Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m^-2 d^-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m^-2 d^-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution.     

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.     

遥感GPP模型在中亚干旱区4个典型生态系统的适用性评价

总初级生产力(GPP)是全球生态系统碳循环的重要组成部分,对全球气候变化有重要影响。目前有多种遥感模型可以模拟总初级生产力,比较不同遥感模型在中亚干旱区上的适用性对推进全球干旱区碳收支估算具有重要意义。基于涡度协相关技术观测的四个地面站数据验证MOD17、VODCA2、VPM、TG、SANIRv五种模型的模拟精度。结果表明：(1)基于光能利用率理论的MOD17、VPM模型模拟咸海荒漠植被和阜康荒漠植被GPP的精度最高(R²分别为0.52和0.80),但在模拟草地、农田生态系统生产力时存在较明显的低估(RE>20%);基于植被指数的遥感模型TG模型、SANIRv模型模拟巴尔喀什湖草地生态系统和乌兰乌苏农田生态系统GPP的精度最高(R²分别为0.91和0.81),同时模拟值与实测值的相对误差也较低;基于微波的VODCA2模型模拟各生态系统生产力的效果最差。(2)水分亏缺是限制植被GPP的主要因素,因此是否合理考虑水分胁迫是影响GPP模型在中亚干旱区适用性的重要因素。研究揭示了遥感GPP模型在中亚干旱区的应用潜力,为推进全球植被碳通量的准确估...     

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.     

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.     

长江流域陆地植被总初级生产力时空变化特征及其气候驱动因子

长江流域是我国重要的工农业生产区和生态安全屏障。深入开展长江流域陆地植被总初级生产力（GPP）时空变化特征和驱动因子研究,对了解变化环境下区域植被生长状况和生物固碳能力、掌握生态环境质量具有重要意义。基于MODIS GPP遥感数据产品、土地利用和气象观测数据,采用趋势分析和偏相关分析法,系统研究了2000-2015年间长江流域陆地植被GPP时空变化特征,探讨了不同二级水资源区内气候因子对GPP变化影响的空间差异,揭示了不同土地利用类型GPP变化特征以及气候因子作用。结果表明：1）长江流域陆地植被覆盖区GPP在0.3-2765 gC m^-2 a^-1之间,均值约990.46 gC m^-2 a^-1,多年平均GPP总量为1.735 P gC;2）近年来,长江流域GPP呈不显著上升趋势,趋势率为2.39 gC m^-2 a^-1。空间上,GPP上升区和下降区分别占总流域面积的68%和32%。各二级水资源区内,除了洞庭湖流域和太湖流域GPP呈下降趋势外,其他区GPP均呈上升趋势;3）不同土地利用类型GPP均值在198.50-1276.90 gC m^-2 a^-1之间。各土地利用类型中除水田GPP呈微弱下降外,其他均呈上升趋势,尤其是高、中、低覆盖度草地GPP上升趋势较为显著;4）不同气候因子对植被GPP变化的影响程度在不同二级水资源区、不同土地利用类型间均存在一定差异,但就长江流域整体而言,GPP年际变化受温度影响显著,其次是蒸发,而降水等其他气候因子的影响不大。     

Interannual variability of plant phenology in tussock tundra: modelling interactions of plant productivity, plant phenology, snowmelt and soil thaw

We present a linked model of plant productivity, plant phenology, snowmelt and soil thaw in order to estimate interannual variability of arctic plant phenology and its effects on plant productivity. The model is tested using 8 years of soil temperature data, and three years of bud break data of Betula nana. Because the factors that trigger the end of the growing season of arctic vegetation are less well known than those of the start of the growing season, three hypotheses were formulated and tested for their effects on productivity and its sensitivity to climate change; the hypothesised factors determining the end of the growing season were frost, photoperiod and periodic constraints. The performance of the soil thermal model was good; both the onset of soil thaw in spring and the initiation of freezing in autumn were predicted correctly in most cases. The phenology model predicted the bud break date of Betula nana closely for the three different years. The soil thaw model predicted similar growing season start dates under current climate as the models based on sum of temperatures, but it made significantly different predictions under climate change scenarios, probably because of the non‐linear interactions between snowmelt and soil thaw. The uncertainty about the driving factors for the end of the growing season, in turn, resulted in uncertainty in the interannual variability of the simulated annual gross primary productivity (GPP). The interannual variability ranged from ? 25 to + 26% of the mean annual GPP for the frost hypothesis, from ? 20 to + 20% for the photoperiod hypothesis and only from ? 7 to + 7% for the periodic hypothesis. The different hypotheses also resulted in different sensitivity to climate change, with the frost hypothesis resulting in 30% higher annual GPP values than the periodic hypothesis when air temperatures were increased by 3 °C.     

