The first Chinese Carbon Dioxide Observation Satellite Mission(TanSat), which was launched on December 21, 2016, is intended to measure atmospheric CO_2 concentration.The high spectral resolution(0.044 nm) and high SN...The first Chinese Carbon Dioxide Observation Satellite Mission(TanSat), which was launched on December 21, 2016, is intended to measure atmospheric CO_2 concentration.The high spectral resolution(0.044 nm) and high SNR(360 at 15.2 mW m^(-1) sr^(-1) nm^(-1)) measurements in the region of the O_2-A band of the Atmospheric Carbon dioxide Grating Spectroradiometer(AGCS) module onboard TanSat make it possible to retrieve solar-induced chlorophyll fluorescence(SIF) from TanSat observations at the global scale.This paper aims to explore the potential of the TanSat data for global SIF retrieval.A singular vector decomposition(SVD) statistical method was employed to retrieve SIF using radiance over a micro spectral window(～2 nm) around the Fe Fraunhofer lines(centered at 758.8 nm).The global SIF at 758.8 nm was successfully retrieved with a low residual error of 0.03 mW m^(-1) sr^(-1) nm^(-1).The results show that the spatial and temporal patterns of the retrieved SIF agree well with the global terrestrial vegetation pattern.The monthly SIF products retrieved from the TanSat data were compared with other remote sensing datasets, including OCO-2 SIF, MODIS NDVI, EVI and GPP.The overall consistency between TanSat and OCO-2 SIF products(R^2= 0.86) and the consistency of the spatial patterns and temporal variations between the TanSat SIF and MODIS vegetation indices and GPP enhance our confidence in the potential and feasibility of TanSat data for SIF retrieval.TanSat, therefore, provides a new opportunity for global sampling of SIF at fine spatial resolution(2 km × 2 km), thus improving photosynthesis observations from space.展开更多
Background:Vegetation phenology research has largely focused on temperate deciduous forests,thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions...Background:Vegetation phenology research has largely focused on temperate deciduous forests,thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions.Results:Using satellite solar-induced chlorophyll fluorescence(SIF)and MODIS enhanced vegetation index(EVI)data,we applied two methods to evaluate temporal and spatial patterns of the end of the growing season(EGS)in subtropical vegetation in China,and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation.Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods(dynamic threshold method and derivative method)was later than that derived from gross primary productivity(GPP)based on the eddy covariance technique,and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks,respectively.We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation(accounting for more than 73%and 62%of the study areas,respectively),but negatively correlated with preseason maximum temperature(accounting for more than 59%of the study areas).In addition,EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors,and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests,shrub and grassland.Conclusions:Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China.We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region.These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China,and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.展开更多
The Chinese Carbon Dioxide Observation Satellite Mission(TanSat)is the third satellite for global CO2 monitoring and is capable of detecting weak solar-induced chlorophyll fluorescence(SIF)signals with its advanced te...The Chinese Carbon Dioxide Observation Satellite Mission(TanSat)is the third satellite for global CO2 monitoring and is capable of detecting weak solar-induced chlorophyll fluorescence(SIF)signals with its advanced technical characteristics.Based on the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing(IAPCAS)platform,we successfully retrieved the TanSat global SIF product spanning the period of March 2017 to February 2018 with a physically based algorithm.This paper introduces the new TanSat SIF dataset and shows the global seasonal SIF maps.A brief comparison between the IAPCAS TanSat SIF product and the data-driven SVD(singular value decomposition)SIF product is also performed for follow-up algorithm optimization.The comparative results show that there are regional biases between the two SIF datasets and the linear correlations between them are above 0.73 for all seasons.The future SIF data product applications and requirements for SIF space observation are discussed.展开更多
