Application and comparison of remote sensing GPP models with multi-site data in China

Aims Estimation of gross primary productivity (GPP) of vegetation at the global and regional scales is important for understanding the carbon cycle of terrestrial ecosystems. Due to the heterogeneous nature of land surface, measurements at the site level cannot be directly up-scaled to the regional scale. Remote sensing has been widely used as a tool for up-saling GPP by integrating the land surface observations with spatial vegetation patterns. Although there have been many models based on light use efficiency and remote sensing data for simulating terrestrial ecosystem GPP, those models depend much on meteorological data; use of different sources of meteorological datasets often results in divergent outputs, leading to uncertainties in the simulation results. In this study, we examines the feasibility of using two GPP models driven by remote sensing data for estimating regional GPP across different vegetation types. Methods Two GPP models were tested in this study, including the Temperature and Greenness Model (TG) and the Vegetation Index Model (VI), based on remote sensing data and flux data from the China flux network (ChinaFLUX) for different vegatation types for the period 2003-2005. The study sites consist of eight ecological stations located in Xilingol (grassland), Changbaishan (mixed broadleaf-conifer forest), Haibei (shrubland), Yucheng (cropland), Damxung (alpine meadow), Qianyanzhou (evergreen needle-leaved forest), Dinghushan (evergreen broad-leaved forest), and Xishuangbanna (evergreen broad-leaved forest), respectively. Important findings All the remote sensing parameters employed by the TG and VI models had good relationships with the observed GPP, with the values of coefficient of determination, R², exceeding 0.67 for majority of the study sites. However, the root mean square errors (RMSEs) varied greatly among the study sites: the RMSE of TG ranged from 0.29 to 6.40 g·m^-2·d^-1, and that of VI ranged from 0.31 to 7.09 g·m^-2·d^-1, respectively. The photosynthetic conversion coefficients m and a can be up-scaled to a regional scale based on their relationships with the annual average nighttime land surface temperature (LST), with 79% variations in m and 58% of variations in a being explainable in the up-scaling. The correlations between the simulated outputs of both TG and VI and the measured values were mostly high, with the values of correlation coefficient, r, ranging from 0.06 in the TG model and 0.13 in the VI model at the Xishuangbanna site, to 0.94 in the TG model and 0.89 in the VI model at the Haibei site. In general, the TG model performed better than the VI model, especially at sites with high elevation and that are mainly limited by temperature. Both models had potential to be applied at a regional scale in China.     

北京永定河沿河沙地杨树人工林光能利用效率

光能利用效率(LUE)是影响生态系统生产力大小和质量的主要因素。以位于北京市大兴区永定河沿河沙地的杨树(欧美107/108,Populus euramericana cv.)人工林生态系统作为研究对象,依托涡度相关观测系统,对该生态系统的LUE进行研究,从而确定LUE在不同时间尺度上的影响因子,并确定最大光能利用利用效率(LUEmax)。结果表明:LUE存在明显的季节变化趋势,4月份生长季开始后LUE迅速升高,到7—8月达到最大,而后逐渐降低;在生长季不同阶段,LUE日动态的影响因子不同:4月份气温(Ta)、蒸散比(EF)和饱和水汽压差(VPD)是影响LUE日动态的主要因子,7、8月份光合有效辐射(PAR)和冠层导度(gc)是主要影响因子,5—6月与9—10月LUE日动态则与土壤水分(VWC)有较大关系;而LUE月动态则与月蒸散比(EFm)和月平均土壤温度(Tsm)有关。由于该人工林各月光能利用最适宜环境条件不同,各月LUEmax也各有差异,该生态系统年LUEmax为0.44 g C/MJ PAR,7、8月LUEmax最大,分别为0.66和0.69 g C/MJ PAR。研究结果表明,在利用光能利用模型进行区域乃至全球初级生产力估算时需要根据研究的不同时间尺度确定LUEmax。     

