柽柳灌丛关键物候参数多种植被指数遥感提取的适用性——基于CO2通量观测和Sentinel-2数据

本研究以额济纳绿洲四道桥超级站为研究区,结合2018—2019年涡度通量、气象数据和2017—2020年Sentinel-2遥感影像,分析通量塔总初级生产力(GPP)与环境因子的关系,评估12种遥感植被指数对柽柳灌丛长势模拟和关键物候参数提取的适用性。采用7参数双逻辑斯蒂函数(DL-7)+全局模型函数(GMF)拟合GPP和各植被指数生长曲线,并逐年提取生长季始期(SOS)、生长季峰期(POS)和生长季末期(EOS)3种关键物候参数。结果表明: 有效积温(GDD)和土壤含水量是影响柽柳灌丛物候动态的主要环境因子。与2018年相比,2019年由于气温较低,SOS前的积温累积速率较慢,柽柳灌丛需要更长时间的热量积累来进入生长季,从而导致2019年SOS比2018年晚。在SOS与POS之间,2018和2019年水热条件相似,但2019年POS比2018年晚8 d,可能是2019年SOS较晚所致。POS以后,2019年较高的GDD和较低的土壤含水量使柽柳灌丛遭受水分胁迫,导致其生长季后期时间缩短。标准化的Sentinel-2植被指数与10:00—14:00 GPP均值的线性回归结果表明,宽波段植被指数中的增强型植被指数和窄波段植被指数中的叶绿素红边指数、倒红边叶绿素指数、红边归一化植被指数(NDVI705)能够较好地反映与柽柳灌丛GPP具有较高的一致性。柽柳灌丛SOS和EOS的遥感提取结果表明,Sentinel-2窄波段植被指数比宽波段植被指数的准确性更高,尤其是修正叶绿素吸收反射率指数提取SOS最准确,MERIS陆地叶绿素指数提取EOS最准确;Sentinel-2宽波段植被指数提取POS的准确性更高,尤其是两波段增强型植被指数和植被近红外反射率指数最准确。综合所有物候参数来看,NDVI705综合表现最佳。     

Application of two remote sensing GPP algorithms at a semiarid grassland site of North China

Aims Estimation of gross primary production (GPP) from remote sensing data is an important approach to study regional or global carbon cycle. However, for a given algorithm, it usually has its limitation on applications to a wide range of vegetation types and/or under diverse environmental conditions. This study was conducted to compare the performance of two remote sensing GPP algorithms, the MODIS GPP and the vegetation photosynthesis model (VPM), in a semiarid temperate grassland ecosystem.Methods The study was conducted at a typical grassland site in Ujimuqin of Inner Mongolia, North China, over 2 years in 2006 and 2007. Environmental controls on GPP measured by the eddy covariance (EC) technique at the study site were first investigated with path analysis of meteorological and soil moisture data at a daily and 8-day time steps. The estimates of GPP derived from the MODIS GPP and the VPM with site-specific inputs were then compared with the values of EC measurements as ground truthing at the site. Site-specific ? max (α) was estimated by using rectangular hyperbola function based on the 7-day flux data at 30-min intervals over the peak period of the growing season (May to September).Important findings Between the two remote sensing GPP algorithms and various estimates of the fraction of absorbed photosynthetic active radiation (FPAR), the VPM based on FPAR derived from the enhanced vegetation index (EVI) works the best in predicting GPP against the ground truthing of EC GPP. A path analysis indicates that the EC GPP in this semiarid temperate grassland ecosystem is controlled predominantly by both soil water and temperature. The site water condition is slightly better simulated by the moisture multiplier in the VPM than in the MODIS GPP algorithm, which is a most probable explanation for a better performance of the VPM than MODIS GPP algorithm in this semiarid grassland ecosystem.     