Quantifying how climate factors affect vegetation phenology is crucial for understanding climate-vegetation interactions and carbon and water cycles under a changing climate.However,the effects of different intensitie...Quantifying how climate factors affect vegetation phenology is crucial for understanding climate-vegetation interactions and carbon and water cycles under a changing climate.However,the effects of different intensities of extreme climatic events on vegetation phenology remain poorly understood.Using a long-term solar-induced chlorophyll fluorescence dataset,we investigated the response of vegetation phenology to extreme temperatures and precipitation events of different intensities across the Tibetan Plateau(TP)from 2000 to 2018.We found that the effect of maximum temperature exposure days(TxED)and minimum temperature exposure days(TnED)on the start of the growing season(SOS)was initially delayed and shifted to advance along the increasing temperature gradients.However,the response of the end of the growing season(EOS)to TxED and TnED shifted from an advance to a delay with increasing temperature gradients until the temperature thresholds were reached,above which thresholds produced an unfavorable response to vegetation growth and brought the EOS to an early end.The corresponding maximum and minimum temperature thresholds were 10.12 and 2.54℃,respectively.In contrast,cumulative precipitation(CP)was more likely to advance SOS and delay EOS as the precipitation gradient increased,but the advance of SOS is gradually weakening.Four vegetation types(i.e.,forest,shrubland,meadow,and steppe)showed similar trends in response to different climates,but the optimal climatic conditions varied between the vegetation types.Generally,meadow and steppe had lower optimal temperatures and precipitation than forest and shrubland.These findings revealed the divergent responses of vegetation phenology to extreme climate events of different intensities,implying that the SOS will continue to advance with warming,whereas the EOS may undergo a partial transformation from delayed areas to advanced areas with continued warming.展开更多
Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems.The normalized ...Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems.The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI),extracted from the Moderate Resolution Imaging Spectrometer(MODIS),are widely used to monitor phenology by calculating land surface reflectance.However,the applicability of the vegetation index based on‘greenness'to monitor photosynthetic activity is hindered by poor observation conditions(e.g.,ground shadows,snow,and clouds).Recently,satellite measurements of solar-induced chlorophyll fluorescence(SIF)from OCO-2 sensors have shown great potential for studying vegetation phenology.Here,we tested the feasibility of SIF in extracting phenological metrics in permafrost regions of the northeastern China,exploring the characteristics of SIF in the study of vegetation phenology and the differences between NDVI and EVI.The results show that NDVI has obvious SOS advance and EOS lag,and EVI is closer to SIF.The growing season length based on SIF is often the shortest,while it can represent the true phenology of vegetation because it is closely related to photosynthesis.SIF is more sensitive than the traditional remote sensing indices in monitoring seasonal changes in vegetation phenology and can compensate for the shortcomings of traditional vegetation indices.We also used the time series data of MODIS NDVI and EVI to extract phenological metrics in different permafrost regions.The results show that the length of growing season of vegetation in predominantly continuous permafrost(zone I)is longer than in permafrost with isolated taliks(zone II).Our results have certain significance for understanding the response of ecosystems in cold regions to global climate change.展开更多
