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The Advanced Geosynchronous Radiation Imager (AGRI) is a mission-critical instrument for the Fengyun series of satellites. AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands, enabling the detection of highly variable aerosol optical depth (AOD). Quantitative retrieval of AOD has hitherto been challenging, especially over land. In this study, an AOD retrieval algorithm is proposed that combines deep learning and transfer learning. The algorithm uses core concepts from both the Dark Target (DT) and Deep Blue (DB) algorithms to select features for the machine-learning (ML) algorithm, allowing for AOD retrieval at 550 nm over both dark and bright surfaces. The algorithm consists of two steps: ① A baseline deep neural network (DNN) with skip connections is developed using 10 min Advanced Himawari Imager (AHI) AODs as the target variable, and ② sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters. Out-of-station validation shows that the retrieved AOD attains high accuracy, characterized by a coefficient of determination (R2) of 0.70, a mean bias error (MBE) of 0.03, and a percentage of data within the expected error (EE) of 70.7%. A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm, as well as the surface reflectance at 650 nm, are the two largest sources of uncertainty impacting the retrieval. In a case study of monitoring an extreme aerosol event, the AGRI AOD is found to be able to capture the detailed temporal evolution of the event. This work demonstrates the superiority of the transfer-learning technique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events.

Chlorophyll-a (Chl-a) concentrations serve as a pivotal metric for assessing the biological content and eutrophication levels of waterbodies. In optical remote sensing, empirical and semi-analytical methods are commonly employed for Chl-a retrieval. Leveraging the Level-1B remote sensing data from the Directional Polarimetric Camera (DPC) aboard the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS), this study amalgamates empirical algorithms (OC2, OC3, Aiken-P, and Aiken-C) and look-up-table (LUT) techniques to estimate Chl-a in open water. Test data acquired from DPC on 20 August 2022, are validated against the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite and AERONET-Ocean Color water-leaving radiance data for the same area and date. The results reveal that among the tested algorithms, particularly in the log10 scale space, the Aiken-P algorithm demonstrates the highest correlation coefficient (R2 = 0.6333), with the lowest bias of 1.0366 and a minimum mean absolute error (MAE) of 1.2097. This underscores the suitability and accuracy of the Aiken-P algorithm for water colour retrieval based on DPC-derived data, highlighting its practical applicability. [ABSTRACT FROM AUTHOR]

This paper proposes a ResGA-Net algorithm for cloud top height (CTH) retrieval using FY-4A satellite data. The algorithm utilizes genetic algorithms for data selection and employs a residual module-based neural network for modeling. It takes the spectral channel data from the FY-4A satellite as input features and uses CTH extracted from ground-based millimeter-wave cloud radar reflectivity as the target. By combining the large observation scale of the FY-4A satellite and the high accuracy of ground-based cloud radar observations, the model can generate satellite CTH products with higher precision. To validate the effectiveness of the algorithm, experiments were conducted using data from the Beijing area spanning from January 2020 to January 2022. The experimental results show that the metrics of the proposed ResGA-Net outperform those of various contrastive algorithms, and compared to the original FY-4A CTH product, the RMSE and MAE have decreased by 37.89% and 34.77%, while the PCC and SRCC have increased by 11.17% and 9.47%, respectively, demonstrating the superiority of the proposed method presented in this paper. [ABSTRACT FROM AUTHOR]

The satellite-based aerosol optical depth (AOD), which can provide continuous spatial observations of aerosol loadings, is widely adopted to estimate atmospheric environmental quality and evaluate its risk for human health. However, current satellite-retrieved AOD products characterized by a comparatively coarse spatial resolution (≥1 km) can hardly analyze the structure of atmospheric pollution or its correlation with urban landscapes over populated urban areas. Existed studies have tried to address this deficiency by retrieving high-resolution AOD using Landsat images, whose long revisit period (16 days nominally), however, largely limits its applications related to urban air pollution research. To achieve both high spatial resolution and expected revisit period from satellite observation, in this study, a comprehensive AOD retrieval framework is developed for Wide-Field-of-View (WFV) satellite sensors to yield a daily AOD dataset over urban areas with 160 m spatial resolution, based on the Gaofen-1 and Gaofen-6 synergistic observations. To address the crucial challenge that the high spatial resolution and complex urban landscape both contribute a dramatic variation of land surface bidirectional reflectance, a Simulated-Annealing-coupled Semiempirical Modified Rahman-Pinty-Verstraete (SAS-MRPV) scheme is proposed to model and estimate the land surface reflectance (LSR) in the AOD retrieval framework, where the SAS-MRPV scheme is implemented using simulated annealing iteration initialized by the bidirectional reflectance distribution function (BRDF) products from the Moderate Resolution Imaging Spectrometer (MODIS) as a priori knowledge. The validation results demonstrate that the Gaofen WFV AOD retrievals exhibit good agreement with the ground-based AErosol RObotic NETwork (AERONET) and SONET (Sun-sky radiometer Observation NETwork) AOD, demonstrating the correlation coefficient and the expected error (EE)±(0.05 + 0.15AOD AERONET/SONET) ratio respectively reaching up to 0.97 and over 80 %, and only slight bias fluctuations are found under different land cover types, aerosol loading, and seasonal conditions. Moreover, the validation results of Gaofen WFV AOD retrievals, with LSR estimated based on the SAS-MRPV scheme, the MODIS BRDF Products, and the Minimum Reflectance Technique (MRT) method, demonstrate better accuracy and completeness of AOD retrievals using the SAS-MPRV scheme over both natural and impervious land surfaces, indicating the stability and reliability of proposed SAS-MRPV LSR determination scheme in WFV AOD retrieval. In addition to that, the error analyses and quality control results demonstrate that the SAS-MRPV scheme is effective and necessary to guarantee the reliability and accuracy of land surface bidirectional reflectance estimation by identifying and excluding the land surface pixels unsuitable for LSR modeling, which mitigates the uncertainty and yield better accuracy in the final WFV AOD products. Furthermore, the inter-comparison between WFV-derived AOD against the operational MODIS Deep Blue (10 km), Dark Target (3 km), and Multi-Angle Implementation of Atmospheric Correction (MAIAC, 1 km) AOD products demonstrates that the 160 m WFV AOD retrievals, on the basis of obtaining similar aerosol overall descriptions as MODIS AOD retrievals, possess the capability to characterize the spatial distribution of atmospheric pollution at a finer scale with smoother variation under both clean and polluted conditions. With outstanding accuracy and reliable performance, the developed comprehensive high-resolution AOD retrieval framework exhibits substantial potential for operational AOD retrieval of Gaofen WFV satellite observations, which could be directly applied to other urban areas supporting further studies related to air pollution emission management and health risk assessment at extra-fine spatial scale. [ABSTRACT FROM AUTHOR]

