In this paper,we present long term observations of atmospheric nitrogen dioxide(NO2)and formaldehyde(HCHO)in Nanjing using a Multi-AXis Differential Optical Absorption Spectroscopy(MAX-DOAS)instrument.Ground based MAX...In this paper,we present long term observations of atmospheric nitrogen dioxide(NO2)and formaldehyde(HCHO)in Nanjing using a Multi-AXis Differential Optical Absorption Spectroscopy(MAX-DOAS)instrument.Ground based MAX-DOAS measurements were performed from April 2013 to February 2017.The MAX-DOAS measurements of NO2 and HCHO vertical column densities(VCDs)are used to validate OMI satellite observations over Nanjing.The comparison shows that the OMI observations of NO2 correlate well with the MAX-DOAS data with Pearson correlation coefficient(R)of 0.91.The comparison result of MAX-DOAS and OMI observations of HCHO VCD shows a good agreement with R of 0.75 and the slope of the regression line is 0.99.The age weighted backward propagation approach is applied to the MAX-DOAS measurements of NO2 and HCHO to reconstruct the spatial distribution of NO2 and HCHO over the Yangtze River Delta during summer and winter time.The reconstructed NO2 fields show a distinct agreement with OMI satellite observations.However,due to the short atmospheric lifetime of HCHO,the backward propagated HCHO data does not show a strong spatial correlation with the OMI HCHO observations.The result shows the MAX-DOAS measurements are sensitive to the air pollution transportation in the Yangtze River Delta,indicating the air quality in Nanjing is significantly influenced by regional transportation of air pollutants.展开更多
In this study,a large time series of Terra SAR-X Stripmap co-polarized(HH-VV)Synthetic Aperture Radar(SAR)imagery collected over the Taylor Energy oil platform site in the Gulf of Mexico is exploited to investigate th...In this study,a large time series of Terra SAR-X Stripmap co-polarized(HH-VV)Synthetic Aperture Radar(SAR)imagery collected over the Taylor Energy oil platform site in the Gulf of Mexico is exploited to investigate this 13 year-long unconventional oil spill.TheσCPDapproach is used to estimate the polluted area along time.In addition,a sensitivity analysis is undertaken to point out the dependence ofσCPDto imaging(noise floor,incidence angle)and environment(sea state)parameters.Experimental results demonstrate thatσCPDcan be effectively used to monitor the Taylor Energy oil spill,estimating the polluted area.For the TSX SAR data avail-ability most dense period(year 2013),a daily spill of about 2.2 km^(2) is observed in average,even though high variability(about 2.0 km^(2))is experienced due to the un-conventional characteristics of the spill.展开更多
The Ozone Monitoring Suite-Nadir(OMS-N),a state-of-the-art hyperspectral ultraviolet-visible(UV-VIS)sensor onboard China's FengYun-3F(FY-3F)satellite,was launched in August 2023.Designed for a morning orbit,OMS-N ...The Ozone Monitoring Suite-Nadir(OMS-N),a state-of-the-art hyperspectral ultraviolet-visible(UV-VIS)sensor onboard China's FengYun-3F(FY-3F)satellite,was launched in August 2023.Designed for a morning orbit,OMS-N represents a significant advancement in global atmospheric composition monitoring,offering an unprecedented spatial resolution of 7 km×7 km.The total ozone column(TOC)product derived from OMS-N is critical for climate modeling and UVradiation assessment.This study presents the first TOC retrievals from OMS-N,utilizing an adapted Differential Optical Absorption Spectroscopy(DOAS)algorithm.The retrieval algorithm overcomes traditional DOAS limitations by incorporating key innovations,including optimized radiative transfer calculations and refined a priori information on surface properties and ozone profiles,which are derived directly from OMS-N spectra rather than relying on external datasets or climatologies.Validation against ground-based measurements from Brewer,Dobson,and SAOZ instruments at 33 sites demonstrated strong agreement,with correlation coefficients mostly greater than 0.9.Comparisons with other well-established satellite instruments,including TROPOMI and GOME-2B,showed that OMS-N can consistently capture global seasonal ozone patterns,with biases typically within 2%across hemispheres and seasons.These results establish OMS-N as a reliable tool for high-resolution dynamic ozone monitoring,significantly enhancing our ability to address climate and environmental challenges.展开更多