Comparing land surface phenology derived from satellite and GPS network microwave remote sensing

The land surface phenology (LSP) start of season (SOS) metric signals the seasonal onset of vegetation activity, including canopy growth and associated increases in land-atmosphere water, energy and carbon (CO₂) exchanges influencing weather and climate variability. The vegetation optical depth (VOD) parameter determined from satellite passive microwave remote sensing provides for global LSP monitoring that is sensitive to changes in vegetation canopy water content and biomass, and insensitive to atmosphere and solar illumination constraints. Direct field measures of canopy water content and biomass changes desired for LSP validation are generally lacking due to the prohibitive costs of maintaining regional monitoring networks. Alternatively, a normalized microwave reflectance index (NMRI) derived from GPS base station measurements is sensitive to daily vegetation water content changes and may provide for effective microwave LSP validation. We compared multiyear (2007–2011) NMRI and satellite VOD records at over 300 GPS sites in North America, and their derived SOS metrics for a subset of 24 homogenous land cover sites to investigate VOD and NMRI correspondence, and potential NMRI utility for LSP validation. Significant correlations (P?<?0.05) were found at 276 of 305 sites (90.5 %), with generally favorable correspondence in the resulting SOS metrics (r ²?=?0.73, P?<?0.001, RMSE = 36.8 days). This study is the first attempt to compare satellite microwave LSP metrics to a GPS network derived reflectance index and highlights both the utility and limitations of the NMRI data for LSP validation, including spatial scale discrepancies between local NMRI measurements and relatively coarse satellite VOD retrievals.     

基于多种遥感植被指数、叶绿素荧光与CO2通量数据的温带针阔混交林物候特征对比分析

植被物候学作为研究植被与环境条件相互作用的科学,在全球气候变化的大背景下已成为国际热点研究领域,其中森林植被在调节全球碳平衡、维护全球气候稳定的过程中有着至关重要的作用。随着遥感技术的发展,多种遥感指数被应用到森林植被物候研究中,其中以MODIS NDVI和EVI应用最为广泛,而叶绿素荧光(SIF)作为植被光合作用的"探针"也被广泛应用于森林植被物候研究中。为了探究3种指数在森林植被物候研究中的差异与特性,本文以长白山温带红松阔叶林通量观测站为研究区域,采用模型拟合结合动态阈值法提取2007—2013森林物候特征参数,并使用通量数据(总初级生产力GPP)进行验证。结果表明:NDVI与EVI、SIF相比,表现为生长季开始时间与结束时间的明显提前和滞后,与GPP数据偏差较大,且夏季生长季峰期曲线形态过宽且平坦,无法较好反映生长季变化特征;EVI相较于NDVI有所改善,整体变化趋势与SIF、GPP基本吻合,但依然存在秋季衰减时间稍迟于SIF与GPP的问题;SIF虽然存在夏季骤降现象,但依然与GPP数据一致性最好,可以较好反映出森林植被季节变化特征。SIF数据与植被光合作用的紧密关联使其在植被物候研究中具有优于植被指数的准确性,并随着遥感平台的增加和反演方法的改善,将会在多尺度、多类型的植被物候监测中发挥更加重要的作用。     

Terrestrial gross primary production inferred from satellite fluorescence and vegetation models

Determining the spatial and temporal distribution of terrestrial gross primary production (GPP) is a critical step in closing the Earth's carbon budget. Dynamical global vegetation models (DGVMs) provide mechanistic insight into GPP variability but diverge in predicting the response to climate in poorly investigated regions. Recent advances in the remote sensing of solar‐induced chlorophyll fluorescence (SIF) opens up a new possibility to provide direct global observational constraints for GPP. Here, we apply an optimal estimation approach to infer the global distribution of GPP from an ensemble of eight DGVMs constrained by global measurements of SIF from the Greenhouse Gases Observing SATellite (GOSAT). These estimates are compared to flux tower data in N. America, Europe, and tropical S. America, with careful consideration of scale differences between models, GOSAT, and flux towers. Assimilation of GOSAT SIF with DGVMs causes a redistribution of global productivity from northern latitudes to the tropics of 7–8 Pg C yr^?1 from 2010 to 2012, with reduced GPP in northern forests (~3.6 Pg C yr^?1) and enhanced GPP in tropical forests (~3.7 Pg C yr^?1). This leads to improvements in the structure of the seasonal cycle, including earlier dry season GPP loss and enhanced peak‐to‐trough GPP in tropical forests within the Amazon Basin and reduced growing season length in northern croplands and deciduous forests. Uncertainty in predicted GPP (estimated from the spread of DGVMs) is reduced by 40–70% during peak productivity suggesting the assimilation of GOSAT SIF with models is well‐suited for benchmarking. We conclude that satellite fluorescence augurs a new opportunity to quantify the GPP response to climate drivers and the potential to constrain predictions of carbon cycle evolution.     