基于植被羰基硫通量估算陆地生态系统总初级生产力研究进展

羰基硫(COS)是大气中的长周期痕量气体,其分子结构、对流层大气混合比的昼夜和季节动态类似于二氧化碳(CO₂)。植物光合作用及其水解过程中,受扩散通路导度和酶活性影响,气孔的COS与CO₂吸收紧密相关,同时,植物自养呼吸并不释放COS。最新研究中,采用植被COS通量直接指示生态系统总初级生产力(GPP)。综述了植被COS通量与光合作用中碳固定过程的关联机制,以及采用涡度相关观测、整合大气COS监测和生态系统过程模型等方法开展植被COS通量与GPP研究的最新进展,探讨了关键生态过程和参数,发现方法存在以下瓶颈：(1)生理过程、尺度效应和解析效应影响了COS与CO₂的叶片相对吸收率,(2)观测与模拟手段有待进一步耦合,(3)全球COS观测密度限制了方法验证,(4)硫循环过程影响了多区域模拟精度。方法发展的前沿领域包括：(1)开展重点地区植被COS通量观测,(2)提高COS卫星柱浓度的覆盖范围,(3)完善生态系统过程模型的COS吸收机理。展望未来研究关注的科学问题是：对于亚热带等尚待开展COS连续观测的区域,采用植被COS通量...     

基于遥感和过程模型的亚洲东部陆地生态系统初级生产力分布特征

利用美国环境预测中心的再分析气象资料和由GIMMS NDVI 资料生成的叶面积指数对BEPS生态模型进行驱动,模拟分析了2000-2005年亚洲东部地区总初级生产力（GPP）和总净初级生产力（NPP）的时空变化特征.在进行区域模拟计算前,使用15个站点不同生态系统的GPP观测数据及1300个样点的NPP观测数据对模型进行验证.结果表明： BEPS模型能较好地模拟不同生态系统的GPP和NPP变化,模拟的GPP与观测数据之间的R2为0.86～0.99,均方根误差（RMSE）为0.2～1.2 g C·m-2·d-1;BEPS模拟值能够解释78%的年NPP变化,其RMSE为118 g C·m-2·a-1.2000-2005年,亚洲东部地区GPP和NPP总量平均值分别为21.7和10.5 Pg C·a-1.NPP和GPP具有相似的时空变化特征.研究期间,NPP总量的变化范围为10.2～10.7 Pg C·a-1, 变异系数为2.2%.NPP由东南向西北显著减少,高值区〖JP2〗（>1000 g C·m-2·a-1）出现在东南亚海岛国家,我国的西北干旱沙漠地区为低值区（<30 g C·m-2·a-1）,〖JP〗其空间格局主要由气候因子决定.不同国家的人均NPP差异很大,其中,蒙古最高,达70217 kg C·a-1,远高于中国的人均NPP（1921 kg C·a-1）,印度的人均NPP最小,为757 kg C·a-1.     

基于FORCCHN模型的中国典型森林生态系统碳通量模拟

森林生态系统是陆地碳循环的重要组成部分,其固碳能力显著高于其他陆地生态系统,研究森林生态系统碳通量是认识和理解全球变化对碳循环影响的关键。碳循环模型是研究森林生态系统碳通量有效工具。以长白山温带落叶阔叶林、千烟洲亚热带常绿针叶林、鼎湖山亚热带常绿阔叶林和西双版纳热带雨林等4种中国典型森林生态系统为研究对象,利用涡度相关2003-2012年观测数据,评估FORCCHN模型对生态系统呼吸（ER）,总初级生产力（GPP）,净生态系统生产力（NEP）的模型效果。结果表明：（1） FORCCHN模型能够较好的模拟中国4种典型森林生态系统不同时间尺度的碳通量。落叶阔叶林和常绿针叶林ER和GPP的逐日变化模拟效果较好（ER的相关系数分别为0.94和0.92,GPP的相关系数分别为0.86和0.74）;（2）4种森林生态系统碳通量季节动态模拟值和观测值显著相关（P<0.01）,ER、GPP、NEP的观测值和模拟值的R²分别为0.77-0.93、0.54-0.88和0.15-0.38;模型可以很好地模拟森林生态系统不同季节碳汇（NEP>0）,碳源（NEP<0）的变化规律;（3）4种森林生态系统碳通量模拟值与观测值的年际变化有很好的吻合度,但在数值大小上存在差异,模型高估了常绿阔叶林的ER和GPP,略微低估了其他3种森林生态系统ER和GPP。     