Ecosystem properties of semiarid savanna grassland in West Africa and its relationship with environmental variability

The Dahra field site in Senegal, West Africa, was established in 2002 to monitor ecosystem properties of semiarid savanna grassland and their responses to climatic and environmental change. This article describes the environment and the ecosystem properties of the site using a unique set of in situ data. The studied variables include hydroclimatic variables, species composition, albedo, normalized difference vegetation index (NDVI), hyperspectral characteristics (350–1800 nm), surface reflectance anisotropy, brightness temperature, fraction of absorbed photosynthetic active radiation (FAPAR), biomass, vegetation water content, and land‐atmosphere exchanges of carbon (NEE) and energy. The Dahra field site experiences a typical Sahelian climate and is covered by coexisting trees (~3% canopy cover) and grass species, characterizing large parts of the Sahel. This makes the site suitable for investigating relationships between ecosystem properties and hydroclimatic variables for semiarid savanna ecosystems of the region. There were strong interannual, seasonal and diurnal dynamics in NEE, with high values of ~?7.5 g C m^?2 day^?1 during the peak of the growing season. We found neither browning nor greening NDVI trends from 2002 to 2012. Interannual variation in species composition was strongly related to rainfall distribution. NDVI and FAPAR were strongly related to species composition, especially for years dominated by the species Zornia glochidiata. This influence was not observed in interannual variation in biomass and vegetation productivity, thus challenging dryland productivity models based on remote sensing. Surface reflectance anisotropy (350–1800 nm) at the peak of the growing season varied strongly depending on wavelength and viewing angle thereby having implications for the design of remotely sensed spectral vegetation indices covering different wavelength regions. The presented time series of in situ data have great potential for dryland dynamics studies, global climate change related research and evaluation and parameterization of remote sensing products and dynamic vegetation models.     

Instantaneous-to-daily GPP upscaling schemes based on a coupled photosynthesis-stomatal conductance model: correcting the overestimation of GPP by directly using daily average meteorological inputs

Daily canopy photosynthesis is usually temporally upscaled from instantaneous (i.e., seconds) photosynthesis rate. The nonlinear response of photosynthesis to meteorological variables makes the temporal scaling a significant challenge. In this study, two temporal upscaling schemes of daily photosynthesis, the integrated daily model (IDM) and the segmented daily model (SDM), are presented by considering the diurnal variations of meteorological variables based on a coupled photosynthesis-stomatal conductance model. The two models, as well as a simple average daily model (SADM) with daily average meteorological inputs, were validated using the tower-derived gross primary production (GPP) to assess their abilities in simulating daily photosynthesis. The results showed IDM closely followed the seasonal trend of the tower-derived GPP with an average RMSE of 1.63 g C m^?2 day^?1, and an average Nash–Sutcliffe model efficiency coefficient (E) of 0.87. SDM performed similarly to IDM in GPP simulation but decreased the computation time by >66 %. SADM overestimated daily GPP by about 15 % during the growing season compared to IDM. Both IDM and SDM greatly decreased the overestimation by SADM, and improved the simulation of daily GPP by reducing the RMSE by 34 and 30 %, respectively. The results indicated that IDM and SDM are useful temporal upscaling approaches, and both are superior to SADM in daily GPP simulation because they take into account the diurnally varying responses of photosynthesis to meteorological variables. SDM is computationally more efficient, and therefore more suitable for long-term and large-scale GPP simulations.     

