基于MODIS GPP数据产品的辽宁省碳源/汇空间格局分布研究

陆地生态系统的碳汇功能是生态系统服务功能的重要方面，在减缓气候变化中起着重要作用，准确地评估陆地生态系统碳源/汇时空变化是有效预测气候变化的重要基础。基于碳源/汇形成过程中各分量间的相互关系，结合MODIS GPP数据产品和区域统计年鉴数据，对2000~2014年辽宁省陆地碳源/汇的强度及空间格局分布进行定量化评估。结果表明：①辽宁省陆地碳源/汇呈现东部高、西部低的变化趋势，东部呈现显著的碳吸收功能，碳吸收强度超过250 gC m^-2 a^-1，但在辽宁中部、西部及北部地区则出现明显的碳排放。②沈阳的年均碳排放量（1.43 TgC a^-1）约占辽宁省各地市净碳排放总量（4.56 TgC a^-1）的三分之一，是全省碳排放的主体。③沈阳陆地生态系统总体表现为碳源，城区碳排放强度相对较弱，仅为26 gC m^-2 a^-1，近似表现为碳中性。本文基于碳源/汇形成过程定量分析辽宁陆地碳源/汇强度及其空间分布规律，为今后其他区域碳源/汇的模拟提供理论依据和方法借鉴。     

基于机器学习和大数据平台的陆地生态系统碳收支遥感监测

陆地生态系统碳收支是全球碳循环研究的重要指标,也是气候变化的重要参数。针对该指标估测的不确定性,基于陆地生态系统通量观测研究网络的实测碳通量数据及遥感卫星观测数据产品,利用机器学习方法进行建模研究。研究选用随机森林算法自动从高质量的星—地训练数据集中学习特征、挖掘数据中的隐含信息以及时序间依赖关系的差异,建立了基于随机森林算法的碳收支参数GPP(Gross Primary Production)、NEP(Net Ecosystem Production)估算模型,并选择标准指标利用验证数据集对模型进行了客观评价。结果分析表明：与MODIS GPP产品相比,该方法在估算精度上有了提高,其中落叶阔叶林预测结果最优,决策系数为R2为0.82,均方根误差为1.93 gCm-2d-1,在其他植被类型上也明显优于传统光能利用率模型产品,更接近于地面通量观测数据。基于相同方法建立的NEP模型也得到了较好的估测结果,落叶阔叶林预测模型的输出结果与通量塔获得的NEP相关关系R2为0.70,RMSE=1.75 g C m-2d-1。GPP和NEP模型精度差异也表明,在进行机器学习建模时,训练数据集自变量的...     

Temporal-Spatial Variation Analysis of Net Primary Productivity and its Relationship with Climate in Central Asia

The Net Primary Productivity (NPP) of vegetation and its response to climate change is one of the key areas in research of global change. The study on spatial and temporal changes of NPP in central Asia is important to understand the mechanism of vegetation-environment action and to cope with global change. Therefore, based on the MOD17A3 dataset and meteorological data and GIS analysis method, this paper is intended to analyze the spatial pattern, temporal variation and the driving factors to NPP in Central Asia during 2000~2014. The results shows that: ①the spatial variation of NPP in Central Asia is ranged from 0 to 874 gC/m²·a, with an average of 151.90 gC/m²·a. The average annual total NPP is 482.41TgC (1 Tg=10¹² g), and both the average NPP and total NPP showed a decrease trend. ②The average NPP was higher in southeastern alpine regions and high latitudes areas than in central and southern desert areas in Central Asia. ③From 2000 to 2014, the annual NPP in central Asia showed a decrease trend with a rate of -2.05 gC/m²·a², covering 39.89% of the region with significant reduction. The areas in which NPP decreased were mainly distributed in Kazakhstan, with typical steppe zone being the most significant in five ecological zones. ④The effect of precipitation on NPP in Central Asia was stronger than that of temperature. Precipitation influenced NPP of typical steppe，desert and desert steppe more seriously, while alpine meadow and alpine forest were jointly affected by precipitation and temperature.     