Due to the quick development of urbanization,it is important to provide a healthy urban environment for the dweller.Previous studies have obtained valuable conclusions of how to improve the urban airflow distribution ...Due to the quick development of urbanization,it is important to provide a healthy urban environment for the dweller.Previous studies have obtained valuable conclusions of how to improve the urban airflow distribution under isothermal conditions.How to adopt and interpret those conclusions when considering the solar-induced atmospheric stability conditions have not been clarified yet.In this study,the characteristics of atmospheric stability condition and influence of diurnal varying solar-induced thermal effect on urban airflow inside the idealized building arrays were investigated at five cities located at five climate zones in China.Urban energy model,CitySim,was employed to simulate the annual distribution of solar-induced walls’temperatures inside the idealized building arrays.The diurnal varying wall temperatures at the hottest days were set as thermal boundary conditions in computational fluid dynamic(CFD)simulations.With albedo value of 0.5,the possibility of adopting the results from isothermal condition directly ranged from 7%to 11%for the five cities in China throughout the year.The unstable condition ocuppied from 19%to 24%annually and the stable condition of more than 40%annually was observed.Under the diurnal varying solar-induced thermal effect,the spatially-averaged air speeds and airflow patterns were significantly different from the isothermal conditions.The percent of Richardson number under different atmospheric stability conditions annually based on the Citysim simulation results indicated that the atmospheric stability was most likely determined by the local climate characteristics and albedo value rather than the building layouts at the five selected cities in China,but this should be further investigated when the shadow effects of surrounding buildings were considered in simulations.展开更多
为实现冬小麦条锈病早期探测、提高冬小麦产量和品质,研究了日光诱导叶绿素荧光(Solar-induced chlorophyll fluorescence,SIF)对冬小麦条锈病早期探测的可行性。基于3波段夫琅和费暗线(3-band Fraunhofer line discrimination,3FLD)和...为实现冬小麦条锈病早期探测、提高冬小麦产量和品质,研究了日光诱导叶绿素荧光(Solar-induced chlorophyll fluorescence,SIF)对冬小麦条锈病早期探测的可行性。基于3波段夫琅和费暗线(3-band Fraunhofer line discrimination,3FLD)和反射率荧光指数2种方法提取了冠层SIF数据,计算了对小麦条锈病敏感的光谱指数(Spectral index,SI),通过相关性分析优选了遥感探测小麦条锈病早期的特征参量,利用偏最小二乘(Partial least squares,PLS)算法构建冬小麦条锈病早期光谱探测模型。研究结果表明:O2-A波段的荧光强度(SIF-A)以及反射率荧光指数ρ440/ρ690、ρ675ρ690/ρ2683、ρ690/ρ655、ρ690/ρ600、DλP/D744、D705/D722均与小麦条锈病早期病情指数(Disease index,DI)达到了极显著相关,相关系数分别为-0.793、-0.523、-0.539、-0.497、0.541、0.446、0.490,可作为冬小麦条锈病早期光谱探测的荧光特征参量;基于3组SIF数据构建的PLS-SIF检验模型的决定系数分别为0.801、0.772、0.807,均方根误差分别为3.3%、3.1%、3.2%,较反射率光谱指数构建的SI-PLS模型决定系数至少提高了27%,均方根误差至少减少了24%。因此,冠层SIF数据更适于冬小麦条锈病的早期探测。本研究结果对及时进行冬小麦条锈病防控具有重要应用价值,可为利用卫星荧光遥感数据对小麦条锈病早期大面积、无损探测提供参考依据。展开更多
基金supported by the National Key Research and Development Program of China (2017YFA0603001)Scientific Research Satellite Engineering of Civil Space Infrastructure Projectthe National Natural Science Foundation of China (41671349, 41701396)
文摘The first Chinese Carbon Dioxide Observation Satellite Mission(TanSat), which was launched on December 21, 2016, is intended to measure atmospheric CO_2 concentration.The high spectral resolution(0.044 nm) and high SNR(360 at 15.2 mW m^(-1) sr^(-1) nm^(-1)) measurements in the region of the O_2-A band of the Atmospheric Carbon dioxide Grating Spectroradiometer(AGCS) module onboard TanSat make it possible to retrieve solar-induced chlorophyll fluorescence(SIF) from TanSat observations at the global scale.This paper aims to explore the potential of the TanSat data for global SIF retrieval.A singular vector decomposition(SVD) statistical method was employed to retrieve SIF using radiance over a micro spectral window(～2 nm) around the Fe Fraunhofer lines(centered at 758.8 nm).The global SIF at 758.8 nm was successfully retrieved with a low residual error of 0.03 mW m^(-1) sr^(-1) nm^(-1).The results show that the spatial and temporal patterns of the retrieved SIF agree well with the global terrestrial vegetation pattern.The monthly SIF products retrieved from the TanSat data were compared with other remote sensing datasets, including OCO-2 SIF, MODIS NDVI, EVI and GPP.The overall consistency between TanSat and OCO-2 SIF products(R^2= 0.86) and the consistency of the spatial patterns and temporal variations between the TanSat SIF and MODIS vegetation indices and GPP enhance our confidence in the potential and feasibility of TanSat data for SIF retrieval.TanSat, therefore, provides a new opportunity for global sampling of SIF at fine spatial resolution(2 km × 2 km), thus improving photosynthesis observations from space.
基金supported by the National Natural Science Foundation of China(Grant No.41901117)Natural Science Foundation of Hunan Province,China(Grant No.2020JJ5362)+1 种基金the Outstanding Youth Project of Hu’nan Provincial Education Department(No.18B001)the Natural Sciences and Engineering Research Council of Canada(NSERC)Discover Grant.
