The accurate estimation of atmospheric turbulence dissipation rate (ε) is crucial for better characterizing the atmospheric physical processes and understanding the boundary layer structure. Nevertheless, the ε derived from the traditional spectral width method (SWM) tends to be overestimated using the radar wind profiler (RWP) measurements, due to the non-turbulent broadening effects. To tackle this challenge, here we propose a novel intelligent algorithm that combines SWM and Random Forest (SWM-RF) to estimate ε. The experimental data were synchronized observations from RWP and meteorological tower at the Boulder Atmospheric Observatory site from March to April 2015. Firstly, a systematic analysis was conducted on the overestimation of the SWM under different turbulence intensity, time, and height scenarios. The SWM-RF model is then constructed by introducing the difference function as a physical constraint and combining multiple observation parameters of RWP. The determination coefficient of the ε between sonic anemometers and SWM-RF reached 0.59, and the mean absolute error is 0.007 m² s⁻³. The comparison of results with sonic anemometers under different times and height indicates that SWM-RF model is superior to the SWM and is not affected by variations in height and time. Results shows that the overestimation of SWM is reduced under strong turbulence conditions, while the correction effect is poor in weak turbulence scenarios. Finally, the semi-diurnal cycles of ε for March and April in 2015 are analyzed. The accurate intelligent detection of ε profiles will lay a solid foundation for advancing the understanding of atmospheric physical processes and improving turbulence parameterization.

Over 150 countries have signed the Global Methane Pledge, aiming to reduce anthropogenic methane emissions by 30% by 2030. Reducing methane emissions from the energy sector is crucial to achieving this target. The current emission inventories for the energy sector have a spatial resolution of 1 km, suitable for regional-scale methane flux inversion but inadequate for identifying and monitoring point source emissions which is the most important type of anthropogenic methane emissions in the energy sector. To address this issue, we propose a multiattention mechanism, MAM-YOLOv9, for identifying emission facilities in the oil and gas industry, based on YOLOv9. We integrate SimAM and cascaded group attention (CGA) modules into the network, focusing on target objects under complex backgrounds while improving detection accuracy. In addition, we introduce the dynamic convolution module to replace the convolution in the YOLOv9 backbone network, improving computational efficiency and accurate object detection capability. Using submeter-level optical images provided by the high-resolution satellite images, we achieve large-scale monitoring of facility-level emission sources on a regional scale. Experiments demonstrate that our new method achieved SOTA performance, achieving the best results across various metrics compared with the baseline. We also conduct batch detection tasks in Shengli Oilfield, the second-largest oilfield in China, identifying over 38000 emission facilities. Based on the results, we further compile a facility-level methane emission inventory, which can better serve the global efforts for mitigating methane emissions from the oil and gas industry.

The boundary layer height (BLH) is a vital parameter that affects the vertical distribution of matter within the atmospheric boundary layer (ABL). However, the traditional algorithms determine the BLH based on changes in gradient within the signal-to-noise ratio (SNR) profile. It often leads to significant uncertainty under complex atmospheric conditions. Here, a convolutional neural network (CNN) algorithm considering multiple atmospheric parameter profiles is proposed for determining the BLH from radar wind profiler (RWP) data. The CNN algorithm is applied to the RWP dataset of atmospheric radiation measurement (ARM) site at Southern Great Plains (SGP) from August 2019 to July 2023. The sensitivity analysis shows that the CNN algorithm overcomes the shortcomings of the traditional algorithms that are susceptible to multiple local peaks. Moreover, the CNN algorithm performs well under complex conditions. It exhibits strong consistency with the BLH estimated by radiosonde (RS), with correlation coefficients, mean absolute error (MAE), and root-mean-square error (RMSE) of 0.81, 0.24, and 0.34 km, respectively. The CNN algorithm is then compared with the covariance wavelet transform (CWT) algorithm and the peak detection algorithm (PDA) using the BLH estimated by RS as a reference. The results indicate that the accuracy of BLH estimated by the CNN algorithm is higher than that of the two traditional algorithms. The MAE and RMSE of the CNN algorithm reduce from$0.53~\pm ~0.56$km ($0.57~\pm ~0.60$km) and 0.77 km (0.83 km) of CWT (PDA) to$0.24~\pm ~0.25$and 0.34 km, respectively. Finally, the diurnal and seasonal variation patterns of BLH are explored. The BLH shows a high correlation with solar radiation, rising from sunrise and then decreasing after sunset. Regarding seasonal variation, BLH peaks in summer and troughs in winter. Overall, the CNN algorithm proposed here can improve the accuracy and stability of BLH estimation. This study verifies the great potential of deep learning algorithms in the BLH estimation.

The planetary boundary layer height (PBLH) has a significant impact on the energy and material exchange in the atmosphere. The traditional gradient method (GM) determines the PBLH based on the gradient change of the aerosol profile. It is susceptible to the effect of complex atmospheric conditions, which leads to uncertainties in the estimation of PBLH. Here, a random forest (RF) algorithm that considers the vertical distribution of aerosols is proposed to find the PBLH under complex atmospheric conditions. The height of the three minimum local peaks of the range correction signal profile and 7 other variables, such as aerosol layer number (ALN), relative humidity, solar radiation, and other meteorological parameters, from January 2017 to December 2021 is used as RF model input. The radiosonde estimated PBLH (PBLHRS) is used as reference value. The sensitivity analysis indicates that the relative error of RF-estimated PBLH (PBLHRF) is smaller than that of GM-estimated PBLH (PBLHGM), and it decreases with an increase in aerosol optical depth. Moreover, RF achieves good performance under different atmospheric conditions. It can effectively overcome the effects of complex atmospheric conditions in PBLH estimation. Based on the correlation analysis, it is found that the estimation accuracy of the RF algorithm is greatly improved compared with the GM. The correlation coefficient between the PBLHRFand the PBLHRSreaches 0.8, which is much larger than that of the PBLHGM(0.47). Finally, long-term PBLHRFanalysis show that there are obvious diurnal and seasonal variations of PBLH. It increases and then decreases from early morning to late evening. It is highest in summer and lowest in winter. Overall, RF can effectively overcome the shortcomings of traditional GM and has high accuracy and robustness for various atmospheric conditions. The findings obtained here have great potential for lidar application in obtaining reliable PBLH estimations.

