Aims accurate remote estimation of the fraction of absorbed photosynthetically active radiation(fAPAR)is essential for the light use efficiency(LUE)models.Currently,one challenge for the LUE models is lack of knowledg...Aims accurate remote estimation of the fraction of absorbed photosynthetically active radiation(fAPAR)is essential for the light use efficiency(LUE)models.Currently,one challenge for the LUE models is lack of knowledge about the relationship between fAPAR and the normalized difference vegetation index(NDVI).Few studies have tested this relationship against field measurements and evaluated the accuracy of the remote estimation method.this study aimed to reveal the empirical relationship between NDVI and fAPAR and to improve algorithms for remote estimation of fAPAR.Methods to investigate the method of remote estimation of fAPAR seasonal dynamics,the CASA(Carnegie-ames-stanford approach)model and spectral vegetation indices(VIs)were used for in situ measure-ments of spectral reflectance and fAPAR during the growing season of a maize canopy in Northeast China.Important Findingsthe results showed that the fAPAR increased rapidly with the day of year during the vegetative stage,it remained relatively stable at the stage of reproduction,and finally decreased slowly during the senescence stage.In addition,fAPAR green[fAPAR_(green)=fAPAR_(green) -fAPAR_(green) LAI_(max))]showed clearer seasonal trends than fAPAR.the NDVI,red-edge NDVI,wide dynamic range vegetation index,red-edge position(REP)and REP with sentinel-2 bands derived from hyperspectral remote sensing data were all significantly positively related to fAPAR green during the entire growing season.In a comparison of the predictive performance of VIs for the whole growing season,REP was the most appropriate spectral index,and can be recommended for monitoring seasonal dynamics of fAPAR in a maize canopy.展开更多
Photosynthetically active radiation （PAR） is the energy source of plant photosyn thesis, and the diffuse component can enhance canopy light use efficiency, thereby increasing the carbon uptake. Therefore, diffuse PA...Photosynthetically active radiation （PAR） is the energy source of plant photosyn thesis, and the diffuse component can enhance canopy light use efficiency, thereby increasing the carbon uptake. Therefore, diffuse PAR is an important driving factor of ecosystem productivity models. In this study, we estimated the diffuse PAR of over 700 meteorological sites in China from 1981 to 2010 using an empirical model based on observational data from Chinese Ecosystem Research Network （CERN） and China Meteorology Administration. Then we derived the spatial data set of 10 km monthly diffuse PAR using ANUSPLIN software, and analyzed the spatiotemporal variation characteristics of diffuse PAR through GIS and trend analysis techniques. The results showed that： （1） The spatial patterns of annual average diffuse PAR during 1981-2010 are heterogeneous across China, lower in the northeast and higher in the west and south. The nationwide average value for 30 years ranges from 6.66 mol m-2 d-1 to 15.27 mol m-2 d-1, and the value in summer is the biggest while the value in winter is the smallest. （2） There is an evident increasing trend of annual diffuse PAR during recent 30 years, with the increasing amplitude at 0.03 mol m-2 d-l/10a. But a significant declining trend is shown in the first 10 years, and obvious anomalies can be seen in 1982, 1983, 1991 and 1992. And there is a downtrend in spring and an uptrend in all the other seasons. （3） The spatial distribution of temporal variation rates of diffuse PAR is inhomogeneous across the country, generally decreasing in the north and increasing in the south.展开更多
