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High water tables (WT) stabilise peatland carbon (C) through regulation of biogeochemical processes. The impact of peatland WT on ecosystem function, including C exchange, alters over time, and the factors that cause some peatlands to display resilience and others to undergo degradation are poorly understood. Here we use CO 2 flux measurements, measured by eddy covariance, to compare ecosystem function between two raised bogs; one drainage-affected, with a deep and fluctuating water table and the other near-natural, with a shallow and stable water table. The drainage-affected bog was found to be a moderate sink for CO 2 (69 g C m -2  yr -1 ), which was 134 g C m -2  yr -1 less than the near-natural bog (203 g C m -2  yr -1 ). Greater ecosystem productivity has allowed the drainage-impacted bog to act as a CO 2 sink despite higher ecosystem respiration; most likely due to an increase in photosynthetic capacity caused by expansion of ericaceous shrub cover. The tolerance of the vegetation community, particularly the main peat former Empodisma robustum (Restionaceae), to low and fluctuating WT appears to have been key in allowing the site to remain a sink. Despite the current resilience of the ecosystem CO 2 sink, we found gross primary production to be limited under both high and low water tables, even in a year with typical rainfall. This is best explained by the limited physiological ability of ericaceous shrubs to tolerate a fluctuating WT. As such we hypothesise that if the WT continues to drop and become even more unstable, then without further vegetation change, a reduction in gross primary production is likely which may in turn cause the site to become a source for CO 2 ., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect of nitrogen content on V max and J max , leading to considerable bias in the estimation of gross primary productivity (GPP). Here, we developed a model driven by the leaf area index, climate, and atmospheric CO 2 concentration to estimate global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 128 flux tower sites, which yielded an accuracy of 72.3%. We found that the global GPP ranged from 116.4 PgC year − 1 to 133.94 PgC year − 1 from 2000 to 2022, with an average of 125.93 PgC year − 1 . We also found that the global GPP showed an increasing trend of 0.548 PgC year − 1 during the study period. Further analyses using the structure equation model showed that atmospheric CO 2 concentration and air temperature were the main drivers of the global GPP changes, total associations of 0.853 and 0.75, respectively, while precipitation represented a minor but negative contribution to global GPP. [ABSTRACT FROM AUTHOR]

The terrestrial ecosystem stores a huge amount of carbon in biomass and acts as a major carbon sink. Gross primary productivity (GPP) measures the carbon assimilation rate in terrestrial ecosystems. Accurate quantification and spatiotemporal analysis of GPP have become the essential indicators of various management, policy developments, and restoration activities in recent decades with the installation of new ground stations, development of robust models, and use of Earth Observation satellite data. The model-estimated and satellite data-derived GPP values greatly differ from ground observations due to model structure and approach, input driving data, coefficients and parameters, and various assumptions. Consequently, considerable ambiguity prevails among datasets and their benchmarking. Moreover, the productivity of ecosystems is regulated by physiological traits coupled with the local environmental conditions. This review provides an overview of the environmental and anthropogenic variables that regulate productivity and pose challenges in GPP estimation and evaluation of the available GPP products. It also evaluates the various available GPP datasets/ products and estimation methods/ models and compares the ecosystem productivity in broad natural and human-modified ecosystems in South Asia. Moreover, this study includes a case study on evaluating five globally available GPP products with variable spatiotemporal resolutions, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Vegetation Photosynthesis Model (VPM), the Global Land Surface Satellite (GLASS), Global OCO-2-based SIF product (GOSIF), and the Penman-Monteith-Leuning (PML) in three major land cover type of South Asia (forest, cropland, and grassland) comparing with eddy covariance (EC) flux tower data. Results showed a better performance of GOSIF and GLASS data than other GPP products. The study aims to provide an overview of the prevailing environmental conditions and carbon sequestration in different ecosystems and assist in developing sustainable landscape management strategies to reduce carbon emissions and mitigate climate change impact. Article highlights: • A critical review of the methods employed for GPP estimation and their limitations. • Impact of various management practices, extreme events, and climate change on GPP. • Comparison among available GPP datasets. [ABSTRACT FROM AUTHOR]

Maize gross primary productivity (GPP) contributes the most to the global cropland GPP, making it crucial to accurately estimate maize GPP for the global carbon cycle. Previous research validated machine learning (ML) methods using remote sensing and meteorological data to estimate plant GPP, yet they disregard vegetation physiological dynamics driven by phenology. Leaf nitrogen content per unit leaf area (i.e., specific leaf nitrogen (SLN)) greatly affects photosynthesis. Its maximum allowable value correlates with a phenological factor conceptualized as normalized maize phenology (NMP). This study aims to validate SLN and NMP for maize GPP estimation using four ML methods (random forest (RF), support vector machine (SVM), convolutional neutral network (CNN), and extreme learning machine (ELM)). Inputs consist of vegetation index (NDVI), air temperature, solar radiation (SSR), NMP, and SLN. Data from four American maize flux sites (NE1, NE2, and NE3 sites in Nebraska and RO1 site in Minnesota) were gathered. Using data from three NE sites to validate the effect of SLN and MMP shows that the accuracy of four ML methods notably increased after adding SLN and MMP. Among these methods, RF and SVM achieved the best performance of Nash–Sutcliffe efficiency coefficient (NSE) = 0.9703 and 0.9706, root mean square error (RMSE) = 1.5596 and 1.5509 gC·m−2·d−1, and coefficient of variance (CV) = 0.1508 and 0.1470, respectively. When evaluating the best ML models from three NE sites at the RO1 site, only RF and CNN could effectively incorporate the impact of SLN and NMP. But, in terms of unbiased estimation results, the four ML models were comprehensively enhanced by adding SLN and NMP. Due to their fixed relationship, introducing SLN or NMP alone might be more effective than introducing both simultaneously, considering the data redundancy for methods like CNN and ELM. This study supports the integration of phenology and leaf-level photosynthetic factors in plant GPP estimation via ML methods and provides a reference for similar research. [ABSTRACT FROM AUTHOR]

Solar‐induced chlorophyll fluorescence (SIF) shows enormous promise as a proxy for photosynthesis and as a tool for modeling variability in gross primary productivity and net biosphere exchange (NBE). In this study, we explore the skill of SIF and other vegetation indicators in predicting variability in global atmospheric CO2 observations, and thus global variability in NBE. We do so using a 4‐year record of CO2 observations from NASA's Orbiting Carbon Observatory 2 satellite and using a geostatistical inverse model. We find that existing SIF products closely correlate with space‐time variability in atmospheric CO2 observations, particularly in the extratropics. In the extratropics, all SIF products exhibit greater skill in explaining variability in atmospheric CO2 observations compared to an ensemble of process‐based CO2 flux models and other vegetation indicators. With that said, other vegetation indicators, when multiplied by photosynthetically active radiation, yield similar results as SIF and may therefore be an effective structural SIF proxy at regional to global spatial scales. Furthermore, we find that using SIF as a predictor variable in the geostatistical inverse model shifts the seasonal cycle of estimated NBE and yields an earlier end to the growing season relative to other vegetation indicators. These results highlight how SIF can help constrain global‐scale variability in NBE. Plain Language Summary: When plants undergo photosynthesis, the chlorophyll in the plant emits small amounts of radiation, known as solar‐induced fluorescence (SIF). SIF can therefore serve as a predictor of photosynthesis in plants, a key component of net biosphere carbon exchange (NBE). Several satellite missions now provide global SIF observations, including NASA's Orbiting Carbon Observatory 2 (OCO‐2) satellite, which also collects observations of atmospheric CO2. In this study, we explore the relationships between existing, global SIF products and NBE, as estimated using CO2 observations from OCO‐2. Overall, we find that SIF is a more skilled predictor of NBE across most regions of the globe compared to other proxies of photosynthesis, including measures of plant greenness, and compared to state‐of‐the‐art, process‐based models of the global carbon cycle. In addition, SIF indicates an earlier seasonal decline in CO2 uptake during the Northern Hemisphere fall compared to NBE estimated without SIF. Overall, our results highlight several advantages and challenges of using SIF products to help predict global patterns in photosynthesis and NBE. Key Points: Solar‐induced fluorescence (SIF) products adeptly explain global variability in atmospheric CO2 observations, and thus in net biosphere exchange (NBE)Inverse estimates of NBE that are informed by SIF exhibit a different seasonality in the extratropics compared to other vegetation productsOther vegetation products, when multiplied by photosynthetically active radiation, may also be an effective structural SIF proxy [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems play a central role in the global carbon cycle and climate mitigation due to their offering of a large carbon sink. More than one‐fifth of the geographical area of India, one of the largest nations on the Earth, is forested, which is highly diverse in vegetation and climate types, offers huge potential for carbon sequestration, but remains vulnerable to climate change. Hence, it is imperative to know the future changes in the terrestrial carbon budget over this region. Gross primary productivity (GPP) represents the carbon uptake by terrestrial ecosystems. The multimodel ensembles of GPP simulated by the sixth phase of the Coupled Model Intercomparison Project (CMIP6) provide a useful means in this regard. In this work, we study the strength and variability of GPP over India in the near‐past and future using these simulations. In future, all the models show an increasing trend in GPP, however, with widely varying trends. The preferred month of carbon uptake differs among the models. A comparison with a satellite biophysical record shows the models underestimated the GPP during the near‐past over India. The carbon uptake in the Eastern Himalaya dominates the Western Himalaya and central Indian regions. Specifically, till 2100, the growth rate of GPP varies from 4.9 to 16.69 gC m−2 y−2, from 2.47 to 18.91 gC m−2 y−2, and from 0.32 to 21.95 gC m−2 y−2 over these three regions, respectively. Plain Language Summary: Climate change is an imminent problem driven by greenhouse gases, with adverse impacts on the Earth system. Carbon dioxide is a major greenhouse gas. Plants offer a natural strategy to mitigate climate change by their photosynthetic carbon uptake. For climate change impact assessment, projections of climate models are widely used, developed from our understanding of the Earth system, as measured by the observations. India is a large and populous country that remains highly vulnerable to climate change; it also plays a key role in global climate change and its mitigation. However, to devise a suitable mitigation strategy the future carbon sequestration by the natural ecosystems in this region needs to be quantified. The World Climate Research Program developed the Coupled Model Intercomparison Project, a multi‐model ensemble study providing future projections of climate variables under different climate change scenarios. In this work, we use these ensembles to study the future photosynthetic carbon uptake over India and its three key forested regions. We find the photosynthetic carbon uptake to increase manifold over India, with significant inter‐model uncertainties. We also find the Eastern Himalaya to have stronger biospheric carbon removal from the atmosphere than the Western Himalaya and Central Indian regions. Key Points: Photosynthetic carbon uptake increases over India in future with the annual growth rate varying from 3 to 15 gC m−2 y−2The photosynthetic carbon uptake is larger in Eastern Himalaya compared to Western Himalaya and Central IndiaThere is a large discord on the strength, trend and variability of Gross primary productivity over India among CMIP6 models and Moderate Resolution Imaging Spectroradiometer [ABSTRACT FROM AUTHOR]