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。     

中国温带草地物候对气候变化的响应及其对总初级生产力的贡献

气候变暖引起的植物物候变化影响了陆地生态系统功能和碳循环。目前研究着重关注温带和热带森林物候变化趋势、驱动因素,关于干旱半干旱地区草地物候变化及其对生态系统总初级生产力（gross primary productivity, GPP）影响仍知之甚少。因此,开展草地植物物候与生产力之间的关系研究对预测草地生态系统响应未来气候变化和区域碳循环至关重要。基于1982-2015年气象资料和GIMMS NDVI3g数据,分析了中国温带草原植被返青期（start of the growing season, SGS）和枯黄期（end of the growing season, EGS）变化及其对气候的响应,并借助一阶差分法量化物候对GPP动态变化的贡献。结果表明：（1）季前1-2个月的夜间温度增温会显著提前SGS, 而当月至季前2个月的白天温度对SGS有着微弱的促进作用;季前3个月的累积降水对SGS提前作用最为强烈,累积太阳辐射在各个时期对SGS影响相对较弱。（2）不同季前时间尺度昼夜温度对草地EGS均表现出相反的作用,短期累积降水对EGS起到显著延迟的区域范围最大,太阳辐射随着季前时间的增加对草地枯黄期的延迟作用逐渐转变为提前作用。（3）EGS对草地GPP年际变化趋势的相对贡献率强于返青期。研究结果有助于深化陆地生态系统与气候变化、碳循环之间相互作用的认识,为草地适应未来气候变化和生态建设提供科学依据。     

Carbon Dioxide Dynamics and Controls in a Deep-water Wetland on the Qinghai-Tibetan Plateau

To initially characterize the dynamics and environmental controls of CO₂, ecosystem CO₂ fluxes were measured for different vegetation zones in a deep-water wetland on the Qinghai-Tibetan Plateau during the growing season of 2002. Four zones of vegetation along a gradient from shallow to deep water were dominated, respectively by the emergent species Carex allivescens V. Krez., Scirpus distigmaticus L., Hippuris vulgaris L., and the submerged species Potamogeton pectinatus L. Gross primary production (GPP), ecosystem respiration (Re), and net ecosystem production (NEP) were markedly different among the vegetation zones, with lower Re and GPP in deeper water. NEP was highest in the Scirpus-dominated zone with moderate water depth, but lowest in the Potamogeton-zone that occupied approximately 75% of the total wetland area. Diurnal variation in CO₂ flux was highly correlated with variation in light intensity and soil temperature. The relationship between CO₂ flux and these environmental variables varied among the vegetation zones. Seasonal CO₂ fluxes, including GPP, Re, and NEP, were strongly correlated with aboveground biomass, which was in turn determined by water depth. In the early growing season, temperature sensitivity (Q₁₀) for Re varied from 6.0 to 8.9 depending on vegetation zone. Q₁₀ decreased in the late growing season. Estimated NEP for the whole deep-water wetland over the growing season was 24 g C m⁻². Our results suggest that water depth is the major environmental control of seasonal variation in CO₂ flux, whereas photosynthetic photon flux density (PPFD) controls diurnal dynamics.     

Typha productivity in a Texas pond: Implications for energy and nutrient dynamics in freshwater wetlands

Above-and below-ground biomass of Typha angustifolia L. was sampled monthly for 18 months from a small Texas pond. Maximum above-ground biomass was 2559±284 g AFDW (ash-free dry weight) m⁻² in 1983 and 2895±217 g AFDW m⁻² in 1984. Peak below-ground biomass for these 2 years was 2506±278 g AFDW m⁻² and 2314±226 g AFDW m^t-2, respectively. Stepwise multiple linear regression analyses revealed that mean above-ground biomass accrual was related to duration of growing season, cumulative precipitation, cumulative degree days and/or cumulative pan evaporation. The same was not true for below-ground biomass increases. Analysis of covariance revealed that the rates of above-ground biomass production were not significantly different (F test, p < 0.05) between the 1983 and 1984 growing seasons. Below-ground biomass turnover times for 1983 and 1984 were 2.47 and 1.21 years, respectively.     

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.     

CO2 flux in alpine wetland ecosystem on the Qinghai-Tibetan Plateau, China

Using the CO₂ flux data measured by the eddy covariance method in the northeast of Qinghai-Tibetan Plateau in 2005, we analyzed the carbon flux dynamics in relation to meteorological and biotic factors. The results showed that the alpine wetland ecosystem was the carbon source, and it emitted 316.02 gCO₂ · m⁻² to atmosphere in 2005 with 230.16 gCO₂ · m⁻² absorbed in the growing season from May to September and 546.18 gCO₂ · m⁻² released in the non-growing season from January to April and from October to December. The maximum of the averaged daily CO₂ uptake rates and release rates was (0.45 ± 0.0012) mgCO₂ · m⁻² · s⁻¹ (Mean ± SE) in July and (0.22 ± 0.0090) mgCO₂ · m⁻² · s⁻¹ in August, respectively. The averaged diurnal variation showed a single-peaked pattern in the growing season, but exhibited very small fluctuation in the non-growing season. Net ecosystem exchange (NEE) and gross primary production (GPP) were all correlated with some meteorological factors, and they showed a negatively linear correlation with aboveground biomass, while a positive correlation existed between the ecosystem respiration (R_es) and those factors.     