Carbon observation satellites based on passive theory (e.g., OCO-2/3, GOSAT-1/2, and TanSat) have relatively high carbon dioxide column concentration (XCO2) accuracy when the observation conditions are met. Passive satellites have data bias and coverage deficiencies due to cloud cover, low albedo, low-light conditions, and aerosol scattering, resulting in carbon observation satellites based on passive theory that cannot meet the demand for high-precision, all-day, all-weather XCO2 monitoring. Active detection satellites are urgently needed to support global carbon sources, sinks, and carbon neutrality. China intends to launch a sensor satellite with active detection of XCO2 in the coming years. In this work, based on the satellite’s scaled-down airborne experiments, a spectral energy model was developed to optimize the conventional inversion algorithm and achieve a more accurate XCO2 inversion. The 1.572-$\mu \text{m}$integrated path differential absorption (IPDA) lidar column length is used indirectly to evaluate the accuracy of the spectral energy model for signal extraction. Also, the experimental results show that the accuracy of the signal extracted by the 1.572-$\mu \text{m}$IPDA lidar column length is 0.74 and 6.20 m at sea and on land based on the indirect evaluation of the length of the 1.572-$\mu \text{m}$IPDA lidar column length. The optimized XCO2 was evaluated (standard deviation as an evaluation metric) and its XCO2 standard deviation reduced by 31%, 63%, and 66% in the ocean, plains, and mountains, respectively. Our algorithm can obtain the XCO2 with a consistent trend by using XCO2 from the OCO-2 satellite as a reference. The calculated XCO2 is more accurate in areas dominated by anthropogenic factors (plains), due to the accuracy of the IPDA detection mechanism. This algorithm improves the accuracy and robustness of XCO2 inversion and has important reference significance for the IPDA lidar carried by China’s satellites to be launched in this year.

High temporal resolution aerosol optical depth (AOD) observations derived from new-generation geostationary (GEO) satellite possess unique advantages in analyzing aerosol fast variation processes and thereby providing more accurate assessments on their climate effects and health risks. Unfortunately, the expected advantages and values are dramatically limited by relatively large proportion of data missing in the GEO AOD products due to cloud obscuration and intrinsic retrieval algorithm. Although several data recovery algorithms have been proposed in recent years to improve the spatial coverage for GEO AOD products, yet most of them aims at filling up the data blanks rather than reconstructing the temporally continuous variation of aerosol. Accordingly, in this study, a novel framework of nested spatiotemporal fusion blending GEO with sun-synchronous orbit (SSO) satellite observations based on Bayesian maximum entropy (BME) theorem is developed for GEO Advanced Himawari-8 Imager (AHI) AOD recovery with the sufficient excavation of complementary information from GEO and SSO satellite observations, where the minute-stage and hour-stage BME fusion are jointly employed to reconcile temporal inconsistency and data discrepancies between GEO and SSO observations. The results demonstrate that the AOD spatial coverage is dramatically increased by 240.9% (from 20.5% to 70%) with ensured accuracy after Nested-BME fusion. Additionally, two case analyses, during the development and dispersion processes of haze respectively, both demonstrate that the proposed Nested-BME fusion framework could reconstruct the reliable aerosol diurnal variation trends on the basis of recovering missing data for Himawari-8 AHI AOD datasets, while the AHI official level-2 and level-3 AOD products fail to capture these key trends. Furthermore, the developed Nested-BME AOD fusion framework is also applicable for other geostationary satellites over other regions, which could substantially enhance the availability and value of high temporal resolution AOD products for better scientific applications.

A novel framework was proposed to measure atmospheric concentration of$\delta ^{13}C$using a multiwavelength integrated path differential absorption (IPDA) LiDAR. The spectroscopy range of the multiwavelength IPDA LiDAR is recommended from 2264.5 to 2265.5 cm$^{-1}$. Using the proposed retrieving method, the relative error of$\delta ^{13}C$retrievals would be within 0.16‰ under reasonable settings. Moreover, the proposed method shows reliable performances in different circumstances. It would be of great significance for exploring the characteristic of$\delta ^{13}C$in the ecosystem and anthropogenic emissions in the future.

Ground-based differential absorption lidar (DIAL) can measure vertical CO2concentration profiles in the troposphere. Here, we propose a method of improving the accuracy and precision of CO2concentration profiles measurements. This method combines a conditional adjustment with Chebyshev fitting to reduce the error of the retrieved results in view of the received signal around the atmospheric boundary layer (ABL) with a high signal-to-noise ratio (SNR). Simulation experiments verified the effectiveness of this method. The accuracy of CO2concentration profiles can be improved larger than 83.4% when compared with that via traditional methods, and the standard deviation of the measured CO2concentration profiles calculated by our method was reduced by approximately 0.43–22.51 ppm when compared with the results calculated by traditional methods. Two real cases in different locations were also examined with the proposed technique. The results indicated the applicability of our method in measuring other trace gases by using DIAL.

