Variation In Gross Primary Production Research Articles

Spatial, Phenological, and Inter-Annual Variations of Gross Primary Productivity in the Arctic from 2001 to 2019

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.

Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

BackgroundVegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain.MethodsWe evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively.ResultsWe found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP (R2: 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP (R2: 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP (R2: 0.40–0.67).ConclusionThese findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

Divergent impacts of atmospheric water demand on gross primary productivity in three typical ecosystems in China

Atmospheric water demand is practically characterized as vapor pressure deficit (VPD) and has been identified as a critical driver of ecosystem function, by affecting plant mortality, wildfires, and carbon loss. In this study, we used daily eddy covariance data across Chinese forest, grassland and shrubland ecosystems, in combination with remote sensing data, to investigate the impacts of VPD on gross primary productivity (GPP). We found divergent VPD impacts on GPP among grassland, shrubland and forest ecosystems. The VPD yielded substantial inhibitory impacts on GPP in grassland ecosystems and this suppressing impact was regulated by soil water content (SWC), showing that GPP declined with VPD under dry conditions but increased with VPD under wet conditions. This GPP variance was attributed to VPD, SWC and their interactions. More than 50% of the variability in GPP was explained by SWC in grassland ecosystems and by VPD in forest and shrubland ecosystems. Partial correlation analysis, random forest, and multiple linear regression revealed similar results when temperature, radiation and SWC were considered. Compared with shrubland and forest ecosystems, grassland has drier environmental conditions and poorer soil water-holding capacity that led to lower SWC and stronger negative impacts on VPD. Thus, GPP was susceptible to the negative impacts of higher VPD, especially under dryness stress. Our results highlight the need to comprehensively consider divergent VPD impacts for different ecosystems to more accurately assess climate impacts on ecosystem function.

Relative importance of climatic variables, soil properties and plant traits to spatial variability in net CO2 exchange across global forests and grasslands

Compared to the well-known drivers of spatial variability in gross primary productivity (GPP), the relative importance of climatic variables, soil properties and plant traits to the spatial variability in net ecosystem exchange of CO2 between terrestrial ecosystem and atmosphere (NEE) is poorly understood. We used principal component regression to analyze data from 147 eddy flux sites to disentangle effects of climatic variables, soil properties and plant traits on the spatial variation in annual NEE and its components (GPP and ecosystem respiration (RE)) across global forests and grasslands. Our results showed that the largest unique contribution (proportion of variance only explained by one class of variables) to NEE variance came from climatic variables for forests (24%-30%) and soil properties for grasslands (41%-54%). Specifically, mean annual precipitation and potential evapotranspiration were the most important climatic variables driving forest NEE, whereas available soil water capacity, clay content and cation exchange capacity mainly influenced grassland NEE. Plant traits showed a small unique contribution to NEE in both forests and grasslands. However, leaf phosphorus content strongly interacted with soil total nitrogen density and clay content, and these combined factors represented a major contribution for grassland NEE. For GPP and RE, the majority of spatial variance was attributed to the common contribution of climate, soil and plant traits (50% - 62%, proportion of variance explained by more than one class of variables), rather than their unique contributions. Interestingly, those factors with only minor influences on GPP and RE variability (e.g., soil properties) have significant contributions to the spatial variability in NEE. Such emerging factors and the interactions between climatic variables, soil properties and plant traits are not well represented in current terrestrial biosphere models, which should be considered in future model improvement to accurately predict the spatial pattern of carbon cycling across forests and grasslands globally.

Atmospheric water vapor and soil moisture jointly determine the spatiotemporal variations of CO2 fluxes and evapotranspiration across the Qinghai-Tibetan Plateau grasslands