Climate change leads to increasing temperature and more extreme hot and drought events. Ecosystem capability to cope with climate warming depends on vegetation's adjusting pace with temperature change. How environmental stresses impair such a vegetation pace has not been carefully investigated. Here we show that dryness substantially dampens vegetation pace in warm regions to adjust the optimal temperature of gross primary production (GPP) (ToptGPP) in response to change in temperature over space and time. ToptGPP spatially converges to an increase of 1.01°C (95% CI: 0.97, 1.05) per 1°C increase in the yearly maximum temperature (Tmax) across humid or cold sites worldwide (37oS–79oN) but only 0.59°C (95% CI: 0.46, 0.74) per 1°C increase in Tmax across dry and warm sites. ToptGPP temporally changes by 0.81°C (95% CI: 0.75, 0.87) per 1°C interannual variation in Tmax at humid or cold sites and 0.42°C (95% CI: 0.17, 0.66) at dry and warm sites. Regardless of the water limitation, the maximum GPP (GPPmax) similarly increases by 0.23 g C m−2 day−1 per 1°C increase in ToptGPP in either humid or dry areas. Our results indicate that the future climate warming likely stimulates vegetation productivity more substantially in humid than water‐limited regions. [ABSTRACT FROM AUTHOR]

Wildfire modifies the short- and long-term exchange of carbon between terrestrial ecosystems and the atmosphere, with impacts on ecosystem services such as carbon uptake. Dry western US forests historically experienced low-intensity, frequent fires, with patches across the landscape occupying different points in the fire-recovery trajectory. Contemporary perturbations, such as recent severe fires in California, could shift the historic stand-age distribution and impact the legacy of carbon uptake on the landscape. Here, we combine flux measurements of gross primary production (GPP) and chronosequence analysis using satellite remote sensing to investigate how the last century of fires in California impacted the dynamics of ecosystem carbon uptake on the fire-affected landscape. A GPP recovery trajectory curve of more than five thousand fires in forest ecosystems since 1919 indicated that fire reduced GPP by 157:4 ± 7:3 g C m-2 y-1(mean ± SE, n = 1926) in the first year after fire, with average recovery to prefire conditions after ~12 y. The largest fires in forested ecosystems reduced GPP by 393:8 ± 15:7 g C m-2 y-1 (n = 401) and took more than two decades to recover. Recent increases in fire severity and recovery time have led to nearly 9:9 ± 3:5 MMT CO2 (3-y rolling mean) in cumulative forgone carbon uptake due to the legacy of fires on the landscape, complicating the challenge of maintaining California's natural and working lands as a net carbon sink. Understanding these changes is paramount to weighing the costs and benefits associated with fuels management and ecosystem management for climate change mitigation. [ABSTRACT FROM AUTHOR]

Vegetation gross primary production (GPP) is the largest terrestrial carbon flux and plays an important role in regulating the carbon sink. Current terrestrial ecosystem models (TEMs) are indispensable tools for evaluating and predicting GPP. However, to which degree the TEMs can capture the interannual variability (IAV) of GPP remains unclear. With large data sets of remote sensing, in situ observations, and predictions of TEMs at a global scale, this study found that the current TEMs substantially underestimate the GPP IAV in comparison to observations at global flux towers. Our results also showed the larger underestimations of IAV in GPP at nonforest ecosystem types than forest types, especially in arid and semiarid grassland and shrubland. One cause of the underestimation is that the IAV in GPP predicted by models is strongly dependent on canopy structure, that is, leaf area index (LAI), and the models underestimate the changes of canopy physiology responding to climate change. On the other hand, the simulated interannual variations of LAI are much less than the observed. Our results highlight the importance of improving TEMs by precisely characterizing the contribution of canopy physiological changes on the IAV in GPP and of clarifying the reason for the underestimated IAV in LAI. With these efforts, it may be possible to accurately predict the IAV in GPP and the stability of the global carbon sink in the context of global climate change. Key Points: Current terrestrial ecosystem models (TEMs) substantially underestimate the interannual variability (IAV) of gross primary production (GPP) in comparison to observations at global flux sitesThe IAV of GPP in TEMs is strongly depended on leaf area index (LAI), which is one of the causes for the underestimation of IAV in GPP and the simulated IAV in LAI from TEMs is much less than the observationPrecisely characterizing the contribution of vegetation physiological changes may improve the performance of predicting IAV in GPP from TEMs [ABSTRACT FROM AUTHOR]

Vegetation green leaf phenology directly impacts gross primary productivity (GPP) of terrestrial ecosystems. Satellite observations of land surface phenology (LSP) provide an important means to monitor the key timing of vegetation green leaf development. However, differences between satellite‐derived LSP proxies and in situ measurements of GPP make it difficult to quantify the impact of climate‐induced changes in green leaf phenology on annual GPP. Here, we used 1,110 site‐years of GPP measurements from eddy‐covariance towers in association with time series of satellite LSP observations from 2000 to 2014 to show that while satellite LSP explains a large proportion of variation in annual GPP, changes in green‐leaf‐based growing season length (GSL, leaf development period from spring to autumn) had less impact on annual GPP by ∼30% than GSL changes in GPP‐based photosynthetic duration. Further, maximum leaf greenness explained substantially more variance in annual GPP than green leaf GSL, highlighting the role of future vegetation greening trends on large‐scale carbon budgets. Site‐level variability contributes a substantial proportion of annual GPP variance in the model based on LSP metrics, suggesting the importance of local environmental factors altering regional GPP. We conclude that satellite LSP‐based inferences regarding large‐scale dynamics in GPP need to consider changes in both green leaf GSL and maximum greenness. Plain Language Summary: The ongoing global trends of vegetation green leaf phenology and physiology caused by climate change have drawn great attention in the climate science community as they significantly impact carbon cycle dynamics. By analyzing field‐measured vegetation productivity data from an extensive global eddy covariance flux data set and the corresponding satellite remote sensing observations, we found that changes in green leaf growing season length (GSL) had less impact on annual vegetation productivity by ∼30% than that in photosynthetic duration. Further, maximum leaf greenness affects vegetation productivity more than observed green leaf GSL, highlighting the role of future vegetation greening trends on large‐scale carbon budgets. We conclude that inferences regarding large‐scale dynamics in vegetation productivity based on satellite observations need to consider changes in both green leaf GSL and maximum greenness. Key Points: Changes in green leaf growing season length (GSL) had less impact on vegetation productivity by ∼30% than changes in photosynthetic durationMaximum leaf greenness affects vegetation productivity more than green leaf GSLStudies of large‐scale vegetation productivity need to consider changes in both green leaf GSL and maximum greenness [ABSTRACT FROM AUTHOR]

This study analyzed the sensitivities of carbon cycle to surface air temperature using the CO _2 flux data collected from June to September for six years (2014–2019) over a moist tundra site in Council, Alaska. The tundra ecosystem was a strong sink of carbon in June and July, a weak sink in August with rapidly decreasing photosynthesis, and a carbon source in September. The ecosystem respiration (Re) and gross primary production (GPP) were obtained from the net ecosystem exchange (NEE) of eddy-covariance system. Both the Re and GPP increased with temperature, enhancing carbon emission and uptake during observation period. Notably, Re showed higher sensitivity to temperature than GPP did. This result means that as global warming continues, the increase in carbon release is greater than the increase in carbon uptake. In other words, the tundra ecosystem is expected to become a weaker carbon sink in June and July and a stronger source of carbon in September. Possible mechanism of different temperature sensitivities of Re and GPP as well as temporal variations of temperature sensitivities are suggested. Present results highlight the importance of understanding the temperature sensitivities of Re and GPP in various tundra ecosystems to accurately understand changes in the carbon cycle in the Arctic region.