Vertical distributions of temperature and humidity are two essential factors for understanding the atmospheric structure, extreme weather events, and regional and global climate. The ground-based microwave radiometer (MWR), which acts as a passive sensor and operates continuously under all weather conditions, has an irreplaceable role in measuring the vertical information of the temperature and water content in the atmosphere. In this letter, we proposed a four-layer back-propagation neural network (BPNN) method to retrieve temperature and relative humidity (RH) profiles from the bright temperature measured by the MWR. In contrast to the traditional BPNN, this method has greater advantages in dealing with the problems of overfitting, gradient disappearance, and gradient explosion in vertical atmospheric retrieval. By adding dropout layers, it can also help to describe the nonlinear relationships for RH profiles. Results showed that the performance of the four-layer BPNN method was better than the quadratic regression (QR, provided by MWR manufacturer) method under both cloud and cloud-free conditions. Compared with measurements of radiosonde data, root-mean-square error of temperature and RH, BPNN achieves 1.88 K and 19.30% under cloud conditions and 2.03 K and 15.10% under cloud-free conditions, respectively, whereas the corresponding values by using the QR method were only 3.07 K and 24.28% under cloud conditions and 4.14 K and 18.96% under cloud-free conditions, respectively. Temperature and RH profiles retrieval with high precision have increased the efficiency of the MWR observations and provided a data foundation for further atmospheric climate research.

Particulate pollution threatens the ecological environment, air quality, and public health. Therefore, it has become an increasing concern for the public and governments in recent decades. In this study, a full coverage PM2.5(aerodynamic diameter of less than 2.5 microns) estimation strategy is proposed based on spatiotemporal machine learning approaches including the Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and Random Forest (RF). The RF estimates PM2.5by considering the features of a single pixel, while the introduction of the CNN-LSTM (size of 7 × 7 × 4) assists in exploiting the spatiotemporal correlation of surrounding pixel features. Compared with linear models and empirical spatiotemporal weight methods, our CNN-LSTM+RF avoids the uncertainty and complexity owing to actual measurements of the surrounding sites. In addition, full coverage is achieved using both satellite data and reanalysis data. Results showed that, the Root Mean Squared Error (RMSE) and coefficient of determination (R2) of the CNN-LSTM+RF were 12.790 μg/m3and 0.910, respectively, in sample-based Cross-Validation (CV). From the perspective of the season, the best performance of the CNN-LSTM+RF was found in autumn (R2of 0.915) and the lowest was in summer (R2of 0.848). In the meantime, for the different regions of China, the CNN-LSTM+RF also showed stable performance. The proposed method can generate high-precision continuous PM2.5distribution maps that provide beneficial support for improving environmental and public health, and provide a reference for using deeper networks.

With the rapid growth of GHG monitoring satellites, more and more studies focused on the issue of inversion/optimization of CO2 fluxes using satellite-derived XCO2 observations in recent years. A common and critical challenge in this framework is the separation of background and anomalies from XCO2 observations, which directly affect performance of the CO2 fluxes inversion. We proposed a novel method to accurately extract background XCO2 from satellite observations. A series of observing system simulation experiments were performed to test the performance of the method. We found that the bias and uncertainty of the background concentration are below 0.01 ppm and 0.05 ppm in the given cases, respectively. Based on this method, we selected five overpasses from 2014 to 2016 to demonstrate a regional-scale flux inversion near Riyadh. The comparison with the two previous methods shows that the posterior simulated XCO2 by the method proposed in this paper can match better with the observed XCO2 from OCO-2.

The full-waveform hyperspectral light detection and ranging (FWHSL) data have been widely used in surface topography, vegetation detection, and 3-D urban terrain modeling, capable of revealing the spatial distribution of a target and more detailed spectral information in the vertical direction. However, the echo signals of a target would significantly vary between different spectral channels due to the reflectance characteristics and the uneven energy distribution of supercontinuum laser source. Especially, band channels with weak reflectance over a long distance would affect the extraction accuracy of waveform parameters, which are essential for retrieving the spatial and spectral information of targets. This article proposes a multichannel interconnection decomposition method to improve the extraction accuracy of distance and spectral information at each pulse using hyperspectral waveform data. Two experiments were conducted to verify the performance of long-distance detection of targets using FWHSL. The first experiment detected a standard whiteboard, a green leaf, and a yellow leaf at roughly 518 m. Results demonstrated a considerable improvement in ranging precision and spectral detection using the proposed method compared with using the optimal channel with the best data quality. The second experiment simultaneously detected two adjacent targets at a distance of approximately 518 m. Results presented clear superiority of adding waveform channels in terms of discovering overlapping components and retrieving accurate waveform parameters. The success rate of extracting two targets 60 cm apart was greatly increased from 47% to 73% through the multichannel interconnection waveform decomposition (MIWD) method.

Satellite-derived aerosol optical depth (AOD) is an important parameter for studies related to atmospheric environment, climate change, and biogeochemical cycle. Unfortunately, the relatively high data missing ratio of satellite-derived AOD limits the atmosphere-related research and applications to a certain extent. Accordingly, numerous AOD fusion algorithms have been proposed in recent years. However, most of these algorithms focused on merging AOD products from multiple passive sensors, which cannot complementarily recover the AOD missing values due to cloud obscuration and the misidentification between optically thin cloud and aerosols. In order to address these issues, a spatiotemporal AOD fusion framework combining active and passive remote sensing based on Bayesian maximum entropy methodology (AP-BME) is developed to provide satellite-derived AOD data sets with high spatial coverage and good accuracy in large scale. The results demonstrate that AP-BME fusion significantly improves the spatial coverage of AOD, from an averaged spatial completeness of 27.9%–92.8% in the study areas, in which the spatial coverage improves from 91.1% to 92.8% when introducing Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD data sets into the fusion process. Meanwhile, the accuracy of recovered AOD nearly maintains that of the original satellite AOD products, based on evaluation against ground-based Aerosol Robotic Network (AERONET) AOD. Moreover, the efficacy of the active sensor in AOD fusion is discussed through overall accuracy comparison and two case analyses, which shows that the provision of key aerosol information by the active sensor on haze condition or under thin cloud is important for not only restoring the real haze situations but also avoiding AOD overestimation caused by cloud optical depth (COD) contamination in AOD fusion results.

