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A Study on the retrieval of Ozone Profiles Using FY-3D/HIRAS Infrared Hyperspectral Data

REMOTE SENSING 2023年第4期15卷 1009-1009页

作者： Xie, Mengzhen Gu, Mingjian Hu, Yong Huang, Pengyu Zhang, Chunming Yang, Tianhang Yang, Chunlei Chinese Acad Sci Shanghai Inst Tech Phys Key Lab Infrared Syst Detect & Imaging Technol Shanghai 200083 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Shanghai Inst Tech Phys Shanghai 200083 Peoples R China Chinese Acad Sci Shanghai Inst Tech Phys Suzhou Acad Suzhou 215000 Peoples R China Sun Yat sen Univ Sch Atmospher Sci Zhuhai 519082 Peoples R China

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

关键词： FY-3D HIRAS ozone profile retrieval algorithm

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ON THE ASYMMETRY QUESTION OF LARGE-SCALE SLOPES OF SURFACE WAVES

ON THE ASYMMETRY QUESTION OF LARGE-SCALE SLOPES OF SURFACE W...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Karaev, Vladimir Kovaldov, Dmitry Titchenko, Yury Panfilova, Maria Ryabkova, Maria Meshkov, Eugeny Ponur, Kirill Zuikova, Emma Russian Acad Sci Inst Appl Phys Moscow Russia

ISBN: (纸本)9798350320107

At small incidence angles the dominant microwave backscattering mechanism is quasi-specular. In this case, reflection occurs from facets of the wave profile oriented perpendicular to the incident electromagnetic waves. A new theoretical algorithm was developed for determining the mean square slopes ( mss) of large-scale, in comparison with radar wavelength, sea waves from the azimuthal dependence of the backscattering radar cross section (RCS). In this paper the results of an experiment on measuring the azimuthal dependence of the backscattering RCS during the experiment on the Oka River within the city of Nizhny Novgorod is discussed. For measurements, two Ka-band radars installed on the metro bridge were used. To determine the mss of large-scale waves, the azimuthal dependence of the RCS, measured at one incidence angle was used. The comparison of the mss retrieved by the new algorithm, with the results of processing by the well-known algorithm using the dependence of the RCS on the incidence angle, confirmed the effectiveness of the new approach.

关键词： microwave radar backscattering radar cross section mean square slopes of large-scale waves experiment retrieval algorithm

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A generalized land surface reflectance reconstruction method for aerosol retrieval: Application to the Particulate Observing Scanning Polarimeter (POSP) onboard GaoFen-5 (02) satellite

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REMOTE SENSING OF ENVIRONMENT 2023年第1期295卷

作者： Shi, Zheng Xie, Yisong Li, Zhengqiang Zhang, Ying Chen, Cheng Mei, Linlu Xu, Hua Wang, Haofei Zheng, Yang Liu, Zhenhai Hong, Jin Zhu, Mengyao Qie, Lili Zhang, Luo Fan, Cheng Guang, Jie Chinese Acad Sci Aerosp Informat Res Inst State Environm Protect Key Lab Satellite Remote Se Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Lille CNRS UMR 8518 LOA Lab Opt Atmospher F-59000 Lille France GRASP SAS Remote Sensing Dev Lille France Univ Bremen Inst Environm Phys D-28359 Bremen Germany China Meteorol Adm Natl Satellite Meteorol Ctr Natl Ctr Space Weather Key Lab Radiometr Calibrat & Validat Environm Sate Beijing 100081 Peoples R China Innovat Ctr FengYun Meteorol Satellite FYSIC Beijing 100081 Peoples R China Chinese Acad Sci Anhui Inst Opt & Fine Mech Hefei Inst Phys Sci Hefei 230031 Peoples R China Jilin Univ Coll Geoexplorat Sci & Technol Changchun 130026 Peoples R China