The terrestrial vegetation GPP of Qinghai Province is an important variable that characterizes the carbon cycling pattern. However, there is still a lack of a high-resolution GPP dataset for Qinghai Province. To address this issue, we processed all Landsat images of Qinghai from 1987 to 2021 using the GEE, and we combined multi-source auxiliary data to estimate GPP using the revised EC-LUE model. We compared our GPP dataset with flux observations to verify its accuracy. The results showed that our GPP dataset had a high correlation with the flux tower observations, with correlation coefficients of 0.984 at CF-AM site and 0.976 at CN-Ha2 site, respectively, and each site had an RMSE of $11.960\, \rm g C\cdot m^{-2}\cdot 16d^{-1}$ 11.960 g C ⋅ m − 2 ⋅ 16 d − 1 and 12.986 $\rm g C\cdot m^{-2}\cdot 16d^{-1}$ g C ⋅ m − 2 ⋅ 16 d − 1 , respectively. There are different deviations between our GPP dataset and the mainstream GPP datasets in various vegetation types, with the average correlation coefficient ranging from 0.431 to 0.943. By comparing with the flux observations and the related analysis, we demonstrated that our GPP dataset features better accuracy, higher spatial resolution, and more temporal coverage than mainstream GPP datasets. This study offers the first long-term high-resolution GPP dataset for Qinghai Province, and we believe that this dataset has important implications for ecological management and climate research. [ABSTRACT FROM AUTHOR]

Gross primary production (GPP) is an important indicator that measures the carbon uptake by vegetation through photosynthesis. How the latest climate models project GPP is critical for climate change evaluation and ecosystem prediction. This study compares the historical runs of seven climate models joining CMIP6 with an observation‐based dataset from 1980 to 2013 in China. It is found that BCC‐CSM2‐MR and MPI‐ESM1‐2‐HR from Beijing Climate Center and the Max Planck Institute give the best estimation of climatological GPP at both regional and national scales. MPI‐ESM1‐2‐HR performs much better than others in characterizing the spatial structure in regions other than the temperate continental, while CMCC‐CM2‐SR5 from Italy performs the best in the temperate monsoonal. No climate model can capture well the GPP interannual variation even over one climate zone. BCC‐CSM2‐MR is a not‐too‐bad choice as it provides the most positively and significantly correlated GPP grids with observations. Further analyses reveal that BCC‐CSM2‐MR and CMCC‐CM2‐SR5 can well capture ecosystem response to climate over regions except for the Tibetan Plateau. With the response parameters and the observational climate, the two climate models can simply rebuild the GPP variabilities as the observational. Over the Tibetan Plateau, all climate models produce spuriously too large precipitation, which turns precipitation from the most confining into no longer significantly influential to the ecosystem. It highlights the urgency to improve the modelling of the Plateau climate and the corresponding ecosystem‐climate feedbacks. [ABSTRACT FROM AUTHOR]

Highlights: Two drought indices (SPEI and VPD) were used to characterize the degree of dryness/wetness. The water deficit represented by two drought indices was mostly negatively correlated with vegetation GPP, especially in summer and autumn. The negative impact of water deficit/drought as measured by SPEI on vegetation GPP was more severe than that revealed by VPD. During drought, both SPEI and VPD showed that drought had a negative impact on vegetation GPP in North China, Southwest China, and the Qinghai–Tibet Plateau. Climate change has exacerbated the frequency and severity of droughts worldwide. Evaluating the response of gross primary productivity (GPP) to drought is thus beneficial to improving our understanding of the impact of drought on the carbon cycle balance. Although many studies have investigated the relationship between vegetation productivity and dry/wet conditions, the capability of different drought indices of assessing the influence of water deficit is not well understood. Moreover, few studies consider the effects of drought on vegetation with a focus on periods of drought. Here, we investigated the spatial-temporal patterns of GPP, the standardized precipitation evapotranspiration index (SPEI), and the vapor pressure deficit (VPD) in China from 2001 to 2020 and examined the relationship between GPP and water deficit/drought for different vegetation types. The results revealed that SPEI and GPP were positively correlated over approximately 70.7% of the total area, and VPD was negatively correlated with GPP over about 66.2% of the domain. Furthermore, vegetation productivity was more negatively affected by water deficit in summer and autumn. During periods of drought, the greatest negative impact was on deciduous forests and croplands, and woody savannas were the least impacted. This research provides a scientific reference for developing mitigation and adaptation measures to lessen the impact of drought disasters under a changing climate. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is a key and a limiting nutrient in ecosystems and plays an important role in many physiological and biochemical processes, affecting both terrestrial ecosystem productivity and soil carbon storage. However, only a few global land surface models have incorporated P cycle and used to investigate the interactions of C-N-P and its limitation on terrestrial ecosystems. The overall objective of this study was to integrate the P cycle and its interaction with carbon (C) and nitrogen (N) into new processes model of TRIPLEX-CNP. In this study, key processes of the P cycle, including P pool sizes and fluxes in plant, litter, and soil were integrated into a new model framework, TRIPLEX-CNP. We also added dynamic P:C ratios for different ecosystems. Based on sensitivity analysis results, we identified the phosphorus resorption coefficient of leaf (rpleaf) as the most influential parameter to gross primary productivity (GPP) and biomass, and determined optimal coefficients for different plant functional types (PFTs). TRIPLEX-CNP was calibrated with 49 sites and validated against 116 sites across eight biomes globally. The results suggested that TRIPLEX-CNP performed well on simulating the global GPP and soil organic carbon (SOC) with respective R2 values of 0.85 and 0.78 (both p < 0.01) between simulated and observed values. The R2 of simulation and observation of total biomass are 0.67 (p < 0.01) by TRIPLEX-CNP. The overall model performance had been improved in global GPP, total biomass and SOC after adding the P cycle comparing with the earlier version. Our work represents the promising step toward new coupled ecosystem process models for improving the quantifications of land carbon cycle and reducing uncertainty. [ABSTRACT FROM AUTHOR]

Droughts significantly impact land carbon uptake by reducing the gross primary productivity (GPP). Projections suggest an increase in severe and extreme droughts in the twenty‐first century. Despite the importance of droughts on carbon uptakes, it remains unclear about the GPP responses under different drought severity and its changes under current climate changes. Using four state‐of‐the‐science GPP products, we showed that moderate (−1 ≤ SPEI < −1.5), severe (−1.5 ≤ SPEI < −2), and extreme (SPEI < −2) droughts caused on average 5%, 7%, and 9% GPP reductions, respectively, across the globe during the recent decades. Although the mean reductions varied among GPP products, we observed similar spatial patterns. The reductions increased significantly along the severity gradient, while the sharpest increases were observed in semi‐arid and grassland ecosystems. On a biome level, we found that the reductions increased nonlinearly with increasing drought severities for almost all vegetation types, but the trends and slopes varied significantly. In addition, we found that the mean drought‐induced GPP reductions had increased in dry ecosystems but decreased in humid ecosystems from 2001–2008 to 2009–2016. The result highlights the potential increase of drought‐induced land carbon sink loss responding to more extreme climates and offers new perspectives to improve the prediction of global carbon fluxes in a changing climate. Plain Language Summary: Drought is a natural disaster characterized by a long period of water shortage. In ecosystems, the gross primary productivity (GPP) is the amount of energy or carbon biomass produced by plants used for all living creatures' activities. Drought causes substantial GPP loss, depending on its severity. In this study, we calculate the loss of GPP under different drought severities. The result shows that rising drought severities increase GPP loss, while the largest increases are found in dry ecosystems and grasslands. In addition, we find that GPP reductions in droughts have increased in dry areas and decreased in humid areas from 2001–2008 to 2009–2016. Key Points: The drought‐induced gross primary productivity (GPP) reductions across global terrestrial ecosystems were evaluated along severity gradients during 1987–2018 using four GPP productsThe drought‐induced GPP reductions increased significantly along the severity gradient, while the sharpest increases were observed in semi‐arid and grassland ecosystemsDuring 2001–2016, the size of drought‐induced GPP reductions increased in arid ecosystems but decreased in humid ecosystems [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SIF) is a promising optical remote sensing signal which is directly linked to photosynthesis, allowing for the monitoring of gross primary production (GPP). Although empirical relationships between these variables have demonstrated the potential of SIF for site-specific GPP estimations, a better physiological understanding of the link between SIF and GPP would pave the way for a more robust model of photosynthesis. The mechanistic light response (MLR) model is a novel approach which determines GPP from SIF by using only a small set of equations and parameters with physiological significance. This study combines the MLR model with the unified stomatal optimality (USO) model to estimate both GPP and transpiration (Tr) at the ecosystem scale. Top-of-canopy SIF measurements were collected over a winter crop with a field spectrometer installed next to an eddy covariance station. MLR-USO model parameters were determined from gas exchange and active chlorophyll fluorescence measurements at the leaf level and interpolated on a half-hourly basis using solar irradiance and canopy temperature. GPP and Tr estimated by the MLR-USO model and eddy covariance measurements were highly correlated at half-hourly and daily timescales (R2 ≥ 0.91, rRMSE ≤ 13.7%) under a wide range of environmental conditions, including soil water stress. These results highlight the potential of the MLR-USO model as an important step towards an improvement of our understanding of the coupling between the water and carbon cycles at the ecosystem scale and beyond. • GPP and Tr are estimated from SIF using a mechanistic light-response approach. • Model parameters are determined from PAR and canopy temperature. • High correlation between model estimates and eddy-covariance is observed. • The model is robust for a wide variety of meteorological conditions. [ABSTRACT FROM AUTHOR]

In many ecosystems, nitrogen is the most limiting nutrient for plant growth and productivity. However, current Earth System Models (ESMs) do not mechanistically represent functional nitrogen allocation for photosynthesis or the linkage between nitrogen uptake and root traits. The current version of CLM (4.5) links nitrogen availability and plant productivity via (1) an instantaneous downregulation of potential photosynthesis rates based on soil mineral nitrogen availability, and (2) apportionment of soil nitrogen between plants and competing nitrogen consumers assumed to be proportional to their relative N demands. However, plants do not photosynthesize at potential rates and then downregulate; instead photosynthesis rates are governed by nitrogen that has been allocated to the physiological processes underpinning photosynthesis. Furthermore, the role of plant roots in nutrient acquisition has also been largely ignored in ESMs. We therefore present a new plant nitrogen model for CLM4.5 with (1) improved representations of linkages between leaf nitrogen and plant productivity based on observed relationships in a global plant trait database and (2) plant nitrogen uptake based on root-scale Michaelis-Menten uptake kinetics. Our model improvements led to a global bias reduction in GPP, LAI, and biomass of 70%, 11%, and 49%, respectively. Furthermore, water use efficiency predictions were improved conceptually, qualitatively, and in magnitude. The new model's GPP responses to nitrogen deposition, CO2 fertilization, and climate also differed from the baseline model. The mechanistic representation of leaf-level nitrogen allocation and a theoretically consistent treatment of competition with belowground consumers led to overall improvements in global carbon cycling predictions.