Estimating canopy photosynthetic parameters in maize field based on multi-spectral remote sensing

Aims Determination of canopy photosynthetic parameters is key to accurate simulation of ecosystem function by using remote sensing methods. Currently, remote estimation of vegetation canopy structure characteristics has been widely adopted. However, directly estimating photosynthetic variables (photosynthetic capacity and efficiency) at canopy scale based on field spectrometry combined with CO₂ flux measurements is rare.
Methods In this study, we remotely estimated solar radiation use efficiency (εN, net ecosystem CO₂ exchange/absorbed photosynthetically active radiation (NEE_CO2/APAR); εG, gross primary productivity/absorbed photosynthetically active radiation (GPP/APAR); α, apparent quantum efficiency) and photosynthetic capacity (P_max) based on in situ measurements of spectral reflectance and ecosystem CO₂ fluxes, along with observational data on micrometeorological factors during the entire growing season for a maize canopy in Northeast China.
Important findings Results showed that the seasonal variations in P_max and α exhibited a single peak; whereas the values of εN and εG were higher at the start of vegetative stage and then rapidly decreased with the development of maize until displaying a single peak at the intermediate and late stages of the growing season, coinciding with the occurrence of peak values in P_max. A comparison was made on the predictive performance based on normalized difference vegetation index (NDVI), ratio vegetation index (RVI), wide dynamic range vegetation index (WDRVI), 2-band enhanced vegetation index (EVI2), and chlorophyll index (CI) in estimating four canopy photosynthetic parameters with any combination of two separate wavelengths at the range of 400–1 300 nm, which showed that EVI2 was most closely and linearly related to photosynthetic capacity and efficiency. This study demonstrates that multi-spectral remote sensing information is sensitive to the variations in canopy photosynthetic parameters in maize field and can be used to quantitatively monitor seasonal dynamics of canopy photosynthesis, and to accurately assess crop productivity and ecosystem CO₂ exchange capacity.     

基于MODIS EVI时间序列的鄱阳湖湿地植被覆盖和生产力的动态变化

鄱阳湖是我国最大的淡水湖, 是与长江保持自由连通的两个湖泊之一, 也是最为重要的候鸟越冬地之一, 其生境质量对全球的生物多样性保护至关重要。枯水期的鄱阳湖由众多子湖构成, 不同子湖具有不同的水文控制与管理模式, 尤其是位于长江上游的三峡大坝2006年正式运行之后, 不同水文控制模式下的子湖展现出不同的退水机制, 对退水期洲滩出露的时间与湿生植被覆盖和生产力产生了不同的影响。近年来, 遥感和生态模型在研究植被变化中应用广泛。本文以MODIS增强植被指数(enhanced vegetation index, EVI)时间序列表示地表属性, 并利用EVI时间序列模型, 建立了2000-2014年植被覆盖和生产力的时空变化趋势。在研究区内建立的网格中, 随机提取了107个斑块, 采集其每16天间隔的MODIS EVI时间序列(2000年2月至2015年4月), 将自适应Savitzky-Golay平滑算法应用于EVI时间序列分析, 提取了4个关键的植被生长指标, 即生长季开始的日期、生长季长度、生长季EVI峰值和生产力。研究结果表明: (1)具有不同水文控制模式下的湿地植被生长特征表现出显著的差异, 尤其位于自由连通子湖的植被与其他模式的子湖相比: 生长季开始的时间更晚, 生长季较短, EVI峰值较低, 并且生长季节的初级生产力较低; (2)由于水文情势的改变, 自由连通子湖2006年前后的双生长周期湿地植被的生长特征差异明显, 秋季生长季提前, 导致了生物量的过度积累, 降低了越冬雁类食源的适口性; 但位于局部水文控制子湖的湿地植被不存在这种差异。(3)自由连通与局部水文控制的子湖对鄱阳湖越冬候鸟的保护均具有十分重要的意义, 需要保证这两种类型子湖的面积, 为越冬候鸟提供更广阔的食源; 当水文情势发生改变时, 局部的水文人为控制可在一定程度上减缓鄱阳湖水情变化对湿地植被生长带来的影响。     