Alpine grasslands play important functions in mitigating climate change and regulating water resources. However, the spatiotemporal variability of their carbon and water budgets remains unquantified. Here, 47 site-year observations of CO2 and water vapor fluxes (ET) are analyzed at sites situated along a hydrothermal gradient across the Qinghai-Tibetan Plateau, including an alpine wetland (wettest), an alpine shrub (coldest), an alpine meadow, an alpine meadow-steppe, and an alpine steppe (driest and warmest). The results show that the benchmarks for annual net ecosystem exchange (NEE) are −79.3, −77.8, −66.7, 20.2, and 100.9 g C m−2 year−1 at the meadow, shrub, meadow-steppe, steppe, and wetland, respectively. The peak daily NEE normalized by peak leaf area index converges to 0.93 g C m−2 d−1 at the 5 sites. Except in the wetland (722.8 mm), the benchmarks of annual ET fluctuate from 511.0 mm in the steppe to 589.2 mm in the meadow. Boosted regression trees-based analysis suggests that the enhanced vegetation index (EVI) and net radiation (Rn) determine the variations of growing season monthly CO2 fluxes and ET, respectively, although the effect is to some extent site-specific. Inter-annual variability in NEE, ecosystem respiration (RES), and ET are tightly (R2 > 0.60) related to the inter-growing season NEE, RES, and ET, respectively. Both annual RES and annual NEE are significantly constrained by annual gross primary productivity (GPP), with 85% of the per-unit GPP contributing to RES (R2 = 0.84) and 15% to NEE (R2 = 0.12). Annual GPP significantly correlates with annual ET alone at the drier sites of the meadow-steppe and the steppe, suggesting the coupling of carbon and water is moisture-dependent in alpine grasslands. Over half of the inter-annual spatial variability in GPP, RES, NEE, and ET is explained by EVI, atmospheric water vapor, topsoil water content, and bulk surface resistance (rs), respectively. Because the spatial variations of EVI and rs are strongly regulated by atmospheric water vapor (R2 = 0.48) and topsoil water content (R2 = 0.54), respectively, we conclude that atmospheric water vapor and topsoil water content, rather than the expected air/soil temperatures, drive the spatiotemporal variations in CO2 fluxes and ET across temperature-limited grasslands. These findings are critical for improving predictions of the carbon sequestration and water holding capacity of alpine grasslands.

Gross primary productivity of Brazilian Savanna (Cerrado) estimated by different remote sensing-based models

Gross primary production (GPP) is the total amount of fixed carbon and depends on vegetation health and water and energy availability. GPP has been monitored worldwide by flux towers and models that are coupled with remotely sensed data, such as by Moderate Resolution Imaging Spectroradiometer (MODIS). However, models have not been evaluated for tropical savanna, which presumably represent a challenge because of large spatial and seasonal variation in GPP. Thus, our goal was to evaluate the Vegetation Photosynthesis Model (VPM), Temperature and Greenness Model (TG), Vegetation Index Model (VI), and the MOD17A2 product of MODIS in a tropical mixed woodland-grassland. The Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), Land Surface Temperature (LST) and Photosynthetically Active Radiation Fraction (fPAR) derived from the MODIS sensor were used as model inputs and integrated with ground-based micrometeorological variables. GPP varied significantly between the wet and dry seasons and was positively correlated with seasonal variations in soil volumetric water content (VSWC) and precipitation, and negatively correlated with the vapor pressure deficit (VPD). Satellite vegetation indices (NDVI, LSWI, and to a lesser extent, the EVI) and derived quantities (fPAR and LUE) also exhibited similar correlations with VSWC and precipitation. Thus, there were strong positive correlations between the SVIs and GPP. All of the models were able to simulate the seasonal variations in GPP; however, VPM had the best performance with the highest correlation and smallest errors. TG, VI, and MOD17A2 models performed similarly, except for the VI model based only on the EVI. Given their ability to capture seasonal dynamics, remote-sensing based models, such as those tested here, will likely be an important tool for assessing how climate variability alters C cycling dynamics in these spatially and temporally heterogeneous landscapes.

Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically defined MEMS 2.0 model