The Arctic–Boreal Region (ABR) has a large impact on global vegetation–atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO2. The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots‐on‐the‐ground science. [ABSTRACT FROM AUTHOR]

Gross Primary Productivity (GPP) is the core component of the terrestrial and global carbon cycle and Earth's climate research. In this study, GPP estimation was performed with the Boreal Ecosystem Productivity Simulator (BEPS) model to check its performance for hemi-boreal forests on the example of the Soontaga area in Estonia. The model was run by using a combination of remote sensing (leaf area index (LAI), clumping index) and meteorological data inputs (air temperature, global radiation, air humidity, precipitation and wind speed). The results were validated against GPP derived from the available flux tower measurements. The spatial representativeness of the site was evaluated using multiple spatial thresholds (500 m–2 km), as well. We found that the BEPS model can track the GPP changes with the season and inter-annual variation very well in a coniferous hemi-boreal forest, given that good quality input data are provided. [ABSTRACT FROM AUTHOR]

Gross primary production (GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems. A set of validated monthly GPP data from 1957 to 2010 in 0.5° × 0.5° grids of China was weighted from the Multi-scale Terrestrial Model Intercomparison Project using Bayesian model averaging (BMA). The spatial anomalies of detrended BMA GPP during the growing seasons of typical El Niño years indicated that GPP response to El Niño varies with Pacific Decadal Oscillation (PDO) phases: when the PDO was in the cool phase, it was likely that GPP was greater in northern China (32°–38°N, 111°–122°E) and less in the Yangtze River valley (28°–32°N, 111°–122°E); in contrast, when PDO was in the warm phase, the GPP anomalies were usually reversed in these two regions. The consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this phenomenon. The previously published findings on how El Niño during different phases of PDO affecting rainfall in eastern China make the statistical relationship between GPP and El Niño in this study theoretically credible. This paper not only introduces an effective way to use BMA in grids that have mixed plant function types, but also makes it possible to evaluate the carbon cycle in eastern China based on the prediction of El Niño and PDO. [ABSTRACT FROM AUTHOR]

Solar radiation is a fundamental driver of ecosystem productivity, but widespread estimates of light available for primary producers in rivers are lacking. We developed a model to predict light available for river primary producers and used it to estimate river primary production across the contiguous United States (CONUS). Successively accounting for riparian and water column processes improved predictions of primary production as a function of light. We calculated the ratio of river width to riparian tree height and used this metric to predict whether riparian zones or water column processes most limit productivity for over 2 million reaches. Water column processes limited productivity for 50% of the nation's river length and 80% of its surface area, with variations across ecoregions related to riparian forest cover. Our findings facilitate large‐scale predictions of stream and river ecosystem productivity, as well as understanding the processes controlling productivity across networks. Plain Language Summary: The conversion of solar energy to organic matter through photosynthesis is an important component of global carbon cycling. While there are global estimates of solar radiation at the Earth's surface, primary producers in rivers often only receive a small percentage of incoming light due to reductions from riparian shading, water depth, and water clarity. We developed a model to predict light available for river primary producers so that we could make estimates of productivity based solely on light. We predicted light and primary productivity for 173 rivers across the United States and also examined the degree to which riparian zones or water column processes limited productivity. Our model allowed us to make first approximations of productivity across many sites with varying conditions based solely on light availability. We also developed a metric to predict whether river productivity was most limited by riparian zones or water column processes and calculated this metric for over 2 million rivers in the United States. Our results suggested that riparian zones and water column process limit an equal percentage of river length nationally; however, these results varied across ecoregions and appear to be driven by changes in forest cover. Key Points: Riparian zones limit light availability, and thus primary productivity, until channel width is 1.7 times the height of riparian treesLight attenuation from water depth and clarity limit productivity for 50% of river length and 80% of river surface areaThe percentage of river length and surface area limited by riparian zones and water column processes varies across ecoregions [ABSTRACT FROM AUTHOR]

Drought largely affects terrestrial gross primary productivity (GPP) by its duration (i.e., drought time‐scales). However, the large‐scale impact of drought time‐scales on GPP is still not well understood, partly due to limited understanding of ecological processes and difficulties in quantifying drought time‐scales. Here, we examined the responses of GPP to drought in China during the last four decades, using state‐of‐the‐art GPP data estimates and a multiscalar drought metric. Most of the GPP data suggested that the time‐scales at which drought has the largest influence on annual GPP (dominant drought time‐scales) were well related to climate region and vegetation type. The GPP responded to drought more rapidly in arid regions than that in semiarid and subhumid regions, with a mean dominant drought time‐scale of 15 and 20 months, respectively; as plants in arid regions can rapidly adapt to water shortage through physiological strategies, while, in semiarid and subhumid regions plants usually can withstand a degree of water deficits. In a humid region, GPP did not rapidly respond to drought, probably because the high soil water availability buffered the effect of drought. In addition, most of the GPP estimates suggested that grasslands and croplands tended to respond to drought at shorter time‐scales than that of forests, shrublands, and sparse vegetation. The study highlights the importance of improving the effects of water availability on vegetation activity and further consideration of vegetation adaptability to drought in both remote sensing GPP algorithms and parameterizations of current terrestrial ecosystem models. Plain Language Summary: Gross primary productivity (GPP) is the entire photosynthetic production of organic compounds in an ecosystem. It plays an important role in carbon cycles, and is the basis for food, fiber, and wood production. Drought widespreadly and negatively affects GPP by its duration (i.e., drought time‐scales). Thus, it is critical to understand the dominant time‐scale at which drought has the largest influence on GPP. We investigated dominant time‐scales of GPP response to drought in China using six GPP estimates and a multiscalar drought metric. We found that the dominant time‐scales were well related to climate region and vegetation type. The GPP responded to drought more quickly in arid regions than that in semiarid and subhumid regions. This might be due to plants in arid regions can rapidly adapt them to water shortage, while, in semiarid and subhumid regions, plants can withstand a degree of water deficits. In a humid region, GPP did not rapidly respond to drought, probably because the high soil water availability buffered the effect of drought. The grasslands and croplands responded to drought at shorter time‐scales than forests, shrublands, and sparse vegetation. The study could benefit the management and mitigation of drought effects on GPP. Key Points: The magnitudes of gross primary productivity (GPP) response to drought highly vary among different GPP estimates and across time‐scalesGPP responds to drought at shorter time‐scales in arid regions than that in other climate regionsGPP of forests responds to drought at longer time‐scales than that of shrublands, grasslands, and croplands [ABSTRACT FROM AUTHOR]

Phosphorus (P) is a key and a limiting nutrient in ecosystems and plays an important role in many physiological and biochemical processes, affecting both terrestrial ecosystem productivity and soil carbon storage. However, only a few global land surface models have incorporated P cycle and used to investigate the interactions of C-N-P and its limitation on terrestrial ecosystems. The overall objective of this study was to integrate the P cycle and its interaction with carbon (C) and nitrogen (N) into new processes model of TRIPLEX-CNP. In this study, key processes of the P cycle, including P pool sizes and fluxes in plant, litter, and soil were integrated into a new model framework, TRIPLEX-CNP. We also added dynamic P:C ratios for different ecosystems. Based on sensitivity analysis results, we identified the phosphorus resorption coefficient of leaf (rpleaf) as the most influential parameter to gross primary productivity (GPP) and biomass, and determined optimal coefficients for different plant functional types (PFTs). TRIPLEX-CNP was calibrated with 49 sites and validated against 116 sites across eight biomes globally. The results suggested that TRIPLEX-CNP performed well on simulating the global GPP and soil organic carbon (SOC) with respective R2 values of 0.85 and 0.78 (both p < 0.01) between simulated and observed values. The R2 of simulation and observation of total biomass are 0.67 (p < 0.01) by TRIPLEX-CNP. The overall model performance had been improved in global GPP, total biomass and SOC after adding the P cycle comparing with the earlier version. Our work represents the promising step toward new coupled ecosystem process models for improving the quantifications of land carbon cycle and reducing uncertainty.