The Imaging InfraRed Spectrometer (IIRS) on board Chandrayaan-2 has been providing high spatial and spectral resolution observations of the lunar surface in 256 spectral bands (0.7-5 µm) since September 2019. It is primarily intended for mineral mapping and identifying hydration features on the lunar surface using reflectance spectra in the range of 0.7-3.2 µm. Here, we have used the IIRS observations in the 3-5 µm range to retrieve daytime lunar surface temperature and spectral emissivity using an optimal estimation theory-based retrieval algorithm. The surface temperature is retrieved at every pixel, while spectral emissivity is retrieved at every third pixel of the hyperspectral image. The mean uncertainty of the retrieved spectral emissivity varies from 0.04 to 0.08, while for surface temperature, it is about 3.5 K. The retrieved spectral emissivity is found to be in close agreement with the emissivity of the Apollo-16 return soil samples. [ABSTRACT FROM AUTHOR]

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 severity of the global warming issue emphasizes the critical importance of utilizing carbon satellite data to estimate ground-level carbon dioxide emissions. However, existing reviews have not kept pace with the latest research developments. Therefore, this article provides an overview of relevant work in the global carbon emissions field to address this knowledge gap. Through visual analysis using Citespace software, the article outlines two methods for quantifying carbon dioxide: 1) ground-level observations; and 2) satellite remote sensing. Despite the unique advantages of ground-level observations, satellite remote sensing is crucial for its extensive spatial coverage and long-term continuity in understanding carbon cycling, drawing significant attention. In addition, the article integrates the application of machine learning in the carbon emissions field, dividing it into two parts: Direct estimation based on ground emission inventory data and estimation of ground-level carbon emissions based on carbon satellite data. This innovative approach combines satellite observational data with ground data to accurately estimate the current ground-level carbon emissions with robust spatial distribution characteristics.

The atmospheric infrared ultraspectral sounder (AIUS) is China's first infrared occultation spectrometer onboard the GaoFen-5 satellite. This instrument provides measurements in the near-infrared and mid-infrared spectral regions with a spectral resolution of 0.02 cm$^{-1}$, monitoring key greenhouse gases like methane (CH$_{4}$). In this study, we have developed a physics-based retrieval algorithm for accurately deriving CH$_{4}$ mole fraction profiles from infrared absorption spectra. The retrieval algorithm is applied to the AIUS and successfully obtains CH$_{4}$ mole fraction profiles from January to December 2019 over Antarctic. The validation results show that the retrieval algorithm has a satisfactory performance using AIUS measurements, with average relative standard deviation of the differences less than 7.2% throughout the altitude range of 10–70 km.

The Tibet Plateau is known as the "third pole" of the world, and its environmental change profoundly impacts East Asia and even the global climate. HDO is the stable isotope of water vapor, which acts as an ideal tracer for studying the water cycle, and which is commonly used for atmospheric circulation and climatic studies. To monitor the water vapor isotopic abundance in the Tibetan Plateau, a portable laser heterodyne radiometer was operated in Golmud in August 2019. The radiometer utilizes a narrow-linewidth 3.66 μm distributed feedback interband cascade laser as the local oscillator, the heterodyne module is been optimized and the radiometer performs with high resolution and stability in obtaining spectral data. Furthermore, the absorption spectra of atmospheric HDO and H2O are obtained, and the retrieval method for water vapor isotopic abundance is discussed. The optimal estimation method is adopted to retrieve the density of HDO and H2O. The average column density of H2O was 1.22 g/cm2, and the HDO/H2O ratio in Golmud was 178 ± 15 × 10−6 during the observation. For a better understanding of the retrieval, the retrieval errors are analyzed and compared. The results indicate that the smoothing error is significantly higher than the measurement error in this work. The backward trajectory analysis of atmospheric transport is used to investigate the relationship between water vapor density and atmospheric motion. The results indicate that the variation of H2O column density and HDO/H2O ratio have a relationship with atmospheric movements. [ABSTRACT FROM AUTHOR]

In this paper, for the common live streaming with cargo dispute cases in the context of the information technology era, the live streaming with cargo industry dispute case retrieval algorithm based on case-based reasoning calculates the attribute similarity measure and weights of the dispute cases in order to realize the live streaming with cargo industry dispute case retrieval. Three thousand judicial documents with live streaming with cargo disputes as the cause of the case were crawled from the China Judicial Documents Network as the initial data for the study and the method of BIOES was used to annotate the initial data and formulate eight basic legal elements of live streaming with cargo dispute cases. The form of extracting legal elements of live streaming with goods industry is transformed into a multi-label text classification task, and the neural network modeling method combined with the attention mechanism is used to design the extraction model of legal elements of the live streaming with goods dispute cases, and then the live streaming with goods dispute cases on the Internet are analyzed by examples. The results show that the Acc value of this paper’s model is 0.973, the Loss value is 0.051, and the F1 value is 0.981, and on the whole, this paper’s model performs better compared with the other five models, i.e., it shows that this paper’s model can be more intelligent and accurate to collect the case element information under the field of live streaming with goods disputes. This study aims to contribute to the development of a new sales model of live streaming with goods benignly.

This paper proposes a ResGA-Net algorithm for cloud top height (CTH) retrieval using FY-4A satellite data. The algorithm utilizes genetic algorithms for data selection and employs a residual module-based neural network for modeling. It takes the spectral channel data from the FY-4A satellite as input features and uses CTH extracted from ground-based millimeter-wave cloud radar reflectivity as the target. By combining the large observation scale of the FY-4A satellite and the high accuracy of ground-based cloud radar observations, the model can generate satellite CTH products with higher precision. To validate the effectiveness of the algorithm, experiments were conducted using data from the Beijing area spanning from January 2020 to January 2022. The experimental results show that the metrics of the proposed ResGA-Net outperform those of various contrastive algorithms, and compared to the original FY-4A CTH product, the RMSE and MAE have decreased by 37.89% and 34.77%, while the PCC and SRCC have increased by 11.17% and 9.47%, respectively, demonstrating the superiority of the proposed method presented in this paper.

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.

Characterization of an isolated attosecond pulse (IAP) in the extreme ultraviolet (XUV) or soft x-ray (SXR) region is essential for its applications. Here we propose to retrieve an IAP in the time domain directly through the modulation of high-harmonic generation (HHG) spectra in the presence of a time-delayed intense few-cycle infrared or mid-infrared laser. The retrieval algorithm is derived based on the strong-field approximation and an extended quantitative rescattering model. We show that both isolated XUV pulses with a narrow spectral bandwidth and isolated SXR pulses with a broad bandwidth can be well characterized through the HHG streaking spectra. Such an all-optical method for characterizing the IAP differs from the commonly used approach based on the streaked photoelectron spectra that would require electron spectrometers. We check the robustness of the retrieval method by changing the dressing laser or by adjusting the steps of time delay. We also show that the XUV pulse can be accurately retrieved by treating the HHG streaking spectra calculated from solving the time-dependent Schrödinger equation for single atoms as the 'experimental' data. [ABSTRACT FROM AUTHOR]