The Environmental Trace Gases Monitoring Instrument(EMI)is the first Chinese satellite-borne UV–Vis spectrometer aiming to measure the distribution of atmospheric trace gases on a global scale.The EMI instrument onbo...The Environmental Trace Gases Monitoring Instrument(EMI)is the first Chinese satellite-borne UV–Vis spectrometer aiming to measure the distribution of atmospheric trace gases on a global scale.The EMI instrument onboard the GaoFen-5 satellite was launched on 9 May 2018.In this paper,we present the tropospheric nitrogen dioxide(NO2)vertical column density(VCD)retrieval algorithm dedicated to EMI measurement.We report the first successful retrieval of tropospheric NO_(2) VCD from the EMI instrument.Our retrieval improved the original EMI NO_(2) prototype algorithm by modifying the settings of the spectral fit and air mass factor calculations to account for the on-orbit instrumental performance changes.The retrieved EMI NO_(2) VCDs generally show good spatiotemporal agreement with the satellite-borne Ozone Monitoring Instrument and TROPOspheric Monitoring Instrument(correlation coefficient R of ~0.9,bias<50%).A comparison with ground-based MAX-DOAS(Multi-Axis Differential Optical Absorption Spectroscopy)observations also shows good correlation with an R of 0.82.The results indicate that the EMI NO_(2) retrieval algorithm derives reliable and precise results,and this algorithm can feasibly produce stable operational products that can contribute to global air pollution monitoring.展开更多
Despite tons of advanced classification models that have recently been developed for the land cover mapping task,the monotonicity of a single remote sensing data source,such as only using hyperspectral data or multisp...Despite tons of advanced classification models that have recently been developed for the land cover mapping task,the monotonicity of a single remote sensing data source,such as only using hyperspectral data or multispectral data,hinders the classification accuracy from being further improved and tends to meet the performance bottleneck.For this reason,we develop a novel superpixel-based subspace learning model,called Supace,by jointly learning multimodal feature representations from HS and MS superpixels for more accurate LCC results.Supace can learn a common subspace across multimodal RS data,where the diverse and complementary information from different modalities can be better combined,being capable of enhancing the discriminative ability of to-be-learned features in a more effective way.To better capture semantic information of objects in the feature learning process,superpixels that beyond pixels are regarded as the study object in our Supace for LCC.Extensive experiments have been conducted on two popular hyperspectral and multispectral datasets,demonstrating the superiority of the proposed Supace in the land cover classification task compared with several well-known baselines related to multimodal remote sensing image feature learning.展开更多
The increased number of free and open Sentinel satellite images has led to new applications of these data.Among them is the systematic classification of land cover/use types based on patterns of settlements or agricul...The increased number of free and open Sentinel satellite images has led to new applications of these data.Among them is the systematic classification of land cover/use types based on patterns of settlements or agriculture recorded by these images,in particular,the identification and quantification of their temporal changes.In this paper,we will present guidelines and practical examples of how to obtain rapid and reliable image patch labelling results and their validation based on data mining techniques for detecting these temporal changes,and presenting these as classification maps and/or statistical analytics.This represents a new systematic validation approach for semantic image content verification.We will focus on a number of different scenarios proposed by the user community using Sentinel data.From a large number of potential use cases,we selected three main cases,namely forest monitoring,flood monitoring,and macro-economics/urban monitoring.展开更多
Throughout the years,various Earth Observation(EO)satellites have generated huge amounts of data.The extraction of latent information in the data repositories is not a trivial task.New methodologies and tools,being ca...Throughout the years,various Earth Observation(EO)satellites have generated huge amounts of data.The extraction of latent information in the data repositories is not a trivial task.New methodologies and tools,being capable of handling the size,complexity and variety of data,are required.Data scientists require support for the data manipulation,labeling and information extraction processes.This paper presents our Earth Observation Image Librarian(EOLib),a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data,in general.The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments(PGS).EOLib is composed of several modules which offer functionalities such as data ingestion,feature extraction from SAR(Synthetic Aperture Radar)data,meta-data extraction,semantic definition of the image content through machine learning and data mining methods,advanced querying of the image archives based on content,meta-data and semantic categories,as well as 3-D visualization of the processed images.EOLib is operated by DLR’s(German Aerospace Center’s)Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen,Germany.展开更多