Accurate partitioning of net ecosystem exchange (NEE) of CO2 to gross primary production (GPP) and ecosystem respiration (Reco) is crucial for understanding carbon cycle dynamics under changing climate. However, it remains as a long‐standing problem in global ecology due to lack of independent constraining information for the two offsetting component fluxes. solar‐induced chlorophyll fluorescence (SIF), a mechanistic proxy for photosynthesis, holds great promise to improve NEE partitioning by constraining GPP. We developed a parsimonious SIF‐based approach for NEE partitioning and examined its performance using synthetic simulations and field measurements. This approach outperforms conventional approaches in reproducing simulated GPP and Reco, especially under high vapor pressure deficit. For field measurements, it results in lower daytime GPP and Reco than conventional approaches. This study made the first attempt to demonstrate SIF's potential for improving NEE partitioning accuracy and sets the stage for future efforts to examine its robustness and scalability under real‐world environmental conditions. Plain Language Summary: Knowing the exchanges of CO2 fluxes between terrestrial ecosystems (both natural and agricultural systems) and atmosphere is crucial for understanding and predicting the strength of land carbon sink under climate change. To date, we are only able to directly measure the net terrestrial CO2 fluxes at the ecosystem level and beyond, but not its constituent fluxes from photosynthesis and respiration. In this study, we developed an approach to partition the net CO2 flux to its two components with opposite signs: CO2 flux absorbed by plants (via photosynthesis, denoted as GPP), and CO2 flux emitted to the atmosphere by the plant and the soil (via autotrophic and heterotrophic respiration, denoted as ecosystem respiration), using remote sensing of Solar‐Induced chlorophyll fluorescence (SIF), a mechanistic proxy for photosynthesis. Our results show that using SIF to anchor GPP has the potential to enhance the accuracy of partitioned component fluxes, setting the stage for future efforts to further examine its global scalability toward closing the long‐standing knowledge gap in carbon flux partitioning. Key Points: We develop a parsimonious solar‐induced chlorophyll fluorescence (SIF)‐based net ecosystem exchange (NEE) partitioning method and examine its performance using synthetic simulations and field measurementsThe SIF‐based NEE partitioning more accurately reproduces the simulated true gross primary production (GPP) and ecosystem respiration (Reco) component fluxes than conventional approachesThe SIF‐based approach results in lower daytime GPP and Reco than conventional approaches for field measurements [ABSTRACT FROM AUTHOR]

Gross primary production (GPP) and evapotranspiration (ET) are the key variables in global carbon and water cycle, respectively. Therefore, it is important to understand how they respond to land use and land cover change (LUCC). Northern China has experienced dramatic LUCC because of a large‐scale ecological restoration project implemented since 1999. This study uses a diagnostic model (PML‐V2) driven by satellite data to quantify LUCC‐driven changes in GPP and ET, at 500 m resolution in northern China from 2004 to 2017. The results indicate that the GPP and ET of northern China has increased by 164 TgC year−1 and 13 km3 year−1. The GPP and ET are strongly increased in the Loess Plateau and Northeast China Plain in the research period, especially after 2009. Cropland has the highest GPP change of 184 gC m−2 year−1 was in Loess Plateau, followed by the Northeast China Plain (128 gC m−2 year−1) and then the Inner Mongolia Plateau (82 gC m−2 year−1). The highest ET increase of 45 mm year−1 occurs in shrubland in the Loess Plateau, followed by the increase of 20 mm year−1 in the Northeast China Plain. The increase in leaf area index is the major cause of GPP and ET increases for these regions. Our results suggest that it is necessary to carefully plan the afforestation and other land use change patterns for sustainable water resources management. Key Points: LUCC‐driven GPP and ET changes estimated using PML‐V2 and continuous annual MODIS land cover data setThe Loess Plateau shows the strongest increase in GPP and ET that are mainly driven by increase in leaf area indexStronger increases in GPP and ET are found for the post‐2009 period, compared to the pre‐2009 period [ABSTRACT FROM AUTHOR]

Remotely sensed plant fluorescence signals have the potential to facilitate a better understanding of vegetation photosynthetic dynamics. Space based observations of solar-induced chlorophyll fluorescence (SIF) provides an alternative non-invasive approach to investigate gross primary production (GPP), a key element of ecological research. Orbiting carbon observatory (OCO-2) is providing continuous global measurements of SIF at 757 nm and 771 nm wavelengths with nadir and glint mode observations. Using one of the three spectrometer instruments, measurements of SIF complements OCO-2's Carbon dioxide data with information on when and where plants are drawing carbon from the atmosphere. Here, we present spatio-temporal patterns of space borne observations of SIF and its correlation with GPP and vegetation indices over diverse terrestrial vegetation of India. The results indicate a spatial variability in SIF ranging from 0.001 to 0.6 (± 0.16) Wm−2 µm−1 sr−1 and 0.001 to 0.7 (± 0.2) Wm−2 µm−1 sr−1 in nadir and glint modes, respectively. OCO-2 derived SIF at finer scale shows a similar trend with MODIS derived GPP during September–October–November and June–July–August seasons. A linear correlation of satellite derived SIF with GPP (Pearson's coefficient, r = 0.84), EVI (r = 0.72) and NDVI (r = 0.63) at 757 nm for 0.05° × 0.05° grid over terrestrial India is reported, besides seasonal variations. Amongst the diverse vegetation types, SIF and GPP are well correlated over Evergreen broadleaf forests. The study provides new insights and useful inputs to GPP modelling and improved understanding of terrestrial carbon cycle research in India. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new global GPP product (DFRF-GPP) was produced by data fusion. • The accuracy of DFRF-GPP is superior to that of mainstream GPP products. • DFRF-GPP can better reflect the response of GPP to high temperature and drought. Understanding the global ecosystem carbon cycle requires a quantitative estimation of terrestrial gross primary production (GPP). Although most existing GPP estimates can accurately reflect the spatial distribution, interannual trends and even seasonal cycles of GPP under average conditions to some extent, the GPP estimates of different models under extreme climate conditions are still unsatisfactory. In this study, based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. The quality of DFRF-GPP was evaluated by comparison with current commonly used GPP datasets and GPP observations at FLUXNET flux sites. The results showed that DFRF-GPP is in great agreement with other GPP products and can reproduce the spatial and temporal patterns and interannual trends of global GPP, with optimal performance in regions such as those north of 30° in the Northern Hemisphere where there are more FLUXNET flux sites. DFRF-GPP has higher accuracy and excellent performance on drought and high-temperature samples, with higher sensitivity to drought and high temperature. This study not only provides a set of time-series products of global gross primary production but also provides a convenient and practical solution for enhancing the applicability of GPP estimation products in large-scale extreme climate studies. [ABSTRACT FROM AUTHOR]

Quantifying the spatial, seasonal (phenological), and inter-annual variations of gross primary productivity (GPP) in the Arctic is critical for comprehending the terrestrial carbon cycle and its feedback to climate warming in this region. Here, we evaluated the accuracy of the MOD17A2H GPP product using the FLUXNET 2015 dataset in the Arctic, then explored the spatial patterns, seasonal variations, and interannual trends of GPP, and investigated the dependence of the spatiotemporal variations in GPP on land cover types, latitude, and elevation from 2001 to 2019. The results showed that MOD17A2H was consistent with in situ measurements (R = 0.8, RMSE = 1.26 g C m−2 d−1). The functional phenology was also captured by the MOD17A2H product (R = 0.62, RMSE = 9 days) in the Arctic. The spatial variation of the seasonal magnitude of GPP and its interannual trends is partly related to land cover types, peaking in forests and lowest in grasslands. The interannual trend of GPP decreased as the latitude and elevation increased, except for the latitude between 62°~66° N and elevation below 700 m. Our study not only revealed the variation of GPP in the Arctic but also helped to understand the carbon cycle over this region.

Forest disturbances induced by clear-cutting (CC) lead to the transformation of the natural biogeochemical processes determining the main greenhouse gas fluxes (primarily CO2) between forest ecosystems and the atmosphere. Effects of CC on CO2 exchange substantially vary depending on local environmental and climate conditions. This study focuses on an estimation of the net ecosystem exchange of СО2 (NEE), gross primary production (GPP), and total ecosystem respiration (TER) and soil respiration in the southern taiga in European Russia. The results are based on continuous eddy covariance measurements during two growing seasons (2016 and 2017). The research has shown that CC was a consistent source of CO2 for the atmosphere during the first years following harvest (NEE from May to October is 553.3 gС m–2 in 2016 and 193.3 in 2017). Interannual variability of the cumulative NEE showed increase of GPP (777.5 gС m–2 in 2016 and 1020.5 gС m–2) and decrease of TER (1330.9 gС m–2 in 2016 and 1213.7 gС m–2 in 2017). The results of chamber measurements have shown that soil respiration in the midday hours in summer varied between 3.6 ± 0.7 and 11.8 ± 3.0 μmol m–2 s–1 in 2016 and between 6.0 ± 1.3 and 14.8 ± 3.5 μmol m–2 s–1 in 2017 at the different plots within the clear-cut site. The estimates of the cumulative GPP at the clear-cut in the southern taiga of European Russia exceed the GPP rates obtained previously in the other clear-cut forest ecosystems in boreal and subboreal ecozones. [ABSTRACT FROM AUTHOR]