中亚地区植被净初级生产力时空动态及其与气候因子关系

植被净初级生产力(Net Primary Productivity,NPP)及其对气候变化的响应是全球变化的核心研究内容之一,研究中亚地区NPP的时空格局变化对理解植被—环境的作用机理以及应对全球变化具有重要的意义。基于MOD17A3数据集、气象数据结合GIS分析方法研究中亚地区2000~2014年的植被NPP时空动态特征及其与气候因子的关系。结果表明：①中亚地区空间上NPP的变化范围在0~874 gC/m²·a之间,平均值为151.90 gC/m²·a,NPP年总量平均值为482.41TgC (1 Tg=10¹² g),NPP平均值与总量均呈现出下降趋势;②中亚地区NPP的高值区主要分布在高纬度地区和东南部高山地区,中部和南部荒漠区则为NPP的低值区;③中亚地区2000~2014年间NPP在空间上总体呈现下降趋势,达到显著下降的区域总体面积的39.89%。NPP呈下降趋势的区域主要集中在哈萨克斯坦的大部分区域,不同分区内以典型草原区最为显著;④中亚地区NPP受降水量的影响作用高于气温,荒漠草原区、典型草原区以及荒漠区主要受到降水量的控制,高山草甸区与高山林地区则受到降水和气温的共同作用。     

多源日光诱导叶绿素荧光产品在中国地区的一致性研究

日光诱导叶绿素荧光(Sun-Induced Chlorophyll Fluorescence,SIF)是表征植被光合的有效探针。基于不同卫星遥感衍生的各种SIF产品已被广泛应用,然而,不同产品在中国区域表现及一致性仍不明晰。以中国生态系统光谱观测网络(China Spec)农田站SIF观测数据为参考评估CSIF、GOSIF、SIF_oco2-005、SIF_LUE(SIF_LUE＿JJ/SIF_LUE＿PK)、SIF₀₀₅等产品,并从不同时空尺度探讨它们的一致性。结果表明：(1)不同产品在4个农田站表现不尽相同,3个农田站存在高估现象。SIF_LUE整体表现较好,GOSIF次之,而后是SIF₀₀₅和SIF_oco2＿005。(2)各产品空间格局具有较高一致性,但其幅度存在差异：年均值方面,SIF_LUE＿PK最大(0.21 W m^-2μm^-1sr^-1<...     

黑河流域湿地、农田、草地生态系统碳通量变化特征及驱动因子分析

为了认识黑河流域湿地、农田、草地生态系统不同时间尺度的碳通量特征及与环境因子的关系,并为干旱区生态系统碳源/汇效应评估提供理论依据。采用涡度相关技术对黑河流域湿地、农田、草地生态系统进行长达7 a的碳通量、气象因子观测,分析了净生态系统生产力(NEP)、生态系统呼吸(Reco)、总初级生产力(GPP)在日际、季节、年际3种尺度的动态变化机制,并比较了碳通量与植被指数NDVI、EVI的季节变化异同。经分析发现：(1)黑河流域湿地与草地、农田生态系统均在日尺度上呈现明显的单峰“倒U”分布,草地于12:00到达峰值,湿地与农田于13:00到达峰值,峰值碳通量农田>湿地>草地;(2)季节尺度上,湿地与农田、草地生态系统碳通量以及NDVI、EVI均呈单峰“倒U”分布,6～9月生长季为明显碳吸收,7月份到达全年峰值,碳吸收峰值为农田>湿地>草地,NDVI、EVI峰值则为阿柔站>湿地站>大满站>大沙龙站。(3)年固碳能力为农田(648.90 gC/m²/a)>湿地(627.51 gC/m²/a)>草地(...     

基于Landsat TM/ETM+影像的武汉市冬季NPP估算及其时空变化分析

植被净第一生产力(NPP)作为反映植被固碳能力的重要指标,在全球CO2浓度上升的背景下,成为研究全球及区域生态系统对气候环境变化响应的热点之一。基于Landsat TM/ETM+遥感影像数据,采用改进的CASA模型,估算得到武汉市2001~2010年空间分辨率为30m的冬季NPP,并对其进行时空变化分析。研究结果表明:武汉市过去10a冬季平均NPP为8.55gC/m2·m。2001~2010年武汉市冬季NPP整体呈现波动上升的趋势,各区域具有不同的增长速率,其中以江夏区最快,而各植被类型中灌木林具有最快的增长速率和最高的平均NPP。武汉市冬季NPP均呈现从三环区域向四周增大的空间分布特征,过去10a武汉市冬季NPP最高的区域由黄陂区转移到了江夏区。     