文摘In this paper,we present long term observations of atmospheric nitrogen dioxide(NO2)and formaldehyde(HCHO)in Nanjing using a Multi-AXis Differential Optical Absorption Spectroscopy(MAX-DOAS)instrument.Ground based MAX-DOAS measurements were performed from April 2013 to February 2017.The MAX-DOAS measurements of NO2 and HCHO vertical column densities(VCDs)are used to validate OMI satellite observations over Nanjing.The comparison shows that the OMI observations of NO2 correlate well with the MAX-DOAS data with Pearson correlation coefficient(R)of 0.91.The comparison result of MAX-DOAS and OMI observations of HCHO VCD shows a good agreement with R of 0.75 and the slope of the regression line is 0.99.The age weighted backward propagation approach is applied to the MAX-DOAS measurements of NO2 and HCHO to reconstruct the spatial distribution of NO2 and HCHO over the Yangtze River Delta during summer and winter time.The reconstructed NO2 fields show a distinct agreement with OMI satellite observations.However,due to the short atmospheric lifetime of HCHO,the backward propagated HCHO data does not show a strong spatial correlation with the OMI HCHO observations.The result shows the MAX-DOAS measurements are sensitive to the air pollution transportation in the Yangtze River Delta,indicating the air quality in Nanjing is significantly influenced by regional transportation of air pollutants.
基金partially funded by European Space Agency(ESA)within the frame of ESA-MOST(Ministry of Science and Technology)Dragon 4 Cooperation(“Microwave satellite measurements for coastal area and extreme weather monitor”,project ID 32235)。
文摘In this study,a large time series of Terra SAR-X Stripmap co-polarized(HH-VV)Synthetic Aperture Radar(SAR)imagery collected over the Taylor Energy oil platform site in the Gulf of Mexico is exploited to investigate this 13 year-long unconventional oil spill.TheσCPDapproach is used to estimate the polluted area along time.In addition,a sensitivity analysis is undertaken to point out the dependence ofσCPDto imaging(noise floor,incidence angle)and environment(sea state)parameters.Experimental results demonstrate thatσCPDcan be effectively used to monitor the Taylor Energy oil spill,estimating the polluted area.For the TSX SAR data avail-ability most dense period(year 2013),a daily spill of about 2.2 km^(2) is observed in average,even though high variability(about 2.0 km^(2))is experienced due to the un-conventional characteristics of the spill.
基金supported by the National Key R&D Program of China(Grant No.2023YFB3907500)the National Natural Science Foundation of China(Grant Nos.42375142 and 42305154)+2 种基金the Open Fund of Innovation Center for FengYun Meteorological Satellite(FYSIC)FengYun Application Pioneering Project(Grant No.FY-APPZX-2022.0214)the National Civilian Space Infrastructure Project(Grant No.Y5BZ31AC60)。
文摘The Ozone Monitoring Suite-Nadir(OMS-N),a state-of-the-art hyperspectral ultraviolet-visible(UV-VIS)sensor onboard China's FengYun-3F(FY-3F)satellite,was launched in August 2023.Designed for a morning orbit,OMS-N represents a significant advancement in global atmospheric composition monitoring,offering an unprecedented spatial resolution of 7 km×7 km.The total ozone column(TOC)product derived from OMS-N is critical for climate modeling and UVradiation assessment.This study presents the first TOC retrievals from OMS-N,utilizing an adapted Differential Optical Absorption Spectroscopy(DOAS)algorithm.The retrieval algorithm overcomes traditional DOAS limitations by incorporating key innovations,including optimized radiative transfer calculations and refined a priori information on surface properties and ozone profiles,which are derived directly from OMS-N spectra rather than relying on external datasets or climatologies.Validation against ground-based measurements from Brewer,Dobson,and SAOZ instruments at 33 sites demonstrated strong agreement,with correlation coefficients mostly greater than 0.9.Comparisons with other well-established satellite instruments,including TROPOMI and GOME-2B,showed that OMS-N can consistently capture global seasonal ozone patterns,with biases typically within 2%across hemispheres and seasons.These results establish OMS-N as a reliable tool for high-resolution dynamic ozone monitoring,significantly enhancing our ability to address climate and environmental challenges.
