Global land 1° mapping dataset of XCO_(2) from satellite observations of GOSAT and OCO-2 from 2009 to 2020

A global mapping data of atmospheric carbon dioxide(CO_(2))concen-trations can help us to better understand the spatiotemporal varia-tions of CO_(2) and the driving factors of the variations to support the actions for emissions reduction and control.Greenhouse gases satel-lites that measure atmospheric CO_(2),such as the Greenhouse Gases Observing Satellite(GOSAT)and Orbiting Carbon Observatory(OCO-2),have been providing global observations of the column averaged dry-air mole fractions of CO_(2)(XCO_(2))since 2009.However,these XCO_(2) retrievals are irregular in space and time with many gaps.In this paper,we mapped a global spatiotemporally continuous XCO_(2) data-set(Mapping-XCO_(2))using the XCO_(2) retrievals from GOSAT and OCO-2 during the period from April 2009 to December 2020 based on a geostatistical approach that fills those data gaps.The dataset covers a geographic range from 56°S to 65°N and 169°W to 180°E for a 1°grid interval in space and 3-day time interval.The uncer-tainties of the mapped XCO_(2) values are generally less than 1.5 parts per million(ppm).The spatiotemporal characteristics of global XCO_(2) that are revealed by the Mapping-XCO_(2) are similar to the model data obtained from CarbonTracker.Compared to the ground observa-tions,the overall standard bias is 1.13 ppm.The results indicate that this long-term Mapping-XCO_(2) dataset can be used to investigate the spatiotemporal variations of global atmospheric XCO_(2) and can support studies related to the carbon cycle and anthropogenic CO_(2) emissions.The dataset is available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.

China's sizeable and uncertain carbon sink: a perspective from GOSAT

Despite the agreement that China’s terrestrial ecosystems can provide a carbon sink and offset carbon dioxide(CO2)emissions from fossil fuels,the magnitude and spatial distribution of the sink remain uncertain.Accurate quantification of the carbon sequestration capacity of China’s terrestrial ecosystems has profound scientific and policy implications.Here,we report on the magnitude and patterns of China’s terrestrial carbon sink using the global monthly CO2flux data product from the Greenhouse gases Observing SATellite(GOSAT),the world’s first satellite dedicated to global greenhouse gas observation.We use the first year’s data from GOSAT(June 2009–May2010)that are currently available to assess China’s biospheric carbon fluxes.Our results show that China’s terrestrial ecosystems provide a carbon sink of-0.21 Pg C a-1.The consumption of fossil fuels in China leads to carbon dioxide emissions of 1.90 Pg C a-1into the atmosphere,approximately 11.1%of which is offset by China’s terrestrial ecosystems.China’s terrestrial ecosystems play a significant role in offsetting fossil fuel emissions and slowing down the buildup of CO2in the atmosphere.Our analysis based on GOSAT data offers a new perspective on the magnitude and distribution of China’s carbon sink.Our results show that China’s terrestrial ecosystems provide a sizeable and uncertain carbon sink,and further research is needed to reduce the uncertainty in its magnitude and distribution.

Spatial distribution and inducement of collapsed buildings in Yushu earthquake based on remote sensing analysis

At 7:49 a.m. on April 14th, 2010, an earthquake of 7.1 on the Richter scale occurred in Yushu County, Yushu Tibetan Autonomous Prefecture in Qinghai Province. There was great loss of property and life.

Dynamic analysis of the Wenchuan Earthquake disaster and reconstruction with 3-year remote sensing data

Earth observation is an effective technique that plays an important role in earthquake damage reduction and reconstruction.This paper introduces the results of dynamic analysis on monitoring and assessing heavily impacted areas affected by the Wenchuan Earthquake using remote sensing data acquired in the past 3 years from 2008 to 2010.Immediately after the disaster on 12 May 2008,the Chinese Academy of Sciences launched a project entitled‘Wenchuan Earthquake Disasters Monitoring and Assessment Using Remote Sensing Technology.’More than 400 images from 17 satellites and 20.2TB airborne remote sensing data were acquired to facilitate quick monitoring and evaluation of severely damaged areas in 14 counties.Results of the image analyses were forwarded on a timely basis to assist with consultative service and decisionmaking support.In subsequent years,in order to monitor the process of environmental restoration and reconstruction,airborne optical remote sensing images covering most of the severely damaged areas were again acquired in May 2009 and April 2010.These images were analyzed and compared along with images from 2008.Results were useful in support of further work on environmental protection and reconstruction in earthquake-damaged areas.Three typical areas were selected for illustrative purposes including Tangjiashan Barrier Lake,Beichuan County,and counties of Yingxiu and the new Beichuan.These results well demonstrate the importance and effectiveness of the utility of earth observation for disaster mitigation and reconstruction.