华北平原玉米田生态系统光合作用特征及影响因素

采用涡度相关法对华北平原夏玉米田进行了连续4a(2003-2006年)的碳通量观测,结果表明:夏玉米田生态系统初始量子效率(α)、最大光合速率(P_max)、暗呼吸速率(R_d)和总初级生产力(GPP)随作物生长发育而变化。在夏玉米生育前期和后期,α、P_max、R_d和GPP都比较小,其最大值出现在抽穗期/灌浆期。2003-2006年,夏玉米生长季平均α、P_max、R_d的范围分别为0.054-0.124 μmol/μmol、1.72-2.93 mg CO₂ · m^-2 · s^-1、0.23-0.38 mg CO₂ · m^-2 · s^-1。α、P_max和R_d均随叶面积指数(LAI)增加呈指数增长。2003-2006年夏玉米生长季GPP总量分别为806.2、741.5、703.0、817.4 g C/m²,年际差异较大。玉米田生态系统GPP随温度升高呈指数增长。在玉米营养生长阶段,GPP随LAI增加而增大,两者之间的关系可用直角双曲线方程来表示;生殖生长阶段,GPP随LAI降低而下降.相同LAI下,生殖生长阶段的GPP明显低于营养生长阶段。     

安徽省植被净初级生产力估算——基于改进的CASA模型

定量估算植被净初级生产力(NPP)对预测陆地碳循环趋势具有重要意义,目前广泛应用于NPP估算的CASA模型其精度仍有待提高。在已有CASA模型优化的基础上,考虑最大光能利用率(LUE_max)的动态变化来改进CASA模型,对改进前后的模拟结果进行比较,并利用改进后的模型估算2001—2020年安徽省植被NPP。结论如下：(1)改进的CASA模型可应用于研究区的植被NPP估算,NPP模拟值与实测值之间的相关性达到显著水平(R²=0.736,P<0.01)。(2)改进后模拟的安徽省植被NPP在空间表达上能够呈现更多细节,时间上较改进前在生长季NPP值更高,非生长季值更低,拉大了NPP的年内变化。(3)2001—2020年安徽省植被NPP整体呈波动上升趋势,多年平均值为547.61 gC m^-2 a^-1,年均增长量达2.18 gC m^-2 a^-1,2016—2020年间NPP增长最快。年内NPP具有明显的季节差异,表现为夏季>秋季>春季>冬...     

2001—2018年中国总初级生产力时空变化的遥感研究

总初级生产力（GPP）是绿色植被吸收大气中CO₂进行光合作用生产的有机质,是陆地生态系统碳循环研究的一个关键参数。利用遥感数据和气象数据驱动的双叶光能利用率DTEC模型计算了2001-2018年中国逐月GPP,并结合日光诱导叶绿素荧光（SIF）反演的GOSIF GPP数据集,分析了中国陆地生态系统2001-2018年GPP的时空变化特征。结果表明：（1） GOSIF和DTEC模拟的中国多年GPP平均值分别为7.23 Pg C和6.93 Pg C,在空间分布上呈现东南部高西北部低的特征;（2）2001-2018年,中国GPP呈显著增长（P<0.01）,年增长幅度分别为0.094 PgC/a （GOSIF）和0.073 PgC/a （DTEC）。而已有研究估计的中国GPP年增长幅度约为0.02-0.057 PgC/a,低估了GPP增长趋势。（3）在中国通量网6个通量站的GPP验证表明,两种模型精度高、表现好,都能较好地模拟观测站的GPP季节变化。（4） GOSIF GPP的精度优于DTEC GPP模型,这可能是由于SIF与GPP存在直接机理联系。GOSIF GPP算法能客观地反映植被生产力状况,而DTEC模型更适合自然条件下植被生产力的模拟。     