植被吸收光合有效辐射(Absorbed Photosynthetically Active Radiation,APAR)是植被进行光合作用中实际吸收的太阳辐射量,是植被净第一性生产力的重要指标,也是生态系统的功能模型、作物生长模型、净初级生产力模型、气候模型等的重要...植被吸收光合有效辐射(Absorbed Photosynthetically Active Radiation,APAR)是植被进行光合作用中实际吸收的太阳辐射量,是植被净第一性生产力的重要指标,也是生态系统的功能模型、作物生长模型、净初级生产力模型、气候模型等的重要参数。因此高空间分辨率和精确性的植被吸收光合有效辐射对于高精度的区域生产力及光能利用率的研究具有重要意义。对CASA(Carnegie-Ames-Stanford Approach)模型进行了改进,利用30m×30m的数字高程模型(Digital Elevation Model,DEM)数据直接计算太阳辐射,从而将其作为CASA模型的输入参数。结合多源遥感数据、气象数据,研究2015-2020年江汉平原APAR的时空分布及其影响因素。顾及江汉平原的土地利用分布特点,着重分析了江汉平原农田APAR的时空特性,研究结果较好的反映了江汉平原APAR分布。实验结果表明:(1)2015-2020年APAR年总值在3.42×10^(13)MJ-3.73×10^(13)MJ之间,总体空间分布与植被类型的分布情况相符;(2)农田月均APAR值在4月、7月高于其他月份,表现出“双峰”的特征;(3)在空间分布上,水田APAR表现出明显的纬度地带性,而旱地APAR正好相反,这可能源于种植结构重心转移;(4)通过借助地理探测器,着重考虑与植被生长相关的12个因子(包括≧10℃积温、年总日照时数、年均气温、年总降雨量、农田种植结构、年散射辐射、农田施肥、土壤类型、土壤质地(砂土、粉砂土、黏土))进行分析,结果表明这12个因素对APAR空间变异性都具有很明显的影响。对CASA的改进方法可以适用于大范围高空间精度的计算。展开更多
为了探究无人机多光谱遥感影像估算作物光合有效辐射吸收比例(Fraction of absorbed photosynthetically active radiation,FPAR)的潜力,以无人机多光谱影像提取的植被指数、纹理指数、叶面积指数为模型输入参数,在分析不同参数与FPAR...为了探究无人机多光谱遥感影像估算作物光合有效辐射吸收比例(Fraction of absorbed photosynthetically active radiation,FPAR)的潜力,以无人机多光谱影像提取的植被指数、纹理指数、叶面积指数为模型输入参数,在分析不同参数与FPAR相关性的基础上优选植被指数与纹理指数,并分别以一元线性模型、多元逐步回归模型、岭回归模型、BP神经网络模型等方法估算玉米FPAR。结果表明:植被指数、纹理指数、叶面积指数3种参数与FPAR都具有较强的相关性,其中植被指数相关系数最大;在不同类型的FPAR估算模型中,BP神经网络模型的估算效果最优,FPAR估算模型决定系数R^(2)、均方根误差(RMSE)分别为0.857、0.173,验证模型R^(2)、RMSE分别为0.868、0.186,模型估算值与田间实测值间相对误差(RE)为8.71%;在不同形式的模型参数组合中,均以植被指数、纹理指数、叶面积指数3种参数融合的FPAR模型的估算与验证效果最优,说明多特征参数融合能有效改善FPAR估算效果。该研究为基于无人机多光谱遥感数据精准估算玉米FPAR及生产潜力提供了科学依据。展开更多
海洋初级生产过程是海洋碳循环的重要组成部分,影响生物地球化学循环和全球气候变化。浮游植物作为海洋初级生产的主要贡献者,按粒径大小可分为小型(micro粒级,>20μm)、微型(nano粒级,2~20μm)和微微型(pico粒级,<2μm)。不同粒...海洋初级生产过程是海洋碳循环的重要组成部分,影响生物地球化学循环和全球气候变化。浮游植物作为海洋初级生产的主要贡献者,按粒径大小可分为小型(micro粒级,>20μm)、微型(nano粒级,2~20μm)和微微型(pico粒级,<2μm)。不同粒级浮游植物初级生产力(size-fractionated primary production,PP_(size))对总初级生产力贡献不同,在海洋物质能量流动及碳循环中扮演着不同角色。本文基于2019年南海西部夏季航次12个站位的生物光学剖面数据,研究了南海西部分粒级浮游植物叶绿素a浓度和初级生产力的空间分布及它们对总叶绿素a浓度和总初级生产力的贡献百分比。利用各粒级670nm波段的浮游植物吸收系数[size-fractionated phytoplankton absorption coefficient at 670nm,a_(ph-size)(670)]与光合有效辐射(photosynthetically active radiation,PAR)的乘积[a_(ph-size)(670)×PAR]建立了南海分粒级初级生产力算法,对于小型、微型和微微型浮游植物数据集,log[a_(ph-size)(670)×PAR]与log(PP_(size))之间的决定系数R^(2)分别为0.64、0.76和0.67。交叉验证的结果表明,该算法具有良好的泛化性能。其性能显著优于仅利用浮游植物吸收系数估算分粒级初级生产力的算法,表明PAR是影响分粒级初级生产力变化的重要因素之一。采用基于叶绿素a浓度的算法估算各粒级初级生产力时,针对小型和微微型浮游植物数据集,该算法的性能与本文构建的算法近似;但针对微型浮游植物数据集时,基于叶绿素a浓度的算法性能显著较低,这可能归因于微型浮游植物吸收系数与叶绿素a浓度间的弱相关性。展开更多
Non-photosynthetic components within a forest ecosystem account for a large proportion of the canopy but are not involved in photosynthesis.Therefore,the accuracy of gross primary production(GPP)estimates is expected ...Non-photosynthetic components within a forest ecosystem account for a large proportion of the canopy but are not involved in photosynthesis.Therefore,the accuracy of gross primary production(GPP)estimates is expected to improve by removing these components.However,their infl uence in GPP estimations has not been quantitatively evaluated for deciduous forests.Several vegetation indices have been used recently to estimate the fraction of photosynthetically active radiation absorbed by photosynthetic components(FAPAR_(green))for partitioning APAR green(photosynthetically active radiation absorbed by photosynthetic components).In this study,the enhanced vegetation index(EVI)estimated FAPAR_(green)and to separate the photosynthetically active radiation absorbed by photosynthetic components(APAR green)from