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Ustin SL, Middleton EM. Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications. SENSORS (BASEL, SWITZERLAND) 2024; 24:3488. [PMID: 38894281 PMCID: PMC11175343 DOI: 10.3390/s24113488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/05/2024] [Accepted: 05/13/2024] [Indexed: 06/21/2024]
Abstract
Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth's status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth's surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements.
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Affiliation(s)
- Susan L. Ustin
- Institute of the Environment, University of California, Davis, Davis, CA 95616, USA
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2
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Shan C, Wang W, Xie Y, Wu P, Xu J, Zeng X, Zha L, Zhu Q, Sun Y, Hu Q, Liu C, Jones N. Observations of atmospheric CO 2 and CO based on in-situ and ground-based remote sensing measurements at Hefei site, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 851:158188. [PMID: 35995161 DOI: 10.1016/j.scitotenv.2022.158188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
The characteristics of long time series of CO2 and CO surface concentrations, tropospheric and total column dry-air mole fractions (DMF) from May 2015 to December 2019 were investigated. Both CO2 and CO show different seasonality for the three datasets. The annual increasing trend of CO2 is similar for all three datasets. However, the annual decreasing trend of CO for surface concentration is high compared to the other two measurements, mainly due to the improved combustion efficiency from power generation in recent years. The correlation between the tropospheric and total atmospheric CO2 and CO is higher than that between the surface concentration and tropospheric CO2 and CO. This is because the tropospheric and total atmospheric results both have common vertical profiles for CO2 and CO respective mole fractions that were observed in troposphere. Furthermore, the enhancement ratios of CO2 to CO derived from the three datasets during the period from 2016 to 2019 were compared. The ratio of ∆CO2 to ∆CO has an obvious increase with altitude each year, which means that the combustion efficiencies obtained from the three datasets are different. All ratios for the three datasets showed a slight increasing trend in recent years, which is attributed to increased combustion efficiency due to governmental measures for energy savings and emission reductions.
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Affiliation(s)
- Changgong Shan
- Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Wei Wang
- Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China.
| | - Yu Xie
- Department of Automation, Hefei University, Hefei 230601, Anhui, China
| | - Peng Wu
- Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
| | - Jiaqing Xu
- Department of Automation, Hefei University, Hefei 230601, Anhui, China
| | - Xiangyu Zeng
- Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
| | - Lingling Zha
- Department of Automation, Hefei University, Hefei 230601, Anhui, China
| | - Qianqian Zhu
- Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
| | - Youwen Sun
- Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
| | - Qihou Hu
- Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
| | - Cheng Liu
- Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China; Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, 230026 Hefei, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei 230026, China; Anhui Province Key Laboratory of Polar Environment and Global Change, University of Science and Technology of China, Hefei 230026, China
| | - Nicholas Jones
- School of Earth, Atmospheric and Life Sciences, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia
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Shan C, Wang W, Liu C, Guo Y, Xie Y, Sun Y, Hu Q, Zhang H, Yin H, Jones N. Retrieval of vertical profiles and tropospheric CO 2 columns based on high-resolution FTIR over Hefei, China. OPTICS EXPRESS 2021; 29:4958-4977. [PMID: 33726041 DOI: 10.1364/oe.411383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/04/2021] [Indexed: 06/12/2023]
Abstract
High-resolution solar absorption spectra, observed by ground-based Fourier Transform Infrared spectroscopy (FTIR), are used to retrieve vertical profiles and partial or total column concentrations of many trace gases. In this study, we present the tropospheric CO2 columns retrieved by mid-infrared solar spectra over Hefei, China. To reduce the influence of stratospheric CO2 cross-dependencies on tropospheric CO2, an a posteriori optimization method based on a simple matrix multiplication is used to correct the tropospheric CO2 profiles and columns. The corrected tropospheric CO2 time series show an obvious annual increase and seasonal variation. The tropospheric CO2 annual increase rate is 2.71 ± 0.36 ppm yr-1, with the annual peak value in January, and CO2 decreases to a minimum in August. Further, the corrected tropospheric CO2 from GEOS-Chem simulations are in good agreement with the coincident FTIR data, with a correlation coefficient between GEOS-chem model and FTS of 0.89. The annual increase rate of XCO2 observed from near-infrared solar absorption spectra is in good agreement with the tropospheric CO2 but the annual seasonal amplitude of XCO2 is only about 1/3 of dry-air averaged mole fractions (DMF) of tropospheric CO2. This is mostly attributed to the seasonal variation of CO2 being mainly dominated by sources near the surface.