High-frequency aerosol observation from new-generation geostationary meteorological satellite is capable to capture and monitor the spatiotemporal dynamic variation of aerosols, which is of vital significance to environmental research and climate studies. Due to the diversity and complexity of land cover, it is a challenge to retrieve aerosol properties with high accuracy over land especially over heterogeneous land surfaces. In this study, a Geometry-Discrete Minimum Reflectance Aerosol Retrieval Algorithm (GeoMRA) has been proposed to retrieve 10-min high temporal resolution aerosol optical depth (AOD) datasets for geostationary Himawari-8 AHI sensor, aiming at providing universal bidirectional reflectance distribution function (BRDF) descriptions for different land surfaces with different heterogeneous extent. The AOD retrievals from GeoMRA demonstrate good consistency against the ground-based AERONET measurements in the East Asia from 2015 to 2020, with a correlation coefficient (R) of 0.883 and approximately 65.6% of matchups falling within the expected error envelope of ±(0.05 + 15%). Intercomparison between the GeoMRA retrieved AOD and other operational AOD products shows that the GeoMRA AOD retrievals, which generally possess similar spatial distribution and accuracy as MODIS AOD products, have better performances than the Japan Aerospace Exploration Agency (JAXA) AOD products by providing more accurate AOD retrievals with higher spatial coverage. Moreover, the AOD bias analyses further demonstrate the robustness of GeoMRA algorithm, and an extreme haze event shows that the continuous GeoMRA AOD images illustrate smoother temporal variations than JAXA AOD products, demonstrating its efficacy and reliability in capturing the process of haze transport and monitoring the continuous spatiotemporal variation of aerosol. The above results suggest the considerable accuracy of GeoMRA algorithm for scientific application requirement, and demonstrate the robustness of proposed BRDF scheme in describing heterogeneous surfaces with diverse reflectance distribution.

The vertical distribution of fine particles with a diameter$ < 2.5~\mu \text{m}$(PM2.5) plays an important role in understanding the transport of air pollution and in making decisions regarding the prevention and control of regional air pollution. However, the studies of the vertical distribution of PM2.5were limited by the lack of monitoring data obtained with vertical sampling strategies. The lidar system can obtain the aerosol profile, which provides the possibility to measure PM2.5profile. Here, the vertical distributions of PM2.5concentrations were investigated on the basis of lidar data from January 2014 to October 2015. Linear regression, improved linear regression, and random forest (RF) models were used to retrieve the PM2.5concentration profile from lidar data. The models were built based on the relationship among extinction coefficient (EC), temperature ($T$), relative humidity (RH), and surface PM2.5mass concentration. Comparison of the estimated and observed PM2.5showed that the RF model exhibited the best inversion effect. The correlation coefficient reached 0.75, and the root mean absolute error (RMAE) and root mean square error (RMSE) were 3.94 and 21.1$\mu \text{g}/\text{m}^{3}$, respectively. Error analysis indicated that the estimated PM2.5retrieved using the linear and improved linear models (ILMs) was smaller than the observed PM2.5when EC was less than 0.7 km−1, whereas PM2.5was evidently overestimated during winter pollution days. The reason might be that the effects of$T$and RH were inaccurately considered. Finally, the seasonal variation of the PM2.5profiles was investigated. Results indicated that the mass concentration of PM2.5was relatively large within 0.5–1.5 km, with a maximum of 60$\mu \text{g}/\text{m}^{3}$. The findings obtained here provide guidance for PM2.5vertical observation and regional pollutant transport.

Continued drought can lead to vegetation mortality and reduced carbon sink capacity of terrestrial ecosystems. However, the complexity of the causes and processes of drought has led to a limited understanding of how vegetation performances under drought. Here we used solar-induced chlorophyll fluorescence (SIF) and enhanced vegetation index (EVI) data to explore the impact of U.S Midwest drought on vegetation in 2012. At the whole study area and flux tower scale, SIF is more sensitive to the decrease in precipitation than EVI. SIF also can more accurately monitor the growth of vegetation under drought. SIF is an effective index for monitoring environmental stress on vegetation.

Multispectral lidar has become a promising technology with the rise in capability of 3-D spectral imaging. However, the precise acquisition of spectral information is interfered by measurement geometry, namely, incidence angle and detection distance. These issues may cause discrepancy within the spectral information, thus limiting the classification capabilities of multispectral lidar. To fill this gap, a hue-saturation-intensity (HSI) color space-based method for multispectral lidar classification is proposed in this study. The proposed scheme does not require radiometric calibration, as the HSI color space is robust to spectral intensity variations within a single target. In this method, spectral data are transformed from red-green-blue (RGB) color space to HSI color space. The three components of the HSI color space are inputted for the classification. Then, a reference target-based radiometric calibration is conducted for comparison. The complex indoor scene and the random forest classifier are used for the validation. The classification results of using raw RGB data, raw HSI data, calibrated RGB data, and calibrated HSI data are compared. Results show that the raw HSI data outperform the raw RGB data in terms of classification accuracy. In particular, the raw HSI data can correct the classification error caused by the measurement geometry more effectively than the calibrated RGB data. The improvement resulting from using the HSI color space is demonstrated by both the three-wavelength multispectral lidar and the 32-channel multispectral lidar. That indicates that HSI color space is a promising tool for enhancing the classification capability of multispectral lidar.