Terrestrial gross primary production (GPP) is the largest carbon flux entering the biosphere from the atmosphere, which serves as a key driver of global carbon cycle and provides essential matter and energy for life on land. However, terrestrial GPP variability is still poorly understood and difficult to predict, especially at the annual scale. As a major internal climate oscillation, El Niño‐Southern Oscillation (ENSO) influences global climate patterns and thus may strongly alter interannual terrestrial GPP variation. Using a remote sensing‐driven ecosystem model with long‐term satellite and climate data, we comprehensively examined the impacts of ENSO on global GPP dynamics from 1982 to 2016, focusing on lag effects of ENSO and their spatial heterogeneity. We found a clear seasonal lag effect of previous‐year ENSO indices on current‐year global GPP variability. The composite Oceanic Niño Index in the previous‐year's August‐October showed the strongest correlation with global annual GPP (R = −0.51, p < 0.01). Spatially, 20.1% and 11.7% of vegetated land area showed significant negative and positive correlations with the ENSO cycle, respectively. ENSO effects on annual GPP exhibited diverse seasonal evolutions, and the timings of peak ENSO influences were heterogeneous across the globe. Annual GPP from TRENDY land surface model ensemble did not capture the major lag effects of ENSO identified in the satellite‐derived GPP and top‐down‐based land sink. Despite the complexity of the climate system, our efforts linking ENSO with global GPP dynamics provide a simple framework to understand and project climatic influences on the terrestrial carbon cycle. Key Points: The composite ONI in the previous‐year's August‐October shows the strongest correlation with global annual GPPENSO effects on annual GPP exhibit diverse seasonal evolutions, and the timing of peak ENSO influences are heterogeneous across the globeTRENDY ensemble appears to miss the major lag effects of ENSO identified in the satellite‐derived GPP and top‐down‐based land carbon sink [ABSTRACT FROM AUTHOR]

Highlights • An empirical GPP model based on the remotely-sensed plant phenology index (PPI) outperformed three commonly used GPP models. • The PPI-based GPP model was able to explain 77% of the variability in in situ GPP at four arid and semi-arid sites in Africa. • PPI is a new and promising tool for the estimation of GPP in the semi-arid ecosystems of Africa. Abstract The importance of semi-arid ecosystems in the global carbon cycle as sinks for CO 2 emissions has recently been highlighted. Africa is a carbon sink and nearly half its area comprises arid and semi-arid ecosystems. However, there are uncertainties regarding CO 2 fluxes for semi-arid ecosystems in Africa, particularly savannas and dry tropical woodlands. In order to improve on existing remote-sensing based methods for estimating carbon uptake across semi-arid Africa we applied and tested the recently developed plant phenology index (PPI). We developed a PPI-based model estimating gross primary productivity (GPP) that accounts for canopy water stress, and compared it against three other Earth observation-based GPP models: the temperature and greenness (T-G) model, the greenness and radiation (GöR) model and a light use efficiency model (MOD17). The models were evaluated against in situ data from four semi-arid sites in Africa with varying tree canopy cover (3–65%). Evaluation results from the four GPP models showed reasonable agreement with in situ GPP measured from eddy covariance flux towers (EC GPP) based on coefficient of variation (R2), root-mean-square error (RMSE), and Bayesian information criterion (BIC). The GöR model produced R2 = 0.73, RMSE = 1.45 g C m−2 d−1, and BIC = 678; the T-G model produced R2 = 0.68, RMSE = 1.57 g C m−2 d−1, and BIC = 707; the MOD17 model produced R2 = 0.49, RMSE = 1.98 g C m−2 d−1, and BIC = 800. The PPI-based GPP model was able to capture the magnitude of EC GPP better than the other tested models (R2 = 0.77, RMSE = 1.32 g C m−2 d−1, and BIC = 631). These results show that a PPI-based GPP model is a promising tool for the estimation of GPP in the semi-arid ecosystems of Africa. [ABSTRACT FROM AUTHOR]

The Orbiting Carbon Observatory‐2 (OCO‐2) collects solar‐induced chlorophyll fluorescence (SIF) at high spatial resolution along orbits (SIF¯oco2_orbit), but its discontinuous spatial coverage precludes its full potential for understanding the mechanistic SIF‐photosynthesis relationship. This study developed a spatially contiguous global OCO‐2 SIF product at 0.05° and 16‐day resolutions (SIF¯oco2_005) using machine learning constrained by physiological understandings. This was achieved by stratifying biomes and times for training and predictions, which accounts for varying plant physiological properties in space and time. SIF¯oco2_005 accurately preserved the spatiotemporal variations of SIF¯oco2_orbit across the globe. Validation of SIF¯oco2_005 with Chlorophyll Fluorescence Imaging Spectrometer airborne measurements revealed striking consistency (R2 = 0.72; regression slope = 0.96). Further, without time and biome stratification, (1) SIF¯oco2_005 of croplands, deciduous temperate, and needleleaf forests would be underestimated during the peak season, (2) SIF¯oco2_005 of needleleaf forests would be overestimated during autumn, and (3) the capability of SIF¯oco2_005 to detect drought would be diminished. Plain Language Summary: Newly available observations of solar‐induced chlorophyll fluorescence (SIF) from satellite sensors represent a major step toward quantifying photosynthesis globally in real time. However, existing satellite SIF records are restricted to low spatial resolutions, sparse data acquisition, or both. These limitations impede the full capability of SIF for improving our understanding of dynamics of photosynthesis and its response to environmental changes (particularly in heterogeneous landscapes) to better support carbon source/sink attribution and verification. This study developed a novel high‐resolution time series of spatially contiguous SIF for the globe, leveraging NASA's Orbiting Carbon Observatory‐2 measurements. We combined machine learning algorithms with known physiological constraints for this effort. Comparison with independent airborne SIF measurements revealed strong consistency, confirming the high quality of this new SIF data set. The high‐resolution and global contiguous coverage of this data set will greatly enhance the synergy between satellite SIF and photosynthesis measured on the ground at consistent spatial scales. Potential applications with this data set include advancing dynamic drought monitoring and mitigation, informing agricultural planning and yield estimation in a more spatially explicit way, and providing a benchmark for upcoming satellite missions with SIF capabilities at higher spatial resolutions. Key Points: A spatially contiguous global OCO‐2 SIF data set at 0.05° and 16‐day resolutions (SIF¯oco2_005) was developed using machine learning and physiological constraintsSIF¯oco2_005 successfully preserves the spatiotemporal variability of the original OCO‐2 SIF retrievals, captures water stress, and reduces noiseSIF¯oco2_005 is highly consistent with independent airborne measurements, demonstrating the effectiveness and validity of the prediction framework [ABSTRACT FROM AUTHOR]

• The soil water content and temperature determined the maximum photosynthesis. • Sufficient soil water elevated the optimal temperature and maximum photosynthesis. • The optimal environmental configuration for the alpine meadow was detected. • If soil water is high, higher temperature and VPD are beneficial for carbon uptake. The changes in the carbon sink function of sensitive and fragile alpine ecosystems under climate change are widely concerned. Radiation, temperature, and water resources play important roles in regulating carbon assimilation. Therefore, it is important to detect the optimal configuration of environmental factors for the photosynthesis of alpine meadow ecosystems. In this study, based on 10-year flux and corresponding environmental observations in a typical alpine meadow ecosystem, the optimal environmental configuration that could realistically occur in the natural world for photosynthesis was detected. The results indicated that the temperature and water conditions were the primary limiting factors, as radiation resources are abundant on the Tibetan Plateau. The optimal temperature for photosynthesis (TA opt) was determined to be 11.92 ℃ based on the entire observation dataset, with a maximum photosynthetic capacity (GPP max) of 0.20 mg CO 2 m−2 s−1. However, the TA opt and the corresponding GPP max could be elevated when the soil water content (SWC) was abundant. When SWC exceeded 0.26 m3 m−3, TA opt increased to 13.41 ℃, and the corresponding GPP max reached as high as 0.30 mg CO 2 m−2 s−1, which is the maximum the ecosystem could reach. Thus, the optimal environmental configuration for photosynthesis that could realistically occur in the natural world in this alpine meadow was air temperature (TA)=13.41 ℃, SWC=0.28 m3 m−3, vapor pressure deficit (VPD)=0.61 kPa, net radiation (RN)=411.53 J m−2 s−1. According to this study, it could be inferred that within a reasonable range, higher temperatures and VPD would benefit photosynthesis in this alpine meadow as long as the soil water supply is sufficient. The climate on the Tibetan Plateau is arid with high VPD and is experiencing warming due to climate change. Consequently, alpine ecosystems situated in relatively humid regions with sufficient soil water resources may exhibit greater potential for carbon sequestration than previously estimated. [ABSTRACT FROM AUTHOR]

• We compared GPP and MODIS VIs for 47 North American evergreen forest sites. • Snow had substantial effects on GPP-VI relationships. • GPP-VI relationships varied among ecoregions and climate classes. • CCI and NIRv outperformed other indices in estimating GPP. • CCI was able to track seasonal GPP in Mediterranean sites. North American evergreen forests cover large areas and influence the global carbon cycle. Satellite remote sensing has been used to track the phenology of ecosystem photosynthesis of these forests by detecting variation in vegetation optical properties associated with physiological and structural features, and most of these methods have been closely tied to vegetation greenness. However, in evergreens, the application of satellite data to monitor photosynthetic phenology is often limited by the lack of sensitivity of greenness-based indices. In this study, we identified 47 evergreen forest flux sites in North America that had MODIS observation overlapping with the flux tower records. We then calculated four vegetation indices using MODIS MAIAC data (MCD19A1), including NDVI, CCI, NIRv, and kNDVI, for the 47 flux sites and evaluated relationships between gross primary productivity (GPP) and vegetation indices across the North American evergreen forests. Our results showed that snow had substantial effects on the performance of all vegetation indices in tracking GPP phenology, particularly in the early spring when rapid changes occurred to both GPP and snow cover. Different vegetation indices were affected differently, indicating contradictory and confounding effects of snow on these indices. After correcting for the snow effects, both CCI and NIRv performed well in tracking GPP phenology, albeit for different reasons. CCI is sensitive to seasonal changes in the relative levels of chlorophyll and carotenoid pigments, which are closely tied to GPP phenology in evergreens. NIRv is sensitive to the absorbed photosynthetically active radiation and to the contribution of deciduous components to the overall optical properties. We also found that correlations between GPP and vegetation indices varied among ecoregions and climate classes. In general, regions with pronounced seasonal GPP patterns had stronger correlations between GPP and greenness-based indices than regions with weaker seasonal GPP patterns. These biome differences were less pronounced for CCI. The snow artifacts and complementary vegetation index effects reported here should be considered in any large-scale studies of GPP using reflectance-based indices from optical satellites. [ABSTRACT FROM AUTHOR]