total APAR observations(APAR_(total))at two deciduous forest sites.The eddy covariance-light use effi ciency(EC-LUE)algorithm was employed to evaluate the infl uence of non-photosynthetic components and to test the performance of APAR green in GPP estimation.The results show that the infl uence of non-photosynthetic components have a seasonal pattern at deciduous forest sites,large diff erences are observed with normalized root mean square error(RMSE*)values of APAR green-based GPP and APAR_(total)-based GPP between tower-based GPP during the early and end stages,while slight diff erences occurred during peak growth seasons.In addition,daily GPP estimation was significantly improved using the APAR green-based method,giving a higher coeffi cient of determination and lower normalized root mean square error against the GPP estimated by the APAR_(total)-based method.The results demonstrate the signifi cance of partitioning APAR green from APAR_(total)for accurate GPP estimation in deciduous forests.展开更多
Among the many approaches for studying the net primary productivity (NPP), a new method by using remote sensing was introduced in this paper. With spectral information source (the visible band, near infrared band and ...Among the many approaches for studying the net primary productivity (NPP), a new method by using remote sensing was introduced in this paper. With spectral information source (the visible band, near infrared band and thermal infrared band) of NOAA-AVHRR, we can get the relative index and parameters, which can be used for estimating NPP of terrestrial vegetation. By means of remote sensing, the estimation of biomass and NPP is mainly based on the models of light energy utilization. In other words, the biomass and NPP can be calculated from the relation among NPP, absorbed photosynthetical active radiation (APAR) and the rate (epsilon) of transformation of APAR to organic matter, thus: NPP = ( FPAR x PAR) x [epsilon * x sigma (T) x sigma (E) x sigma (S) x (1 - Y-m) x (1 - Y-g)]. Based upon remote sensing ( RS) and geographic information system (GIS), the NPP of terrestrial vegetation in China in every ten days was calculated, and the annual NPP was integrated. The result showed that the total NPP of terrestrial vegetation in China was 6.13 x 10(9) t C . a(-1) in 1990 and the maximum NPP was 1 812.9 g C/m(2). According to this result, the spatio-temporal distribution of NPP was analyzed. Comparing to the statistical models, the RS model, using area object other than point one, can better reflect the distribution of NPP, and match the geographic distribution of vegetation in China.展开更多
地球表面的瞬时光合有效辐射(Photosynthetically Active Radiation,PAR)和植被吸收的光合有效辐射(Absorbed Photosynthetically Active Radiation,APAR)是许多集中于生物圈和大气之间交换过程研究的关键参量。光合有效辐射吸收比例(Fr...地球表面的瞬时光合有效辐射(Photosynthetically Active Radiation,PAR)和植被吸收的光合有效辐射(Absorbed Photosynthetically Active Radiation,APAR)是许多集中于生物圈和大气之间交换过程研究的关键参量。光合有效辐射吸收比例(Fraction of Photosynthetically Active Radiation,FPAR)是APAR和PAR的比值,是许多气候模型及生产力模型的基本特征参量。因此,研究PAR/APAR/FPAR的时空变化规律,对准确估算草地NPP具有十分重要的作用。本文利用MODIS遥感影像和TOMS紫外波段反射率数据,在GIS和RS软件支持下,计算分析了内蒙古草地生长季的PAR/FPAR/APAR及其时空变化,并分析了气候因子对FPAR/APAR的影响。结果表明:内蒙古草地生长季平均PAR为1973.8MJ/m2,FPAR为0.275,APAR为516.64MJ/m2。内蒙古草地生长季的PAR/FPAR/PAR时空变化明显:FPAR及APAR空间上呈东北向西南地区递减的趋势,PAR与之相反。自生长季初期(4月份),随着时间的推移PAR/FPAR/APAR都开始增加,只是出现峰值的时间不同。内蒙古草地生长季不同草地类型的FPAR及APAR差异明显,在草地NPP估算及建模时应引起重视。展开更多
基金National Natural Science Foundation of China(41330531)the R&D Special Fund for Public Welfare Industry(Meteorology)Project(GYHY201106027)the State Key Development Program of Basic Research(2010CB951303).