Abstract. For decades, predominant soil biogeochemical models have used conceptual soil organic matter (SOM) pools and only simulated them to a shallow depth in soil. Efforts to overcome these limitations have prompted the development of the new generation SOM models, including MEMS 1.0, which represents measurable biophysical SOM fractions, over the entire root zone, and embodies recent understanding of the processes that govern SOM dynamics. Here we present the result of continued development of the MEMS model, version 2.0. MEMS 2.0 is a full ecosystem model with modules simulating plant growth with above- and belowground inputs, soil water and temperature by layer, decomposition of plant inputs and SOM, and mineralization and immobilization of nitrogen (N). The model simulates two commonly measured SOM pools – particulate and mineral-associated organic matter (POM and MAOM, respectively). We present results of calibration and validation of the model with several grassland sites in the US. MEMS 2.0 generally captured the soil carbon (C) stocks (R2 of 0.89 and 0.6 for calibration and validation, respectively) and their distributions between POM and MAOM throughout the entire soil profile. The simulated soil N matches measurements but with lower accuracy (R2 of 0.73 and 0.31 for calibration and validation of total N in SOM, respectively) than for soil C. Simulated soil water and temperature were compared with measurements, and the accuracy is comparable to the other commonly used models. The seasonal variation in gross primary production (GPP; R2 = 0.83), ecosystem respiration (ER; R2 = 0.89), net ecosystem exchange (NEE; R2 = 0.67), and evapotranspiration (ET; R2 = 0.71) was well captured by the model. We will further develop the model to represent forest and agricultural systems and improve it to incorporate new understanding of SOM decomposition.

Drone-Based Hyperspectral and Thermal Imagery for Quantifying Upland Rice Productivity and Water Use Efficiency after Biochar Application

Miniature hyperspectral and thermal cameras onboard lightweight unmanned aerial vehicles (UAV) bring new opportunities for monitoring land surface variables at unprecedented fine spatial resolution with acceptable accuracy. This research applies hyperspectral and thermal imagery from a drone to quantify upland rice productivity and water use efficiency (WUE) after biochar application in Costa Rica. The field flights were conducted over two experimental groups with bamboo biochar (BC1) and sugarcane biochar (BC2) amendments and one control (C) group without biochar application. Rice canopy biophysical variables were estimated by inverting a canopy radiative transfer model on hyperspectral reflectance. Variations in gross primary productivity (GPP) and WUE across treatments were estimated using light-use efficiency and WUE models respectively from the normalized difference vegetation index (NDVI), canopy chlorophyll content (CCC), and evapotranspiration rate. We found that GPP was increased by 41.9 ± 3.4% in BC1 and 17.5 ± 3.4% in BC2 versus C, which may be explained by higher soil moisture after biochar application, and consequently significantly higher WUEs by 40.8 ± 3.5% in BC1 and 13.4 ± 3.5% in BC2 compared to C. This study demonstrated the use of hyperspectral and thermal imagery from a drone to quantify biochar effects on dry cropland by integrating ground measurements and physical models.

A pigment ratio index based on remotely sensed reflectance provides the potential for universal gross primary production estimation

Gross primary production (GPP) estimation usually involves a priori assumptions about biome-specific rules or climate controls, which hampers an objective analysis of driving mechanisms. Observation-based methods that are biome-invariant and globally uniform are thus highly desirable. To facilitate this, a reflectance index representing the ratio of chlorophyll to total pigments (R chl) was proposed to consider the variation of energy conversion efficiency driven by different pigment contents in the canopy. Experiments based on simulated reflectance spectra showed that R chl could explain over 83% of chlorophyll ratio dynamics. A model was then developed which approximates GPP as the product of R chl, the normalized difference vegetation index, the near-infrared reflectance, and the photosynthetically active radiation. The model is simple, fast, with definite physical meaning and independent of climatic parameters such as temperature and humidity. Validated with over one hundred thousand field measurements, the model exhibited comparable accuracy to biome- and climate-based GPP models (r = 0.74 for both types of models), demonstrating satisfactory performance. It also achieved significantly better results compared with a regression model excluding R chl, which emphasizes the important role of R chl. By avoiding circular analyses in mechanism studies on GPP variations, this model may extend our previous understanding of global terrestrial carbon uptake.

