Regional Gross Primary Production Research Articles

Estimation of Global Cropland Gross Primary Production from Satellite Observations by Integrating Water Availability Variable in Light-Use-Efficiency Model

Satellite-based models have been widely used to estimate gross primary production (GPP) of terrestrial ecosystems. Although they have many advantages for mapping spatiotemporal variations of regional or global GPP, the performance in agroecosystems is relatively poor. In this study, a light-use-efficiency model for cropland GPP estimation, named EF-LUE, driven by remote sensing data, was developed by integrating evaporative fraction (EF) as limiting factor accounting for soil water availability. Model parameters were optimized first using CO2 flux measurements by eddy covariance system from flux tower sites, and the optimized parameters were further spatially extrapolated according to climate zones for global cropland GPP estimation in 2001–2019. The major forcing datasets include the fraction of absorbed photosynthetically active radiation (FAPAR) data from the Copernicus Global Land Service System (CGLS) GEOV2 dataset, EF from the ETMonitor model, and meteorological forcing variables from ERA5 data. The EF-LUE model was first evaluated at flux tower site-level, and the results suggested that the proposed EF-LUE model and the LUE model without using water availability limiting factor, both driven by flux tower meteorology data, explained 82% and 74% of the temporal variations of GPP across crop sites, respectively. The overall KGE increased from 0.73 to 0.83, NSE increased from 0.73 to 0.81, and RMSE decreased from 2.87 to 2.39 g C m−2 d−1 in the estimated GPP after integrating EF in the LUE model. These improvements may be largely attributed to parameters optimized for different climatic zones and incorporating water availability limiting factor expressed by EF into the light-use-efficiency model. At global scale, the verification by GPP measurements from cropland flux tower sites showed that GPP estimated by the EF-LUE model driven by ERA5 reanalysis meteorological data and EF from ETMonitor had overall the highest R2, KGE, and NSE and the smallest RMSE over the four existing GPP datasets (MOD17 GPP, revised EC-LUE GPP, GOSIF GPP and PML-V2 GPP). The global GPP from the EF-LUE model could capture the significant negative GPP anomalies during drought or heat-wave events, indicating its ability to express the impacts of the water stress on cropland GPP.

Improving global gross primary productivity estimation by fusing multi-source data products

A reliable estimate of the gross primary productivity (GPP) of terrestrial vegetation is essential for both making decisions to address global climate change and understanding the global carbon balance. The lack of consistency in global terrestrial GPP estimates across various products leads to great uncertainty. In this study, we improve the quantification of global gross primary productivity by integrating multiple source GPP products without using any prior knowledge through the Bayesian-based Three-Cornered Hat (BTCH) method to generate a new weighted GPP data set. The fusion results demonstrate the superiority of weighted GPP, which greatly reduces the random error of individual datasets and fully takes advantage of the characteristics of multi-source data products. The weighted dataset can largely reproduce the interannual variation of regional GPP. Overall, the merging scheme based on the BTCH method can effectively generate a new GPP dataset that integrates information from multiple products and provides new ideas for GPP estimation on a global scale.

Adjusting solar-induced fluorescence to nadir-viewing provides a better proxy for GPP

Solar-induced fluorescence (SIF) provides key information for inferring terrestrial gross primary productivity (GPP). However, there is little research on analyzing the performance of nadir-adjusted SIF (SIFnadir) on GPP, compared to total SIF emitted by all leaves (SIFtotal) for reducing the viewing angle effects and estimating GPP. Besides, there have been controversial opinions on whether SIFtotal is better correlated to GPP than raw SIF observations (SIFobs). Here we systematically analyzed the relationship between raw/nadir/total SIF (i.e., SIFobs, SIFnadir and SIFtotal) and GPP and investigated the underlying mechanism, using multi-angular field measurements and eddy covariance data in wheat and corn crops at sub-daily scale. We further compared the performance of SIFobs, SIFnadir and SIFtotal in estimating GPP using the TROPOspheric Monitoring Instrument (TROPOMI) SIF and concurrent AmeriFlux measurements at daily scale. The results indicate that diurnal SIFnadir has stronger correlations to GPP than SIFobs for field measurements, with the increase of mean coefficient of determination (R2) by 0.05–0.07 for far-red band and 0.11–0.20 for red band. SIFnadir shows comparable performance with SIFtotal for both far-red and red bands. Although the viewing angle effects dominate the difference in estimating GPP between SIFnadir and SIFobs, the correlation between light use efficiency (LUE) and fesc further determines the different performance of SIFnadir and SIFtotal in estimating GPP. TROPOMI-based analysis further confirms that SIFnadir overall has higher correlations to AmeriFlux GPP than SIFobs for different plant functional types and shows similar performance with SIFtotal. Compared to SIFtotal, the estimation of SIFnadir independent of viewing angle effects does not require any canopy structure parameters, and thus offers promising potential for reliably estimating regional and global GPP.