物候变化对北美温带落叶阔叶林生态系统生产力的影响

数据源、时间范围、空间尺度等的差异导致许多物候变化对陆地生态系统碳收支影响的研究缺少可比性。该文基于4级碳通量填充数据, 采用相对阈值方法提取了两个北美典型温带阔叶林站Harvard Forest (HF)和University of Michigan Biological Station (UMBS)共20年的物候参数(返青期、枯黄期和生长季长度), 并研究了物候变化对生态系统生产力的影响。结果表明: 1)生长季长度的延长对年累积总初级生产力(GPP)有显著贡献, 但由于呼吸作用(RE)的干扰, 生长季长度变化对年净生态系统生产力(NEP)的影响并不显著; 2)返青期的提前对上半年生态系统总初级生产力的贡献最为显著, 二者的相关系数分别为0.76 (HF)和0.93 (UMBS); 3)枯黄期的延迟对生产力的影响并不显著; 4)随着春季返青期的提前或秋季枯黄期的延迟, 上、下半年GPP和RE的累积量虽均有增加趋势, 但由于各自增加的幅度不确定, 导致年NEP与二者的响应关系复杂。     

Phenology-based,remote sensing of post-burn disturbance windows in rangelands

Wildland fire activity has increased in many parts of the world in recent decades. Ecological disturbance by fire can accelerate ecosystem degradation processes such as erosion due to combustion of vegetation that otherwise provides protective cover to the soil surface. This study employed a novel ecological indicator based on remote sensing of vegetation greenness dynamics (phenology) to estimate variability in the window of time between fire and the reemergence of green vegetation. The indicator was applied as a proxy for short-term, post-fire disturbance windows in rangelands; where a disturbance window is defined as the time required for an ecological or geomorphic process that is altered to return to pre-disturbance levels. We examined variability in the indicator determined for time series of MODIS and AVHRR NDVI remote sensing data for a database of ∼100 historical wildland fires, with associated post-fire reseeding treatments, that burned 1990–2003 in cold desert shrub steppe of the Great Basin and Columbia Plateau of the western USA. The indicator-based estimates of disturbance window length were examined relative to the day of the year that fires burned and seeding treatments to consider effects of contemporary variability in fire regime and management activities in this environment. A key finding was that contemporary changes of increased length of the annual fire season could have indirect effects on ecosystem degradation, as early season fires appeared to result in longer time that soils remained relatively bare of the protective cover of vegetation after fires. Also important was that reemergence of vegetation did not occur more quickly after fire in sites treated with post-fire seeding, which is a strategy commonly employed to accelerate post-fire vegetation recovery and stabilize soil. Future work with the indicator could examine other ecological factors that are dynamic in space and time following disturbance – such as nutrient cycling, carbon storage, microbial community composition, or soil hydrology – as a function of disturbance windows, possibly using simulation modeling and historical wildfire information.     

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.     

Seasonal,interannual and decadal drivers of tree and grass productivity in an Australian tropical savanna

Tree–grass savannas are a widespread biome and are highly valued for their ecosystem services. There is a need to understand the long‐term dynamics and meteorological drivers of both tree and grass productivity separately in order to successfully manage savannas in the future. This study investigated the interannual variability (IAV) of tree and grass gross primary productivity (GPP) by combining a long‐term (15 year) eddy covariance flux record and model estimates of tree and grass GPP inferred from satellite remote sensing. On a seasonal basis, the primary drivers of tree and grass GPP were solar radiation in the wet season and soil moisture in the dry season. On an interannual basis, soil water availability had a positive effect on tree GPP and a negative effect on grass GPP. No linear trend in the tree–grass GPP ratio was observed over the 15‐year study period. However, the tree–grass GPP ratio was correlated with the modes of climate variability, namely the Southern Oscillation Index. This study has provided insight into the long‐term contributions of trees and grasses to savanna productivity, along with their respective meteorological determinants of IAV.     