Satellite observation is an effective way of obtaining global aerosol information. The study focuses on developing a new scheme to apply the traditional dark target (DT) method to the advanced Medium Resolution Spectral Imager (MERSI II), which is a part of the Chinese Fengyun-3-D satellite. Compared with the Moderate Resolution Imaging Spectroradiometer (MODIS), MERSI II shows higher ratios between red (0.65$\mu \text{m}$) and near-infrared ($2.13~\mu \text{m}$) bands in surface reflectance estimation and the green band ($0.55~\mu \text{m}$) that is more sensitive to cloud screening. Aerosol optical depth (AOD) is retrieved from earlier MERSI II observations by following the adapted DT method over land in Asia in 2018. Overall, AOD from MERSI II has a good performance compared with ground-based measurements with an expected error (EE%) of 66.38% and$R^{2}$of 0.834, which is close to the MODIS EE% of 70.59% and$R^{2}$of 0.829. Both sensors slightly overestimate the AOD over heavy aerosol loading regions, but MERSI-II has larger retrieval area covering a wider swath than MODIS in heavy hazy areas. On a spatial scale, the MERSI II effectively reflects the AOD distribution pattern but tends to overestimate and underestimate AOD at low and high latitudes, respectively, when compared with MODIS. The MERSI II sensor shows good aerosol detection potential, and the DT algorithm can be applied. MERSI II will provide important observation data on climate change and atmospheric pollution for the investigations in the future.

Quantification of the distribution of the CO2dry-air mixing ratio (XCO2) is crucial for understanding the carbon cycle. However, clouds and aerosols in the line of light create spectral interference with CO2signals. This interference can result in a low yield of XCO2retrievals, thus limiting the application of these valuable satellite data. In this study, we developed an innovative methodology to obtain XCO2maps of high spatial and temporal resolution using satellite data. The method first interpolates the spatial properties using an empirical Bayesian kriging (EBK) algorithm. Then, the temporal properties are modulated based on a CO2curve database that was constructed using temporal contours and transfer learning techniques. We applied this method to obtain spatiotemporal XCO2maps over mainland China using the Orbiting Carbon Observatory 2 (OCO-2) data product OCO-2_L2_Lite_FP 9r for the period from January 1 to December 31, 2019. The correlation coefficient ($R^{2}$) was 0.8056, and the average absolute prediction error [root-mean-square error (RMSE)] was 0.9951. In the research area of mainland China, the vacancy validation strategy was adopted and yielded$R^{2}$and RMSE of 0.8230 and 0.9746, respectively. We used the 2018–2019 ground-based data from four Total Carbon Column Observing Network (TCCON) sites in Europe and 2016 Hefei sites in mainland China to evaluate the performance of this new mapping method, respectively. Also, we obtained$R^{2}$of 0.8690 and the RMSE of 0.9056 in Europe and$R^{2}$of 0.8473 and the RMSE of 0.7026 in mainland China, proving the robustness and high precision of our method. This mapping technique is capable of filling the spatiotemporal gaps of satellite measurements with the high accuracy and resolution needed for its scientific application; thus, it has the potential to augment the scientific returns of satellite missions (e.g., USA OCO-2 Japan GOSAT and Chinese TanSat).

The vertical structure of wind is a key factor in modulating air quality, from which the determination of boundary layer height (BLH) remains a major challenge. In this paper, we developed an improved threshold method to determine the BLH from radar wind profiler (RWP) measurements. The normalized signal-to-noise ratio (SNR) profiles were used instead of the original SNR profiles to avoid instrumental inconsistencies. Additionally, a peak filter was designed to indicate the BLH based on the maximum SNR by taking into account the multiple peaks in the SNR profile. This algorithm was then applied to the RWP measurements taken in the summer (June-July-August) of 2018 in Beijing to obtain the BLHs. Validation analyses suggested that the BLH retrievals from RWP exhibited high consistency with those from radiosondes, with an average correlation coefficient of 0.69 (0.66) and a root mean squared error of 0.39 (0.41) in the daytime (nighttime). Additionally, the major features of summertime BLHs in Beijing were examined. In particular, a distinct diurnal variation in BLH was observed with a peak (1630 ± 510 m) occurring at 0600 universal time coordinated (UTC) and a minimum (587 ± 343 m) at 2300 UTC. Therefore, the algorithm presented here has great potential to be applied to other regions to obtain reliable BLHs. The findings obtained here highlight the importance of vertical wind structure in air quality studies.

Comprehensive research was conducted to analyze the characteristics of haze pollution during winter in Wuhan based on data for winter 2014-2015. The results demonstrated that haze pollution could be divided into two types. Type-1 lasted for 1-2 days and peak values of PM2.5exceeded 200 ug.m-3, Type-2 displayed a long duration of 5-6 days, and the hourly concentrations of PM2.5 ranged from 100 to 200 ug·m-3. Meanwhile, our results showed that type-1 haze pollution was mainly due to photochemical pollution process caused by high relative humidity (RH). Type-2 haze pollution was mainly caused by the accumulation of anthropogenic pollutants near the surface. Both haze pollution in winter was mainly fine-mode particles, and sometimes coarse-mode particles appeared. The characteristics of haze pollution revealed in this study can be used in regional climate modeling and can provide guidance to the government regarding prevention of haze pollution over central China.