Impacts of the 2012-2015 Californian drought on carbon, water and energy fluxes in the Californian Sierras: Results from an imaging spectrometry-constrained terrestrial biosphere model.

Accurate descriptions of current ecosystem composition are essential for improving terrestrial biosphere model predictions of how ecosystems are responding to climate variability and change. This study investigates how imaging spectrometry-derived ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional-scale carbon, water and energy fluxes. Incorporating imaging spectrometry-derived composition of five plant functional types (Grasses/Shrubs, Oaks/Western Hardwoods, Western Pines, Fir/Cedar and High-elevation Pines) into the Ecosystem Demography (ED2) terrestrial biosphere model improves predictions of net ecosystem productivity (NEP) and gross primary productivity (GPP) across four flux towers of the Southern Sierra Critical Zone Observatory (SSCZO) spanning a 2250m elevational gradient in the western Sierra Nevada. NEP and GPP root-mean-square-errors were reduced by 23%-82% and 19%-89%, respectively, and water flux predictions improved at the mid-elevation pine (Soaproot), fir/cedar (P301) and high-elevation pine (Shorthair) flux tower sites, but not at the oak savanna (San Joaquin Experimental Range [SJER]) site. These improvements in carbon and water predictions are similar to those achieved with model initializations using ground-based inventory composition. The imaging spectrometry-constrained ED2model was then used to predict carbon, water and energy fluxes and above-ground biomass (AGB) dynamics over a 737km2 region to gain insight into the regional ecosystem impacts of the 2012-2015 Californian drought. The analysis indicates that the drought reduced regional NEP, GPP and transpiration by 83%, 40% and 33%, respectively, with the largest reductions occurring in the functionally diverse, high basal area mid-elevation forests. This was accompanied by a 54% decline in AGB growth in 2012, followed by a marked increase (823%) in AGB mortality in 2014, reflecting an approximately 10-fold increase in per capita tree mortality from ~55 treeskm-2 year-1 in 2010-2011, to ~535 treeskm-2 year-1 in 2014. These findings illustrate how imaging spectrometry estimates of ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional carbon, water, and energy fluxes, and biomass dynamics.

Extreme events driving year-to-year differences in gross primary productivity across the US

Abstract. Solar-induced chlorophyll fluorescence (SIF) has previously been shown to strongly correlate with gross primary productivity (GPP); however this relationship has not yet been quantified for the recently launched TROPOspheric Monitoring Instrument (TROPOMI). Here we use a Gaussian mixture model to develop a parsimonious relationship between SIF from TROPOMI and GPP from flux towers across the conterminous United States (CONUS). The mixture model indicates the SIF–GPP relationship can be characterized by a linear model with two terms. We then estimate GPP across CONUS at 500 m spatial resolution over a 16 d moving window. We observe four extreme precipitation events that induce regional GPP anomalies: drought in western Texas, flooding in the midwestern US, drought in South Dakota, and drought in California. Taken together, these events account for 28 % of the year-to-year GPP differences across CONUS. Despite these large regional anomalies, we find that CONUS GPP varies by less than 4 % between 2018 and 2019.