Simulation of Solar Radiation based on BP Neural Network andIts Spatio-temporal Change Analysis in East China

Solar radiation data are important parameters of crop model,hydrological model and climate change model,however,the distribution of solar radiation sites is scarce and uneven throughout the country,and it is difficult to obtain spatial continuous solar radiation by using only rare radia.Therefore,the lack of solar radiation data restricts the development of the relevant model,and the neural network on the solar radiation has a good predictability,many Artificial Neural Network ensemble models were developed to estimate solar radiation using routinely measured meteorolological variables,but it did not consider cloud,aerosol,and precipitable water vapor influence on solar radiation.In this paper,we used cloud,aerosols,atmospheric precipitable water vapor from MODIS atmosphere remote sensing products and conventional meteorological data including air pressure,temperature,sunshine duration and latitude and elevation,based on the LM-BP neural network model to simulate the 90 conventional weather stations in Eastern China from 2001 to 2014.The results show that the model has a good fit of 0.95,and the root mean square error is controlled within 2 MJ·m^-2.The average deviation error is between -1 MJ·m^-2 and 1 MJ·m^-2.Finally,using the simulated values of the model and the measured values of 13 radiation sites,the spatial distribution of the annual solar radiation in the East China region from 2001 to 2014 is obtained by spatial interpolation and the spatial variation trend is analyzed.      

Mass Change of Glaciers in Mt.Qomolangma(Everest) Region from 1974 to 2012 as derived from Remote Sensing Data

In this study,glacier mass changes are investigated for the period 1974~2012 for 14 glaciers larger than 3 km^2on south and north slopes in Mt.Qomolangma(Everest) region based on ZY-3 images,SRTM DEMs and topographic maps.In general,a continuous mass loss(-0.31±0.03 m w·e·a^-1) for glaciers on south and north slopes of Mt.Qomolangma could be observed between 1974 and 2012.The mass budget of 14 glaciers was -0.27±0.03 w·e·a^-1 for the period 1974~1999 and -0.35±0.06 w·e·a^-1 during 1999~2012.Glaciers on the south slope lost mass at a rate of -0.38±0.03 w·e·a^-1,[JP]was larger than glaciers on the north slope which was at a rate of 0.27±0.03 w·e·a^-1.And these glaciers change are heterogeneous and differ spatially.The main reason for negative mass budget may be attributed to the increasing air temperature,heterogeneous glacier mass balances were responded to different climate conditions.Debris-covered regions obviously exhibited higher thinning rates on the north slope about 5 500~6 000 m.However,the dependence of mass change on altitude is not significant in other regions.The main reason for this may be attributed to the heterogeneous debris thickness except for different climate conditions.Glacial lake expansion is the result of glacier rapid ablation,and also accelerates glacier melting.     

青藏高原积雪对气候反馈的初步研究

积雪具有很高的反照率,能反射回绝大部分的太阳短波辐射;同时,积雪是热的不良导体,其热阻隔性会抑制地表的长波辐射。因此,积雪的积累和消融会强烈地改变大气层顶的辐射平衡,进而对气候产生反馈。采用ERA-Interim再分析资料和MODIS去云积雪产品,通过改进的偏辐射扰动思想,对青藏高原地区2001~2010年积雪影响下大气层顶的辐射能量收支状况进行模拟,计算对应的积雪辐射强迫,并在此基础上估算积雪反馈。结果表明:研究区99.5%以上地区的大气层顶辐射平衡为负,即积雪对气候存在正的辐射强迫,年平均辐射强迫为3.97 W·m-2。时空分布特征表明,积雪辐射强迫的年际差异不大,但空间差异很大,其空间分布与积雪覆盖率有很强的正相关关系,在绝大多数情况下,短波反照率辐射强迫对积雪辐射强迫起着决定性作用,且青藏高原的积雪反馈强度约为9.35 W·m-2·℃-1。     