To facilitate understanding of the relationship between the most significant greenhouse gas carbon dioxide and human activities, we have developed a differential absorption lidar (DIAL) detection system at 1.57 μm. The goal of this lidar system is to detect the temporal and spatial distribution of atmospheric carbon dioxide gas from 0.3 km to 3 km in the atmosphere. Beginning in 2009, the system was initially completed in 2013. Since then, we have been constantly experimenting and repeated instrumentation improvements. From July 2015 to the present, we carried out vertical and horizontal measurement experiments in the urban area of Wuhan, Hubei Province and the suburb of Huainan, Anhui Province, China. This article presents a fast and optimized inversion algorithm to improve the speed and accuracy. Experimental results show that the DIAL system and inversion algorithm are stable and reliable.

To evaluate the performance of the Orbiting Carbon Observatory 2 (OCO-2) Lite File Product (Lite_FP) which has the highest amount of data and the highest utilization efficiency among the three products of OCO-2, we compared global atmospheric CO2observations for 20 months (September 2014 to April 2016) with GGG2014 data from the Total Carbon Column Observing Network (TCCON). We considered the latitude distribution of the TCCON sites and performed a site-by-site comparison at different latitude zones. The result demonstrated that the seasonal fluctuation of XCO2from Lite_FP is consistent with TCCON, and the biases of XCO2measurements ranged from -3 ppm to 4 ppm, with a 1% precision. Bias distribution differed in terms of latitude zones and observing modes. In addition, we analyzed the distribution characteristic of the bias of XCO2observations under land target mode in detail combined with surface and atmospheric properties.

Combining the multispectral rasterized data and the three-dimensional (3D) lidar point cloud has long been a hot topic in the remote sensing field. This facilitates not only target recognition, land-classification, but also understanding for the ecosystems and environment. To address this problem, the concept of novel multispectral lidar (MSL), which captures multispectral reflectance and accurate spatial traits simultaneously, was proposed in this study. The layout of the instrument was described. Four laser diodes were co-aligned into a single beam. The reflectance spectrum at four wavelengths (covering red-edge region) as well as distance were recorded. In a validation experiment, reflectance at four wavelengths and normal vectors obtained by the MSL system were fully utilized to classify different targets including fresh and sere plants, with an overall accuracy of 85.5%. The novel MSL was demonstrated to have great potentials in land-use classification and vegetation monitoring.

The vertical profile of atmospheric CO2is of great scientific significance in identifying carbon sinks and sources, and estimating CO2emissions or uptakes. Differential absorption Light Detection And Ranging (DIAL), has been widely accepted as the most promising technique to sense atmospheric CO2. The classical method to retrieve measurements, generated from range-resolved detection, is derived from differentiating the measured column content, but its performance in dealing with aerosol backscatter signals is poor. To address this issue, this paper proposes a derivative method, which is based on Chebyshev fitting to the measured differential absorption optical depth. We created a performance evaluation model to assess the performance of the proposed method. Simulations revealed that the error of a single CO2profile in data retrieval can be reduced to less than 4 ppm in 6000 m. The precision of long-term mean CO2profile is expected to be less than 1 ppm. We believe that this novel method can be used in other applications also, e.g., trace gas measurements collected using DIAL, especially when the signal-to-noise-ratio of received signal is small.

This work evaluates the performance of OCO (Orbiting Carbon Observatory) -2 on global observations since its launch in Sep 2014. It is 10%~30% coverage that could be detected to obtain the concentration of atmospheric carbon dioxide in space dimension. However, about 65% data of OCO-2 has not be utilized to retrieve because of special atmospheric environment, such as thick aerosol depth and low pressure. In the accuracy aspect, compared with the TCCON, OCO-2 has good consistence of XCO2 at most sites. The average bias of monthly value is about 0.87 ppm and the standard deviation is 1.8 over TCCON sites. The mean monthly value of CO2can represent the actual value at a certain range.

Air pollution is one of the most concerned problems both for researchers and the public. In this study, we collected long-term observation of mass concentrations of PM10, PM2.5, and other gaseous pollutants in Wuhan, China, including sulphur dioxide (SO2), nitrogen oxide (NOx), from 2011 to 2014. The time series analysis is utilized to analyze the long-term trends of particulate matter (PM) and gaseous pollutants. Results show that the concentrations of PM and SO2have the trends to decrease due to the efforts of emission reduction and energy optimization. However, with the increase number of motor vehicles, the upward momentum of NOxwill not be reduced. The joint efforts of the government and the public are still needed. And, the seasonal characteristic for most pollutants is obvious. At last, we demonstrate the linear relationship between PM10and PM2.5, and reveal that PM2.5serves as the primary pollutant in Wuhan region, which should be paid more attention.

A transportable two-wavelength (532 and 355 nm) depolarization Mie Lidar has been described. The 532 nm has a polarization channel. It has the ability to simultaneously measure vertical profiles of tropospheric aerosol extinction coefficients, attenuated depolarization ratio, attenuated color ratio and Ångström coefficient. Comparison with CALIPSO indicates that the measured data by the system is reliable.

Laser-induced fluorescence (LIF) is an active technology that is closely related to excitation wavelength (EW). This study has mainly analyzed the performance of LIF LiDAR with different EWs in distinguishing plant species. The 355-, 460-, and 556-nm lasers were utilized to excite leaf fluorescence. The fluorescence signals were measured by the LIF system built in the laboratory. Subsequently, principal component analysis combined with back-propagation neural network was used to analyze fluorescence spectra. For the three EWs, the overall identification rates of the eight plant species were 75%, 80%, and 87.5%. However, when the plant species of the same genus were taken as a category, the overall classification rates were 92.5%, 81.3%, and 86.3%. Experimental results demonstrated that, when the plant species of the same genus were regarded as a category, 355 nm was the optimal EW. However, 556 nm was superior to 355 and 460 nm in the identification of plant species of the same genus.