文摘Aims accurate remote estimation of the fraction of absorbed photosynthetically active radiation(fAPAR)is essential for the light use efficiency(LUE)models.Currently,one challenge for the LUE models is lack of knowledge about the relationship between fAPAR and the normalized difference vegetation index(NDVI).Few studies have tested this relationship against field measurements and evaluated the accuracy of the remote estimation method.this study aimed to reveal the empirical relationship between NDVI and fAPAR and to improve algorithms for remote estimation of fAPAR.Methods to investigate the method of remote estimation of fAPAR seasonal dynamics,the CASA(Carnegie-ames-stanford approach)model and spectral vegetation indices(VIs)were used for in situ measure-ments of spectral reflectance and fAPAR during the growing season of a maize canopy in Northeast China.Important Findingsthe results showed that the fAPAR increased rapidly with the day of year during the vegetative stage,it remained relatively stable at the stage of reproduction,and finally decreased slowly during the senescence stage.In addition,fAPAR green[fAPAR_(green)=fAPAR_(green) -fAPAR_(green) LAI_(max))]showed clearer seasonal trends than fAPAR.the NDVI,red-edge NDVI,wide dynamic range vegetation index,red-edge position(REP)and REP with sentinel-2 bands derived from hyperspectral remote sensing data were all significantly positively related to fAPAR green during the entire growing season.In a comparison of the predictive performance of VIs for the whole growing season,REP was the most appropriate spectral index,and can be recommended for monitoring seasonal dynamics of fAPAR in a maize canopy.
基金Research on the key technology of effectiveness evaluation and standardized construction of National Nature Reserve,No.201209028"Strategic Priority Research Program–Climate Change:Carbon Budget and Relevant Issues"of the Chinese Academy of Sciences,No.XDA05050600Key Projects in the National Science&Technology Pillar Program during the Twelve Five-year Plan Period,No.2013BAC03B03
文摘Photosynthetically active radiation （PAR） is the energy source of plant photosyn thesis, and the diffuse component can enhance canopy light use efficiency, thereby increasing the carbon uptake. Therefore, diffuse PAR is an important driving factor of ecosystem productivity models. In this study, we estimated the diffuse PAR of over 700 meteorological sites in China from 1981 to 2010 using an empirical model based on observational data from Chinese Ecosystem Research Network （CERN） and China Meteorology Administration. Then we derived the spatial data set of 10 km monthly diffuse PAR using ANUSPLIN software, and analyzed the spatiotemporal variation characteristics of diffuse PAR through GIS and trend analysis techniques. The results showed that： （1） The spatial patterns of annual average diffuse PAR during 1981-2010 are heterogeneous across China, lower in the northeast and higher in the west and south. The nationwide average value for 30 years ranges from 6.66 mol m-2 d-1 to 15.27 mol m-2 d-1, and the value in summer is the biggest while the value in winter is the smallest. （2） There is an evident increasing trend of annual diffuse PAR during recent 30 years, with the increasing amplitude at 0.03 mol m-2 d-l/10a. But a significant declining trend is shown in the first 10 years, and obvious anomalies can be seen in 1982, 1983, 1991 and 1992. And there is a downtrend in spring and an uptrend in all the other seasons. （3） The spatial distribution of temporal variation rates of diffuse PAR is inhomogeneous across the country, generally decreasing in the north and increasing in the south.