基金supported by grants from the National Natural Science Foundation of China(nos.41722501,91544212,51778596,and 41575021)the National Key Research and Development Program of China(nos.2018YFC0213104,2017YFC0210002,and 2016YFC0203302)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(no.XDA23020301)the National Key Project for Causes and Control of Heavy Air Pollution(nos.DQGG0102 and DQGG0205)the National High-Resolution Earth Observation Project of China(no.05-Y30B01-9001-19/20-3).
文摘The Environmental Trace Gases Monitoring Instrument(EMI)is the first Chinese satellite-borne UV–Vis spectrometer aiming to measure the distribution of atmospheric trace gases on a global scale.The EMI instrument onboard the GaoFen-5 satellite was launched on 9 May 2018.In this paper,we present the tropospheric nitrogen dioxide(NO2)vertical column density(VCD)retrieval algorithm dedicated to EMI measurement.We report the first successful retrieval of tropospheric NO_(2) VCD from the EMI instrument.Our retrieval improved the original EMI NO_(2) prototype algorithm by modifying the settings of the spectral fit and air mass factor calculations to account for the on-orbit instrumental performance changes.The retrieved EMI NO_(2) VCDs generally show good spatiotemporal agreement with the satellite-borne Ozone Monitoring Instrument and TROPOspheric Monitoring Instrument(correlation coefficient R of ~0.9,bias<50%).A comparison with ground-based MAX-DOAS(Multi-Axis Differential Optical Absorption Spectroscopy)observations also shows good correlation with an R of 0.82.The results indicate that the EMI NO_(2) retrieval algorithm derives reliable and precise results,and this algorithm can feasibly produce stable operational products that can contribute to global air pollution monitoring.
基金supported by the National Natural Science Foundation of China (Grant Nos. 62161160336, 42030111, and 62101045)the China Postdoctoral Science Foundation Funded Project (Grant No. 2021M690385)
文摘Despite tons of advanced classification models that have recently been developed for the land cover mapping task,the monotonicity of a single remote sensing data source,such as only using hyperspectral data or multispectral data,hinders the classification accuracy from being further improved and tends to meet the performance bottleneck.For this reason,we develop a novel superpixel-based subspace learning model,called Supace,by jointly learning multimodal feature representations from HS and MS superpixels for more accurate LCC results.Supace can learn a common subspace across multimodal RS data,where the diverse and complementary information from different modalities can be better combined,being capable of enhancing the discriminative ability of to-be-learned features in a more effective way.To better capture semantic information of objects in the feature learning process,superpixels that beyond pixels are regarded as the study object in our Supace for LCC.Extensive experiments have been conducted on two popular hyperspectral and multispectral datasets,demonstrating the superiority of the proposed Supace in the land cover classification task compared with several well-known baselines related to multimodal remote sensing image feature learning.
基金The work was supported by the European Commission’s H2020 CANDELA project under Grant Agreement No.776193.
文摘The increased number of free and open Sentinel satellite images has led to new applications of these data.Among them is the systematic classification of land cover/use types based on patterns of settlements or agriculture recorded by these images,in particular,the identification and quantification of their temporal changes.In this paper,we will present guidelines and practical examples of how to obtain rapid and reliable image patch labelling results and their validation based on data mining techniques for detecting these temporal changes,and presenting these as classification maps and/or statistical analytics.This represents a new systematic validation approach for semantic image content verification.We will focus on a number of different scenarios proposed by the user community using Sentinel data.From a large number of potential use cases,we selected three main cases,namely forest monitoring,flood monitoring,and macro-economics/urban monitoring.
基金The work was supported by EOLib—an ESA technological project ESA EOLib project,2019.
文摘Throughout the years,various Earth Observation(EO)satellites have generated huge amounts of data.The extraction of latent information in the data repositories is not a trivial task.New methodologies and tools,being capable of handling the size,complexity and variety of data,are required.Data scientists require support for the data manipulation,labeling and information extraction processes.This paper presents our Earth Observation Image Librarian(EOLib),a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data,in general.The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments(PGS).EOLib is composed of several modules which offer functionalities such as data ingestion,feature extraction from SAR(Synthetic Aperture Radar)data,meta-data extraction,semantic definition of the image content through machine learning and data mining methods,advanced querying of the image archives based on content,meta-data and semantic categories,as well as 3-D visualization of the processed images.EOLib is operated by DLR’s(German Aerospace Center’s)Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen,Germany.