文摘Background:Vegetation phenology research has largely focused on temperate deciduous forests,thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions.Results:Using satellite solar-induced chlorophyll fluorescence(SIF)and MODIS enhanced vegetation index(EVI)data,we applied two methods to evaluate temporal and spatial patterns of the end of the growing season(EGS)in subtropical vegetation in China,and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation.Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods(dynamic threshold method and derivative method)was later than that derived from gross primary productivity(GPP)based on the eddy covariance technique,and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks,respectively.We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation(accounting for more than 73%and 62%of the study areas,respectively),but negatively correlated with preseason maximum temperature(accounting for more than 59%of the study areas).In addition,EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors,and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests,shrub and grassland.Conclusions:Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China.We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region.These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China,and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.
基金This study was supported by the National Key R&D Program of China(No.2016YFA0600203)the Key Research Program of the Chinese Academy of Sciences(ZDRW-ZS-2019-1&ZDRW-ZS-2019-2)the Youth Program of the National Natural Science Foundation of China(41905029).The TanSat L1B data service was provided by the International Reanalysis Cooperation on Carbon Satellite Data(IRCSD)(131211KYSB20180002)and the Cooperation on the Analysis of Carbon Satellite Data(CASA).The authors thank the OCO-2 team for providing the Level-2 SIF data products.
文摘The Chinese Carbon Dioxide Observation Satellite Mission(TanSat)is the third satellite for global CO2 monitoring and is capable of detecting weak solar-induced chlorophyll fluorescence(SIF)signals with its advanced technical characteristics.Based on the Institute of Atmospheric Physics Carbon Dioxide Retrieval Algorithm for Satellite Remote Sensing(IAPCAS)platform,we successfully retrieved the TanSat global SIF product spanning the period of March 2017 to February 2018 with a physically based algorithm.This paper introduces the new TanSat SIF dataset and shows the global seasonal SIF maps.A brief comparison between the IAPCAS TanSat SIF product and the data-driven SVD(singular value decomposition)SIF product is also performed for follow-up algorithm optimization.The comparative results show that there are regional biases between the two SIF datasets and the linear correlations between them are above 0.73 for all seasons.The future SIF data product applications and requirements for SIF space observation are discussed.
基金supported by the National Natural Science Foundation of China(Grant Nos.41901117,U22A20570)the Science and Technology Innovation Program of Hunan Province(Grant No.2022RC4027)。
文摘Quantifying how climate factors affect vegetation phenology is crucial for understanding climate-vegetation interactions and carbon and water cycles under a changing climate.However,the effects of different intensities of extreme climatic events on vegetation phenology remain poorly understood.Using a long-term solar-induced chlorophyll fluorescence dataset,we investigated the response of vegetation phenology to extreme temperatures and precipitation events of different intensities across the Tibetan Plateau(TP)from 2000 to 2018.We found that the effect of maximum temperature exposure days(TxED)and minimum temperature exposure days(TnED)on the start of the growing season(SOS)was initially delayed and shifted to advance along the increasing temperature gradients.However,the response of the end of the growing season(EOS)to TxED and TnED shifted from an advance to a delay with increasing temperature gradients until the temperature thresholds were reached,above which thresholds produced an unfavorable response to vegetation growth and brought the EOS to an early end.The corresponding maximum and minimum temperature thresholds were 10.12 and 2.54℃,respectively.In contrast,cumulative precipitation(CP)was more likely to advance SOS and delay EOS as the precipitation gradient increased,but the advance of SOS is gradually weakening.Four vegetation types(i.e.,forest,shrubland,meadow,and steppe)showed similar trends in response to different climates,but the optimal climatic conditions varied between the vegetation types.Generally,meadow and steppe had lower optimal temperatures and precipitation than forest and shrubland.These findings revealed the divergent responses of vegetation phenology to extreme climate events of different intensities,implying that the SOS will continue to advance with warming,whereas the EOS may undergo a partial transformation from delayed areas to advanced areas with continued warming.