Impact of different ERA reanalysis data on GPP simulation

The performance of the terrestrial biosphere models (TBMs) is limited by the accuracy of climate forcing data. As the reanalysis products of the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA-Interim and ERA5 data are widely used in the simulation of terrestrial carbon budgets and reveal their responses to climate change. However, the discrepancy between simulated carbon budgets driven by ERA-Interim and ERA5 on a global scale has not been evaluated. In this study, driven by ERA-Interim and ERA5, we conducted two simulations by a TBM, BEPS (Boreal Ecosystem Productivity Simulator), to investigate the differences of simulated Gross primary productivity (GPP) in temporal trends and spatial patterns and to identify the differences in climate factors resulted in the spreads of simulated GPPs. We found that by 2015, the relatively stable difference of simulated GPP by ERA-Interim and ERA5 was about 3.55 Pg C yr−1. Since 2016, the differences of simulated GPP increased gradually and peaked in the last year of our simulation in 2018 at 13.16 Pg C yr−1. This significant difference in GPP was due to the changes of GPP in the Amazon Basin, Congo Basin and South Asia, where tropical forests and tropical savannahs & grasslands were widely distributed. In these regions, the GPP of ERA5 in total was at least 3.0 Pg C yr−1 lower than that of ERA-Interim after 2016. The difference of GPP in such regions was the main reason why ERA5 and ERA-Interim GPP showed different interannual variability. And less precipitation and higher temperature of ERA5 in tropical regions mainly results in the reduction of GPP compared with the results driven by ERA-Interim. Our results highlight challenges in using ERA5 and ERA-Interim to evaluate responses of ecosystem to climate change and provide implications for reducing the uncertainty of climate forcing data in simulating terrestrial carbon cycle.

Mismatches between vegetation greening and primary productivity trends in South Asia – A satellite evidence

Vegetation greening contributes to gross carbon sequestration by plants and thus plays a vital role in mitigation of global warming. Terrestrial vegetation has a significant greening trend in South Asia (SA); however, it remains elusive how this greening translates to gross primary production (GPP) (proxy of gross carbon uptake by plants at ecosystem scale) in SA. In this study, we investigated the spatiotemporal dynamics of GPP in correspondence to leaf area increase (based on leaf area index as proxy of greening) over SA using remote sensing driven data on Google Earth Engine (GEE) cloud computation, during 2003–2017. We found a much weaker increase of GPP in SA as compared to the overall greening trend, primarily attributed to the disturbance in cropland activities of the region. Especially, greening was highly significant (p < 0.05) over the most irrigated and fertile Indo-Gangetic Plain (IGP) of SA, however GPP trend was non-significant (p > 0.05) over this region. Further, natural vegetation areas also showed low vegetation greening and GPP increase during the study period. The analysis of the potential drivers revealed that both climatic and anthropogenic stresses (intensive cropping, population pressure) had a negative impact on the regional GPP, especially in IGP. Our study indicated that the overwhelming greening of SA because of enhanced cropland activities, did not contribute much to GPP over the region. The restricted cropland canopy carbon uptake and low GPP increase in forests led to a lower than expected increase of gross carbon assimilation over SA in the context of climate changes and anthropogenic disturbances. Our study inferred that effective reforestation programs and conservation of natural vegetation areas are essential in SA for efficient vegetation carbon assimilation.

Competing effects of nitrogen deposition and ozone exposure on northern hemispheric terrestrial carbon uptake and storage, 1850–2099

Abstract. Tropospheric ozone (O3) and nitrogen deposition affect vegetation growth and, thereby, the ability of the land biosphere to take up and store carbon. However, the magnitude of these effects on the contemporary and future terrestrial carbon balance is insufficiently understood. Here, we apply an extended version of the O–CN terrestrial biosphere model that simulates the atmosphere to canopy transport of O3, its surface and stomatal uptake, the O3-induced leaf injury, and the coupled terrestrial carbon and nitrogen cycles. We use this model to simulate past and future impacts of air pollution against a background of concurrent changes in climate and carbon dioxide concentrations (CO2) for two contrasting representative concentration pathway (RCP) scenarios (RCP2.6 and RCP8.5). The simulations show that O3-related damage considerably reduced northern hemispheric gross primary production (GPP) and long-term carbon storage between 1850 and the 2010s. The simulated O3 effect on GPP in the Northern Hemisphere peaked towards the end of the 20th century, with reductions of 4 %, causing a reduction in the northern hemispheric carbon sink of 0.4 Pg C yr−1. During the 21st century, O3-induced reductions in GPP and carbon storage are projected to decline, through a combination of direct air pollution control methods that reduce near-surface O3 and the indirect effects of rising atmospheric CO2, which reduces stomatal uptake of O3 concurrent with increases of leaf-level water use efficiency. However, in hot spot regions such as East Asia, the model simulations suggest a sustained decrease in GPP by more than 8 % throughout the 21st century. O3 exposure reduces projected carbon storage at the end of the 21st century by up to 15 % in parts of Europe, the US, and East Asia. Our simulations suggest that the stimulating effect of nitrogen deposition on regional GPP and carbon storage is lower in magnitude compared to the detrimental effect of O3 during most of the simulation period for both RCPs. In the second half of the 21st century, the detrimental effect of O3 on GPP is outweighed by nitrogen deposition, but the effect of nitrogen deposition on land carbon storage remains lower than the effect of O3. Accounting for the stimulating effects of nitrogen deposition but omitting the detrimental effect of O3 may lead to an overestimation of projected carbon uptake and storage.