In this article, we introduce the genetic algorithm into the inversion of aerosol size distribution. We are often faced with limited or insufficient observations in remote sensing and the observations are contaminated. The particle spectrum extinction equation is an ill-posed integral equation of the extinction inversion method[1]. To overcome the ill-posed nature, we use a double logarithmic normal distribution function to express the aerosol size distribution. To obtain the optimal solution, we introduce the genetic algorithm to gain the minimum sum of squared errors. Our method can improve accuracy and reduce the computational difficulty. The assumption of parameters in the bimodal distribution function is important to the inversion results. The aerosol size distribution obtained from the GRIMM 180 PM monitor and the TSI Scanning mobility particle sizers is compared with that computed via the method proposed by Dubovik and King(2000)[2]. Obvious difference has been discovered between aerosol size distribution on the ground and in the total atmospheric column. As a result, it is necessary to develop multi-wavelength and multi-function lidar to get observe the three-dimensional distribution characteristics of aerosol.

Atmospheric aerosols have significant effects on raditive forcing and climate systems [1, 2]. Precise measurements of aerosol optical properties are required to be made on a global scale to understand the quantitative aerosol radiation effects. Therefore, we conducted a comprehensive aerosol experiment, which is the first of its kind, in urban Wuhan, central China in 2011. The means of the scattering coefficient, absorption coefficient, and SSA were 405.66 Mm-1, 131.64 Mm-1and 0.75, respectively. Atmospheric boundary layer heights (APLHs) played an important role in annual and diurnal variations of aerosol optical properties. Both scattering and absorption coefficients were large in winter and low in summer. And both were high at 7:00 LT because of the abundant motor vehicle exhaust emissions during the morning rush hours. These results can further provide a scientific basis for local environmental policies for the government.

A wavelength-dependent light detection and ranging (LiDAR) backscatter intensity calibration method was developed to maximize the advantages of a multispectral LiDAR system. We established a spectral ratio calibration method for multispectral LiDAR and investigated the effective calibration procedure for the mixed measurement of the effect of incident angle and surface roughness. Experiment results showed that the proposed LiDAR spectral ratio is insensitive to sensor-related factors and advantageous in calibrating the effect of incidence angle and surface roughness. As the product of the LiDAR calibration procedure based on spectral ratio, extended vegetation indexes significantly improve the classification accuracy.

Differential absorption lidar (DIAL) is widely accepted as the most promising remote sensing means to map the global CO2concentrations. Nevertheless, diurnal variations and vertical distributions of atmospheric CO2cannot be obtained by satellite-borne and airborne measurements. Ground-based DIAL systems are developed to fill this gap, as well as serve as validations for satellite-borne measurements. Atmospheric factors play significant roles in obtaining accurate range-resolved measurements of XCO2. However, the influence of atmospheric factors on the performance of a ground-based DIAL system aiming at CO2measurements has not been dedicatedly discussed yet. The pressure, temperature, and water vapor of the atmosphere have been taken into consideration for performance evaluation after preselection of absorption lines around 1.6 μm in this paper. In addition, errors caused by variations of aerosols have also been analyzed by using theoretical simulations and real measurements. We found that biases caused by temperature and pressure uncertainties were 0.11-0.45 ppm/K and 0.39 ppm/hPa, respectively, if the central wavelength was utilized as the online wavelength. In addition, the water vapor effect could be neglected by cautious selection of online and offline wavelength. Finally, if the online and offline wavelengths were transmitted alternatively, the temporal and range resolutions have to be determined very carefully to balance the signal-to-noise ratio of acquired data and tolerable errors derived from variations of aerosols. A variable range resolution is recommended for CO2measurements at different altitudes to fulfill the target precision.

The vertical profile of atmospheric carbon dioxide is of great significance for carbon cycle and budget study. However, that parameter can be only obtained by flask samples from profiling aircraft till now. Ground-based differential absorption lidar is widely accepted as a promising remote sensing means to obtain the vertical CO2concentration profile. Unfortunately, classic DIAL retrieving method cannot provide results of adequate accuracy and precision. Here, we propose an improved retrieving method to solve this problem. Experiments showed that the accuracy and precision of results are superior to 0.2 ppm (parts per million) and 4E-5 ppm respectively by means of the proposed method. That could be an ideal result for further carbon cycle and climate change research.

Carbon dioxide is considered as the main factor leading to global climate change[1, 3]. Precise measurements, especially the different absorption lidar (DIAL), are needed for analyzing the carbon sources and sinks. The Ground-based DIAL usually emits on-line and offline lasers alternately[5, 8, 9], but aerosols fluctuations will affect the lidar signals. In order to offset the effects caused by aerosols fluctuations, a combined observation of 532 nm Mie lidar and 1572 nm DIAL was introduced and analyzed firstly. This paper analyzes the linear relation of the extinction coefficient between 532 nm lidar and 1572 nm lidar, and applies this connection to DIAL CO2retrieval by using an improved DIAL method, considering the importance of aerosols. The results obtained by 532 nm Mie lidar work as a reference, revealing an appropriate period of calculation and serving as calibration data. The result s of the combined observation show the feasibility of our experiment and method.

This letter proposes a blind image restoration method for the deblurring of remote sensing images. A simple but robust identification method of point spread function (PSF) support is proposed, and a joint estimation method is presented to simultaneously solve the PSF coefficients and restoration image. To narrow the solution space for the best possible definition, the Huber-Markov (Huber-Markov random field) prior model is employed to regularize the two series of unknowns. Experiments were performed to demonstrate the effectiveness of the proposed approach.