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Yang D, Boesch H, Liu Y, Somkuti P, Cai Z, Chen X, Di Noia A, Lin C, Lu N, Lyu D, Parker RJ, Tian L, Wang M, Webb A, Yao L, Yin Z, Zheng Y, Deutscher NM, Griffith DWT, Hase F, Kivi R, Morino I, Notholt J, Ohyama H, Pollard DF, Shiomi K, Sussmann R, Té Y, Velazco VA, Warneke T, Wunch D. Toward High Precision XCO 2 Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements. JOURNAL OF GEOPHYSICAL RESEARCH. ATMOSPHERES : JGR 2020; 125:e2020JD032794. [PMID: 33777605 PMCID: PMC7983077 DOI: 10.1029/2020jd032794] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 07/22/2020] [Accepted: 07/24/2020] [Indexed: 06/12/2023]
Abstract
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.
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Affiliation(s)
- D. Yang
- Earth Observation Science, School of Physics and AstronomyUniversity of LeicesterUK
- Institute of Atmospheric PhysicsChinese Academy of SciencesChina
- Shanghai Advanced Research InstituteChinese Academy of SciencesShanghaiChina
| | - H. Boesch
- Earth Observation Science, School of Physics and AstronomyUniversity of LeicesterUK
- National Centre for Earth ObservationUniversity of LeicesterUK
| | - Y. Liu
- Institute of Atmospheric PhysicsChinese Academy of SciencesChina
- Shanghai Advanced Research InstituteChinese Academy of SciencesShanghaiChina
| | - P. Somkuti
- Earth Observation Science, School of Physics and AstronomyUniversity of LeicesterUK
- National Centre for Earth ObservationUniversity of LeicesterUK
- Colorado State UniversityFort CollinsCOUSA
| | - Z. Cai
- Institute of Atmospheric PhysicsChinese Academy of SciencesChina
| | - X. Chen
- Institute of Atmospheric PhysicsChinese Academy of SciencesChina
| | - A. Di Noia
- Earth Observation Science, School of Physics and AstronomyUniversity of LeicesterUK
- National Centre for Earth ObservationUniversity of LeicesterUK
| | - C. Lin
- Changchun Institute of Optics, Fine Mechanics and PhysicsChina
| | - N. Lu
- National Satellite Meteorological Center, China Meteorological AdministrationChina
| | - D. Lyu
- Institute of Atmospheric PhysicsChinese Academy of SciencesChina
| | - R. J. Parker
- Earth Observation Science, School of Physics and AstronomyUniversity of LeicesterUK
- National Centre for Earth ObservationUniversity of LeicesterUK
| | - L. Tian
- Shanghai Engineering Center for MicrosatellitesChina
| | - M. Wang
- Shanghai Advanced Research InstituteChinese Academy of SciencesShanghaiChina
| | - A. Webb
- Earth Observation Science, School of Physics and AstronomyUniversity of LeicesterUK
- National Centre for Earth ObservationUniversity of LeicesterUK
| | - L. Yao
- Institute of Atmospheric PhysicsChinese Academy of SciencesChina
| | - Z. Yin
- Shanghai Engineering Center for MicrosatellitesChina
| | - Y. Zheng
- Changchun Institute of Optics, Fine Mechanics and PhysicsChina
| | - N. M. Deutscher
- Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life SciencesUniversity of WollongongNSWAustralia
| | - D. W. T. Griffith
- Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life SciencesUniversity of WollongongNSWAustralia
| | - F. Hase
- Karlsruhe Institute of Technology, IMK‐IFUGarmisch‐PartenkirchenGermany
| | - R. Kivi
- Space and Earth Observation CentreFinnish Meteorological InstituteFinland
| | - I. Morino
- National Institute for Environmental Studies (NIES)TsukubaIbarakiJapan
| | - J. Notholt
- Institute of Environmental Physics (IUP)University of BremenBremenGermany
| | - H. Ohyama
- National Institute for Environmental Studies (NIES)TsukubaIbarakiJapan
| | - D. F. Pollard
- National Institute of Water and Atmospheric Research Ltd (NIWA)LauderNew Zealand
| | - K. Shiomi
- Japan Aerospace Exploration AgencyJapan
| | - R. Sussmann
- Karlsruhe Institute of Technology, IMK‐IFUGarmisch‐PartenkirchenGermany
| | - Y. Té
- Laboratoire d'Etudes du Rayonnement et de la Matière en Astrophysique et Atmosphères (LERMA‐IPSL)Sorbonne Université, CNRS, Observatoire de Paris, PSL UniversitéParisFrance
| | - V. A. Velazco
- Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life SciencesUniversity of WollongongNSWAustralia
| | - T. Warneke
- Institute of Environmental Physics (IUP)University of BremenBremenGermany