High-Resolution Monitoring and Assessment of Evapotranspiration and Gross Primary Production Using Remote Sensing in a Typical Arid Region

Land surface evapotranspiration (ET) and gross primary productivity (GPP) are critical components in terrestrial ecosystems with water and carbon cycles. Large-scale, high-resolution, and accurately quantified ET and GPP values are important fundamental data for freshwater resource management and help in understanding terrestrial carbon and water cycles in an arid region. In this study, the revised surface energy balance system (SEBS) model and MOD17 GPP algorithm were used to estimate daily ET and GPP at 100 m resolution based on multi-source satellite remote sensing data to obtain surface biophysical parameters and meteorological forcing data as input variables for the model in the midstream oasis area of the Heihe River Basin (HRB) from 2010 to 2016. Then, we further calculated the ecosystem water-use efficiency (WUE). We validated the daily ET, GPP, and WUE from ground observations at a crop oasis station and conducted spatial intercomparisons of monthly and annual ET, GPP, and WUE at the irrigation district and cropland oasis scales. The site-level evaluation results show that ET and GPP had better performance than WUE at the daily time scale. Specifically, the deviations in the daily ET, GPP, and WUE data compared with ground observations were small, with a root mean square error (RMSE) and mean absolute percent error (MAPE) of 0.75 mm/day and 26.59%, 1.13 gC/m2 and 36.62%, and 0.50 gC/kgH2O and 39.83%, respectively. The regional annual ET, GPP, and WUE varied from 300 to 700 mm, 200 to 650 gC/m2, and 0.5 to 1.0 gC/kgH2O, respectively, over the entire irrigation oasis area. It was found that annual ET and GPP were greater than 550 mm and 500 gC/m2, and annual oasis cropland WUE had strong invariability and was maintained at approximately 0.85 gC/kgH2O. The spatial intercomparisons from 2010 to 2016 revealed that ET had similar spatial patterns to GPP due to tightly coupled carbon and water fluxes. However, the WUE spatiotemporal patterns were slightly different from both ET and GPP, particularly in the early and late growing seasons for the oasis area. Our results demonstrate that spatial full coverage and reasonably fine spatiotemporal variation and variability could significantly improve our understanding of water-saving irrigation strategies and oasis agricultural water management practices in the face of water shortage issues.

Detecting regional GPP variations with statistically downscaled solar-induced chlorophyll fluorescence (SIF) based on GOME-2 and MODIS data

ABSTRACT Solar-Induced chlorophyll Fluorescence (SIF) is associated with vegetation canopy photosynthesis and is potentially used to retrieve Gross Primary Productivity (GPP). However, the coarse resolutions of the currently available SIF satellite data limit their applications. To expand the applicability of the SIF dataset, a framework was developed to disaggregate the Global Ozone Monitoring Experiment-2 (GOME-2) SIF dataset, which was based on statistical relationships between SIF and remotely sensed measurements of the Normalized Difference Vegetation Index (NDVI), the fraction of absorbed photosynthetically active radiation (f PAR), the soil moisture index and Land Surface Temperature (LST). The statistical relationships were established within a zone of n × n pixels (n∈[1, 25]) with a moving window technique. The regression function established within n × n pixels with the smallest Root Mean Square Error (RMSE) and highest coefficient of determination (R 2) was selected for downscaling regression. Compared with the fixed window technique (n = 5) and theglobal regression model, the moving window technique presented low residuals and high R 2 values. Validated with flux-tower eddy covariance measurements, the GPP retrieved within the downscaled SIF data shows the potential to improve vegetation GPP prediction, and the downscaled SIF could trace the seasonal phenology of evergreen forests.