Regional gross primary productivity (GPP) estimates are crucial to estimating carbon-climate feedbacks but are highly uncertain with existing methods. An emerging approach uses atmospheric carbonyl sulphide (COS) as a tracer for carbon dioxide: COS plant uptake is simulated by scaling GPP. A critical parameter for this method is leaf-scale relative uptake (LRU). Plant chamber and eddy covariance studies find a narrow range of LRU values but some atmospheric modelling studies assign values well outside this range. Here we study this discrepancy by conducting new regional chemical transport simulations for North America using the underlying data from previous studies. We find the wide range of ecosystem model GPP estimates can explain the discrepancy in LRU values. We also find that COS concentration uncertainty is more sensitive to GPP uncertainty than to LRU parameter uncertainty. These results support the COS tracer technique as a useful approach for constraining GPP estimates.

Diurnal and vertical patterns of carbonyl sulfide (OCS) and CO2 mixing ratios above and within a 60‐m‐tall old‐growth temperate forest are presented. Canopy air from four different heights was sampled in situ using a continuous integrated cavity output spectroscopy analyzer during August–September 2014. Measurements revealed large vertical gradients in OCS, from which we inferred ecosystem fluxes. The diurnal cycle of OCS mixing ratios at all heights exhibited a typical pattern characterized by nighttime drawdown, an early morning minimum, and a maximum of OCS around midday. Daytime increase in the upper canopy is attributed to entrainment of planetary boundary layer air into the canopy. The ecosystem was found to be a large daytime sink of OCS (mean maximum daytime flux ~ −75 pmol · m−2 · s−1). Mean leaf relative uptake (concentration normalized uptake of OCS flux to CO2 uptake) was found to be 6.9. We discuss this high leaf relative uptake in the context of the presence and distribution of epiphytes at the site. While epiphytic uptake of OCS has been studied before, we show for the first time that this may contribute significantly to ecosystem fluxes under humid or moist conditions. We test this theory using a chamber experiment measuring epiphytic fluxes for two species of lichen and one moss species (in situ and in a laboratory). We suggest that the role of epiphytes should be explicitly considered when using OCS as a tracer of ecosystem‐scale photosynthesis in forest ecosystems with abundant epiphytic cover and biomass. Plain Language Summary: The resilience of old growth forests in changing climates is less well understood, in part due to difficulties in measuring forest productivity. Here we apply a new method using measurements of carbonyl sulfide (OCS) to understand the same in a tall old growth forest in the Pacific northwestern United States. We find that OCS is taken up by plants, soil, and epiphytes (lichens and mosses) and provides biophysical controls on OCS uptake and its utility in estimating productivity in similar forests. Key Points: Large vertical gradients of OCS mixing ratios occur within the canopy of an old‐growth, tall stature forestInferred fluxes show that the forest is a large sink for OCSOCS is also consumed by epiphytes that are abundant in this ecosystem [ABSTRACT FROM AUTHOR]

Canopy nitrogen (N) is a key factor regulating carbon cycling in forest ecosystems through linkages among foliar N and photosynthesis, decomposition, and N cycling. This analysis examined landscape variation in canopy nitrogen and carbon assimilation in a temperate mixed forest surrounding Harvard Forest in central Massachusetts, USA by integration of canopy nitrogen mapping with ecosystem modeling, and spatial data from soils, stand characteristics and disturbance history. Canopy %N was mapped using high spectral resolution remote sensing from NASA’s AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) instrument and linked to an ecosystem model, PnET-II, to estimate gross primary productivity (GPP). Predicted GPP was validated with estimates derived from eddy covariance towers. Estimated canopy %N ranged from 0.5 to 2.9% with a mean of 1.75% across the study region. Predicted GPP ranged from 797 to 1622 g C m−2 year−1 with a mean of 1324 g C m−2 year−1. The prediction that spatial patterns in forest growth are associated with spatial patterns in estimated canopy %N was supported by a strong, positive relationship between field-measured canopy %N and aboveground net primary production. Estimated canopy %N and GPP were related to forest composition, land-use history, and soil drainage. At the landscape scale, PnET-II GPP was compared with predicted GPP from the BigFoot project and from NASA’s MODIS (Moderate Resolution Imaging Spectroradiometer) data products. Estimated canopy %N explained much of the difference between MODIS GPP and PnET-II GPP, suggesting that global MODIS GPP estimates may be improved if broad-scale estimates of foliar N were available. [ABSTRACT FROM AUTHOR]

The Orbiting Carbon Observatory-2 (OCO-2), launched in July 2014, is capable of measuring Solar-Induced chlorophyll Fluorescence (SIF), a functional proxy for terrestrial gross primary productivity (GPP). Although its primary mission is to measure the column-averaged mixing ratio of CO 2 ( X co 2 ) to constrain global carbon source/sink distribution, one of the OCO-2 spectrometers allows for a robust SIF retrieval solely based on solar Fraunhofer lines. Here we present a technical overview of the OCO-2 SIF product, aiming to provide the scientific community guidance on best practices for data analysis, interpretation, and application. This overview consists of the retrieval algorithms, OCO-2 specific bias correction, retrieval uncertainty evaluation, cross-mission comparison with other existing SIF products, and a global-scale examination of the SIF-GPP relationship. With the initial three years of data (September 2014 onward), we compared OCO-2 SIF with retrievals from Greenhouse Gases Observing Satellite (GOSAT) and Global Ozone Monitoring Experiment-2 (GOME-2), and examined its relationship with FLUXCOM and MODIS GPP datasets. Our results show that OCO-2 SIF, along with GOSAT products, closely resemble the mean spatial and temporal patterns of FLUXCOM GPP from regions to the globe. Compared with GOME-2, however, OCO-2 depicts a more realistic spatial contrast between the tropics and extra-tropics. The linear relationship between OCO-2 SIF and existing modeled GPP products diverges somewhat across biomes at the global scale, consistent with previous GOSAT or GOME-2 based findings when modeled GPP products were used, but in contrast to a consistent cross-biome SIF-GPP relationship obtained at flux tower sites with OCO-2 products. This contrast suggests a critical need to reconcile differences in diverse SIF and GPP products and the relationships among them. Overall, the OCO-2 SIF products are robust and valuable for monitoring the global terrestrial carbon cycle and for constraining the carbon source/sink strengths of the Earth system. Finally, insights are offered for future satellite missions optimized for SIF retrievals. [ABSTRACT FROM AUTHOR]

Accurately quantifying gross primary production (GPP) is of vital importance to understanding the global carbon cycle. Light-use efficiency (LUE)models and process-basedmodels have been widely used to estimate GPP at different spatial and temporal scales. However, large uncertainties remain in quantifying GPP, especially for croplands. Recently, remote measurements of solar-induced chlorophyll fluorescence (SIF) have provided a new perspective to assess actual levels of plant photosynthesis. In the presented study, we evaluated the performance of three approaches, including the LUE-based multi-source data synergized quantitative (MuSyQ) GPP algorithm, the process-based boreal ecosystem productivity simulator (BEPS) model, and the SIF-based statistical model, in estimating the diurnal courses of GPP at a maize site in Zhangye, China. A field campaign was conducted to acquire synchronous far-red SIF (SIF760) observations and flux tower-based GPP measurements. Our results showed that both SIF760 and GPP were linearly correlated with APAR, and the SIF760-GPP relationship was adequately characterized using a linear function. The evaluation of the modeled GPP against the GPP measured from the tower demonstrated that all three approaches provided reasonable estimates, with R2 values of 0.702, 0.867, and 0.667 and RMSE values of 0.247, 0.153, and 0.236 mg m-2 s-1 for the MuSyQ-GPP, BEPS and SIFmodels, respectively. This study indicated that the BEPSmodel simulated the GPP best due to its efficiency in describing the underlying physiological processes of sunlit and shaded leaves. TheMuSyQ-GPPmodelwas limited by its simplification of some critical ecological processes and its weakness in characterizing the contribution of shaded leaves. The SIF760-based model demonstrated a relatively limited accuracy but showed its potential in modeling GPP without dependency on climate inputs in short-term studies. [ABSTRACT FROM AUTHOR]