Strong impacts of autumn phenology on grassland ecosystem water use efficiency on the Tibetan Plateau

Vegetation phenology is a commonly used indicator of climate-vegetation interactions, which also plays a crucial role in regulating gross primary productivity (GPP), evapotranspiration (ET) and ecosystem water use efficiency (WUE, defined as the ratio between GPP and ET). Actually, quantitative evaluation of the impact of phenology on WUE is critical yet lacking. In this study we detected the interannual variations of autumn WUE, GPP and ET and conducted Redundancy analysis (RDA) to quantitatively assess their responses to autumn end of growing season (EOS) in the grassland ecosystem across the Tibetan Plateau. Our study found that the delayed EOS increased the daily mean GPP but meanwhile increased the ET in autumn, rather than enhanced the WUE. Over the entire Tibetan Plateau, the daily GPP and ET in the autumn of the steppe were dominated by the delayed EOS, with the explanatory power of EOS to GPP and ET of 75.90% (P < 0.01) and 21.80% (P < 0.05). An one day delay of the EOS increased the daily GPP and ET by 0.05 × 10−1 gC m−1 day−1 (P < 0.01) and 0.27 × 10−1 mm day−1 (P < 0.01), respectively. Furthermore, the EOS agreed on a positive correlation with daily GPP and ET in different ecoregions and the interannual variations of WUE for the meadow and steppe in the different ecoregions across the Tibetan Plateau did not show significant trend because of this same increasing pattern of GPP and ET form the delayed EOS. This study highlights the impacts of EOS on the interannual changes of the GPP, ET and WUE in autumn in a large cold and semiarid/arid area, which indicated that phenology should be regard as a crucial indicator in the process of carbon water coupling cycles and their biological regulatory mechanisms in terrestrial ecosystems.

Potential of Sun‐Induced Chlorophyll Fluorescence for Indicating Mangrove Canopy Photosynthesis

AbstractAccurate characterization of gross primary productivity (GPP) is critically important in assessing mangrove carbon budgets, but the current knowledge of the temporal variations of GPP in evergreen mangroves is very limited. Remote sensing of sun‐induced chlorophyll fluorescence (SIF) has emerged as a promising approach to approximating GPP across ecosystems, but its capability for tracking GPP in evergreen mangroves has not been assessed. The SIF‐GPP link at a subtropical mangrove and its environmental controls are explored using 1‐year time‐series measurements from tower‐based hyperspectral and eddy covariance systems. Both the relationship between SIF and GPP as well as that between SIFy (SIF yield: the ratio of SIF over absorbed photosynthetically active radiation [APAR]) and LUE (light use efficiency: the ratio of GPP over APAR) at diurnal and seasonal time scales are analyzed. The temporal variations of SIF and GPP shared overall similar changing patterns, but their functional relationship tended to be time scale‐dependent. Midday depressions in SIF were observed when environmental stresses occurred around noon (including excess light and high VPD), and the strength of the SIF‐GPP link was affected by changing environmental conditions. The SIFy‐LUE relationship was temporally more dynamic, tending to match during midday hours but diverge from each other during morning and afternoon hours. These findings confirm SIF can serve as a potential remotely sensed indicator of mangrove canopy photosynthesis. This paper provides the first, high temporal‐resolution, continuous SIF measurements in mangroves, and highlights the importance of the impacts of environmental conditions on the SIF‐GPP relationship.

Modulation of Land Photosynthesis by the Indian Ocean Dipole: Satellite‐Based Observations and CMIP6 Future Projections

AbstractIndian Ocean Dipole (IOD), a major climate variability in the tropics which drives the abiotic stress associated with heavy rainfalls and severe droughts, is not much understood in terms of its role in the carbon cycle, while El Niño‐Southern Oscillation (ENSO)‐related terrestrial carbon cycle variation has been intensively studied. Here, we investigate IOD's impact on land photosynthesis over the Indian ocean rim countries during austral spring using satellite‐based gross primary productivity (GPP) and Earth System Model simulations produced in the Coupled Model Intercomparison Project Phase 6 (CMIP6). IOD independently affects GPP with significant positive partial correlation coefficients () over most of Africa and India, and negative over southern China, Indo‐China peninsula, maritime continent, and Australia, mostly driven by precipitation variations; this obviously differs from the widespread significant negative pattern induced by ENSO. The recent extremely positive IOD in 2019 caused the canonical IOD‐affected GPP patterns, however, with its extreme impacts. Furthermore, though large inter‐model spreads exist, the CMIP6 multimodel median can basically capture the main characteristics of IOD‐affected precipitation and GPP patterns. Importantly, IOD is predicted to occur more frequently in future warming scenarios. Model future projections suggest that it will exert larger impacts on GPP variations over central and eastern Africa, Sumatra, western and southeastern Australia with stronger and enhanced explained variance, but less impacts over southern Africa, east India, Indo‐China peninsula, and northeastern Australia. Therefore, besides ENSO, understanding the IOD impacts can provide us new insights into regional and global carbon cycle interannual variability.