植被覆盖度和物候变化对典型黑沙蒿灌丛生态系统总初级生产力的影响

气候变化和人类活动是植被生产力年际尺度变化的重要驱动因素, 明晰二者对植被生产力的共同影响对于生态系统可持续管理至关重要。气候变化可能导致植被物候变化, 进而影响植被生产力。目前尚不清楚毛乌素沙地典型植被物候如何响应气候变化, 并因此影响生态系统总初级生产力(GPP)。此外, 植被恢复(覆盖度增加)和物候变化对GPP的共同影响有待明确。该研究选取典型黑沙蒿(Artemisia ordosica)灌丛生态系统, 结合MODIS遥感数据与涡度相关数据, 利用植被光合模型(VPM), 模拟并分析了2005-2018年间植被覆盖度和物候变化对GPP的影响。结果表明: (1) VPM模型能够较好地模拟涡度相关法观测的GPP动态(GPP_Flux), 而MODIS遥感产品(MOD17A2H)则显著低估GPP_Flux; (2)研究期内年均归一化差异植被指数(NDVI)、最大NDVI (NDVI_max)和年总GPP均显著增加, 表明植被恢复促进了植被生产力增加; (3)基于NDVI和GPP日序列估算的生长季开始日期显著提前(2.1 d·a^-1), 生长季结束日期显著推迟(1.5 d·a^-1), 二者共同促使生长季长度延长(3.6 d·a^-1); (4)物候期延长促进了GPP增加, 生长季长度每延长1天, 全年GPP显著增加6.44 g C·m^-2·a^-1; (5)植被覆盖度增加和生长季延长分别可以解释79%和57%的GPP增加; (6)尽管植被覆盖度和物候变化均促进GPP增加, 但前者是其增加的主要驱动因素。鉴于植被覆盖度增加和生长季延长也可能导致生态系统呼吸和蒸散发增加, 未来研究仍需探究生态系统碳汇能力、水分利用效率和水分承载力对气候变化和人类活动的响应。此外, 该研究主要探讨GPP在年际尺度的变化趋势及影响因素, 未来需要研究GPP的年际变异规律及驱动因素, 尤其是对降水年际变异和极端干旱事件的响应。     

基于耦合模型的干旱区植被净初级生产力估算

净初级生产力是陆地生态系统物质与能量运转研究的基础, 在干旱区生态环境演变及其与气候相互作用和影响方面极为敏感,是揭示干旱区生态环境特征的重要指标.本研究基于RS和GIS技术,利用地面气象数据、涡度相关数据、Landsat 8数据和MODIS数据,通过SEBAL模型和光能利用率模型的耦合,估算了新疆玛纳斯河流域植被净初级生产力(NPP),分析了其空间格局及与高程和坡度的关系.结果表明: SEBAL模型与光能利用率模型的耦合对玛纳斯河流域山地-绿洲-荒漠生态系统植被NPP的模拟效果较合理,能较好地反映植被净初级生产力的实际情况;2013年,玛纳斯河流域植被NPP总量为7066.72 Tg C·a^-1,平均值为278.06 g C·m^-2·a^-1,总体分布趋势是自南向北先增加后减少再增加最后减少,随着地貌和土地利用类型的变化呈现明显的分布规律,且其月变化比较明显,7—8月达到最大值,占总量NPP的52.2%;植被净初级生产力随海拔和坡度的增加整体呈下降趋势,但随海拔增加植被NPP出现3次波动.这些波动主要由地表植被覆盖类型和环境因素所引起.     

