Currently, few-shot learning (FSL) is widely used in image classification hyperspectral image classification (HSIC), owing to its exceptional proficiency in achieving good performance with few training samples. Although the FSL has made good progress, there are still some problems to be solved. On the one hand, existing methods rely on linear distance to learn metrics, which cannot capture the subtle similarities and differences between scarce prior samples. On the other hand, many current methods directly superimpose the features of spatial and spectral information, without deeply fusing the internal relationship between these two kinds of information. To address the aforementioned issues, a deep metric learning method based on Brownian distance covariance (DML-BDC) is proposed for few-shot HSIC. A dual-channel Brownian distance covariance feature extraction network is designed, which uses the Brownian covariance representation to model and fuse the spatial and spectral information and uses two different feature extractors to achieve the effect of information complementarity. Then, a metric loss based on Gaussian kernel distance is proposed to learn the complex nonlinear structure and subtle similarities and differences between support samples. Experiments on three benchmark datasets show that DML-BDC has advantages over the existing mainstream methods in terms of classification accuracy, generalization, and model complexity.

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.

Accurate beach topography mapping is crucial for understanding coastal dynamics and mitigating climate change impacts. However, traditional methods such as airborne LiDAR have limitations, leading to substantial gaps in national-scale elevation data. This study presents an innovative framework to reconstruct missing elevation data along New Zealand’s coastline by integrating airborne LiDAR, Sentinel-2 optical imagery, and geometric features (distance) using machine learning methods. Our results show that Artificial Neural Network (ANN) emerged as the best model (test set: R²=0.79, RMSE=0.91 m; validation set: 0.79, RMSE=0.93 m), outperforming other models in accuracy. The produced 10-m DEM for national-scale sandy beaches expands area coverage by 286.6% (114.15 km²), filling gaps in 1249 beaches, including remote areas such as Stewart Island. This novel framework offers a scalable solution for improving the comprehensiveness and accuracy of beach topography. It provides essential support for inundation prediction, habitat management, and the development of climate adaptation strategies, thereby facilitating more informed decision-making in coastal zone management and climate change mitigation efforts.

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 Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with a new type of photon-counting laser altimetry system, demonstrating significant potential for global mapping. However, the ICESat-2 data contain a large amount of noise photons influenced by solar background, making data processing challenging. In this letter, we propose an end-to-end deep neural network called VOJA-Net, which utilizes semantic information from multilevel decoders to learn multilevel feature representations, thus enhancing denoising performance. First, we design a module called ICESat spatial transformer (IS Transformer), specifically for extracting spatial features from ICESat-2 data to deepen the network’s understanding of the spatial distribution differences between valid photons and noise. Second, we construct a joint attention fusion module named joint attention fusion (JA Fusion), which employs a multibranch attention mechanism to avoid the network from overlearning features of dense parts of the ICESat-2 point cloud, thereby enhancing the network’s ability to recognize sparse valid photons. Finally, we design a multiscale denoising loss (MSDLoss) function to guide network model training, promoting the network to achieve optimal denoising effects. On our carefully annotated ICESat-2 point cloud dataset, the final model achieved$F1$score and mIoU of 93.34% and 73.40%, respectively, demonstrating the competitiveness of the proposed approach.

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.

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.

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.

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.

CO2 is the most important warming gas in atmosphere, it's meaningful to measure the CO2 concentration with high precise by different sensors. In this manuscript, we introduced the campaign of QHD (Qinhuangdao) flights, which equipped with CO2- IPDA (integrated path of different absorption LIDAR), an in-situ CO2 sensor, thermometer, hygrometer and GPS (Global Positioning System). A fast and accurately retrieve method of CO2 by the oral data acquired by the airborne IPDA has been developed by our group. These flight contains three different landforms, including sea, city and mountain. It shows apparent difference on the distribution of CO2 among different landforms. Finally, we compared XCO2 data from OCO-2 and results of XCO2 calculated by IPDA system, it shows little difference.

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).

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.

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.

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.

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.

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.

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.