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Retrieval and Validation of XCO2 from TanSat Target Mode Observations in Beijing. REMOTE SENSING 2020. [DOI: 10.3390/rs12183063] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Satellite observation is one of the main methods used to monitor the global distribution and variation of atmospheric carbon dioxide (CO2). Several CO2 monitoring satellites have been successfully launched, including Japan’s Greenhouse Gases Observing SATellite (GOSAT), the USA’s Orbiting Carbon Observatory-2 (OCO-2), and China’s Carbon Dioxide Observation Satellite Mission (TanSat). Satellite observation targeting the ground-based Fourier transform spectrometer (FTS) station is the most effective technique for validating satellite CO2 measurement precision. In this study, the coincident observations from TanSat and ground-based FTS were performed numerous times in Beijing under a clear sky. The column-averaged dry-air mole fraction of carbon dioxide (XCO2) obtained from TanSat was retrieved by the Department for Eco-Environmental Informatics (DEEI) of China’s State Key Laboratory of Resources and Environmental Information System based on a full physical model. The comparison and validation of the TanSat target mode observations revealed that the average of the XCO2 bias between TanSat retrievals and ground-based FTS measurements was 2.62 ppm, with a standard deviation (SD) of the mean difference of 1.41 ppm, which met the accuracy standard of 1% required by the mission tasks. With bias correction, the mean absolute error (MAE) improved to 1.11 ppm and the SD of the mean difference fell to 1.35 ppm. We compared simultaneous observations from GOSAT and OCO-2 Level 2 (L2) bias-corrected products within a ±1° latitude and longitude box centered at the ground-based FTS station in Beijing. The results indicated that measurements from GOSAT and OCO-2 were 1.8 ppm and 1.76 ppm higher than the FTS measurements on 20 June 2018, on which the daily observation bias of the TanSat XOC2 results was 1.87 ppm. These validation efforts have proven that TanSat can measure XCO2 effectively. In addition, the DEEI-retrieved XCO2 results agreed well with measurements from GOSAT, OCO-2, and the Beijing ground-based FTS.
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Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors. ATMOSPHERE 2020. [DOI: 10.3390/atmos11030231] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Over the past few decades, concentrations of carbon dioxide (CO2), a key greenhouse gas, have risen at a global rate of approximately 2 ppm/a. China is the largest CO2 emitter and is the principle contributor to the increase in global CO2 levels. Based on a satellite-retrieved atmospheric carbon dioxide column average dry air mixing ratio (XCO2) dataset, derived from the greenhouse gas observation satellite (GOSAT), this paper evaluates the spatial and temporal variations of XCO2 characteristics in China during 2009–2016. Moreover, the factors influencing changes in XCO2 were investigated. Results showed XCO2 concentrations in China increased at an average rate of 2.28 ppm/a, with significant annual seasonal variations of 6.78 ppm. The rate of change of XCO2 was greater in south China compared to other regions across China, with clear differences in seasonality. Seasonal variations in XCO2 concentrations across China were generally controlled by vegetation dynamics, characterized by the Normalized Difference Vegetation Index (NDVI). However, driving factors exhibited spatial variations. In particular, a distinct belt (northeast–southwest) with a significant negative correlation (r < −0.75) between XCO2 and NDVI was observed. Furthermore, in north China, human emissions were identified as the dominant influencing factor of total XCO2 variations (r > 0.65), with forest fires taking first place in southwest China (r > 0.47). Our results in this study can provide us with a potential way to better understand the spatiotemporal changes of CO2 concentration in China with NDVI, human activity and biomass burning, and could have an enlightening effect on slowing the growth of CO2 concentration in China.