ECOSTRESS estimates gross primary production with fine spatial resolution for different times of day from the International Space Station

Accurate estimation of gross primary production (GPP), the amount of carbon absorbed by plants via photosynthesis, is of great importance for understanding ecosystem functions, carbon cycling, and climate-carbon feedbacks. Remote sensing has been widely used to quantify GPP at regional to global scales. However, polar-orbiting satellites (e.g., Landsat, Sentinel, Terra, Aqua, Suomi NPP, JPSS, OCO-2) lack the capability to examine the diurnal cycles of GPP because they observe the Earth's surface at the same time of day. The Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), launched in June 2018, observes the land surface temperature (LST) at different times of day with high spatial resolution (70 m × 70 m) from the International Space Station (ISS). Here, we made use of ECOSTRESS data to predict instantaneous GPP with high spatial resolution for different times of day using a data-driven approach based on machine learning. The predictive GPP model used instantaneous ECOSTRESS LST observations along with the daily enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), land cover type from the National Land Cover Database (NCLD), and instantaneous meteorological data from the ERA5 reanalysis dataset. Our model estimated instantaneous GPP across 56 flux tower sites fairly well (R2 = 0.88, Root Mean Squared Error (RMSE) = 2.42 μmol CO2 m−2 s−1). The instantaneous GPP estimates driven by ECOSTRESS LST captured the diurnal variations of tower GPP for different biomes. We then produced multiple high resolution ECOSTRESS GPP maps for the central and northern California. We found distinct changes in GPP at different times of day (e.g., higher in late morning, peak around noon, approaching zero at dusk), and clear differences in productivity across landscapes (e.g., savannas, croplands, grasslands, and forests) for different times of day. ECOSTRESS GPP also captured the seasonal variations in the diurnal cycling of photosynthesis. This study demonstrates the feasibility of using ECOSTRESS data for producing instantaneous GPP (i.e., GPP for the acquisition time of the ECOSTRESS data) for different times of day. The ECOSTRESS GPP can shed light on how plant photosynthesis and water use vary over the course of the diurnal cycle and inform agricultural management and future improvement of terrestrial biosphere/land surface models.

Improved estimation of gross primary production of paddy rice cropland with changing model parameters over phenological transitions

Paddy rice is one of the main grain crops in the world. Accurate estimations of the gross primary production (GPP) of paddy rice are essential for assessing rice grain production and monitoring the carbon cycle in paddy fields with the aim of providing ideal conditions for crops throughout the growing season. Several studies have demonstrated the advantages of combining the eddy covariance technique with remotely sensed data to model GPP at CO2 eddy flux tower sites. As paddy rice continuously changes during its growth and development, and important growth events frequently occur, it is critical to observe the growing conditions at various stages of the process. To better understand the variations in GPP at different growth stages, two key parameters that drive the vegetation photosynthesis model (VPM) are analyzed and estimated at various phenological phases. Specifically, general piecewise logistic functions are used to extract phenological transitions from data at four paddy rice flux tower sites. The maximum light-use efficiency (LUE) and optimum temperature are estimated from these phenological transitions, and these indicators are used to drive the VPM to simulate GPP over multiple years at the four sites. The simulation results show that GPP based on our phenological transition-based VPM (GPPPVPM) agrees reasonably well with the variations of GPP estimated from CO2 flux data (GPPEC) (R2 > 0.9). In addition, a comparison indicates that GPPPVPM tracks the seasonal dynamics of GPPEC better than GPP estimated from the original VPM. Furthermore, GPP based on the improved maximum LUE is lower than GPPEC at most flux sites and GPP based on the improved optimum temperature is higher than GPPEC. These comparisons imply that the maximum LUE and optimum temperature estimated in the phenological transitions of paddy rice are beneficial to enhance the accuracy of GPP estimation. The improved estimation of GPP provides phenological insights into the temporal dynamics of vegetation photosynthesis in paddy fields.