Climate change and intensive anthropogenic activities have severely challenged the water quality of China’s Pearl River Estuary (PRE). Further investigations into long-term water quality variation and associated driving mechanisms are therefore necessary to support the sustainable development of the PRE’s Greater Bay Area (GBA). This study used remote sensing retrieval to address long-term spatiotemporal chlorophyll-a (Chl-a) variation characteristics in the PRE and the relationship between Chl-a concentrations and socioeconomic/environmental indicators. Three decades of Landsat satellite images and measured data were collected, and a two-band global algorithm was used to retrieve Chl-a concentration data. Results reveal significant spatiotemporal variability in Chl-a concentrations. The space-averaged Chl-a concentration exhibited a slight downward trend during the past three decades, and the multi-year mean value was 5.20 mg/L. Changes to environmental protection policies in recent years have improved overall PRE water quality. The western section of the PRE had the highest Chl-a concentration (i.e., 5.92 mg/L average) while the eastern section had the lowest (i.e., 3.98 mg/L average). This discrepancy was likely caused by the western section’s more intensive industrial activities, resulting in a higher overall wastewater discharge volume. Affected by climatic conditions, winter Chl-a concentrations were evenly distributed while summer concentrations were significantly higher. Additionally, Chl-a concentrations significantly and positively correlated with total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), and the biotic oxygen demand (BOD5). Chl-a concentrations also correlated with external factors (i.e., climate and anthropogenic activities). Among these factors, industrial wastewater discharge and the proportion of primary industries in coastal cities significantly and positively correlated with water quality. This study is intended to help direct water quality improvement management and urban sustainable development in the GBA.

The Tibet Plateau is known as the “third pole” of the world, and its environmental change profoundly impacts East Asia and even the global climate. HDO is the stable isotope of water vapor, which acts as an ideal tracer for studying the water cycle, and which is commonly used for atmospheric circulation and climatic studies. To monitor the water vapor isotopic abundance in the Tibetan Plateau, a portable laser heterodyne radiometer was operated in Golmud in August 2019. The radiometer utilizes a narrow-linewidth 3.66 μm distributed feedback interband cascade laser as the local oscillator, the heterodyne module is been optimized and the radiometer performs with high resolution and stability in obtaining spectral data. Furthermore, the absorption spectra of atmospheric HDO and H2O are obtained, and the retrieval method for water vapor isotopic abundance is discussed. The optimal estimation method is adopted to retrieve the density of HDO and H2O. The average column density of H2O was 1.22 g/cm2, and the HDO/H2O ratio in Golmud was 178 ± 15 × 10−6 during the observation. For a better understanding of the retrieval, the retrieval errors are analyzed and compared. The results indicate that the smoothing error is significantly higher than the measurement error in this work. The backward trajectory analysis of atmospheric transport is used to investigate the relationship between water vapor density and atmospheric motion. The results indicate that the variation of H2O column density and HDO/H2O ratio have a relationship with atmospheric movements.

In order to improve the accuracy of the ground-based microwave radiometer retrievals of the atmospheric temperature and humidity profile, and enhance the observation performance of locally deployed devices, this study implements two kinds of neural network methods for ground-based radiometer. One is called neural network direct sample retrieval algorithm, and the other is the neural network indirect sample retrieval algorithm based on the preprocessing of the observed brightness temperature. In this paper, these two retrieval algorithms are applied to the HRA002 domestic ground-based microwave radiometer developed by Wuhan Huameng Technology Co., Ltd., and the comparative observation experiments with soundings and three US MP-3000A microwave radiometers are carried out at the Wuhan National Basic Weather Station. The experiments results show that the mean square error of water vapor density and relative humidity retrieved by the improved network in direct sample retrieval is reduced by 0.94 g·m-3 and 5% respectively. The correlation between the observed brightness temperature and the simulated brightness temperature is improved significantly after preprocessing, the mean square error of the retrieval of lower-level temperature, water vapor density and relative humidity compared with sounding are improved from 2.4 K, 3.26 g·m-3 and 18.79% to 1.58 K, 2.18 g·m-3 and 14.55%. The accuracy of the HRA002 indirect sample retrieval method is slightly lower than that of the direct sample retrieval method, but the accuracy of the two methods is close. Compared with the retrieval results of 3 MP-3000A, the temperature profile of HRA002 using the direct sample retrieval method is generally better than MP-3000A, while the average deviations of water vapor density and relative humidity using indirect sample retrieval method of HRA002 is better than others, but the mean square errors are slightly lower. The research results in this paper show that the improved direct sample inversion method is more suitable for radiometer hardware performance and has higher retrieval accuracy, and the brightness temperature preprocessing of HRA002 can effectively improve the accuracy of indirect sample retrieval, making up for the defect that the direct sample retrieval method needs long-term observation data. The results also show that the use of direct sample and indirect sample retrieval algorithms can improve the localized and individualized observation performance of domestic microwave radiometers, and is useful in retrieving atmospheric parameter profiles.

Atmospheric ozone is a pollutant gas that has an important influence on the process of atmospheric radiation transmission and climate change. The Fengyun-3D (FY-3D) satellite Hyperspectral Infrared Atmospheric Sounder (HIRAS) has better spectral performance than other remote sensing payloads. Its observation radiation data contains abundant atmospheric vertical information, which can be used for ozone retrieval, but there are no ozone profile business products being generated at present. Therefore, for the mainland of Hong Kong, based on HIRAS infrared hyperspectral observation data, we used the traditional one-dimensional variational (1D-VAR) physical retrieval algorithm, combined with the radiative transfer model for TOVS (RTTOV), and selected the spectrum channel according to the optimal sensitive profile algorithm. The artificial neural network (ANN) algorithm was used to optimize the prior profiles, and the atmospheric ozone profile retrieval system was established. Finally, a set of ozone profile retrieval schemes suitable for FY-3D/HIRAS were summarized. We used ERA5 reanalysis data and World Ozone and Ultraviolet Radiation Data Centre (WOUDC) data to determine true values. The retrieval results were compared with Global Forecast System (GFS) forecast data, Ozone Mapping and Profile Suite (OMPS) ozone products, and Atmospheric Infrared Sounder (AIRS) ozone products. The results show that our ozone profile retrieval scheme makes up for the shortcomings of the conventional physical methods in some atmospheric pressure levels. The overall root-mean-square error (RMSE) of the ozone from the ground to the top of the stratosphere is within 30% on average, which was better than that for the GFS forecast data; the retrieval accuracy RMSE (%) was less than 20% in the pressure layer with the highest ozone concentration (15–25 hPa), which is better than that of OMPS ozone products and AIRS ozone products. The retrieval results prove that FY3D/HIRAS observation data allow ozone profile retrieval. This paper provides a reference for generating independent HIRAS ozone profile product data sets in business, and provides support for the subsequent application of Fengyun-3 series meteorological satellites in atmospheric parameter remote sensing. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) products presently available in permafrost regions, especially on the Qinghai–Tibet Plateau (QTP), hardly meet the demands of evaluating and modeling climatic, hydrological, and ecological processes, due to their significant bias and low spatial resolution. This study developed an algorithm to generate high-spatial-resolution SM during the thawing season using Sentinel-1 (S1) and Sentinel-2 (S2) temporal data in the permafrost environment. This algorithm utilizes the seasonal backscatter differences to reduce the effect of surface roughness and uses the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) to characterize vegetation contribution. Then, the SM map with a grid spacing of 50 m × 50 m in the hinterland of the QTP with an area of 505 km × 246 km was generated. The results were independently validated based on in situ data from active layer monitoring sites. It shows that this algorithm can retrieve SM well in the study area. The coefficient of determination (R2) and root-mean-square error (RMSE) are 0.82 and 0.06 m3/m3, respectively. This study analyzed the SM distribution of different vegetation types: the alpine swamp meadow had the largest SM of 0.26 m3/m3, followed by the alpine meadow (0.23), alpine steppe (0.2), and alpine desert (0.16), taking the Tuotuo River basin as an example. We also found a significantly negative correlation between the coefficient of variation (CV) and SM in the permafrost area, and the variability of SM is higher in drier environments and lower in wetter environments. The comparison with ERA5-Land, GLDAS, and ESA CCI showed that the proposed method can provide more spatial details and achieve better performance in permafrost areas on QTP. The results also indicated that the developed algorithm has the potential to be applied in the entire permafrost regions on the QTP. [ABSTRACT FROM AUTHOR]