基于OPI方法的模式城市识别偏差对地表辐射收支的影响分析

城市化影响着局地与区域气候变化。区域气候模式是探讨城市与环境问题演变机制与城市化气候效应的重要研究手段,然而模式地表参数化方法处理地表参数时难以全面反映下垫面状况,可能影响模拟结果的合理性。因此以卫星数据源为基础,分析了OPI方法处理下垫面参数导致的偏差,结合其导致的地表辐射收支差异说明不同空间尺度偏差的辐射收支效应,进而通过与CO²排放关联定量分析OPI方法对城市化气候效应的影响。结果发现随着空间尺度的变大,OPI方法导致的城市面积提取误差也在变大,导致城市下垫面转变成了其周边的土地覆盖类型,增强了城市群区域的反照率,降低了城市区域的辐射收支,弱化了城市气候效应。研究发现城市地表单位面积反照率增加0.01相当于减少了2.82 kg的CO²排放,京津冀城市群2009年案例分析表明其弱化程度相当于减少了CO²排放2.28×10⁵ t,扩展到中国区域尺度上则相当于减少了1.81×10⁶ t CO²排放。     

Satellite-driven estimation of terrestrial carbon flux over Far East Asia with 1-km grid resolution

The terrestrial carbon cycle is strongly affected by natural phenomena, terrain heterogeneity, and human-induced activities that alter carbon exchange via vegetation and soil activities. In order to accurately understand terrestrial carbon cycle mechanisms, it is necessary to estimate spatial and temporal variations in carbon flux and storage using process-based models with the highest possible resolution. We estimated terrestrial carbon fluxes using a biosphere model integrating eco-physiological and mechanistic approaches based on satellite data (BEAMS) and observations with 1-km grid resolution. The study area is the central Far East Asia region, which lies between 30° and 50° north latitude and 125° and 150° east longitude. Aiming to simulate terrestrial carbon exchanges under realistic land surface conditions, we used as many satellite-observation datasets as possible, such as the standard MODIS, TRMM, and SRTM high-level land products. Validated using gross primary productivity (GPP), net ecosystem production (NEP), net radiation and latent heat with ground measurements at six flux sites, the model estimations showed reasonable seasonal and annual patterns. In extensive analysis, the total GPP and NPP were determined to be 2.1 and 0.9 PgC/year, respectively. The total NEP estimation was + 5.6 TgC/year, meaning that the land area played a role as a carbon sink from 2001 to 2006. In analyses of areas with complicated topography, the 1-km grid estimation could prove to be effective in evaluating the effect of landscape on the terrestrial carbon cycle. The method presented here is an appropriate approach for gaining a better understanding of terrestrial carbon exchange, both spatially and temporally.     

“全球生态系统碳循环关键参数立体观测与反演”项目概述与研究进展

准确评估全球碳循环是准确估算未来大气CO₂浓度、预测气候变化的关键。目前全球陆地与海洋碳源汇估算时空不确定性大。除碳循环模式理论和认知存在缺陷外,全球尺度上缺乏精细时空分辨率的生态系统碳循环参数观测数据是造成全球碳循环估算存在巨大不确定性的重要原因之一。为此,项目以立体观测为技术手段,通过协同全球台站观测资料和多源卫星遥感数据,研制要素齐全的高质量陆地与海洋生态系统碳循环关键参数产品（GLOCC）,不仅包括主要碳源汇直接观测产品,如陆地生态系统生产力、生物量、土壤碳库和海水二氧化碳分压、海水颗粒有机碳等;还包括陆地与海洋生态系统光合作用关键参数以及碳循环模型过程关键变量。项目执行3 a多来,收集与处理了1981~2019年来的28种国内外卫星数据和19种全球碳循环产品生产相关的全球遥感产品,攻克了多源卫星遥感数据的一致性处理关键技术,发展了陆地与海洋生态系统碳循环关键参数的高精度卫星反演关键技术,初步研制了GLOCC碳参数产品生产与共享平台,并通过集成国内外卫星遥感数据,将部分陆地生态系统碳参数的时间分辨率从8 d提高到5 d。目前已经有7个GLOCC产品在国内外多个数据中心提供了产品共享服务。项目预期能够为全球变化研究提供时空分辨率高、时间序列长、碳循环参数全的遥感产品,并服务于全球碳源汇准确估算需求,并提供全球和区域碳收支的重要科学数据。     