Newly launched sensors often face challenges in obtaining sufficient observations in a short time to support the development of land surface reflectance (LSR) constraint for high-precision aerosol retrieval. To address this problem, we proposed a generalized spectral LSR reconstruction method. First, short-term overlapping observation of the target sensor and a reference sensor currently in operation is utilized to generate a representative spectral LSR comparison dataset. Then, the differences in spectral LSR caused by spectral response functions (SRF) are explored to construct a spectral LSR reconstruction model, which can convert the reference sensor's spectral LSR to match the spectral LSR of the target sensor. Finally, long-term spectral LSR data of the reference sensor can be reconstructed for the target sensor to support the surface constraint for aerosol retrieval. We applied this method to the newly launched Particulate Observing Scanning Polarimeter (POSP) instrument onboard GaoFen-5 (02) satellite, using MODIS as the reference sensor. We used one-month concurrently overlapping observation of POSP and MODIS to quantify the differences in spectral LSR and built the spectral LSR reconstruction model. Four years of MODIS LSR data were then reconstructed to develop the multispectral surface constraint scheme (B2RE) for POSP, which enables POSP data for high-precision AOD retrieval. We retrieved the global AOD at 550 nm over land for December 2021 to February 2022. Validation results of POSP AOD against AErosol RObotic NETwork (AERONET) AOD show that the POSP AOD derived from 490 nm observation is the most accurate with the correlation coefficient (R) of 0.914, root mean square error (RMSE) of 0.080, mean relative error (MRE) of 1.60% and 82.5% of the values falling into the expected error (EE) envelope of & PLUSMN;(0.05 + 0.15AODAERONET). The spatial distribution of monthly POSP AOD also has good agreement with MODIS Deep Blue AOD product.

关键词： Land surface reflectance Reconstruction Surface constraint Aerosol optical depth retrieval algorithm

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Rainfall rate estimation over India using global precipitation measurement's microwave imager datasets and different variants of fuzzy information system

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GEOCARTO INTERNATIONAL 2022年第21期37卷 6213-6231页

作者： Anand, Akash Dinesh, Anand Singh Srivastava, Prashant K. Chaudhary, Sumit Kumar Verma, Atul Kumar Kumar, Pavan Banaras Hindu Univ Inst Environm & Sustainable Dev Remote Sensing Lab Varanasi Uttar Pradesh India Banaras Hindu Univ DST Mahamana Ctr Excellence Climate Change Res Inst Environm & Sustainable Dev Varanasi Uttar Pradesh India Indian Space Res Org Space Applicat Ctr Ahmadabad Gujarat India Rani Laxmi Bai Cent Agr Univ Coll Hort & Forestry Jhansi Uttar Pradesh India

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.

关键词： GPM microwave imager retrieval algorithm Neuro-fuzzy Models machine learning statistical metrices

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retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

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Chinese Journal of Oceanology and Limnology 2013年第1期31卷 210-218页

作者：赵屹立 College of Information Science and Engineering Ocean University of China Department of Remote Sensing and Mapping National Ocean Technology Center

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center （NDBC）, root mean square errors are 1.2 rn/s and 30~ for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.

关键词： polarimetric microwave radiometer sea surface wind vector retrieval algorithm WindSat

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MULTI-FREQUENCY SIGNALS OF OPPORTUNITY SOIL MOISTURE retrievalS FOR AGRICULTURAL APPLICATIONS

MULTI-FREQUENCY SIGNALS OF OPPORTUNITY SOIL MOISTURE RETRIEV...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Kim, Seho Smith, Eric P. Nold, Benjamin R. Choudhari, Archana S. Garrison, James L. Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Root-zone soil moisture (RZSM) is one of the least measured hydrological variables despite its critical role in understanding the global carbon cycle and forecasting agricultural drought and food production. Signals of opportunity (SoOp) has great potential to overcome the limitation of conventional microwave methods to utilize lower frequencies in space-borne remote sensing. Multi-frequency SoOp reflectometry (SoOp-R) offers a promising solution to measure RZSM by solving the inverse problem for obtaining multi-layer soil moisture profiles. This paper summarizes ongoing work to develop and validate multi-frequency SoOp-R to retrieve RZSM, including forward and inverse methods, sensitivity analysis for the optimal frequency combination, and tower-based field experiments.