Land carbon sink is a vital component for the achievement of China's ambitious carbon neutrality goal, but its magnitude is poorly known. Atmospheric observations and inverse models are valuable tools to constrain the China's land carbon sink. Space‐based CO2 measurements from satellites form an emerging data stream for application of such atmospheric inversions. Here, we reviewed the satellite missions that is dedicated to the monitoring of CO2, and the recent progresses on the inversion of China's land carbon sink using satellite CO2 measurements. We summarized the limitations and challenges in current space platforms, retrieval algorithms, and the inverse modeling. It is shown that there are large uncertainties of contemporary satellite‐based estimates of China's land carbon sink. We discussed future opportunities of continuous improvements in three aspects to better constrain China's land carbon sink with space‐based CO2 measurements. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI), which is closely related to canopy physiological processes such as photosynthesis and water utilization, serves as an important biophysical parameter for monitoring rice (Oryza sativa L.) growth. However, due to significant variations in the water-soil mixed background during the rice growing season, the optimal LAI retrieval method for rice throughout the whole period remains unclear. Here, different LAI retrieval methods, categorized into vegetation index (VI)-, look-up table (LUT)- and machine learning (ML)-based groups, were evaluated for rice at multiple growth stages using Sentinel-2 images. Particularly, the performance of rice LAI derived from the optimal retrieval model was comprehensively analyzed to understand factors influencing LAI estimation during different growth stages. Results suggested that the Gaussian Process Regression (GPR) in the ML-based group achieved the best performance for rice LAI estimation in the whole growth period (R2 = 0.75, RMSE = 0.60, RRMSE = 15.81 %), followed by the normalized difference VI (NDVI) in the VI-based group (R2 = 0.74, RMSE = 0.61, RRMSE = 15.98 %) and the cost function K(x)=log(x)+1/x in the LUT-based group (R2 = 0.70, RMSE = 0.69, RRMSE = 18.09 %). Notably, although the VI-based LAI retrieval method incorporated ground measurements to build empirical LAI-VI relationships, a single VI with parametric regression cannot well capture variations in rice LAI across growth stages compared to the ML-based method using simulation dataset from the physical model. The optimal ML-based method also exhibited better performance in rice LAI estimation than similar studies, with R2 increasing by 0.14 and RMSE decreasing by 0.18. Furthermore, based on ground LAI measurements, the water-soil mixed background is a primary influencing factor for rice LAI estimation in the tillering, jointing, and booting stages (RMSE: 0.39–0.79), whereas the saturation effects should be considered in the full heading stage (RMSE = 0.68). Overall, this study indicates that the GPR-based retrieval strategy is the optimal method for generating rice LAI datasets over the whole growth period, providing valuable reference for precision agriculture application such as field irrigation, fertilization management, and yield estimation. [Display omitted] • Optimal LAI retrieval algorithms were explored for rice at multiple growth stages. • Vegetation index, look-up table and machine learning were used for LAI estimation. • Machine learning-based method effectively estimated rice LAI across growth stages. • Water-soil background mainly affected LAI estimates from tillering to booting stage. [ABSTRACT FROM AUTHOR]

Freeze/Thaw (F/T) surface state detection and imaging has been demonstrated as a capability of NASA's Cyclone Global Navigation Satellite System mission. The objective of the research presented here is the application of an existing F/T retrieval algorithm to multiple target areas over several years in order to demonstrate the ability to provide long term trending of F/T behavior. Metrics are developed and evaluated to characterize F/T trends related to global warming. They include the annual fraction of time spent frozen and thawed as a function of location and year, and the earliest fall freeze and earliest spring thaw as a function of location and year. The results demonstrate the capability of Global Navigation Satellite Systems Reflectometry to provide long term trending of F/T behavior relevant to studies of climate change.

Remote sensing of sea surface salinity (SSS) near land is difficult due to land contamination. In this article, we assess SSS retrieved from the soil moisture active passive (SMAP) mission in coastal region. SMAP SSS products from the Jet Propulsion Laboratory (JPL), and from the remote sensing systems (RSS) are collocated with in situ data collected by saildrones during the North American West Coast Survey. Satellite and saildrone salinity measurements reveal consistent large-scale features: the fresh water (low SSS) assocciated with the Columbia River discharge, and the relatively salty water (high SSS) near Baja California associated with regional upwelling. The standard deviation of the difference for collocations with SMAP Level 3 (eight days average) between 40 and 100 km from land is 0.51 (0.56) psu for JPL V5 (RSS V4 70 km). This is encouraging for the potential application of SMAP SSS in monitoring coastal zone freshwater particularly where there exists large freshwater variance. We analyze the different land correction approaches independently developed at JPL and RSS using SMAP level 2 matchups. We found that JPL's land correction method is more promising in pushing SMAP SSS retrieval towards land. For future improvement, we suggest implementing dynamic land correction versus the current climatology-based static land correction to reduce uncertainty in estimating land contribution. In level 2 to level 3 processing, a more rigorous quality control may help to eliminate outliers and deliver reliable level 3 products without over-smoothing, which is important in resolving coastal processes such as fronts or upwelling.

Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithms, rain rate estimations are continuously improving. This study provides a comparative performance appraisal of three hybrid machine learning algorithms namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) and Hybrid Fuzzy Inference System (HYFIS) for rain rate estimation using the Global Precipitation Measurement (GPM)'s Microwave Imager (GMI) and ground-based Disdrometer data. The in situ sampling was conducted at four different locations (both land and ocean) across the Indian region and different statistical metrics were used to evaluate the performances of these models. The results showed that HYFIS algorithm has provided better rain rate estimation than ANFIS and DENFIS. The study endorses these neuro-fuzzy models for generating accurate precipitation products and can be considered as an alternative for future satellite retrieval algorithms. [ABSTRACT FROM AUTHOR]

Orbital radars are used to monitor the state of the sea ice in the Arctic and Antarctic. The backscattering radar cross section (RCS) is used to determine the type of scattering surface. The power of the reflected signal depends on many factors, so the problem of separating sea ice and sea waves is not always unambiguous. Previous research has shown that microwave Doppler radar installed on aircrafts can be used to determine the boundary of sea ice. The width of the Doppler spectrum for wide or knife-like antenna beam depends on the statistical parameters of the reflecting surface, so sea ice and sea waves are easily separated. However, when installing a Doppler radar on a satellite, the spatial resolution becomes extremely low. In this research, we discuss the possibility of improving the spatial resolution by dividing the antenna footprint into elementary scattering cells. To do this, it is proposed to use the original incoherent synthesis procedure, which allows one to determine the dependence of the RCS on the incidence angle for an elementary scattering cell. Numerical modeling was performed and processing of model data confirmed that sea ice and sea waves are separated. The coefficient of kurtosis was used as a criterion in the algorithm. In addition, for sea waves, it is possible to determine the mean square slopes (mss) of large-scale waves, compared to the electromagnetic wavelength of sea waves along the sounding direction. [ABSTRACT FROM AUTHOR]

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory's microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the "truth", this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR's precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development. [ABSTRACT FROM AUTHOR]

Space-borne visible and infrared instruments can only observe the information of the cloud top or within a limited depth below the top due to their lack of ability to penetrate clouds, while space-borne passive microwave instruments can obtain cloud body information because radiation generated from particles inside cloud can penetrate cloud. Therefore, space-borne passive microwave instruments have more advantages in cloud and precipitation observation. Since the end of 1970s, the United States has invested a large number of space-borne passive microwave instruments to obtain information of cloud and precipitation for weather forecasting and so on, which has also promoted the rapid development of retrieval algorithms on cloud and precipitation parameters remote sensed by passive microwave instruments. This paper focuses on the research progress of retrieval algorithms of cloud liquid water over ocean based on satellite-borne passive microwave remote sensing, aiming to improve the application ability of satellite-borne passive microwave instruments in China. Firstly, several kinds of microwave radiative transfer models are briefly introduced. Secondly, the sensitive studies on passive microwave channel (frequency) to cloud liquid water and oceanic surface parameters are presented. Then, retrieval algorithms of statistical algorithm, physical algorithm, combination method of physical and statistical algorithm, multi-parameter synchronous retrieval algorithm, algorithm based on passive microwave and visible/infrared, of cloud liquid water over ocean are introduced. Finally, the future study directions of this field are discussed.

China successfully launched the Atmospheric Environment Monitoring Satellite (AEMS) equipped with an Atmospheric Carbon Dioxide Lidar (ACDL) on April 16, 2022, which is the world's first satellite based on Integrated Path Differential Absorption (IPDA) technique to detect the atmospheric CO 2 column-weighted dry-air mixing ratio (XCO 2). In order to accurately and quickly process the AEMS measurements, we proposed a systematic retrieval algorithm for the AEMS ACDL and conducted two airborne campaigns to validate its performance. The first airborne campaign was conducted in the land-sea interface region of northeast China in 2019. The CO 2 retrieval algorithm distinguished significant horizontal XCO 2 gradients over different underlying surfaces and obtained an apparent XCO 2 enhancement of 8–18 ppm between the urban and forests. The CO 2 retrievals not only demonstrated the excellent detection capability of the ACDL for carbon sources and sinks, but also proved the feasibility of the retrieval algorithm in complex terrain and variable atmospheric conditions. The second airborne experiment was conducted in 2021 in the interior desert region of China, which is an excellent flight field to explore the accuracy and precision limits of the retrieval algorithm. We validated the XCO 2 retrievals with the airborne in-situ CO 2 profiles and demonstrated that the XCO 2 accuracy and precision were 0.29 ppm and 0.63 ppm with 1.5-km averages over the desert surface, indicating the accuracy of the retrieval algorithm. The hard target elevation (HTE) retrieval validation results indicate that the IPDA lidar ranging precision is 0.69 m and 6.29 m for the ocean and land surface, respectively. In addition, further analysis combined with the space-borne IPDA lidar simulator showed high consistency in CO 2 precision between airborne measurements and simulation results in East Asia, demonstrating the robustness of the retrieval algorithm at continental scales. • A systematic CO 2 retrieval algorithm is provided for China's AEMS space mission. • Performance of the retrieval algorithm is assessed by airborne campaigns. • The precision of the CO 2 retrievals is 0.63 ppm, validated with an in-situ sensor. • Recognize an apparent CO 2 enhancement of 8–18 ppm between carbon source and sink. • The robustness of the retrieval algorithm is confirmed at continental scales. [ABSTRACT FROM AUTHOR]

Sentinel-3 (S3) satellites are equipped with microwave radiometers that perform brightness temperature (TB) measurements at 23.8 and 36.5 GHz to determine the wet tropospheric correction (WTC). The analysis of the two MWR-derived WTC present in S3 products, retrieved from three- and five-input neural network (NN) algorithms, suggest the need for their improvement. Focusing on the inputs and physical component, this paper aims at improving the WTC retrieval for open ocean, considering a suitable learning for S3 and a better accounting for the surface contribution to the WTC retrieval. Adopting a purely empirical approach, the learning database has been built using 1 year (2017) of valid S3A measurements, ERA5-derived WTC, and dynamic sea surface temperature (SST) from ERA5. The proposed approach is a similar NN with four inputs: TB at 23.8 and 36.5 GHz, altimeter backscattering coefficient and SST. Results show that the use of a dynamic SST, instead of static tables as currently adopted in S3, makes the fifth input (vertical temperature decrease) redundant. Comparisons with reference and independent WTC sources show that the WTC derived from this algorithm, when compared with those available in the S3 products, leads to a decrease in the RMS values of WTC differences, with respect to these independent WTC, by about 1 mm globally, that locally can reach almost 1 cm. This study proposes a new approach for the WTC of Sentinel-3 by considering a suitable set of dynamic inputs that better characterize the atmosphere, which is a significant enhancement over the current algorithms. [ABSTRACT FROM AUTHOR]

To acquire daytime land surface temperature (LST) in thin cirrus cloudy skies, we have developed a three-channel LST retrieval algorithm based on a widely used two-channel LST retrieval algorithm for the clear-sky conditions. In this algorithm, the LST is expressed as a multiple linear function of MODIS channels 29, 31 and 32 with the coefficients of the linear function dependent on the cirrus optical depth (COD) and cirrus effective radius (R). The influences from land surface emissivities (LSEs) are also considered in this algorithm. The simulated dataset shows that the LST could be estimated using the proposed algorithm with the root mean squire error (RMSE) less than 2.2 K in thin cirrus cloudy skies (COD less than 0.7) when viewing zenith angle (VZA) equivalent to 0°. As VZA is equivalent to 60°, the maximum RMSE are 2.7 K. The widely used generalized split-window (GSW) algorithm proposed for clear-sky conditions are used in cirrus cloudy skies, and the RMSEs of GSW algorithm estimated LST are 16.89 K and 22.32 K for VZA =0° and VZA =60° respectively when COD is 0.7. It indicates that the proposed three-channel algorithm can significantly improve the LST retrieval accuracy using thermal infrared data in cirrus cloudy skies. To estimate the LST errors caused by the uncertainties of COD, R, LSE and instrument noise, a sensitivity analysis was performed. It shows that the accuracy of cirrus COD is more important for the retrieval of LST compared with other parameters. The maximum total LST errors, taking into account all the input parameters’ uncertainty and algorithm error itself, are 3.8 K and 4.3 K when VZA =0° and VZA =60° respectively.