Optimality principles explaining divergent responses of alpine vegetation to environmental change

Recent increases in vegetation greenness over much of the world reflect increasing CO₂ globally and warming in cold areas. However, the strength of the response to both CO₂ and warming in those areas appears to be declining for unclear reasons, contributing to large uncertainties in predicting how vegetation will respond to future global changes. Here, we investigated the changes of satellite-observed peak season absorbed photosynthetically active radiation (F_max) on the Tibetan Plateau between 1982 and 2016. Although climate trends are similar across the Plateau, we identified robust divergent responses (a greening of 0.31 ± 0.14% year⁻¹ in drier regions and a browning of 0.12 ± 0.08% year⁻¹ in wetter regions). Using an eco-evolutionary optimality (EEO) concept of plant acclimation/adaptation, we propose a parsimonious modelling framework that quantitatively explains these changes in terms of water and energy limitations. Our model captured the variations in F_max with a correlation coefficient (r) of .76 and a root mean squared error of .12 and predicted the divergent trends of greening (0.32 ± 0.19% year⁻¹) and browning (0.07 ± 0.06% year⁻¹). We also predicted the observed reduced sensitivities of F_max to precipitation and temperature. The model allows us to explain these changes: Enhanced growing season cumulative radiation has opposite effects on water use and energy uptake. Increased precipitation has an overwhelmingly positive effect in drier regions, whereas warming reduces F_max in wetter regions by increasing the cost of building and maintaining leaf area. Rising CO₂ stimulates vegetation growth by enhancing water-use efficiency, but its effect on photosynthesis saturates. The large decrease in the sensitivity of vegetation to climate reflects a shift from water to energy limitation. Our study demonstrates the potential of EEO approaches to reveal the mechanisms underlying recent trends in vegetation greenness and provides further insight into the response of alpine ecosystems to ongoing climate change.     

Differences in diffuse photosynthetically active radiation effects on cropland light use efficiency calculated via contemporary remote sensing and crop production models

Diffuse photosynthetically active radiation (PAR_diff) is instrumental to the light use efficiency (LUE) of vegetation. Accurately assessing the impact of PAR_diff on crop LUE can better our understanding of the carbon cycle in cropland ecosystems. LUE estimates from six remote sensing models (including four big-leaf models and two two-leaf models) and two crop production models were compared with measured FLUXNET LUE data from cropland sites under different PAR_diff fraction (FDIFF_PAR) intervals. Compared with the FLUXNET observations, the Eddy Covariance-Light Use Efficiency (EC-LUEa) model exhibited the best LUE estimation (R² = 0.250, RMSE = 0.868 gC·MJ⁻¹, and Bias = −0.005 gC·MJ⁻¹) owing to the use of more accurate calculation scheme of environmental stress factors. LUEs calculated from FLUXNET observational data were positively correlated with FDIFF_PAR, but only LUEs simulated by the Moderate Resolution Imaging Spectroradiometer Photosynthesis (MOD17) and Two-Leaf Light Use Efficiency (TL-LUE) models increased with increasing FDIFF_PAR. This is attributed to the fact that the MOD17 model divides the crop growth types into cereal and broadleaf, while the TL-LUE model considers the change of light interception with increased FDIFF_PAR. Furthermore, the maximum LUE (LUE_max) increased with FDIFF_PAR at FLUXNET observational sites, but the eight models could not capture the effects of PAR_diff on the crop LUE_max. Among the eight models, the LUE_max–FDIFF_PAR relationship simulated by the two-leaf models fluctuated because the crops were divided into sunlit and shaded leaves, while the big-leaf and crop production models used a constant LUE_max and showed a constant LUE_max–FDIFF_PAR relationship. Additionally, big-leaf models performed better than two-leaf models for gross primary production (GPP) simulation in the cropland ecosystem, which is related to the planting density and vegetation structure. These results demonstrate the importance of considering the impact of FDIFF_PAR on LUE_max in LUE modeling.     