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Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China. REMOTE SENSING 2020. [DOI: 10.3390/rs12040718] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Persistent and widespread increase of vegetation cover, identified as greening, has been observed in areas of the planet over late 20th century and early 21st century by satellite-derived vegetation indices. It is difficult to verify whether these regions are net carbon sinks or sources by studying vegetation indices alone. In this study, we investigate greening trends in Eastern China (EC) and corresponding trends in atmospheric CO2 concentrations. We used multiple vegetation indices including NDVI and EVI to characterize changes in vegetation activity over EC from 2003 to 2016. Gap-filled time series of column-averaged CO2 dry air mole fraction (XCO2) from January 2003 to May 2016, based on observations from SCIAMACHY, GOSAT, and OCO-2 satellites, were used to calculate XCO2 changes during growing season for 13 years. We derived a relationship between XCO2 and surface net CO2 fluxes from two inversion model simulations, CarbonTracker and Monitoring Atmospheric Composition and Climate (MACC), and used those relationships to estimate the biospheric CO2 flux enhancement based on satellite observed XCO2 changes. We observed significant growing period (GP) greening trends in NDVI and EVI related to cropland intensification and forest growth in the region. After removing the influence of large urban center CO2 emissions, we estimated an enhanced XCO2 drawdown during the GP of −0.070 to −0.084 ppm yr−1. Increased carbon uptake during the GP was estimated to be 28.41 to 46.04 Tg C, mainly from land management, which could offset about 2–3% of EC’s annual fossil fuel emissions. These results show the potential of using multi-satellite observed XCO2 to estimate carbon fluxes from the regional biosphere, which could be used to verify natural sinks included as national contributions of greenhouse gas emissions reduction in international climate change agreements like the UNFCC Paris Accord.
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Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method. REMOTE SENSING 2020. [DOI: 10.3390/rs12030576] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements.
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Evaluation and Analysis of the Seasonal Cycle and Variability of the Trend from GOSAT Methane Retrievals. REMOTE SENSING 2019. [DOI: 10.3390/rs11070882] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Methane ( CH 4 ) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane ( XCH 4 ). To understand and quantify the spatial differences of the seasonal cycle and trend of XCH 4 in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average XCH 4 seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC CH 4 Proxy algorithm version 2.3.8 and RemoTeC CH 4 Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43 ∘ S and 53.13 ∘ N. At latitude bands, we also compared the trend of GOSAT XCH 4 retrievals to the NOAA’s Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of XCH 4 agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of XCH 4 . These differences are linked to the regional CH 4 sources and sinks, and call for further research.