Patterns and controls of the variability of radiation use efficiency and primary productivity across terrestrial ecosystems

Aim The controls of gross radiation use efficiency (RUE), the ratio between gross primary productivity (GPP) and the radiation intercepted by terrestrial vegetation, and its spatial and temporal variation are not yet fully understood. Our objectives were to analyse and synthesize the spatial variability of GPP and the spatial and temporal variability of RUE and its climatic controls for a wide range of vegetation types. Location A global range of sites from tundra to rain forest. Methods We analysed a global dataset on photosynthetic uptake and climatic variables from 35 eddy covariance (EC) flux sites spanning between 100 and 2200 mm mean annual rainfall and between ?13 and 26°C mean annual temperature. RUE was calculated from the data provided by EC flux sites and remote sensing (MODIS). Results Rainfall and actual evapotranspiration (AET) positively influenced the spatial variation of annual GPP, whereas temperature only influenced the GPP of forests. Annual and maximum RUE were also positively controlled primarily by annual rainfall. The main control parameters of the growth season variation of gross RUE varied for each ecosystem type. Overall, the ratio between actual and potential evapotranspiration and a surrogate for the energy balance explained a greater proportion of the seasonal variation of RUE than the vapour pressure deficit (VPD), AET and precipitation. Temperature was important for determining the intra‐annual variability of the RUE at the coldest energy‐limited sites. Main conclusions Our analysis supports the idea that the annual functioning of vegetation that is adapted to its local environment is more constrained by water availability than by temperature. The spatial variability of annual and maximum RUE can be largely explained by annual precipitation, more than by vegetation type. The intra‐annual variation of RUE was mainly linked to the energy balance and water availability along the climatic gradient. Furthermore, we showed that intra‐annual variation of gross RUE is only weakly influenced by VPD and temperature, contrary to what is frequently assumed. Our results provide a better understanding of the spatial and temporal controls of the RUE and thus could lead to a better estimation of ecosystem carbon fixation and better modelling.     

亚洲中部干旱区3个典型生态系统生长季水碳通量特征

亚洲中部干旱区地处欧亚大陆腹地, 干旱少雨, 生态环境十分脆弱, 研究该地区大气与地表之间的能量和物质交换对干旱区水资源利用和生态环境保护具有重要意义。该文分析了亚洲中部干旱区荒漠与草地生态系统能量、水汽和CO₂通量的日变化及季节变化特征, 探究了水汽和CO₂通量对主要环境因子的响应。通过分析亚洲中部干旱区3个站点的涡度相关资料发现: 亚洲中部干旱区荒漠和草地生态系统在生长季(4-10月)能量、水汽通量、净CO₂通量和总初级生产力的日变化呈“单峰型”, 而荒漠生态系统呼吸日变化相对稳定; 草地生态系统白天的潜热通量占净辐射通量的比例明显高于荒漠生态系统; 草地生态系统在5-8月呈现较强的碳汇, 而荒漠生态系统表现为弱碳汇。亚洲中部干旱区草地和荒漠生态系统水汽通量和总初级生产力对降水、净辐射通量或光合有效辐射、饱和水汽压差、气温均表现出明显的敏感性。     

The use of CO2 flux measurements in models of the global terrestrial carbon budget

CO₂ flux measurements give access to two critical terms of the carbon budget of terrestrial ecosystems, the gross primary productivity (GPP) and the net ecosystem productivity (NEP). CO₂ fluxes measured by micrometeorological methods have spatial and temporal characteristics that make them potentially useful in modelling the global terrestrial carbon budget. The first use is in parameterizing ecosystem physiological processes. We present an example, based on parameterizing the mean light response of GPP. This parameterization can be used in diagnostic, satellite-based GPP models. A global application leads to realistic estimates of global GPP. The second use is in testing the seasonality of fluxes predicted by global models. Our example of this use tests two global GPP models. One is a diagnostic, satellite-based model, and one is a prognostic, process-based model. Despite the limitations of the models, both agree reasonably well with the measurements. The agreements and disagreements are useful in addressing the problems of available input datasets and representation of processes, in global models. Long-term CO₂ flux measurements give access to key variables of terrestrial vegetation models and therefore offer exciting perspectives.     