文摘估算并消除冠层非光合组分(non-photosynthetic vegetation,NPV)吸收的光合有效辐射,对准确估算生态系统总初级生产力(gross primary productivity,GPP)具有重要意义。以落叶阔叶林为例,通过设置不同情景,应用任意倾斜叶片散射(scattering by arbitrary inclined leaves,SAIL)模型进行冠层光合有效辐射吸收分量(fraction of absorbed photosynthetically active radiation,FPAR)的分层模拟,分析冠层NPV的FPAR的变动及其对冠层FPAR的贡献,并初步探讨落叶阔叶林NPV的FPAR的估算方法。结果表明,冠层NPV的FPAR的大小与冠层结构相关,在高覆盖度植被区NPV对冠层FPAR的贡献通常较小,但在低植被覆盖区的贡献会较高;NPV降低了冠层在近红外波段的反射;增强型植被指数(enhanced vegetation index,EVI)与NPV的FPAR存在显著的线性负相关关系,可用来描述NPV的变化。
文摘为了探究无人机多光谱遥感影像估算作物光合有效辐射吸收比例(Fraction of absorbed photosynthetically active radiation,FPAR)的潜力,以无人机多光谱影像提取的植被指数、纹理指数、叶面积指数为模型输入参数,在分析不同参数与FPAR相关性的基础上优选植被指数与纹理指数,并分别以一元线性模型、多元逐步回归模型、岭回归模型、BP神经网络模型等方法估算玉米FPAR。结果表明:植被指数、纹理指数、叶面积指数3种参数与FPAR都具有较强的相关性,其中植被指数相关系数最大;在不同类型的FPAR估算模型中,BP神经网络模型的估算效果最优,FPAR估算模型决定系数R^(2)、均方根误差(RMSE)分别为0.857、0.173,验证模型R^(2)、RMSE分别为0.868、0.186,模型估算值与田间实测值间相对误差(RE)为8.71%;在不同形式的模型参数组合中,均以植被指数、纹理指数、叶面积指数3种参数融合的FPAR模型的估算与验证效果最优,说明多特征参数融合能有效改善FPAR估算效果。该研究为基于无人机多光谱遥感数据精准估算玉米FPAR及生产潜力提供了科学依据。
文摘海洋初级生产过程是海洋碳循环的重要组成部分,影响生物地球化学循环和全球气候变化。浮游植物作为海洋初级生产的主要贡献者,按粒径大小可分为小型(micro粒级,>20μm)、微型(nano粒级,2~20μm)和微微型(pico粒级,<2μm)。不同粒级浮游植物初级生产力(size-fractionated primary production,PP_(size))对总初级生产力贡献不同,在海洋物质能量流动及碳循环中扮演着不同角色。本文基于2019年南海西部夏季航次12个站位的生物光学剖面数据,研究了南海西部分粒级浮游植物叶绿素a浓度和初级生产力的空间分布及它们对总叶绿素a浓度和总初级生产力的贡献百分比。利用各粒级670nm波段的浮游植物吸收系数[size-fractionated phytoplankton absorption coefficient at 670nm,a_(ph-size)(670)]与光合有效辐射(photosynthetically active radiation,PAR)的乘积[a_(ph-size)(670)×PAR]建立了南海分粒级初级生产力算法,对于小型、微型和微微型浮游植物数据集,log[a_(ph-size)(670)×PAR]与log(PP_(size))之间的决定系数R^(2)分别为0.64、0.76和0.67。交叉验证的结果表明,该算法具有良好的泛化性能。其性能显著优于仅利用浮游植物吸收系数估算分粒级初级生产力的算法,表明PAR是影响分粒级初级生产力变化的重要因素之一。采用基于叶绿素a浓度的算法估算各粒级初级生产力时,针对小型和微微型浮游植物数据集,该算法的性能与本文构建的算法近似;但针对微型浮游植物数据集时,基于叶绿素a浓度的算法性能显著较低,这可能归因于微型浮游植物吸收系数与叶绿素a浓度间的弱相关性。
基金funded by Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals(No.CBAS2022IRP01)the National Earth System Science Data Sharing Infrastructure(No.2005DKA32300)the National Natural Science Foundation of China(No.41825002).