关键词： Root-zone soil moisture signals of opportunity multi-frequency observation retrieval algorithm field experiment

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Atmospheric HDO Abundance Measurements in the Tibetan Plateau Based on Laser Heterodyne Radiometer

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REMOTE SENSING 2024年第3期16卷 459页

作者： Lu, Xingji Huang, Yinbo Wu, Pengfei Huang, Jun Luo, Tao Liu, Qiang Cao, Zhensong Chinese Acad Sci Anhui Inst Opt & Fine Mech Key Lab Atmospher Opt HFIPS Hefei 230031 Peoples R China Adv Laser Technol Lab Anhui Prov Hefei 230037 Peoples R China

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 mu m 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 x 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.

关键词： laser heterodyne radiometer isotopic abundance atmospheric transportation retrieval algorithm backward trajectory analysis

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ML-assisted optimization method for the prediction of 3-dimen-sional fractal structures from microscopic images

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JOURNAL OF AEROSOL SCIENCE 2024年 177卷

作者： Singh, Abhishek Kailas, Saket Kohinkar Thajudeen, Thaseem Hogan, Chris Indian Inst Technol Goa Sch Mech Sci Farmagudi 403401 Goa India Indian Inst Technol Goa Off Dean R&D Adm BlockGoa Coll Engn Campus Farmagudi 403401 Goa India

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 (kf) and fractal Dimension (Df)). 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 Df and 0.73 for kf. 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), Df (1.1-2.7), and kf (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 Df, exhibited errors under 10%, and the predicted kf 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 t

关键词： Fractal aggregates retrieval algorithm Machine learning methods Morphological analysis Optimization

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FAQ question Answering method based on semantic similarity matching 6

FAQ question Answering method based on semantic similarity m...

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6th International Symposium on Computer Science and Intelligent Control (ISCSIC)

作者： Ji Tenghao HongKong Polytech Univ Elect Engn Hong Kong Peoples R China

ISBN: (纸本)9781665454889

Frequently asked questions (FAQ) is a typical limited domain-oriented retrieval question answering system, and semantic similarity matching is the key technology of FAQ question answering. Aiming at the FAQ problems of insufficient deep semantic representation and extraction and synonyms and polysemy, this paper proposes a cascade matching model combining coarse-grained recall and finegrained sorting. Firstly, an improved Okapi BM25 algorithm based on inverse word frequency is proposed. This paper proposes a new solution to FAQ answer based on the traditional text matching and retrieval pipeline, and gives a new semantic similarity matching model. Additionally, we further analyze the shortcomings of semantic matching and the advantages of data-driven semantic similarity matching model. Finally, the proposed hybrid matching model is evaluated. Experimental results show that the proposed method not only ensures the retrieval speed of question and answer, but improves its performance

关键词： FAQ Question Answering Semantic Similarity Text matching retrieval algorithm

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Potential of AOD retrieval Using Atmospheric Emitted Radiance Interferometer (AERI)

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REMOTE SENSING 2022年第2期14卷 407-407页

作者： Seo, Jongjin Choi, Haklim Oh, Youngsuk Univ Wisconsin Dept Atmospher & Ocean Sci Madison WI 53706 USA Univ Wisconsin Space Sci & Engn Ctr Madison WI 53706 USA Kyungpook Natl Univ Kyungpook Inst Oceanog Daegu 41566 South Korea Natl Inst Meteorol Sci Innovat Meteorol Res Dept Seogwipo Si 63568 South Korea

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.

关键词： retrieval algorithm AOD AERI hyper-spectral infrared optimal estimation method

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