Atmospheric ozone is a pollutant gas that has an important influence on the process of atmospheric radiation transmission and climate change. The Fengyun-3D (FY-3D) satellite Hyperspectral Infrared Atmospheric Sounder (HIRAS) has better spectral performance than other remote sensing payloads. Its observation radiation data contains abundant atmospheric vertical information, which can be used for ozone retrieval, but there are no ozone profile business products being generated at present. Therefore, for the mainland of Hong Kong, based on HIRAS infrared hyperspectral observation data, we used the traditional one-dimensional variational (1D-VAR) physical retrieval algorithm, combined with the radiative transfer model for TOVS (RTTOV), and selected the spectrum channel according to the optimal sensitive profile algorithm. The artificial neural network (ANN) algorithm was used to optimize the prior profiles, and the atmospheric ozone profile retrieval system was established. Finally, a set of ozone profile retrieval schemes suitable for FY-3D/HIRAS were summarized. We used ERA5 reanalysis data and World Ozone and Ultraviolet Radiation Data Centre (WOUDC) data to determine true values. The retrieval results were compared with Global Forecast System (GFS) forecast data, Ozone Mapping and Profile Suite (OMPS) ozone products, and Atmospheric Infrared Sounder (AIRS) ozone products. The results show that our ozone profile retrieval scheme makes up for the shortcomings of the conventional physical methods in some atmospheric pressure levels. The overall root-mean-square error (RMSE) of the ozone from the ground to the top of the stratosphere is within 30% on average, which was better than that for the GFS forecast data; the retrieval accuracy RMSE (%) was less than 20% in the pressure layer with the highest ozone concentration (15–25 hPa), which is better than that of OMPS ozone products and AIRS ozone products. The retrieval results prove that FY3D/HIRAS observation data allow ozone profile retrieval. This paper provides a reference for generating independent HIRAS ozone profile product data sets in business, and provides support for the subsequent application of Fengyun-3 series meteorological satellites in atmospheric parameter remote sensing.

Characterization of an isolated attosecond pulse (IAP) in the extreme ultraviolet (XUV) or soft x-ray (SXR) region is essential for its applications. Here we propose to retrieve an IAP in the time domain directly through the modulation of high-harmonic generation (HHG) spectra in the presence of a time-delayed intense few-cycle infrared or mid-infrared laser. The retrieval algorithm is derived based on the strong-field approximation and an extended quantitative rescattering model. We show that both isolated XUV pulses with a narrow spectral bandwidth and isolated SXR pulses with a broad bandwidth can be well characterized through the HHG streaking spectra. Such an all-optical method for characterizing the IAP differs from the commonly used approach based on the streaked photoelectron spectra that would require electron spectrometers. We check the robustness of the retrieval method by changing the dressing laser or by adjusting the steps of time delay. We also show that the XUV pulse can be accurately retrieved by treating the HHG streaking spectra calculated from solving the time-dependent Schrödinger equation for single atoms as the ‘experimental’ data.

Soil moisture (SM) products presently available in permafrost regions, especially on the Qinghai–Tibet Plateau (QTP), hardly meet the demands of evaluating and modeling climatic, hydrological, and ecological processes, due to their significant bias and low spatial resolution. This study developed an algorithm to generate high-spatial-resolution SM during the thawing season using Sentinel-1 (S1) and Sentinel-2 (S2) temporal data in the permafrost environment. This algorithm utilizes the seasonal backscatter differences to reduce the effect of surface roughness and uses the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) to characterize vegetation contribution. Then, the SM map with a grid spacing of 50 m × 50 m in the hinterland of the QTP with an area of 505 km × 246 km was generated. The results were independently validated based on in situ data from active layer monitoring sites. It shows that this algorithm can retrieve SM well in the study area. The coefficient of determination (R2) and root-mean-square error (RMSE) are 0.82 and 0.06 m3/m3, respectively. This study analyzed the SM distribution of different vegetation types: the alpine swamp meadow had the largest SM of 0.26 m3/m3, followed by the alpine meadow (0.23), alpine steppe (0.2), and alpine desert (0.16), taking the Tuotuo River basin as an example. We also found a significantly negative correlation between the coefficient of variation (CV) and SM in the permafrost area, and the variability of SM is higher in drier environments and lower in wetter environments. The comparison with ERA5-Land, GLDAS, and ESA CCI showed that the proposed method can provide more spatial details and achieve better performance in permafrost areas on QTP. The results also indicated that the developed algorithm has the potential to be applied in the entire permafrost regions on the QTP.

Orbital radars are used to monitor the state of the sea ice in the Arctic and Antarctic. The backscattering radar cross section (RCS) is used to determine the type of scattering surface. The power of the reflected signal depends on many factors, so the problem of separating sea ice and sea waves is not always unambiguous. Previous research has shown that microwave Doppler radar installed on aircrafts can be used to determine the boundary of sea ice. The width of the Doppler spectrum for wide or knife-like antenna beam depends on the statistical parameters of the reflecting surface, so sea ice and sea waves are easily separated. However, when installing a Doppler radar on a satellite, the spatial resolution becomes extremely low. In this research, we discuss the possibility of improving the spatial resolution by dividing the antenna footprint into elementary scattering cells. To do this, it is proposed to use the original incoherent synthesis procedure, which allows one to determine the dependence of the RCS on the incidence angle for an elementary scattering cell. Numerical modeling was performed and processing of model data confirmed that sea ice and sea waves are separated. The coefficient of kurtosis was used as a criterion in the algorithm. In addition, for sea waves, it is possible to determine the mean square slopes (mss) of large-scale waves, compared to the electromagnetic wavelength of sea waves along the sounding direction.