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Zhang LL, Yue TX, Wilson JP, Zhao N, Zhao YP, Du ZP, Liu Y. A comparison of satellite observations with the XCO 2 surface obtained by fusing TCCON measurements and GEOS-Chem model outputs. THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 601-602:1575-1590. [PMID: 28609846 DOI: 10.1016/j.scitotenv.2017.06.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2017] [Revised: 05/07/2017] [Accepted: 06/02/2017] [Indexed: 06/07/2023]
Abstract
Ground observations can capture CO2 concentrations accurately but the number of available TCCON (Total Carbon Column Observing Network) sites is too small to support a comprehensive analysis (i.e. validation) of satellite observations. Atmospheric transport models can provide continuous atmospheric CO2 concentrations in space and time, but some information is difficult to generate with model simulations. The HASM platform can model continuous column-averaged CO2 dry air mole fraction (XCO2) surface taking TCCON observations as its optimum control constraints and an atmospheric transport model as its driving field. This article presents a comparison of the satellite observations with a HASM XCO2 surface obtained by fusing TCCON measurements with GEOS-Chem model results. We first verified the accuracy of the HASM XCO2 surface using six years (2010-2015) of TCCON observations and the GEOS-Chem model XCO2 results. The validation results show that the largest MAE of bias between the HASM results and observations was 0.85ppm and the smallest MAE was only 0.39ppm. Next, we modeled the HASM XCO2 surface by fusing the TCCON measurements and GEOS-Chem XCO2 model results for the period 9/1/14 to 8/31/15. Finally, we compared the GOSAT and OCO-2 observations with the HASM XCO2 surface and found that the global OCO-2 XCO2 estimates more closely resembled the HASM XCO2 surface than the GOSAT XCO2 estimates.
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Affiliation(s)
- Li Li Zhang
- State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
| | - Tian Xiang Yue
- State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - John P Wilson
- State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Spatial Sciences Institute, Dana and David Dornsife College of Letters, Arts, and Sciences, University of Southern California, Los Angeles, CA 90089-0374, USA
| | - Na Zhao
- State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ya Peng Zhao
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Zheng Ping Du
- State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yu Liu
- State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 2: Application to XCO2 Retrievals from OCO-2. REMOTE SENSING 2017. [DOI: 10.3390/rs9111102] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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12
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Eldering A, Wennberg PO, Crisp D, Schimel DS, Gunson MR, Chatterjee A, Liu J, Schwandner FM, Sun Y, O'Dell CW, Frankenberg C, Taylor T, Fisher B, Osterman GB, Wunch D, Hakkarainen J, Tamminen J, Weir B. The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes. Science 2017; 358:eaam5745. [PMID: 29026012 PMCID: PMC5668686 DOI: 10.1126/science.aam5745] [Citation(s) in RCA: 131] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2016] [Accepted: 07/12/2017] [Indexed: 11/02/2022]
Abstract
NASA's Orbiting Carbon Observatory-2 (OCO-2) mission was motivated by the need to diagnose how the increasing concentration of atmospheric carbon dioxide (CO2) is altering the productivity of the biosphere and the uptake of CO2 by the oceans. Launched on 2 July 2014, OCO-2 provides retrievals of the column-averaged CO2 dry-air mole fraction ([Formula: see text]) as well as the fluorescence from chlorophyll in terrestrial plants. The seasonal pattern of uptake by the terrestrial biosphere is recorded in fluorescence and the drawdown of [Formula: see text] during summer. Launched just before one of the most intense El Niños of the past century, OCO-2 measurements of [Formula: see text] and fluorescence record the impact of the large change in ocean temperature and rainfall on uptake and release of CO2 by the oceans and biosphere.
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Affiliation(s)
- A Eldering
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
| | - P O Wennberg
- Division of Geology and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA
| | - D Crisp
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - D S Schimel
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - M R Gunson
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - A Chatterjee
- Universities Space Research Association, Columbia, MD, USA
- NASA Global Modeling and Assimilation Office, Greenbelt, MD, USA
| | - J Liu
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - F M Schwandner
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Y Sun
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - C W O'Dell
- Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
| | - C Frankenberg
- Division of Geology and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
| | - T Taylor
- Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
| | - B Fisher
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - G B Osterman
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - D Wunch
- Division of Geology and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
| | - J Hakkarainen
- Finnish Meteorological Institute, Earth Observation, Helsinki, Finland
| | - J Tamminen
- Finnish Meteorological Institute, Earth Observation, Helsinki, Finland
| | - B Weir
- Universities Space Research Association, Columbia, MD, USA
- NASA Global Modeling and Assimilation Office, Greenbelt, MD, USA
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13
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Modelling of XCO₂ Surfaces Based on Flight Tests of TanSat Instruments. SENSORS 2016; 16:s16111818. [PMID: 27809272 PMCID: PMC5134477 DOI: 10.3390/s16111818] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Revised: 10/25/2016] [Accepted: 10/26/2016] [Indexed: 11/17/2022]
Abstract
The TanSat carbon satellite is to be launched at the end of 2016. In order to verify the performance of its instruments, a flight test of TanSat instruments was conducted in Jilin Province in September, 2015. The flight test area covered a total area of about 11,000 km2 and the underlying surface cover included several lakes, forest land, grassland, wetland, farmland, a thermal power plant and numerous cities and villages. We modeled the column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) surface based on flight test data which measured the near- and short-wave infrared (NIR) reflected solar radiation in the absorption bands at around 760 and 1610 nm. However, it is difficult to directly analyze the spatial distribution of XCO2 in the flight area using the limited flight test data and the approximate surface of XCO2, which was obtained by regression modeling, which is not very accurate either. We therefore used the high accuracy surface modeling (HASM) platform to fill the gaps where there is no information on XCO2 in the flight test area, which takes the approximate surface of XCO2 as its driving field and the XCO2 observations retrieved from the flight test as its optimum control constraints. High accuracy surfaces of XCO2 were constructed with HASM based on the flight’s observations. The results showed that the mean XCO2 in the flight test area is about 400 ppm and that XCO2 over urban areas is much higher than in other places. Compared with OCO-2’s XCO2, the mean difference is 0.7 ppm and the standard deviation is 0.95 ppm. Therefore, the modelling of the XCO2 surface based on the flight test of the TanSat instruments fell within an expected and acceptable range.
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14
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Mapping Global Atmospheric CO2 Concentration at High Spatiotemporal Resolution. ATMOSPHERE 2014. [DOI: 10.3390/atmos5040870] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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15
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Wang T, Shi J, Jing Y, Zhao T, Ji D, Xiong C. Combining XCO2 measurements derived from SCIAMACHY and GOSAT for potentially generating global CO2 maps with high spatiotemporal resolution. PLoS One 2014; 9:e105050. [PMID: 25119468 PMCID: PMC4132063 DOI: 10.1371/journal.pone.0105050] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2014] [Accepted: 07/20/2014] [Indexed: 11/18/2022] Open
Abstract
Global warming induced by atmospheric CO2 has attracted increasing attention of researchers all over the world. Although space-based technology provides the ability to map atmospheric CO2 globally, the number of valid CO2 measurements is generally limited for certain instruments owing to the presence of clouds, which in turn constrain the studies of global CO2 sources and sinks. Thus, it is a potentially promising work to combine the currently available CO2 measurements. In this study, a strategy for fusing SCIAMACHY and GOSAT CO2 measurements is proposed by fully considering the CO2 global bias, averaging kernel, and spatiotemporal variations as well as the CO2 retrieval errors. Based on this method, a global CO2 map with certain UTC time can also be generated by employing the pattern of the CO2 daily cycle reflected by Carbon Tracker (CT) data. The results reveal that relative to GOSAT, the global spatial coverage of the combined CO2 map increased by 41.3% and 47.7% on a daily and monthly scale, respectively, and even higher when compared with that relative to SCIAMACHY. The findings in this paper prove the effectiveness of the combination method in supporting the generation of global full-coverage XCO2 maps with higher temporal and spatial sampling by jointly using these two space-based XCO2 datasets.