数据驱动的植被总初级生产力估算方法研究

植被总初级生产力（Gross Primary Production,GPP）是指在单位时间和单位面积上,绿色植物通过光合作用固定二氧化碳所产生的全部有机物同化量,对GPP的准确估算有助于碳循环的研究。为了提高GPP的估算精度,将机器学习技术与遥感技术相结合,首先利用GEE平台下的遥感数据以及中国陆地生态系统通量观测研究网络的通量塔实测GPP数据,建立数据集。然后使用随机森林作为估算模型,建模后根据数据特点对模型调参。最后获得模型的预测结果,决定系数R²为0.87,均方根误差RMSE的值为1.132 gC·m^-2·d^-1。这说明随机森林模型可以较为精确地估算GPP。结果发现,以大数据以及人工智能为代表的计算机技术飞速发展,将为遥感技术注入新的活力,使遥感技术走向更加成熟的发展应用阶段。     

Data-Driven Estimation of Gross Primary Production

Gross Primary Production (GPP) of vegetation refers to the assimilation of all organic matter produced by green plants through photosynthesis and fixed carbon dioxide per unit time and unit area. Accurate estimation of GPP is helpful for the study of carbon cycle. In order to improve the estimation accuracy of GPP, this study combines machine learning technology and remote sensing technology. First, the remote sensing data under the GEE platform and the flux tower measurement data of the China Terrestrial Ecosystem Flux Observation Research Network are used to establish a data set. Then use random forest as the estimation model, and adjust the model according to the data characteristics after modeling. Finally, the prediction results of the model are obtained, the determination coefficient R² is 0.87, and the root mean square error RMSE is 1.132 gC·m^-2·d^-1. This shows that the random forest model can estimate GPP more accurately.From the results of this study, we can see that the rapid development of computer technology represented by big data and artificial intelligence will inject new vitality into remote sensing technology and make remote sensing technology enter a more mature stage of development and application.     

多层土壤观测数据同化的森林碳、水通量模拟

为充分考虑森林生态系统土壤水分的垂直运动及改善碳、水通量的模拟精度,利用Biome-BGC MuSo模型模拟了长白山森林通量站点的碳、水通量,该模型包含了多层土壤模块、物候模块以及管理模块;其次,利用集合卡尔曼滤波算法将站点观测的多层土壤参数同化到Biome-BGC MuSo模型中,并用站点涡动通量数据进行了验证。结果表明：与Biome-BGC模型模拟结果相比,Biome-BGC MuSo改善了站点净生态系统交换量（Net ecosystem exchange, NEE）、生态系统呼吸量（Ecosystem respiration, ER）和蒸散发（Evapotranspiration, ET）模拟精度,站点观测的时序土壤温度和水分数据同化到Biome-BGC MuSo后,碳、水通量模拟结果有了进一步的提升（NEE: R² = 0.70, RMSE = 1.16 gC·m^–2·d^–1; ER: R² = 0.85, RMSE = 1.97 gC·m^–2·d^–1 ; ET: R² = 0.81, RMSE = 0.70 mm·d^–1）。数据-模型同化策略为森林生态系统碳、水同量的模拟提供了科学的方法。     

多层土壤观测数据同化的森林碳、水通量模拟

为充分考虑森林生态系统土壤水分的垂直运动及改善碳、水通量的模拟精度，利用Biome-BGC MuSo模型模拟了长白山森林通量站点的碳、水通量，该模型包含了多层土壤模块、物候模块以及管理模块；其次，利用集合卡尔曼滤波算法将站点观测的多层土壤参数同化到Biome-BGC MuSo模型中，并用站点涡动通量数据进行了验证。结果表明：与Biome-BGC模型模拟结果相比，Biome-BGC MuSo改善了站点净生态系统交换量（Net ecosystem exchange, NEE）、生态系统呼吸量（Ecosystem respiration, ER）和蒸散发（Evapotranspiration, ET）模拟精度，站点观测的时序土壤温度和水分数据同化到Biome-BGC MuSo后，碳、水通量模拟结果有了进一步的提升（NEE: R² = 0.70, RMSE = 1.16 gC·m^–2·d^–1; ER: R² = 0.85, RMSE = 1.97 gC·m^–2·d^–1 ; ET: R² = 0.81, RMSE = 0.70 mm·d^–1）。数据-模型同化策略为森林生态系统碳、水同量的模拟提供了科学的方法。     