A novel and efficient inverse method, named Nonlinear least squares four-dimensional variational data Assimilation (NLS-4DVar)-based CO2 Retrieval Algorithm (NARA), is proposed for retrieving atmospheric CO2 from the satellite hyperspectral measurements, in which the NLS-4DVar method is used as the optimization method. As the NLS-4DVar method works independently of the tangent linear model and adjoint model, the time-consuming calculation of the weighting function matrix is unnecessary, and the computation complexity is tremendously reduced while maintaining the retrieval accuracy. This is extremely important for space-based CO2 retrievals with large data volumes. Observing system simulation experiments (OSSEs) over four different sites around the world showed that the NARA algorithm could retrieve XCO2 and CO2 profiles effectively. To further evaluate the NARA algorithm, it was used for real CO2 retrievals from target-mode observations of Orbiting Carbon Observatory-2 (OCO-2) over Lamont, Oklahoma, and Darwin, Australia. The results were compared with that of ground measurements of Total Carbon Column Observing Network (TCCON). The mean difference of XCO2 between NARA and TCCON over Lamont, from 180 observations, was −0.15 ppmv with a standard deviation (SD) of 0.76 ppmv. Over Darwin, the mean difference, from 180 observations (90 points over land and 90 points over the ocean), is −0.17 ppmv (SD: 1.26 ppmv). The preliminary results showed that the efficient NLS-4DVar-based algorithm could provide great help for satellite remote sensing of CO2, and it may be used as an operational procedure after further and extensive evaluations.

Aggregated aerosol particles released from high temperature processes and combustion processes are often described as quasi-fractal aggregates, where the shape of these particles is represented by the scaling law. A detailed understanding of the morphology is quite important as various properties are strongly dependent on the particle shape. Electron microcopy based image analysis is the most commonly used technique to visualize and study the morphological features. In this study, we propose a machine learning (ML)-assisted retrieval method where ML techniques are combined with optimization algorithms to predict the morphological features and the corresponding 3-dimensional structures from microscopic images. The proposed algorithm is comprehensively tested with "synthetic" images as well as Transmission Electron Microcopy images. Various ML models, including Linear regression, Artificial Neural Network, K-nearest neighbours, Random Forest regression, and XGBoost are used for preliminary prediction of the morphological features (Number of monomers (N), fractal prefactor (k f) and fractal Dimension (D f)). These are used to narrow down the search space in the optimization algorithms. Random Forest and XGBoost methods achieved approximately 0.96 R2 score for N, 0.85 for D f and 0.73 for k f. Multiple optimization methods, including PSO, JAYA, and JAYA-SA, were tested in the study. The method was tested across a wide range of parameters, including N (up to 500), D f (1.1–2.7), and k f (0.6–2.1), and the results are quite promising while comparing various 3-dimensional properties of the retrieved structures. The retrieved fractal parameters, N and D f, exhibited errors under 10%, and the predicted k f values were found within approximately 15% using the proposed method. Results also show that the 3-dimensional properties of the predicted structure are quite close to the structures used for testing the algorithm. The algorithm was also parallelized to improve the computational time. The results show that the predicted fractal parameters and the retrieved 3-dimensional structures are quite similar to the structures used for testing across a wide range of particle morphologies. The incorporation of ML models has significantly improved the accuracy and computational speed, compared to the existing retrieval techniques. • ML-assisted optimization to predict 3D fractal morphology from microscopic images. • Linear Regression, ANN, Random Forest Regressor, XGBoost, and kNN are tested. • FracVAL is employed for tuning, generating aggregate structures. • Particle Swarm Optimization, JAYA and self-adaptive JAYA are used for Optimization. • Accurate and fast prediction of 3-dimensional properties from microscopic images. [ABSTRACT FROM AUTHOR]

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development.

The Chinese Carbon Dioxide Observation Satellite Mission (TanSat) is the third satellite for global CO2 monitoring and is capable of detecting weak solar-induced chlorophyll fluorescence (SIF) signals with its advanced technical characteristics. Based on the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing (IAPCAS) platform, we successfully retrieved the TanSat global SIF product spanning the period of March 2017 to February 2018 with a physically based algorithm. This paper introduces the new TanSat SIF dataset and shows the global seasonal SIF maps. A brief comparison between the IAPCAS TanSat SIF product and the data-driven SVD (singular value decomposition) SIF product is also performed for follow-up algorithm optimization. The comparative results show that there are regional biases between the two SIF datasets and the linear correlations between them are above 0.73 for all seasons. The future SIF data product applications and requirements for SIF space observation are discussed. [ABSTRACT FROM AUTHOR]

Remote sensing of HDO and CH4 could provide valuable information on environmental and climatological studies. In a recent contribution, we reported a 3.53 μm distributed feedback (DFB) inter-band cascade laser (ICL)-based heterodyne radiometer. In the present work, we present the details of measurements and inversions of HDO and CH4 at Dunhuang, Northwest of China. The instrument line shape (ILS) of laser heterodyne radiometer (LHR) is discussed firstly, and the spectral resolution is about 0.004 cm−1 theoretically according to the ILS. Furthermore, the retrieval algorithm, optimal estimation method (OEM), combined with LBLRTM (Line-by-line Radiative Transfer Model) for retrieving the densities of atmospheric HDO and CH4 are investigated. The HDO densities were retrieved to be less than 1.0 ppmv, while the CH4 densities were around 1.79 ppmv from 20 to 24 July 2018. The correlation coefficient of water vapor densities retrieved by LHR and EM27/SUN is around 0.6, the potential reasons for the differences were discussed. Finally, in order to better understand the retrieval procedure, the Jacobian value and the Averaging Kernels are also discussed.

Aerosols in the atmosphere play an essential role in the radiative transfer process due to their scattering, absorption, and emission. Moreover, they interrupt the retrieval of atmospheric properties from ground-based and satellite remote sensing. Thus, accurate aerosol information needs to be obtained. Herein, we developed an optimal-estimation-based aerosol optical depth (AOD) retrieval algorithm using the hyperspectral infrared downwelling emitted radiance of the Atmospheric Emitted Radiance Interferometer (AERI). The proposed algorithm is based on the phenomena that the thermal infrared radiance measured by a ground-based remote sensor is sensitive to the thermodynamic profile and degree of the turbid aerosol in the atmosphere. To assess the performance of algorithm, AERI observations, measured throughout the day on 21 October 2010 at Anmyeon, South Korea, were used. The derived thermodynamic profiles and AODs were compared with those of the European center for a reanalysis of medium-range weather forecasts version 5 and global atmosphere watch precision-filter radiometer (GAW-PFR), respectively. The radiances simulated with aerosol information were more suitable for the AERI-observed radiance than those without aerosol (i.e., clear sky). The temporal variation trend of the retrieved AOD matched that of GAW-PFR well, although small discrepancies were present at high aerosol concentrations. This provides a potential possibility for the retrieval of nighttime AOD.

The aircraft structure is an important part of the air-frame, and its manufacturing process is complex. It is difficult to recognize the target in the computer-aided process planning(CAPP) system. According to the characteristics of structural parts, CAPP system is constructed based on manufacturing feature of air-frame parts to improve the production efficiency. Six manufacturing features are summed up according to the machining process by researching on classification of common aircraft structural parts. In the VC++6.0 environment, a coding system based on manufacturing feature is designed according to OPITZ standard. The step retrieval algorithm is designed for feature recognition between target parts and entity library parts. The similarity of features is achieved between the target parts and the physical library parts. Taking the frame parts with 44 slots as an example, 40 manufacturing features are automatically identified by the algorithm program. The automatic recognition rate is 91%. The result shows the feasibility and correctness of the algorithm in CAPP system.