Global land mapping of satellite-observed CO_(2)total columns using spatio-temporal geostatistics

This study presents an approach for generating a global land mapping dataset of the satellite measurements of CO_(2)total column(XCO_(2))using spatio-temporal geostatistics,which makes full use of the joint spatial and temporal dependencies between observations.The mapping approach considers the latitude-zonal seasonal cycles and spatio-temporal correlation structure of XCO_(2),and obtains global land maps of XCO_(2),with a spatial grid resolution of 1°latitude by 1°longitude and temporal resolution of 3 days.We evaluate the accuracy and uncertainty of the mapping dataset in the following three ways:(1)in cross-validation,the mapping approach results in a high correlation coefficient of 0.94 between the predictions and observations,(2)in comparison with ground truth provided by the Total Carbon Column Observing Network(TCCON),the predicted XCO_(2)time series and those from TCCON sites are in good agreement,with an overall bias of 0.01 ppm and a standard deviation of the difference of 1.22 ppm and(3)in comparison with model simulations,the spatio-temporal variability of XCO_(2)between the mapping dataset and simulations from the CT2013 and GEOS-Chem are generally consistent.The generated mapping XCO_(2)data in this study provides a new global geospatial dataset in global understanding of greenhouse gases dynamics and global warming.

Specific patterns of XC02 observed by GOSAT during 2009-2016and assessed with model simulations over China

Spatiotemporal patterns of column-averaged dry air mole fraction of CO2(XCO2)have not been well characterized on a regional scale due to limitations in data availability and precision.This paper addresses these issues by examining such patterns in China using the long-term mapping XCO2 dataset(2009-2016)derived from the Greenhouse gases Observing SATellite(GOSAT).XCO2 simulations are also constructed using the high-resolution nested-grid GEOS-Chem model.The following results are found:Firstly,the correlation coefficient between the anthropogenic emissions and XCO2 spatial distribution is nearly zero in summer but up to 0.32 in autumn.Secondly,on average,XCO2 increases by 2.08 ppm every year from2010 to 2015,with a sharp increase of 2.6 ppm in 2013.Lastly,in the analysis of three typical regions,the GOSAT XCO2 time series is inbetter agreement with the GEOS-Chem simulation of XCO2 in the Taklimakan Desert region(the least difference with bias 0.65±0.78 ppm),compared with the northern urban agglomerationregion(-1.3±1.2 ppm)and the northeastern forest region(-1.4±1.4 ppm).The results are likely attributable to uncertainty in both the satellite-retrieved XCO2 data and the model simulation data.

Feasibility and physics potential of detecting ^(8)B solar neutrinos at JUNO

The Jiangmen Underground Neutrino Observatory(JUNO)features a 20 kt multi-purpose underground liquid scintillator sphere as its main detector.Some of JUNO's features make it an excellent location for^8B solar neutrino measurements,such as its low-energy threshold,high energy resolution compared with water Cherenkov detectors,and much larger target mass compared with previous liquid scintillator detectors.In this paper,we present a comprehensive assessment of JUNO's potential for detecting^8B solar neutrinos via the neutrino-electron elastic scattering process.A reduced 2 MeV threshold for the recoil electron energy is found to be achievable,assuming that the intrinsic radioactive background^(238)U and^(232)Th in the liquid scintillator can be controlled to 10^(-17)g/g.With ten years of data acquisition,approximately 60,000 signal and 30,000 background events are expected.This large sample will enable an examination of the distortion of the recoil electron spectrum that is dominated by the neutrino flavor transformation in the dense solar matter,which will shed new light on the inconsistency between the measured electron spectra and the predictions of the standard three-flavor neutrino oscillation framework.IfDelta m^(2)_(21)=4.8times10^(-5);(7.5times10^(-5))eV^(2),JUNO can provide evidence of neutrino oscillation in the Earth at approximately the 3sigma(2sigma)level by measuring the non-zero signal rate variation with respect to the solar zenith angle.Moreover,JUNO can simultaneously measureDelta m^2_(21)using^8B solar neutrinos to a precision of 20% or better,depending on the central value,and to sub-percent precision using reactor antineutrinos.A comparison of these two measurements from the same detector will help understand the current mild inconsistency between the value of Delta m^2_(21)reported by solar neutrino experiments and the KamLAND experiment.