• Large interannual variability of ecosystem carbon fluxes at a semiarid savanna site. • Flux variability governed by water availability metrics like precipitation and soil moisture. • Positive trend in 19-year soil moisture and carbon uptake record. • Models and satellites often capture less than half the variability in measured fluxes. Eddy covariance measurements of land-atmosphere energy, carbon, and water exchange now span multiple decades at some sites, supporting an improved understanding of flux interannual variability (IAV) and its ecophysiological and physical controls. Most eddy covariance IAV studies have focused on temperate forest ecosystems, where carbon fluxes are large and flux records are longest – but also where IAV is much lower than in dryland regions, which have been identified as an essential driver of the trend and variability in the global terrestrial carbon sink. In this study, we leveraged 19 years of continuous micrometeorological measurements at the AmeriFlux US-SRM mesquite savanna site in southern Arizona, USA to quantify the IAV, trends, and drivers of carbon fluxes during the distinct spring and summer growing seasons. We also assessed the ability of modern satellite and land surface models to capture the IAV of seasonal water and carbon fluxes. Annual net ecosystem production (NEP) was small and highly variable (23 +/- 64 gC m −2 yr−1). Precipitation and associated measures of water availability determined most of the variability in NEP, largely through their influence on annual and seasonal gross ecosystem productivity (GEP) as opposed to ecosystem respiration (ER). Root-zone soil moisture captured between 73% (spring) and 85% (summer) of GEP variability and between 73% (spring) and 58% (summer) of ER variability. Throughout the study period, soil moisture and greenness increased with associated increases in GEP, ER and NEP. These trends were strongly influenced by very productive and wet summer growing seasons during the last two years, which were characterized by abundant understory grass cover. Typically, less than half of the variability in growing season GEP and evapotranspiration was captured by satellite-based estimates and land surface model simulations with local site forcing and calibration, highlighting the ongoing utility of long-term datasets to support careful model testing and improvement. [ABSTRACT FROM AUTHOR]

Monitoring changes in the terrestrial carbon cycle and vegetation health can only be undertaken over large areas and on a regular basis using ecological indicators derived from satellite-based sensors. Climate conditions in Mediterranean ecosystems have undergone, and are projected to undergo, significant change in the future with marked impacts on forest and shrubland vegetation. In the southwest of Australia (SWAU), endemic tree species have experienced significant declines in health and mortality since the early 1990s primarily due to these climatic changes. In this paper we examine trends in Net Primary Production (NPP) from 2000 to 2011 as an indicator of productivity and health condition of the woody vegetation across the SWAU region. To do so, we examine NPP estimates derived from satellite imagery and climate data to answer the questions: (1) what is the extent and rate of change in NPP for the SWAU region over the study period, and (2) how important is fire as a contributing factor in the observed trends? Our results suggest that, similar to the global trend in Mediterranean ecosystems, between 2000 and 2011, overall NPP declined across the study region, with the majority of declines occurring in the ecological transition zone between trees and shrubs. Twenty-six percent of the 37,042 square kilometre of woody vegetation that showed a declining NPP trend, was affected by fire. The overall rate of NPP decline for the region was estimated to be −0.38 megaton C per year since 2000, indicating a reduction in the capacity of the region to act as a carbon sink. Under climate change projections, the observed decline trends are likely to continue and our results suggest that the carbon storage potential in this region is gradually decreasing following an ecological shift from tall tree-dominated to lower shrub-dominated vegetation. [ABSTRACT FROM AUTHOR]

The Southern Great Plains are characterized by a fine-scale mixture of different land-cover types, predominantly winter-wheat and grazed pasture, with relatively small areas of other crops, native prairie, and switchgrass. Recent droughts and predictions of increased drought in the Southern Great Plains, especially during the summer months, raise concern for these ecosystems. We measured ecosystem carbon and water fluxes with eddy-covariance systems over cultivated cropland for 10 years, and over lightly grazed prairie and new switchgrass fields for 2 years each. Growing-season precipitation showed the strongest control over net carbon uptake for all ecosystems, but with a variable effect: grasses (prairie and switchgrass) needed at least 350 mm of precipitation during the growing season to become net carbon sinks, while crops needed only 100 mm. In summer, high temperatures enhanced evaporation and led to higher likelihood of dry soil conditions. Therefore, summer-growing native prairie species and switchgrass experienced more seasonal droughts than spring-growing crops. For wheat, the net reduction in carbon uptake resulted mostly from a decrease in gross primary production rather than an increase in respiration . Flux measurements suggested that management practices for crops were effective in suppressing evapotranspiration and decomposition (by harvesting and removing secondary growth), and in increasing carbon uptake (by fertilizing and conserving summer soil water). In light of future projections for wetter springs and drier and warmer summers in the Southern Great Plains, our study indicates an increased vulnerability in native ecosystems and summer crops over time. [ABSTRACT FROM AUTHOR]

• A cross comparison of GPP models in croplands using various spatial resolutions • Integration of red-edge vegetation indices enhance the vegetation photosynthesis model • GPP estimates from fine resolution satellites align best with in situ estimates • Seasonal anomalies of GPP are best explained by Sentinel-2 red-edge models Gross primary production (GPP) is a fundamental measure of the terrestrial carbon cycle critical to our understanding of ecosystem function under the changing climate and land use. Remote sensing enables access to continuous spatial coverage, but remains challenged in heterogeneous croplands. Coarse resolution products, like MOD17A (500 m), may aggregate fragmented land cover types commonly found in heavily managed landscapes and misrepresent their respective contribution to carbon production. Consequently, this study demonstrates the capability of fine-resolution imagery (20-30 m) and available red-edge vegetation indices to characterize GPP across seven Midwest cropping systems. Four sites were established on a 22-year-old USDA Conservation Reserve Program (CRP); and the other three on land conventionally farmed with corn-soybean-wheat rotation (AGR). We compare in situ GPP estimates from eddy-covariance towers with ten satellite models: eight variants of the vegetation photosynthesis models (VPM), of which five include a red-edge vegetation index, as well as conventional products Landsat CONUS GPP (30 m) and MOD17A2H V6 (500 m). Daily and cumulative fine-resolution imagery integrated within VPM generally agreed with tower-based GPP in heterogeneous landscapes more than those from MODIS 500 m VPM or conventional GPP products from MOD17AH V6 or Landsat 8 CONUS. Replacing EVI2 with red-edge indices NDRE2, NDRE1, and MTCI in Sentinel 2 VPMs notably improved explanation of variance and estimation of cumulative GPP. While existing methods using MODIS- and Landsat-derived GPP are important baselines for regional and global studies, future research may benefit from the higher spatial, temporal, and radiometric resolution. [ABSTRACT FROM AUTHOR]

As a major component of temperate steppes in the Eurasian continent, the Mongolian Plateau (MP) plays a pivotal role in the East Asian and global carbon cycles. This paper describes the use of five remote sensing indices derived from satellite data to characterize vegetation cover on MP, namely: gross primary production (GPP), net primary production (NPP), normalized difference vegetation index (NDVI), leaf area index (LAI) and fractional vegetation cover (FVC). It is found that GPP, NPP, and NDVI exhibit increasing trends, whereas LAI and FVC present decreasing trends on the MP since 1982. The different indices highlight discrepancies in the spatial pattern of vegetation growth, with the greatest increase in the southeast of MP. Only 3.4% of the total land area of MP exhibited consistent trends in the indices (0.1% degradation and 3.3% growth, P < 0.01), with the synchronous change of both LAI and NPP exhibiting higher consistency than that of raw NDVI and NPP. Understanding of the characteristics and status of vegetation change on the MP has far-reaching implications for its ecological protection management, and climate change mitigation. • Five indices were used to quantitatively analyze the vegetation changes on the MP since 1982. • An increasing trend of GPP, NPP and NDVI, and a decreasing trend of LAI and FVC were observed. • Only 3.4% of the MP's area showed consistent trends (0.1% degradation and 3.3% growth, P < 0.01). • The synchronous change of LAI and NPP exhibited higher consistency than that of LAI and NPP. [ABSTRACT FROM AUTHOR]

One of the most important linkages that couple terrestrial carbon and water cycles is ecosystem water use efficiency (WUE), which is relevant to the reasonable utilization of water resources and farming practices. Eddy covariance techniques provide an opportunity to monitor the variability in WUE and can be integrated with Moderate Resolution Imaging Spectroradiometer (MODIS) observations. Scaling up in situ observations from flux tower sites to large areas remains challenging and few studies have been reported on direct estimation of WUE from remotely-sensed data. This study examined the main environmental factors driving the variability in WUE of corn/soybean croplands, and revealed the prominent role of solar radiation and temperature. Time-series of MODIS-derived enhanced vegetation indices (EVI), which are proxies for the plant responses to environmental controls, were also strongly correlated with ecosystem WUE, thereby implying great potential for remote quantification. Further, both performance of the indirect MODIS-derived WUE from gross primary productivity (GPP) and evapotranspiration (ET), and the direct estimates by exclusive use of MODIS EVI data were evaluated using tower-based measurements. The results showed that ecosystem WUE were overpredicted at the beginning and ending of crop-growth periods and severely underestimated during the peak periods by the indirect estimates from MODIS products, which was mainly attributed to the error source from MODIS GPP. However, a simple empirical model that is solely based on MODIS EVI data performed rather well to capture the seasonal variations in WUE, especially for the growing periods of croplands. Independent validation at different sites indicates the method has potential for broad application. [ABSTRACT FROM AUTHOR]

Changes to the carbon cycle in tropical forests could affect global climate, but predicting such changes has been previously limited by lack of field-based data. Here we show seasonal cycles of the complete carbon cycle for 14, 1 ha intensive carbon cycling plots which we separate into three regions: humid lowland, highlands, and dry lowlands. Our data highlight three trends: (1) there is differing seasonality of total net primary productivity (NPP) with the highlands and dry lowlands peaking in the dry season and the humid lowland sites peaking in the wet season, (2) seasonal reductions in wood NPP are not driven by reductions in total NPP but by carbon during the dry season being preferentially allocated toward either roots or canopy NPP, and (3) there is a temporal decoupling between total photosynthesis and total carbon usage (plant carbon expenditure). This decoupling indicates the presence of nonstructural carbohydrates which may allow growth and carbon to be allocated when it is most ecologically beneficial rather than when it is most environmentally available. [ABSTRACT FROM AUTHOR]