Evaluating drought effect on MODIS Gross Primary Production (GPP) with an eco-hydrological model in the mountainous forest, East Asia

Surface soil moisture dynamics is a key link between climate fluctuation and vegetation dynamics in space and time. In East Asia, precipitation is concentrated in the short monsoon season, which reduces plants water availability in the dry season. Furthermore, most forests are located in mountainous areas because of high demand for agricultural land, which results in increased lateral water flux and uneven distribution of plant available water. These climatic and topographic features of the forests make them more vulnerable to drought conditions. In this study, the eco‐hydrological model (Regional Hydro‐Ecological Simulation System) is validated with various water and carbon flux measurements in a small catchment in Korea. The model is then extended to the regional scale with fine‐resolution remote sensing data to evaluate the Moderate Resolution Imaging Radiometer (MODIS) leaf area index and gross primary productivity (GPP) products. Long‐term model runs simulated severe drought effect in 2001 well, which is clearly shown in the ring increment data. However, MODIS GPP does not capture this drought effect in 2001, which might be from a simplified treatment of water stress in the MODIS GPP algorithm. This study shows that the MODIS GPP products can potentially overestimate carbon uptake specifically during drought conditions driven by soil water stress.     

A cross-biome comparison of daily light use efficiency for gross primary production

Vegetation light use efficiency is a key physiological parameter at the canopy scale, and at the daily time step is a component of remote sensing algorithms for scaling gross primary production (GPP) and net primary production (NPP) over regional to global domains. For the purposes of calibrating and validating the light use efficiency ( ε _g) algorithms, the components of ε _g– absorbed photosynthetically active radiation (APAR) and ecosystem GPP – must be measured in a variety of environments. Micrometeorological and mass flux measurements at eddy covariance flux towers can be used to estimate APAR and GPP, and the emerging network of flux tower sites offers the opportunity to investigate spatial and temporal patterns in ε _g at the daily time step. In this study, we examined the relationship of daily GPP to APAR, and relationships of ε _g to climatic variables, at four micrometeorological flux tower sites – an agricultural field, a tallgrass prairie, a deciduous forest, and a boreal forest. The relationship of GPP to APAR was close to linear at the tallgrass prairie site but more nearly hyperbolic at the other sites. The sites differed in the mean and range of daily ε _g, with higher values associated with the agricultural field than the boreal forest. ε_g decreased with increasing APAR at all sites, a function of mid‐day saturation of GPP and higher ε _g under overcast conditions. ε _g was generally not well correlated with vapor pressure deficit or maximum daily temperature. At the agricultural site, a ε _g decline towards the end of the growing season was associated with a decrease in foliar nitrogen concentration. At the tallgrass prairie site, a decline in ε _g in August was associated with soil drought. These results support inclusion of parameters for cloudiness and the phenological status of the vegetation, as well as use of biome‐specific parameterization, in operational ε _g algorithms.     

Improving the light use efficiency model for simulating terrestrial vegetation gross primary production by the inclusion of diffuse radiation across ecosystems in China

Qualification of gross primary production (GPP) of terrestrial ecosystem over large areas is important in understanding the response of terrestrial ecosystem to global climate change. While light use efficiency (LUE) models were widely used in regional carbon budget estimates, few studies consider the effect of diffuse radiation on LUE caused by clouds using a big leaf model. Here we developed a cloudiness index light use efficiency (CI-LUE) model based on the MOD17 model algorithm to estimate the terrestrial ecosystem GPP, in which the base light use efficiency encompassed the cloudiness index, maximum LUE and clear sky LUE. GPP measured at seven sites from 2003 to 2007 in China were used to calibrate and validate the CI-LUE model. The results showed that at forest sites and cropland site the CI-LUE model outperformed the Vegetation Photosynthesis Model (VPM), Terrestrial Ecosystem Carbon flux model (TEC), MOD17 model algorithm driven by in situ meteorological measurements and MODIS GPP products, especially the R² of simulated GPP against flux measurements at Dinghushan forest site increased from 0.17 (MODIS GPP products) to 0.61 (CI-LUE). Instead, VPM model had the best agreement with GPP measurements followed by CI-LUE model and lastly TEC model at two grassland sites. Meanwhile, GPP calculated by CI-LUE model has less underestimation under cloudy skies in comparison with MOD17 model. This study demonstrated the potential of the CI-LUE model in improving GPP simulations resulting from the inclusion of diffuse radiation in regulating the base light use efficiency and maximum light use efficiency.