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Affiliation(s)
- Tianxing Wang
- State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. Beijing, China
- * E-mail:
| | - Jiancheng Shi
- State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. Beijing, China
| | - Yingying Jing
- State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. Beijing, China
| | - Tianjie Zhao
- State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. Beijing, China
| | - Dabin Ji
- State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. Beijing, China
| | - Chuan Xiong
- State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. Beijing, China
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16
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Retrieval of column-averaged volume mixing ratio of CO2 with ground-based high spectral resolution solar absorption. CHINESE SCIENCE BULLETIN-CHINESE 2014. [DOI: 10.1007/s11434-014-0261-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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17
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Wang T, Shi J, Jing Y, Xie Y. Investigation of the consistency of atmospheric CO2 retrievals from different space-based sensors: Intercomparison and spatiotemporal analysis. CHINESE SCIENCE BULLETIN-CHINESE 2013. [DOI: 10.1007/s11434-013-5996-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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18
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Multi-Year Comparison of Carbon Dioxide from Satellite Data with Ground-Based FTS Measurements (2003–2011). REMOTE SENSING 2013. [DOI: 10.3390/rs5073431] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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19
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Guo M, Wang X, Li J, Yi K, Zhong G, Tani H. Assessment of global carbon dioxide concentration using MODIS and GOSAT data. SENSORS 2012; 12:16368-89. [PMID: 23443383 PMCID: PMC3571787 DOI: 10.3390/s121216368] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2012] [Revised: 11/22/2012] [Accepted: 11/23/2012] [Indexed: 11/25/2022]
Abstract
Carbon dioxide (CO2) is the most important greenhouse gas (GHG) in the atmosphere and is the greatest contributor to global warming. CO2 concentration data are usually obtained from ground observation stations or from a small number of satellites. Because of the limited number of observations and the short time series of satellite data, it is difficult to monitor CO2 concentrations on regional or global scales for a long time. The use of the remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) or Moderate Resolution Imaging Spectroradiometer (MODIS) data can overcome these problems, particularly in areas with low densities of CO2 concentration watch stations. A model based on temperature (MOD11C3), vegetation cover (MOD13C2 and MOD15A2) and productivity (MOD17A2) of MODIS (which we have named the TVP model) was developed in the current study to assess CO2 concentrations on a global scale. We assumed that CO2 concentration from the Thermal And Near infrared Sensor for carbon Observation (TANSO) aboard the Greenhouse gases Observing SATellite (GOSAT) are the true values and we used these values to check the TVP model accuracy. The results indicate that the accuracy of the TVP model is different in different continents: the greatest Pearson’s correlation coefficient (R2) was 0.75 in Eurasia (RMSE = 1.16) and South America (RMSE = 1.17); the lowest R2 was 0.57 in Australia (RMSE = 0.73). Compared with the TANSO-observed CO2 concentration (XCO2), we found that the accuracy throughout the World is between −2.56∼3.14 ppm. Potential sources of TVP model uncertainties were also analyzed and identified.
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Affiliation(s)
- Meng Guo
- Graduate School of Agriculture, Hokkaido University, Sapporo 060-8589, Japan; E-Mails: (K.Y.); (G.Z.)
- Author to whom correspondence should be addressed; E-Mail: ; Tel./Fax: +81-11-706-4174
| | - Xiufeng Wang
- Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589, Japan; E-Mails: (X.W.); (H.T.)
| | - Jing Li
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; E-Mail:
| | - Kunpeng Yi
- Graduate School of Agriculture, Hokkaido University, Sapporo 060-8589, Japan; E-Mails: (K.Y.); (G.Z.)
| | - Guosheng Zhong
- Graduate School of Agriculture, Hokkaido University, Sapporo 060-8589, Japan; E-Mails: (K.Y.); (G.Z.)
| | - Hiroshi Tani
- Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589, Japan; E-Mails: (X.W.); (H.T.)
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20
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Wunch D, Toon GC, Blavier JFL, Washenfelder RA, Notholt J, Connor BJ, Griffith DWT, Sherlock V, Wennberg PO. The total carbon column observing network. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2011; 369:2087-2112. [PMID: 21502178 DOI: 10.1098/rsta.2010.0240] [Citation(s) in RCA: 140] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
A global network of ground-based Fourier transform spectrometers has been founded to remotely measure column abundances of CO(2), CO, CH(4), N(2)O and other molecules that absorb in the near-infrared. These measurements are directly comparable with the near-infrared total column measurements from space-based instruments. With stringent requirements on the instrumentation, acquisition procedures, data processing and calibration, the Total Carbon Column Observing Network (TCCON) achieves an accuracy and precision in total column measurements that is unprecedented for remote-sensing observations (better than 0.25% for CO(2)). This has enabled carbon-cycle science investigations using the TCCON dataset, and allows the TCCON to provide a link between satellite measurements and the extensive ground-based in situ network.
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Affiliation(s)
- Debra Wunch
- Department of Earth Science and Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
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