Divergent response of seasonally dry tropical vegetation to climatic variations in dry and wet seasons

Interannual variations of photosynthesis in tropical seasonally dry vegetation are one of the dominant drivers to interannual variations of atmospheric CO₂ growth rate. Yet, the seasonal differences in the response of photosynthesis to climate variations in these ecosystems remain poorly understood. Here using Normalized Difference Vegetation Index (NDVI), we explored the response of photosynthesis of seasonally dry tropical vegetation to climatic variations in the dry and the wet seasons during the past three decades. We found significant (p < 0.01) differences between dry and wet seasons in the interannual response of photosynthesis to temperature (γ^int) and to precipitation (δ^int). γ^int is ~1% °C^?1 more negative and δ^int is ~8% 100 mm^?1 more positive in the dry season than in the wet season. Further analyses show that the seasonal difference in γ^int can be explained by background moisture and temperature conditions. Positive γ^int occurred in wet season where mean temperature is lower than 27°C and precipitation is at least 60 mm larger than potential evapotranspiration. Two widely used Gross Primary Productivity (GPP) estimates (empirical modeling by machine‐learning algorithm applied to flux tower measurements, and nine process‐based carbon cycle models) were examined for the GPP–climate relationship over wet and dry seasons. The GPP derived from empirical modeling can partly reproduce the divergence of γ^int, while most process models cannot. The overestimate by process models on negative impacts by warmer temperature during the wet season highlights the shortcomings of current carbon cycle models in representing interactive impacts of temperature and moisture on photosynthesis. Improving representations on soil water uptake, leaf temperature, nitrogen cycling, and soil moisture may help improve modeling skills in reproducing seasonal differences of photosynthesis–climate relationship and thus the projection for impacts of climate change on tropical carbon cycle.     

Gross Primary Productivity of a High Elevation Tropical Montane Cloud Forest

For decades, the productivity of tropical montane cloud forests (TMCF) has been assumed to be lower than in tropical lowland forests due to nutrient limitation, lower temperatures, and frequent cloud immersion, although actual estimates of gross primary productivity (GPP) are very scarce. Here, we present the results of a process-based modeling estimate of GPP, using a soil–plant–atmosphere model, of a high elevation Peruvian TMCF. The model was parameterized with field-measured physiological and structural vegetation variables, and driven with meteorological data from the site. Modeled transpiration corroborated well with measured sap flow, and simulated GPP added up to 16.2 ± SE 1.6 Mg C ha^?1 y^?1. Dry season GPP was significantly lower than wet season GPP, although this difference was 17% and not caused by drought stress. The strongest environmental controls on simulated GPP were variation of photosynthetic active radiation and air temperature (T _air). Their relative importance likely varies with elevation and the local prevalence of cloud cover. Photosynthetic parameters (V _cmax and J _max) and leaf area index were the most important non-environmental controls on GPP. We additionally compared the modeled results with a recent estimate of GPP of the same Peruvian TMCF derived by the summing of ecosystem respiration and net productivity terms, which added up to 26 Mg C ha^?1 y^?1. Despite the uncertainties in modeling GPP we conclude that at this altitude GPP is, conservatively estimated, 30–40% lower than in lowland rainforest and this difference is driven mostly by cooler temperatures than changes in other parameters.     

Calibration of Terra/MODIS gross primary production over an irrigated cropland on the North China Plain and an alpine meadow on the Tibetan Plateau

This paper evaluated the MODerate resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) product (MOD17) by using estimated GPP from eddy‐covariance flux measurements over an irrigated winter wheat and maize double‐cropping field on the North China Plain in 2003–2004, and an alpine meadow on the Tibetan Plateau in 2002–2003. The mean annual GPP from MOD17 accounted for 1/2–2/3 of the surface estimated mean annual GPP for the alpine meadow, but only about 1/5–1/3 for the cropland. This underestimation was partly attributed to low estimates of leaf area index by a MODIS product (MOD15) because it is used to calculate absorbed photosynthetically active radiation in the MOD17 algorithm. The main reason is that the parameter maximum light use efficiency (ε_max) in the MOD17 algorithm was underestimated for the two biomes, especially for the cropland. Contrasted to the default, ε_max was optimized using surface measurements. The optimized ε_max for winter wheat, maize and meadow was 1.18, 1.81 and 0.73 g C/MJ, respectively. By using the surface measurements and optimized ε_max , the MOD17 algorithm significantly improved the accuracy of GPP estimates. The optimum MOD17 algorithm explained about 82%, 68%, and 79% of GPP variance for winter wheat, maize, and meadow, respectively. These results suggest that it is necessary to adjust the MOD17 parameters for the estimation of cropland and meadow GPP, particularly over cropland.