基金Under the auspices of National Key Research and Development Projects(No.2018YFE0207800)National Natural Science Foundation of China(No.41871103)。
文摘Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems.The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI),extracted from the Moderate Resolution Imaging Spectrometer(MODIS),are widely used to monitor phenology by calculating land surface reflectance.However,the applicability of the vegetation index based on‘greenness'to monitor photosynthetic activity is hindered by poor observation conditions(e.g.,ground shadows,snow,and clouds).Recently,satellite measurements of solar-induced chlorophyll fluorescence(SIF)from OCO-2 sensors have shown great potential for studying vegetation phenology.Here,we tested the feasibility of SIF in extracting phenological metrics in permafrost regions of the northeastern China,exploring the characteristics of SIF in the study of vegetation phenology and the differences between NDVI and EVI.The results show that NDVI has obvious SOS advance and EOS lag,and EVI is closer to SIF.The growing season length based on SIF is often the shortest,while it can represent the true phenology of vegetation because it is closely related to photosynthesis.SIF is more sensitive than the traditional remote sensing indices in monitoring seasonal changes in vegetation phenology and can compensate for the shortcomings of traditional vegetation indices.We also used the time series data of MODIS NDVI and EVI to extract phenological metrics in different permafrost regions.The results show that the length of growing season of vegetation in predominantly continuous permafrost(zone I)is longer than in permafrost with isolated taliks(zone II).Our results have certain significance for understanding the response of ecosystems in cold regions to global climate change.
基金The authors acknowledge the scholarship(JY201704)from Guangzhou Elite Project(GEP)granted for Guoxing Chen’s Ph.D.study at Aarhus University.
文摘Due to the quick development of urbanization,it is important to provide a healthy urban environment for the dweller.Previous studies have obtained valuable conclusions of how to improve the urban airflow distribution under isothermal conditions.How to adopt and interpret those conclusions when considering the solar-induced atmospheric stability conditions have not been clarified yet.In this study,the characteristics of atmospheric stability condition and influence of diurnal varying solar-induced thermal effect on urban airflow inside the idealized building arrays were investigated at five cities located at five climate zones in China.Urban energy model,CitySim,was employed to simulate the annual distribution of solar-induced walls’temperatures inside the idealized building arrays.The diurnal varying wall temperatures at the hottest days were set as thermal boundary conditions in computational fluid dynamic(CFD)simulations.With albedo value of 0.5,the possibility of adopting the results from isothermal condition directly ranged from 7%to 11%for the five cities in China throughout the year.The unstable condition ocuppied from 19%to 24%annually and the stable condition of more than 40%annually was observed.Under the diurnal varying solar-induced thermal effect,the spatially-averaged air speeds and airflow patterns were significantly different from the isothermal conditions.The percent of Richardson number under different atmospheric stability conditions annually based on the Citysim simulation results indicated that the atmospheric stability was most likely determined by the local climate characteristics and albedo value rather than the building layouts at the five selected cities in China,but this should be further investigated when the shadow effects of surrounding buildings were considered in simulations.
文摘为实现冬小麦条锈病早期探测、提高冬小麦产量和品质,研究了日光诱导叶绿素荧光(Solar-induced chlorophyll fluorescence,SIF)对冬小麦条锈病早期探测的可行性。基于3波段夫琅和费暗线(3-band Fraunhofer line discrimination,3FLD)和反射率荧光指数2种方法提取了冠层SIF数据,计算了对小麦条锈病敏感的光谱指数(Spectral index,SI),通过相关性分析优选了遥感探测小麦条锈病早期的特征参量,利用偏最小二乘(Partial least squares,PLS)算法构建冬小麦条锈病早期光谱探测模型。研究结果表明:O2-A波段的荧光强度(SIF-A)以及反射率荧光指数ρ440/ρ690、ρ675ρ690/ρ2683、ρ690/ρ655、ρ690/ρ600、DλP/D744、D705/D722均与小麦条锈病早期病情指数(Disease index,DI)达到了极显著相关,相关系数分别为-0.793、-0.523、-0.539、-0.497、0.541、0.446、0.490,可作为冬小麦条锈病早期光谱探测的荧光特征参量;基于3组SIF数据构建的PLS-SIF检验模型的决定系数分别为0.801、0.772、0.807,均方根误差分别为3.3%、3.1%、3.2%,较反射率光谱指数构建的SI-PLS模型决定系数至少提高了27%,均方根误差至少减少了24%。因此,冠层SIF数据更适于冬小麦条锈病的早期探测。本研究结果对及时进行冬小麦条锈病防控具有重要应用价值,可为利用卫星荧光遥感数据对小麦条锈病早期大面积、无损探测提供参考依据。