文摘Non-photosynthetic components within a forest ecosystem account for a large proportion of the canopy but are not involved in photosynthesis.Therefore,the accuracy of gross primary production(GPP)estimates is expected to improve by removing these components.However,their infl uence in GPP estimations has not been quantitatively evaluated for deciduous forests.Several vegetation indices have been used recently to estimate the fraction of photosynthetically active radiation absorbed by photosynthetic components(FAPAR_(green))for partitioning APAR green(photosynthetically active radiation absorbed by photosynthetic components).In this study,the enhanced vegetation index(EVI)estimated FAPAR_(green)and to separate the photosynthetically active radiation absorbed by photosynthetic components(APAR green)from total APAR observations(APAR_(total))at two deciduous forest sites.The eddy covariance-light use effi ciency(EC-LUE)algorithm was employed to evaluate the infl uence of non-photosynthetic components and to test the performance of APAR green in GPP estimation.The results show that the infl uence of non-photosynthetic components have a seasonal pattern at deciduous forest sites,large diff erences are observed with normalized root mean square error(RMSE*)values of APAR green-based GPP and APAR_(total)-based GPP between tower-based GPP during the early and end stages,while slight diff erences occurred during peak growth seasons.In addition,daily GPP estimation was significantly improved using the APAR green-based method,giving a higher coeffi cient of determination and lower normalized root mean square error against the GPP estimated by the APAR_(total)-based method.The results demonstrate the signifi cance of partitioning APAR green from APAR_(total)for accurate GPP estimation in deciduous forests.
文摘Among the many approaches for studying the net primary productivity (NPP), a new method by using remote sensing was introduced in this paper. With spectral information source (the visible band, near infrared band and thermal infrared band) of NOAA-AVHRR, we can get the relative index and parameters, which can be used for estimating NPP of terrestrial vegetation. By means of remote sensing, the estimation of biomass and NPP is mainly based on the models of light energy utilization. In other words, the biomass and NPP can be calculated from the relation among NPP, absorbed photosynthetical active radiation (APAR) and the rate (epsilon) of transformation of APAR to organic matter, thus: NPP = ( FPAR x PAR) x [epsilon * x sigma (T) x sigma (E) x sigma (S) x (1 - Y-m) x (1 - Y-g)]. Based upon remote sensing ( RS) and geographic information system (GIS), the NPP of terrestrial vegetation in China in every ten days was calculated, and the annual NPP was integrated. The result showed that the total NPP of terrestrial vegetation in China was 6.13 x 10(9) t C . a(-1) in 1990 and the maximum NPP was 1 812.9 g C/m(2). According to this result, the spatio-temporal distribution of NPP was analyzed. Comparing to the statistical models, the RS model, using area object other than point one, can better reflect the distribution of NPP, and match the geographic distribution of vegetation in China.
文摘地球表面的瞬时光合有效辐射(Photosynthetically Active Radiation,PAR)和植被吸收的光合有效辐射(Absorbed Photosynthetically Active Radiation,APAR)是许多集中于生物圈和大气之间交换过程研究的关键参量。光合有效辐射吸收比例(Fraction of Photosynthetically Active Radiation,FPAR)是APAR和PAR的比值,是许多气候模型及生产力模型的基本特征参量。因此,研究PAR/APAR/FPAR的时空变化规律,对准确估算草地NPP具有十分重要的作用。本文利用MODIS遥感影像和TOMS紫外波段反射率数据,在GIS和RS软件支持下,计算分析了内蒙古草地生长季的PAR/FPAR/APAR及其时空变化,并分析了气候因子对FPAR/APAR的影响。结果表明:内蒙古草地生长季平均PAR为1973.8MJ/m2,FPAR为0.275,APAR为516.64MJ/m2。内蒙古草地生长季的PAR/FPAR/PAR时空变化明显:FPAR及APAR空间上呈东北向西南地区递减的趋势,PAR与之相反。自生长季初期(4月份),随着时间的推移PAR/FPAR/APAR都开始增加,只是出现峰值的时间不同。内蒙古草地生长季不同草地类型的FPAR及APAR差异明显,在草地NPP估算及建模时应引起重视。