The influence of lithology on stream metabolism in headwater systems

AbstractPhysical disturbances in streams have important effects on rates of gross primary production (GPP) and ecosystem respiration (ER). Underlying lithology can control sediment size, amount, and evolution in the stream, influencing substrate stability and in turn benthic organisms. We assessed patterns of disturbance and recovery for metabolic processes of GPP and ER associated with periods of increased flow and suspended sediment flux between December and April in two streams in the Oregon Coast Range with differing lithologies (basalt and sandstone). The results of whole‐stream metabolism modelling indicate that the two study streams have varying patterns of response and recovery rates after storm events. Both streams were heterotrophic during the entirety of the study period with changes in heterotrophy driven by changes in ER. Poststorm GPP decreased in both streams, but the basalt basin had greater proportional decreases and recovered slower than the sandstone basin. This result was unexpected and appeared to be associated with lower light availability in the basalt basin driven by increased turbidity during storm events; the coarser basalt substrate weathers into smaller size fractions than the finer sandstone substrate, remaining in suspension over longer periods and limiting light availability to benthic primary producers. The rates of ER in the sandstone basin did not change from prestorm to poststorm, whereas rates of ER in the basalt basin had varying responses. Overall, our results indicated that the underlying lithology of small mountain streams can drive variability in GPP by controlling sediment size and light availability during storms events.

A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States

Abstract. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time and observation-based GPP products. To address this critical gap, here we leverage a state-of-the-art vegetation index, near-infrared reflectance of vegetation (NIRV), along with accurate photosynthetically active radiation (PAR), to produce a SatelLite Only Photosynthesis Estimation (SLOPE) GPP product for the contiguous United States (CONUS). Compared to existing GPP products, the proposed SLOPE product is advanced in its spatial resolution (250 m versus &gt;500 m), temporal resolution (daily versus 8 d), instantaneity (latency of 1 d versus &gt;2 weeks) and quantitative uncertainty (on a per-pixel and daily basis versus no uncertainty information available). These characteristics are achieved because of several technical innovations employed in this study: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRV (SANIRV) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Cropland Data Layer (CDL) product to predict dynamic C4 crop fraction. Through developing a parsimonious model with only two slope parameters, the proposed SLOPE product explains 85 % of the spatial and temporal variations in GPP acquired from 49 AmeriFlux eddy-covariance sites (324 site years), with a root-mean-square error (RMSE) of 1.63 gC m−2 d−1. The median R2 over C3 and C4 crop sites reaches 0.87 and 0.94, respectively, indicating great potentials for monitoring crops, in particular bioenergy crops, at the field level. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for biological and environmental research, carbon cycle research, and a broad range of real-time applications at the regional scale. The archived dataset is available at https://doi.org/10.3334/ORNLDAAC/1786 (download page: https://daac.ornl.gov/daacdata/cms/SLOPE_GPP_CONUS/data/, last access: 20 January 2021) (Jiang and Guan, 2020), and the real-time dataset is available upon request.

Multi-year eddy covariance measurements of net ecosystem exchange in tropical dry deciduous forest of India

Tropical deciduous forests are unique in terms of their geographical distribution, strong seasonality and their contribution to global carbon dynamics, but yet their carbon sequestration potential is poorly sampled. In the current study, we report the carbon balance of 65-year-old tropical dry deciduous forest in central India using the multi-year eddy covariance measurements from November 2011 to May 2019. Over the study period, the forest site was observed to be a net sink of atmospheric CO2 with a mean annual net ecosystem productivity (NEP) of 524 (± 40; ±1 SD across different years) g C m−2 yr−1 with a 233 (±15) day growing season. The NEP was partitioned into gross primary productivity (GPP) of 3358 (± 167) g C m−2 yr−1 and annual carbon loss due to respiration and decomposition (Reco) of 2834 (± 157) g C m−2 yr−1. The ecosystem showed a strong seasonality as a source of carbon during the leaf-off season (March to June), and as a carbon sink during the rest of the year with significant sequestration during the winter season (October to December). The intra-annual analysis suggested that CO2 flux is primarily controlled by canopy greenness with mean Reco/GPP ratio varying from 1.90 (± 0.12) to 0.79 (± 0.04) during leaf-off to leaf-on seasons. The monthly C fluxes in the growing season are found to be strongly correlated to the environmental variables rather than in the leaf-off season, while variability in monthly ecosystem respiration was better explained by air temperature during the leaf-off season than the growing season. Further, the inter-annual variability of NEP was mainly dependent on growing season length and mean annual temperature. The variations in annual GPP and Reco were directly dependent on the increase in mean annual temperature. The eddy covariance-based NEP was complemented by close independent biometric estimates, imparting confidence in our measurements. In conclusion, this tropical dry deciduous forest site is a substantial carbon sink and would add crucial information regarding carbon budgeting of tropical forests in global carbon balance models.