Simulation of Solar Radiation based on Three Machine Learning Methods

Quantitative simulation of solar radiation is essential for understanding climate change 0n the Loess Plateau, Many machine learning methods were developed to estimate solar radiation well, but different machine learning methods have different simulation accuracy in different regions, In order to achieve optimal simulation of solar radiation on the Loess Plateau, this provides more higher precision solar radiation data for crop models, hydrological models, and climate change models. In this study, three machine learning methods, including Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM), were applied to estimate solar radiation on the Loess Plateau, three machine learning methods were trained using ground measurements at fourteen radiation sites from 2003 to 2009 and ten radiation sites from 2010 to 2016 and corresponding parameter pressure, cloud fraction, cloud optical thickness, ozone, precipitation water vapor and DEM, slope, and aspect to train the three model, The solar radiation estimates based on three machine learning methods were evaluated using ground measurements at four radiation sites from 2010 to 2016. The validation results show that the RF model has the best simulation effect on the Loess Plateau and surrounding areas. The average deviation is -0.17 MJ·m^-2, the root mean square error is 1.48 MJ·m^-2, and has a good fit of 0.96. The results show that combined RF model and meteorological data and remote sensing data can effectively solve the problem about solar radiation simulation on the non-radiation observation area of the Loess Plateau, which is of great significance to the research of regional solar radiation.     

不同叶面积指数遥感数据模拟中国总初级生产力的时空差异

陆地生态系统总初级生产力（GPP）反映了植物吸收固定大气中CO₂的能力,是碳循环过程中的重要环节。光能利用率（LUE）模型被广泛应用于GPP模拟。叶面积指数（LAI）数据是LUE模型的重要输入数据,不同的LAI数据差异较大,从而导致GPP模拟存在很大差异。利用3种常用的卫星遥感LAI数据（MCD15、GLASS和GlobMap）和气象数据模拟中国2003~2017年的GPP,比较了3种LAI数据在中国区域的时空差异,分析不同LAI数据模拟的中国GPP的时空差异。研究结果表明：3种LAI数据在中国区域的年平均值和LAI变化趋势的空间分布格局存在明显差异,森林区域的差异较大;2003~2017年间,中国区域3种LAI年平均值均呈显著增加趋势（p<0.01）,但不同LAI数据年平均值的年际变化差异明显;站点尺度GLASS LAI模拟的GPP与观测值相关性较好;不同LAI数据模拟的中国GPP总量多年平均值差异明显,最大值为7.46 Pg C a^-1 （GLASS）,最小值为6.39 Pg C a^-1 （GlobMap）;3种LAI数据模拟的中国GPP总量在2003~2017年呈显著增加趋势（p<0.05）,但不同的LAI数据模拟的中国GPP年总量的年际变化差异明显;不同LAI数据模拟的年均GPP和GPP变化趋势的空间分布格局存在明显差异,森林和农田区域的差异较大。研究结果有助于评估由于LAI数据造成的区域GPP模拟结果的不确定性。     

基于3种机器学习法的太阳辐射模拟研究

定量模拟太阳辐射对认识黄土高原区气候变化至关重要,现有研究表明机器学习可以很好地模拟太阳辐射,但不同的机器学习法在不同区域模拟精度不同,为了实现黄土高原区太阳辐射数据的最优模拟,从而为农作物模型、水文模型以及气候变化模型提供精度更高的太阳辐射数据。基于随机森林（RF,Random Forest）、人工神经网络（ANN,Artificial Neural Network）和支持向量机（SVM,Support Vector Machine）3种机器学习法来模拟黄土高原地区的太阳辐射并对这3种算法进行比较研究,选取了2003~2009年14个辐射站点和2010~2016年10个辐射站点的实测数据和对应参数气压、云量、云光学厚度、臭氧、可降水水汽以及DEM、坡度、坡向作为模型的训练数据,随机选取2010~2016年4个辐射站点的太阳辐射实测数据对模拟结果进行验证。验证结果表明：RF模型在黄土高原及周边地区的模拟效果最优,平均偏差(MBE)为-0.17 MJ·m^-2,均方根误差(RMSE)为1.48 MJ·m^-2,拟合优度达到0.96。研究结果表明:RF模型与气象数据及遥感数据结合能够有效解决黄土高原无辐射观测区的太阳辐射模拟问题,对区域太阳辐射的研究具有重要意义。