the remaining regions during strong EASM. The difference in surface O3 between strong and weak EASM was around −15~7 ppbv. After analyzing relevant meteorological fields, it is found that the decrease in northeast China was mainly attributed to the large increase in vertical upward transport. The considerable decrease in the Huang-Huai-Hai region depended on the dilution and diffusion of eastward anomalous horizontal circulation. The increase in Hunan-Hubei-Guangdong Province was largely due to input from the north. In addition, the vast areas between the Yangtze River and the Yellow River were supported by higher temperatures and stronger shortwave solar radiation that promoted photochemical reactions. The reasons for changes in Shanxi-Sichuan-Yunnan Province were relatively more complex and thus require more in-depth exploration. [ABSTRACT FROM AUTHOR]

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region's boundary in a manner that is more substantially consistent with the true color image. Using the PM2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM2.5 ≥ 35 μg/cm3 is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM2.5 ≥ 50 μg/cm3 is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite. [ABSTRACT FROM AUTHOR]

Atmospheric carbon dioxide (CO2) is recognized as the most important component of the greenhouse gases, the concentration of which has increased rapidly since the pre-industrial era due to anthropogenic emissions of greenhouse gases (GHG). The accurate monitoring of carbon dioxide is essential to study the global carbon cycle and radiation budget on Earth. The Aerosol and Carbon Detection Lidar (ACDL) instrument onboard the Atmospheric Environmental Monitoring Satellite (AEMS) was successfully launched in April 2022, which allows a new perspective to quantify the global spatial distribution of atmospheric CO2 with high accuracy. In this work, the impact of the Doppler shift on CO2 measurements for an integrated-path differential absorption (IPDA) light detection and ranging (lidar) system was evaluated to meet the weighted column-averaged mixing ratio of carbon dioxide (XCO2) measurement requirements of less than one part per million (ppm). The measurement uncertainties due to the Doppler shift were first evaluated in airborne IPDA observations. The result shows that most of the Doppler shift is in the range of 6–8 MHz, resulting in 0.26-0.39 ppm deviations in the XCO2 results. The deviations between the XCO2 retrievals and in situ measurements decreased to 0.16 ppm after the correction of the Doppler shift from 11:28:29 to 11:28:49 in the flight campaign. In addition, the online Doppler shift accounts for 98% of the deviations between XCO2 retrievals and in situ measurements. Furthermore, the impact of the Doppler shift on ACDL measurements is also assessed. The differences between the XCO2 retrievals with and without Doppler shift are used to quantify measurement uncertainties due to the Doppler effect. The simulations reveal that a pointing misalignment of 0.067 mrad can lead to a mean bias of about 0.30 ppm (0.072%) in the CO2 column. In addition, CO2 measurements are more sensitive to the Doppler shift at high altitudes for IPDA lidar, so the largest differences in the CO2 columns are found on the Qinghai–Tibet Plateau in China. [ABSTRACT FROM AUTHOR]

The Fourier Transform Spectrometer (FTS) at the Beijing Satellite Meteorological Ground Station observed XCO2 (the dry carbon dioxide column) from 2 March 2016 to 4 December 2018. The validation results of ground-based XCO2, as well as GOSAT, OCO-2, and TanSat XCO2, show that the best temporal matching setting for ground-based XCO2 and satellite XCO2 is ±1 h, and the best spatial matching setting for GOSAT is 0.5° × 0.5°. Consistent with OCO-2, the best spatial matching setting of TanSat is 5° × 5° or 6° × 6°. Among GOSAT, OCO-2, and TanSat, the satellite observation validation characteristics near 5° × 5° from the ground-based station are obviously different from other spatial matching grids, which may be due to the different observation characteristics of satellites near 5° × 5°. To study the influence of local CO2 sources on the characteristics of satellite observation validation, we classified the daily XCO2 observation sequence into concentrated, dispersive, increasing, and decreasing types, respectively, and then validated the satellite observations. The results showed that the concentrated and decreasing sub-datasets have better validation performance. Our results suggest that it is best to use concentrated and decreasing sub-datasets when using the Beijing Satellite Meteorological Ground Station XCO2 for satellite validation. The temporal matching setting should be ±1 h, and the spatial matching setting should consider the satellites observation characteristics of 5° × 5° distance from the ground-based station. [ABSTRACT FROM AUTHOR]