Disaggregating the effects of nitrogen addition on gross primary production in a boreal Scots pine forest

Adding nitrogen to boreal forest ecosystems commonly increases gross primary production (GPP). The effect of nitrogen addition on ecosystem GPP is convoluted due to the impacts of and interactions among leaf scale photosynthetic productivity, canopy structure, site fertility, and environmental constraints. We used a unique controlled nitrogen fertilisation experiment combined with eddy covariance measurements and the calibration of a LUE-based (light use efficiency) photosynthetic production model in order to reveal differences in photosynthetic capacity due to nitrogen addition.A systematically designed soil moisture survey was conducted to characterise the within-site spatial heterogeneity and validate the difference of water stress between fertilised and control sites. The canopy photosynthetic light responses and environmental constraints were evaluated using an inverse modelling approach. We found that nitrogen fertilisation elevated ecosystem GPP by 24% according to model simulations. This was caused by increases in ecosystem light interception (through an increase in leaf area index (LAI)) and LUE by 7% and 17%, respectively. Nitrogen addition increased canopy potential LUE for both low and high photosynthetic photon flux density (PPFD) conditions. The calculations of leaf area and light interception indicated that the understorey vegetation contributed 9% of ecosystem GPP in the fertilised site and 7% in the control site when assuming a same LUE for trees and shrubs. The constraint arising from atmospheric water demand, rather than soil water stress, was the dominating control of the intra- and inter-annual GPP variations. The uncertainty propagated from soil moisture data is negligible for GPP predictions, but influential in the inference on the severity of the drought. This study demonstrates the combination of the controlled field experiment with the inverse modelling approach provides a powerful tool to quantitatively describe and disaggregate N addition effects on forest ecosystem GPP.

Toward a comprehensive understanding of global vegetation CO2 assimilation from space.

Large-scale global analysis of the relationship between growing season solar-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP), indicated by the GPP/SIF ratio, varied greatly with higher values found in wet-and-cold climate regions and lower values found in hot-and-dry climate regions. Such pattern has been shown to be most influenced by the environmental factor of moisture availability.

Observational Constraints on the Response of High‐Latitude Northern Forests to Warming

AbstractSince the 1960s, carbon cycling in the high‐latitude northern forest (HLNF) has experienced dramatic changes: Most of the forest is greening and net carbon uptake from the atmosphere has increased. During the same time period, the CO2 seasonal cycle amplitude (SCA) has increased by ~50% or more. Disentangling complex processes that drive these changes has been challenging. In this study, we substitute spatial sensitivity to temperature for time to quantify the impact of temperature increase on gross primary production (GPP), total ecosystem respiration (TER), the fraction of Photosynthetic Active Radiation (fPAR), and the resulted contribution of these changes in amplifying the CO2 SCA over the HLNF since 1960s. We use the spatial heterogeneity of GPP inferred from solar‐induced chlorophyll Fluorescence in combination with net ecosystem exchange (NEE) inferred from column CO2 observations made between 2015 and 2017 from NASA's Orbiting Carbon Observatory‐2. We find that three quarters of the spatial variations in GPP can be explained by the spatial variation in the growing season mean temperature (GSMT). The long term hindcast captures both the magnitude and spatial variability of the trends in observed fPAR. We estimate that between 1960 and 2010, the increase in GSMT enhanced both GPP and the SCA of NEE by ~20%. The calculated enhancement of NEE due to increase in GSMT contributes 56–72% of the trend in the CO2 SCA at high latitudes, much larger than simulations by most biogeochemical models.