TanSat is the 1st Chinese carbon dioxide (CO2) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL‐FP) algorithm is implemented for TanSat nadir mode XCO2 retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8th order Fourier series. The spectrum‐correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O2 A band retrieval. Accordingly, we extend the previous TanSat single CO2 weak band retrieval to a combined O2 A and CO2 weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post‐screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO2 retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO2 retrieval. We show that our new approach produces a significant improvement on the XCO2 retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of −0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO2 retrieval from TanSat and subsequently the Level‐2 data production, and hence will be applied in the TanSat operational XCO2 processing. Key Points: First using O2 A and 1.61 um CO2 band approaching TanSat XCO2 retreivalDevelopment a method on radiometric correction of TanSat L1B data in O2 A and 1.61 um CO2Validation of new TanSat retrieval against TCCON and recived significant improved results compare to previously retrieval [ABSTRACT FROM AUTHOR]

The present study aims to develop a retrieval algorithm for total precipitable water (TPW) over the ocean using Indian geostationary satellite INSAT-3D observations. The algorithm computes TPW as the sum of two broad layers using thermal infrared observations of the Imager for clear sky and upper tropospheric humidity (UTH) products. First, the differential absorption of the atmospheric water vapour in split-window channels is exploited to estimate atmospheric water vapour concentration from surface to mid-troposphere. Secondly, the contribution from UTH products available from the INSAT-3D Imager is added to estimate TPW. The algorithm performance has been assessed by comparing the INSAT-3Dretrieved TPW with collocated TPW products from radiosonde, INSAT-3D Sounder and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua satellite for the year 2018. The standard deviation of the error was found to be around 0.5 cm for each of the months. [ABSTRACT FROM AUTHOR]

An advanced algorithm for atmospheric water vapor column (WVC) retrieval from the Advanced Microwave Scanning Radiometer (AMSR) measurements over the Arctic sea ice (SI) and open ocean waters is presented. The algorithm is built on the physical modeling of the brightness temperature (BT) of the microwave radiation of the SI–open ocean–atmosphere system at the AMSR frequencies and polarizations. The BTs are calculated using a data set of the SI, atmospheric, and oceanic parameters changing in the range of their natural variability in the Arctic, and using the SI microwave emission coefficients varied according to the published experimental data. The inverse operator explores neural networks (NNs), trained on an ensemble of modeled BTs. The algorithm is applied both to the AMSR-E and to the AMSR2 measurement data. Validation of the algorithm is performed with radiosonde (r/s) WVC measurements from the four Arctic coastal stations at different SI conditions during 2014–2017. The results of the application of the new algorithm to satellite radiometer measurements are also compared with the Era-Interim reanalysis WVC, as well as with other satellite WVC products, based on the data of the Moderate Resolution Imaging Spectrometer (MODIS) and on the data of the Advanced Microwave Sounding Unit-B (AMSU-B) for 2008 and 2015. To justify the usage of the Era-Interim WVC as a reference data set for the algorithm accuracy estimation in the Arctic area, Era-Interim WVC is also compared with the r/s WVC measurements. [ABSTRACT FROM AUTHOR]

• Global optical classification system is applied to turbid coastal waters. • Optimal retrieval Chla algorithms are identified for each optical class. • Chla retrieval in high accuracy is achieved over optically complex waters. Accurate chlorophyll a concentration (Chla) retrieval in coastal waters from ocean color remote sensing faces challenges due to the significant optical complexity compared to clear oceanic waters. In this paper, a novel technique for Chla retrieval in turbid coastal waters was proposed and tested in the Bohai Sea based on a global optical water classification system. Firstly, the in situ measured spectral remote sensing reflectance spectra (R rs (λ)) (n = 559) were classified into different optical water types. Secondly, optimal algorithms were identified with newly tuned model parameters for each water type to achieve accurate Chla retrieval. We found that (1) among all the 23 optical water types (1–23), the water types of 9–22 are present in the Bohai Sea, indicative of moderate or high turbidity, while other water types corresponding to clear oceanic waters are generally absent from our data. (2) Clear spatio-temporal patterns of the water types are revealed. Three basins of the Bohai Sea (the Bohai Bay, Laizhou Bay and Liaodong Bay) receiving tremendous terrestrial inputs and with high turbidity are dominated by the optical water types of 15–22. Water types 9–14 that are less turbid, are mainly distributed in the Bohai Strait and the central Bohai Sea, which are far from the coast (and thus with less terrestrial influences) and relatively deep (and thus with less bottom suspension influences), compared to the 3 basins. (3) Through the identification of the optimal retrieval algorithm and parameter tuning for each water type, the uncertainty of chlorophyll a retrievals has been reduced from 54% (root mean square error of 2.76 mg/m3) to about 36% (1.84 mg/m3). Independent validation with the in situ -satellite match-ups further demonstrates the algorithm's validity (uncertainty of about 35%).The global optical classification system together with the optimal retrieval algorithm for each optical class, is proved to be a feasible way for ocean color retrieval in high accuracy over optically complex waters. [ABSTRACT FROM AUTHOR]

The Doppler spectrum (DS) of the signal backscattered at the low incidence angles from the sea surface was measured by the Ka-band radar in an experiment on a marine oceanographic platform. The directional elevation spectrum of sea waves was measured simultaneously by a six-string wave gauge mounted on a platform. At low incidence angles the backscattering mechanism is quasi-specular, and DS of the backscattered signal is calculated in the Kirchhoff approximation with the antenna pattern being taken into account. In the case of pure wind sea, the parameters of the sea waves spectrum model (wind speed and fetch) were retrieved by 2-D optimization method from measured DS shift and width. Sea wave propagation direction was obtained from the azimuthal dependence of Doppler shift. Analytical dependence of DS shift and width on incidence angle calculated from the spectrum model is in a good agreement with the experiment for incidence angles from 0° to 12°. Close values of significant wave height, the average wave period, and the variance of orbital velocities were obtained from the retrieved model spectrum and from the spectrum directly measured by the string wave gauge, which also proves the correctness of DS model. [ABSTRACT FROM AUTHOR]

Single-shot non-invasive imaging through a scattering medium has been an area of research focus for several years. Achieving an accurate autocorrelation distribution is crucial for effective image reconstruction. However, the obtained autocorrelation is always contaminated by various types of noise. In this study, we propose an effective method to extract the autocorrelation distribution of the sample from the noise-laden raw data before image retrieval. This is accomplished by incorporating an intensity threshold and a spatial Gaussian filter into the retrieval algorithm, which effectively eliminates the noise artifacts in the reconstructed image. The proposed algorithm exhibited remarkable robustness for both visible (laser, white light, and light-emitting diode light) and X-ray light sources, and its efficacy was verified through multiple experiments. [ABSTRACT FROM AUTHOR]