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). This paper performs sea ice monitoring with the CSCAT backscatter measurements in polar areas. The CSCAT measurements have the characteristics of diverse incidence and azimuth angles and separation between open water and sea ice. Hence, five microwave feature parameters, which show different sensitivity to ice or water, are defined and derived from the CSCAT measurements firstly. Then the random forest classifier is selected for sea ice monitoring because of its high overall accuracy of 99.66% and 93.31% in the Arctic and Antarctic, respectively. The difference of features ranked by importance in different seasons and regions shows that the combination of these parameters is effective in discriminating sea ice from water under various conditions. The performance of the algorithm is validated against the sea ice edge data from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) on a global scale in a period from 1 January 2019 to 10 May 2021. The mean sea ice area differences between CSCAT and OSI SAF product in the Arctic and Antarctic are 0.2673 million km2 and −0.4446 million km2, respectively, and the sea ice area relative errors of CSCAT are less than 10% except for summer season in both poles. However, the overall sea ice area derived from CSCAT is lower than the OSI SAF sea ice area in summer. This may be because the CSCAT is trained by radiometer sea ice concentration data while the radiometer measurement of sea ice is significantly affected by melting in the summer season. In conclusion, this research verifies the capability of CSCAT in monitoring polar sea ice using a machine learning-aided random forest classifier. This presented work can give guidance to sea ice monitoring with radar backscatter measurements from other spaceborne scatterometers, particular for the recently launched FY-3E scatterometer (called WindRad). [ABSTRACT FROM AUTHOR]

Satellite observation is one of the main methods used to monitor the global distribution and variation of atmospheric carbon dioxide (CO2). Several CO2 monitoring satellites have been successfully launched, including Japan's Greenhouse Gases Observing SATellite (GOSAT), the USA's Orbiting Carbon Observatory-2 (OCO-2), and China's Carbon Dioxide Observation Satellite Mission (TanSat). Satellite observation targeting the ground-based Fourier transform spectrometer (FTS) station is the most effective technique for validating satellite CO2 measurement precision. In this study, the coincident observations from TanSat and ground-based FTS were performed numerous times in Beijing under a clear sky. The column-averaged dry-air mole fraction of carbon dioxide (XCO2) obtained from TanSat was retrieved by the Department for Eco-Environmental Informatics (DEEI) of China's State Key Laboratory of Resources and Environmental Information System based on a full physical model. The comparison and validation of the TanSat target mode observations revealed that the average of the XCO2 bias between TanSat retrievals and ground-based FTS measurements was 2.62 ppm, with a standard deviation (SD) of the mean difference of 1.41 ppm, which met the accuracy standard of 1% required by the mission tasks. With bias correction, the mean absolute error (MAE) improved to 1.11 ppm and the SD of the mean difference fell to 1.35 ppm. We compared simultaneous observations from GOSAT and OCO-2 Level 2 (L2) bias-corrected products within a ±1° latitude and longitude box centered at the ground-based FTS station in Beijing. The results indicated that measurements from GOSAT and OCO-2 were 1.8 ppm and 1.76 ppm higher than the FTS measurements on 20 June 2018, on which the daily observation bias of the TanSat XOC2 results was 1.87 ppm. These validation efforts have proven that TanSat can measure XCO2 effectively. In addition, the DEEI-retrieved XCO2 results agreed well with measurements from GOSAT, OCO-2, and the Beijing ground-based FTS. [ABSTRACT FROM AUTHOR]

A cloud structure construction algorithm adapted for the nighttime condition is proposed and evaluated. The algorithm expands the vertical information inferred from spaceborne radar and lidar via matching of infrared (IR) radiances and other properties at off-nadir locations with their counterparts that are collocated with active footprints. This nighttime spectral radiance matching (NSRM) method is tested using measurements from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud layer heights are estimated up to 400 km on both sides of the ground track and reconstructed with the dead zone setting for an approximate evaluation of the reliability. By mimicking off-nadir pixels with a dead zone around pixels along the ground track, reconstruction of nadir profiles shows that, at 200 km from the ground track, the cloud top height (CTH) and the cloud base height (CBH) reconstructed by the NSRM method are within 1.49 km and 1.81 km of the original measurements, respectively. The constructed cloud structure is utilized for cloud classification in the nighttime. The same method is applied to the daytime measurements for comparison with collocated MODIS classification based on the International Satellite Cloud Climatology Project (ISCCP) standard. The comparison of eight cloud types over the expanded distance shows good agreement in general. [ABSTRACT FROM AUTHOR]

Horizontal visibility (HVIS) is a primary index used for assessing air quality. Although satellite images provide information regarding atmospheric aerosols, atmospheric visibility is not directly measured. In this paper, a deep learning approach is proposed to retrieve HVIS using moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, the European Centre for Medium-Range Weather Forecasts reanalysis dataset, and ground-based visibility observations. The deep neural network model comprises a multi-layer unsupervised restricted Boltzmann machine (RBM) and a layer for supervised learning. The dropout mechanism was used in the training process to overcome the errors caused by over-fitting. The results demonstrate that the correlation coefficient values between HVIS observations and retrievals during training, pre-validating, and evaluation were 0.74, 0.723, and 0.697, respectively. The retrieved HVIS in Eastern China exhibited a north-to-south increasing trend, increasing and decreasing in summer and winter, respectively. In conclusion, the proposed model presents an effective and more reliable method for HVIS retrieval. However, the small samples, low AOD, low albedo, high total column water, high longitude, and the low vertical wind component at 10 m likely cause HVIS bias. [ABSTRACT FROM AUTHOR]