Variability and evolution of global land surface phenology over the past three decades (1982–2012)

Monitoring land surface phenology (LSP) is important for understanding both the responses and feedbacks of ecosystems to the climate system, and for representing these accurately in terrestrial biosphere models. Moreover, by shedding light on phenological trends at a variety of scales, LSP provides the potential to fill the gap between traditional phenological (field) observations and the large‐scale view of global models. In this study, we review and evaluate the variability and evolution of satellite‐derived growing season length (GSL) globally and over the past three decades. We used the longest continuous record of Normalized Difference Vegetation Index data available to date at global scale to derive LSP metrics consistently over all vegetated land areas and for the period 1982–2012. We tested GSL, start‐ and end‐of‐season metrics (SOS and EOS, respectively) for linear trends as well as for significant trend shifts over the study period. We evaluated trends using global environmental stratification information in place of commonly used land cover maps to avoid circular findings. Our results confirmed an average lengthening of the growing season globally during 1982–2012 – averaging 0.22–0.34 days yr^?1, but with spatially heterogeneous trends. About 13–19% of global land areas displayed significant GSL change, and over 30% of trends occurred in the boreal/alpine biome of the Northern Hemisphere, which showed diverging GSL evolution over the past three decades. Within this biome, the ‘Cold and Mesic’ environmental zone appeared as an LSP change hotspot. We also examined the relative contribution of SOS and EOS to the overall changes, finding that EOS trends were generally stronger and more prevalent than SOS trends. These findings constitute a step towards the identification of large‐scale phenological drivers of vegetated land surfaces, necessary for improving phenological representation in terrestrial biosphere models.     

Ecosystem functional units characterized by satellite observed phenology and productivity gradients: A case study for Europe

The present study demonstrates remote sensing derived phenological and productivity indicators of ecosystem functional dynamism. The indices were derived from SPOT VEGETATION NDVI data on 1 km spatial resolution across the pan-European continent using the Phenolo approach. The phenological and productivity indices explained 78% of the variance in the European ecosystem gradient measured by bio-climatic zones. Along this gradient climatic predictors could only explain 57% of the variance in the satellite metrics. Reclassification of the bio-climatic zones into phenology and productivity related ecosystem functional units (EFUs) selected five metrics related to the cyclic and permanent fraction of productivity, to the background, to the growing season start and the timing of the maximum NDVI value. Along the EFU gradient the climatic predictors explained over 90% of the variance of the remote sensing variables, 30% more than along the bio-climatic gradient. The EFUs showed strong correspondence to 14 land-cover types in Europe and the selected remote sensing metrics explained 86% of the variation in the land-cover classes. These results show that remote sensing derived parameters have tremendous potential for the quantification of ecosystem functional dynamism. Phenological and productivity metrics offer an indicator system for ecosystems that climatic indicators alone cannot manifest. Their potential to monitor the spatial pattern, status and inter-annual variability of ecosystems and vegetation cover can deliver reference status information for future assessments of the impacts of human or climate change induced ecosystem changes.     

1990-2020年地表物候时空变化对高寒湿地景观演变过程的响应

地表物候是生态系统环境变化的敏感指示器。为探讨物候时空变化和湿地景观生态格局与过程之间的关系,论文以若尔盖高寒湿地为例,基于1990—2020年GIMMS3g NDVI和MODIS NDVI数据集、7期Landsat TM/OLI卫星遥感数据,采用阈值法提取地表物候参数,基于面向对象的分类方法解译出土地利用数据,利用土地覆盖转换指数模型(PNTI)刻画高寒湿地动态变化过程,分析地表物候时空变化与高寒湿地景观格局演变过程的关系。结果表明：(1)1990—2020年研究区呈现前期湿地面积减少后期趋于稳定的特征,根据土地利用类型演变路径和强度分为动态平衡区、退化演变区和恢复演变区,面积占比分别56.84%、 28.14%和15.02%。(2)植被返青期(SOS)、生长盛期(POS)呈南早北晚,枯黄期(EOS)呈中间早周边晚,生长期长度(LOS)呈中间短周边长、西北短东南长的空间分布特征。SOS分布在第96—149天,EOS分布在第249—284天,LOS持续125—173d, POS分布在第179—209天。SOS、POS先推迟后提前、EOS先提前后推迟,LOS呈现先缩短后延长的规律,199...     