In this paper, an adjustable model-based image fusion method for multispectral (MS) and panchromatic (PAN) images is developed. The relationships of the desired high spatial resolution (HR) MS images to the observed low-spatial-resolution MS images and HR PAN image are formulated with image observation models. The maximum a posteriori framework is employed to describe the inverse problem of image fusion. By choosing particular probability density functions, the fused HR MS images are solved using a gradient descent algorithm. In particular, two functions are defined to adaptively determine most regularization parameters using the partially fused results at each iteration, retaining one parameter to adjust the tradeoff between the enhancement of spatial information and the maintenance of spectral information. The proposed method has been tested using QuickBird and IKONOS images and compared to several known fusion methods using quantitative evaluation indices. The experimental results verify the efficacy of this method.

Although the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO) has been widely used in aerosol research, the classification of aerosol and cloud still exist some problems. Tradition classification method used by NASA is probability distribution functions (PDFs), but in reality, when we want to realize this algorithm, we fund it is difficult to describe the multi-modal distribution of cloud backscatter coefficients. Further, because ice cloud and dust aerosol have some similar properties, so it is not easy to identify them. In this paper, we introduce a classification method which based on Support vector machine (SVM), and add another characteristic. Then according to the result of classification inverse the aerosol characteristic, the height of cloud top, at the same time, combine with the CloudSat calculate the other cloud character, these data will be helpful for further climate research.

In this paper a new technique of objects measurement based on spectral ratio lidar system has been proposed and developed to make horizontal-path laser measurements of objects. The two or more wavelengths laser transmitter operates within and adjacent to the sensitive bands exhibited by the characteristics of each object, the result could be used to establish inversion models of the laser transmitting backscatter signals. The application value and the key techniques of the spectral lidar are analyzed. The laser spectral ratio model is established and the lidar system is designed, the lidar measurements were down to testify its feasibility. Also issues to approach the final goal of this new technique are discussed.

The main objectives of this research were to select the best hyperspectral narrow bands in the study of rice under different levels of nitrogen and water was used to study the nutritional status of rice. The methodologies employed used partial least squares (PLS) analysis method and spectral bands inter-correlation method (ICM), on the basis of an experiment using reflectance spectra of rice leaves and the concentration of three foliar biochemicals: nitrogen, chlorophyll-a and water, we select the most appropriate wavelengths. We established several broad-band and narrow-band (wavelengths) combinations and compare the inverse effects with each other to determine the inversion accuracy of narrow bands. From the PLS regression result, we confirmed that a 5 narrow bands combination includes 552 nm, 660 nm, 675 nm, 752 nm, 776 nm is the best inversion model of rice leaf nitrogen content, and the 3 narrow bands combination includes 1158 nm, 1378 nm, 1955 nm is much available for rice leaf water content inversion.

Studying optical properties of atmospheric aerosol is important because aerosol affects people around the world significantly. These effects strongly depend on the physical and optical properties of aerosol particles. In this paper, we propose to use lidar, sun-photometer, and CALIPSO synchronously, then present combined retrieval to investigate the optical properties of aerosol. The observations were performed at Wuhan during the period of December 2007 to May 2008. The primary results show that the proposed method improved the precision of aerosol optical depth effectively. Furthermore, long-term atmospheric and aerosol data could be obtained by consecutive observations. Also these data will be useful for future understanding about their environmental and climate effects.

The stable aerosol retrieval algorithm needs a prior assumption of lidar ratio (the extinction-to-backscatter ratio), and the known aerosol type that is the prerequisite of this assumption, so how to identify the clouds and aerosol from lidar profile is fundamental to acquire atmospheric optical parameter. In this paper, we first employ the CloudSat to validate the CALISPO's classificatory results, which is released in different versions, after choosing more accurate classification, then start retrieving. Second, sun-photometer is used for verifying the CALIPSO's calibration coefficient and supplies the day time records which are relatively more accurate. Finally, aerosol characteristic in Hubei province is analyzed. All the data will supply more available information for further climate change research.

Obtaining high-accuracy optical property of atmosphere timely will lead to good results of atmospheric correction and real remote sensing image inversion. We have developed a multi-function Raman/Mie lidar system. Making use of this lidar, we can get the spatial distribution and the time evolution of many atmospheric parameters. In this paper, we studied the main factors affecting atmospheric correction, namely absorption and scattering by aerosols and several major atmospheric elements. Preliminary experimental results are described. These data are combined with sunphotometer data and another scanning Mie lidar data, integrated with the imagery from such as remote sensing of the Earth satellite to obtain the ground truth.

in this paper, a new method is proposed, which combine CALIPSO lidar data with AERONET data to acquire the unstable aerosol information in Taiwan. First, introduce a CALIPSO retrieval arithmetic to obtain the aerosol optical depth, and then compare the differences between CALIPSO and AERONET. By combining these two techniques we could not only have the precise site data from AERONET, but also own the change information of aerosol in southeast China from CALIPSO. Different from AERONET, we can also display the spatial properties of aerosol from CALIPSO lidar backscatter data, such as, the strength of aerosol in each layer and their change with time in the aerosphere.

In this paper, a hybrid retrieval method of aerosol optical depth based on the combination of active and passive optical remote sensing is proposed. Two methods to retrieve the atmospheric optical depth are introduced: the so-called dark pixel method is used for retrieving the aerosol optical depth from MODIS; the other one is used by CALIPSO lidar data. After analyzing the two methods, the combined MODIS and CALIPSO method is applied for the aerosol optical depth, the primary experimental results show that these data are in agreement with each other in time-space evolvement trend.

A new atmospheric correction method of earth observation images based on the combination of satellite data and lidar data is proposed in this paper. A mobile scanning Mie lidar was developed to detect the aerosols' spatial and temporal distribution for the purpose above. To obtain more accurate data, future development plan of a multi-wavelength, multi-channel Raman lidar is discussed. Earth observation images processed by the radiative transfer model and this new method are presented. Also issues to approach the final goal of this new atmospheric correction method are discussed.