AIUS (Atmospheric Infrared Ultraspectral Sounder) is an infrared occultation spectrometer onboard the Chinese GaoFen-5 satellite, which covers a spectral range of 2.4–13.3 μm (750–4100 cm−1) with a spectral resolution of about 0.02 cm−1. AIUS was designed to measure and to study the chemical processes of ozone (O3) and other trace gases in the upper troposphere and stratosphere over the Antarctic. In this study, the AIUS retrieval methodology is described. A comparison between AIUS measurements and simulated spectra illustrates that AIUS measurements agree well with the simulated spectra. To first evaluate the reliability of the AIUS retrieval algorithm, three retrieval O3 experiments were performed based on ACE-FTS (Atmospheric Chemistry Experiment—Fourier transform spectrometer) observed spectra. A comparison with the ACE-FTS official products shows that the relative difference of these three retrieval experiments was mostly within 10% between 20 and 70 km. These retrieval experiments demonstrate that the retrieval algorithm described in this study provided reliable results and reliably. Furthermore, O3, H2O, and HCl profiles were retrieved from 24 orbits of AIUS measurements and compared with the official Aura /MLS (Microwave Limb Sounder) level-2 v4.2 profiles. The relative difference was mostly within 10% (about 0.02–0.4 ppm) between 18 and 58 km for the O3 retrieval, within 10% (0–0.5 ppm) between 15 and 80 km for the H2O retrieval, and within 10% (about 0.1 ppb) between 30 and 60 km for the HCl retrieval. A good agreement in the retrieved trace gas profiles was reached between AIUS and MLS. [ABSTRACT FROM AUTHOR]

Graphene and its chemically exfoliated derivatives—GO and rGO—are the key members of graphene family materials (GFM). The atomically thick crystal structure and the large continuous π conjugate of graphene imparts it with unique electrical, mechanical, optical, thermal, and chemical properties. Although those properties of GO and rGO are compromised, they have better scalability and chemical tunability. All GFMs can be subject to noncovalent modification due to the large basal plane. Besides, they have satisfying biocompatibility. Thus, GFMs are promising materials for biological, chemical and mechanical sensors. The present review summarizes how to incorporate GFMs into different sensing system including fluorescence aptamer-based sensors, field-effect transistors (FET), and electrochemical sensors, as well as, how to covalently and/or non-covalently modify GFMs to achieve various detection purpose. Sensing mechanisms and fabrication strategies that will influence the sensitivity of different sensing system are also reviewed. [ABSTRACT FROM AUTHOR]

The Atmospheric Infrared Ultraspectral Sounder (AIUS), the first high-resolution (0.02 cm−1) solar occultation sounder, aboard GF5, was launched in May 2018 from China. However, relevant studies about vertical profiles of atmospheric constituents based on its operational data were not conducted until half a year later. Due to an urgent need for Hin-orbit tests, the real spectra (called reference spectra hereafter) were substituted with simulated spectra calculated from the reference forward model (RFM) plus different random noises at different altitudes. In the generation process of the reference spectra for N2O, NO2, and HF species, ACE-FTS (Atmospheric Chemistry Experiment–Fourier Transform Spectrometer instrument on the SCISAT satellite) level 2 products replace corresponding profiles included in the atmospheric background profiles. The optimal estimation method is employed to extract N2O, NO2, and HF profiles in this study. Comparing the retrieved results with ACE-FTS level 2 products, the relative deviations for these three species are calculated. For N2O, the average relative deviation is less than 6% at altitudes below 25 km, while larger deviations are observed in the range of 25–45 km, with the maximum being at ~25%. Additionally, the difference for NO2 is less than 5% in the 20–45 km range, with a larger discrepancy found below 20 km and above 45 km; the maximum deviation reaches ±40%. For HF, the relative deviation is less than 6% for all tangent heights, implying satisfactory retrieval. The vertical resolution, averaging kernel, and number of degrees of freedom are used to assess the retrieval algorithm, which indicate that the retrieved information content is much more attributable to the reference spectra contribution than to the a priori profile. Finally, a large number of retrieval tests are performed for N2O, NO2, and HF in selected areas covering the Arctic region, northern middle latitude, tropics, southern middle latitude, and Antarctic region, and reliable results are obtained. Thus, to a great extent, the algorithm adopted in the AIUS system can process retrievals reliably and precisely. [ABSTRACT FROM AUTHOR]