Modeling the Sensitivity of the Seasonal Cycle of GPP to Dynamic LAI and Soil Depths in Tropical Rainforests

The seasonality of pan-tropical wet forests has been highlighted by recent remote sensing and eddy flux measurements that have recorded both increased and sustained dry-season gross primary productivity (GPP). These observations suggest that wet tropical forests are primarily light limited and that the mechanisms for resilience to drought and projected climate change must be considered in ecosystem model development. Here we investigate two proposed mechanisms for drought resilience of tropical forests, deep soil water access and the seasonality of phenology, using the LPJmL Dynamic Global Vegetation Model. We parameterize a new seasonal phenology module for tropical evergreen trees using remotely sensed leaf area index (LAI) and incoming solar radiation data from the Terra Earth Observing System. Simulations are evaluated along a gradient of dry-season length (DSL) in South America against MODIS GPP estimates. We show that deep soil water access is critical for maintaining dry-season GPP, whereas implementing a seasonal LAI did not enhance simulated dry-season GPP. The Farquhar-Collatz photosynthesis scheme used in LPJmL optimizes leaf nitrogen allocation according to light conditions, causing maximum photosynthetic capacity in the dry season. High LAI, characteristic of tropical forests, also dampens the seasonal amplitude of the fraction of photosynthetically active radiation (FPAR). Given the relatively high uncertainty in tropical phenology observations and their corresponding proximate drivers, we recommend that ecosystem model development focus on belowground processes. An improved representation of soil depths and rooting distributions is necessary for modeling the dynamics of dry-season tropical forest functioning and may have important impacts for modeling tropical forest vulnerability to climate change. Author Contributions  BP conceived of the study, analyzed data, and wrote the paper. UH designed study and contributed new methods. WC designed study and contributed to paper.     

Remote sensing of larch phenological cycle and analysis of relationships with climate in the Alpine region

This research aims at developing a remote sensing technique for monitoring the interannual variability of the European larch phenological cycle in the Alpine region of Aosta Valley (Northern Italy) and to evaluate its relationships with climatic factors. Phenological field observations were conducted in eight test sites from 2005 to 2007 to determine the dates of completion of different phenological phases. MODerate Resolution Imaging Spectrometer (MODIS) 250 m 16‐days normalized difference vegetation index (NDVI) time series were fitted with double logistic curves and the dates corresponding to different features of the curves were determined. Comparison with field data showed that the features of the fitted NDVI curve that allowed the best estimate of the start and end of the growing season were the zeroes of its third derivative (MAE of 6 and 4 days, respectively). The start and end of season were also estimated with the spring warming (SW) and growing season index (GSI) phenological models. MODIS start and end of season dates generally agreed with those obtained by the SW and GSI climate‐driven phenological models. However, phenological models provided erroneous results when applied in years with anomalous meteorological conditions. The relationships between interannual variability of the larch phenological cycle and climate were investigated by comparing the mean start and end of season yearly anomalies with air temperature anomalies. A strong linear relationship (R²=0.91) was found between mean spring temperatures and mean start of season dates, with an increase of 1 °C in mean spring temperature leading to a 7‐day anticipation of mean larch bud‐burst date. Leaf coloring dates were found to be best related with mean September temperature (R²=0.77), but with higher spring temperatures appearing to lead to earlier leaf coloring.     

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.     

A historical meta‐analysis of global terrestrial net primary productivity: are estimates converging?

Net primary productivity (NPP) is one of the most important ecosystem parameters, representing vegetation activity, biogeochemical cycling, and ecosystem services. To assess how well the scientific community understands the biospheric function, a historical meta‐analysis was conducted. By surveying the literature from 1862 to 2011, I extracted 251 estimates of total terrestrial NPP at the present time (NPP_T) and calculated their statistical metrics. For all the data, the mean±standard deviation and median were 56.2±14.3 and 56.4 Pg C yr^–1, respectively. Even for estimates published after 2000, a substantial level of uncertainty (coefficient of variation by ±15%) was inevitable. The estimates were categorized on the basis of methodology (i.e., inventory analysis, empirical model, biogeochemical model, dynamic global vegetation model, and remote sensing) to examine the consistency among the statistical metrics of each category. Chronological analysis revealed that the present NPP_T estimates were directed by extensive field surveys in the 1960s and 1970s (e.g., the International Biological Programme). A wide range of uncertainty remains in modern estimates based on advanced biogeochemical and dynamic vegetation models and remote‐sensing techniques. Several critical factors accounting for the estimation uncertainty are discussed. Ancillary analyses were performed to derive additional ecological and human‐related parameters related to NPP. For example, interannual variability, carbon‐use efficiency (a ratio of NPP to gross photosynthesis), human appropriation, and preindustrial NPP_T were assessed. Finally, I discuss the importance of improving NPP_T estimates in the context of current global change studies and integrated carbon cycle research.     

Interannual variations in GPP in forest ecosystems in Southwest China and regional differences in the climatic contributions

Forest ecosystem plays an important role as carbon sinks in Southwest China. Currently, remote sensing technology has been widely used to substantially model the high temporal and spatial variation in gross primary production (GPP) at a site or regional scale. However, during the growing season, the regional uncertainty of GPP in the forest ecosystem and the relative contributions of climate variations to interannual variation (IAV) of GPP are not well understood across Southwest China. Our research focuses on the joint analysis of the three-cornered hat (TCH) algorithm and uses the contribution index to analyse the model's uncertainties varying with plant functional types (PFTs), climate zones, and the contribution of climate variabilities to GPP IAV. Here, three GPP datasets are used to investigate how climate variabilities contribute to the GPP IAV during the growing season. The uncertainties in GPP vary from 829.33 g C m⁻² year⁻¹ to 2031.86 g C m⁻² year⁻¹ for different models in different climate zones and different PFTs. Additionally, the results highlight that precipitation dominates the interannual variation in GPP in forest ecosystem during the growing season in Southwest China. It makes the largest contribution (34.46%) to the IAV of GPP in the climate zone of E (cold subtropical highland area) and the largest contribution (80.83%) to PFTs of the MF (mixed forest). Our study suggests the availability and applicability of GPP products can be used to assess GPP uncertainties and analyse the contributions of climate factors to GPP IAV in forest ecosystem or other ecosystems.     

气候变化和人类活动对中国陆地生态系统总初级生产力的影响厘定研究

总初级生产力(GPP)是生态系统植被光合作用生成有机物的能力表征,是生态系统服务功能的基础,关系到区域社会经济可持续发展及区域生态安全。基于生态系统过程模型CEVSA2,应用中分辨率成像光谱仪(MODIS)卫星遥感的叶面积指数数据产品(MCD15A2H),以强迫法构建了遥感数据驱动的模型新版本——CEVSA-RS;基于CEVSA-RS模拟分析了气候变化和人类活动对中国陆地生态系统GPP时空变化的相对影响,从气候潜在总初级生产力(GPP_CL)和现实总初级生产力(GPP_RS)的大小和趋势两方面厘定了人类活动影响。2000至2017年全国平均潜在GPP(1016.36 gC m^-2a^-1)略高于对应现实GPP(962.85 gC m^-2a^-1),但存在明显的空间分异:长江以南大部、秦岭、太行山脉以东以及大兴安岭以东和长白山地区等森林植被覆盖区,现实GPP高于潜在GPP;而西部草地及灌丛等地区现实GPP低于潜在GPP。全国GPP呈显著增加趋势(P<0.05)...