Increased carbon uptake and water use efficiency in global semi-arid ecosystems

The semi-arid ecosystems dominate the inter-annual variability of the global carbon sink and the driving role of semi-arid ecosystems is becoming increasingly important. However, the impacts of climate change on the dynamics of carbon and water fluxes in global semi-arid ecosystems are still not well understood. We used a data-driven (or machine learning) approach, along with observations from a number of FLUXNET sites and spatially continuous satellite and meteorological data, to generate gridded carbon and water flux estimates for semi-arid regions globally, and then examined the magnitude, spatial patterns, and trends of carbon and water fluxes and their responses to climate change during the period 1982–2015. The average annual gross primary productivity (GPP), net ecosystem productivity (NEP), evapotranspiration (ET), and water use efficiency (WUE) were 628.6 g C m−2 yr−1, 9.6 g C m−2 yr−1, 463.1 mm yr−1, and 1.60 g C Kg−1 H2O, respectively. The climate conditions during the period 1982–2015 enhanced gross and net carbon uptake in global semi-arid regions. The spatially-averaged annual GPP, NEP, ET, and WUE in semi-arid regions showed significant increases both globally and regionally (Asia, Africa, and Australia). As with GPP and ET, WUE significantly increased in North America, Asia, Africa, and Australia. Australia was the most sensitive semi-arid region in terms of changes in carbon and water fluxes and their responses to climate. Semi-arid forests, shrublands, and savannas were net carbon sinks; croplands were minor carbon sources; grasslands were nearly carbon neutral. Overall, precipitation was the most important climate factor influencing the carbon and water fluxes; WUE in 40.9% of the semi-arid region was significantly influenced by precipitation. The global climate change is expected to influence global semi-arid ecosystems in many ways and our findings have implications for semi-arid ecosystem management and policy making.

Global divergent responses of primary productivity to water, energy, and CO2

The directionality of the response of gross primary productivity (GPP) to climate has been shown to vary across the globe. This effect has been hypothesized to be the result of the interaction between multiple bioclimatic factors, including environmental energy (i.e. temperature and radiation) and water availability. This is due to the tight coupling between water and carbon cycling in plants and the fact that temperature often drives plant water demand. Using GPP data extracted from 188 sites of FLUXNET2015 and observation-driven terrestrial biosphere models (TBMs), we disentangled the confounding effects of temperature, precipitation and carbon dioxide on GPP, and examined their long-term effects on productivity across the globe. Based on the FLUXNET2015 data, we observed a decline in the positive effect of temperature on GPP, while the positive effects of precipitation and CO2 were becoming stronger during 2000–2014. Using data derived from TBMs between 1980 and 2010 we found similar effects globally. The modeled data allowed us to investigate these effects more thoroughly over space and time. In arid regions, the modeled response to precipitation increased since 1950, approximately 30 years earlier than in humid regions. We further observed the negative effects of summer temperature on GPP in arid regions, suggesting greater aridity stress on productivity under global warming. Our results imply that aridity stress, triggered by rising temperatures, has reduced the positive influence of temperature on GPP, while increased precipitation and elevated CO2 may alleviate negative aridity impacts.

Gross Primary Production Estimation in Crops Using Solely Remotely Sensed Data

Gross primary production (GPP) is a measure for crop productivity, indicating yield and expressing C exchange of agro‐ecosystems. A multitude of satellite sensors at varying spatial and spectral resolution brings a possibility to use remotely sensed data for regional and global GPP estimation. More work is still needed to develop algorithms for GPP estimation applicable to multiple, if not all, vegetation types, phenological phases, and environmental conditions. This study employed neural networks (NN), multiple linear regressions (MLR), and vegetation indices (VI) to develop algorithms for GPP estimation based solely on remotely sensed data in two crops, maize (Zea mays L.) and soybean [Glycine max (L.) Merr.], with contrasting canopy architectures, leaf structures, and photosynthetic pathways. The focus of the study was to devise algorithms not requiring re‐parameterization for different crop species. Data used in the models included in situ hyperspectral reflectance and satellite surface reflectance products. For the tested NN, MLR, and VI algorithms, the bands selected to obtain minimal errors in maize and soybean combined were mainly located in red edge and near infrared (NIR) spectral regions. For both in situ reflectance and satellite surface reflectance, a NN estimated GPP with normalized root mean square errors (NRMSE) below 14 and 18.7%, respectively, and VI using bands in red edge and NIR with NRMSE 15.6 and 20.4%, respectively. The results showed that the models based on the red edge and NIR bands may facilitate accurate assessments of crop GPP at multiple scales, from close range to satellite platforms.Core Ideas The models using remotely sensed data allow accurate estimation of gross primary production in two crops. The optimal bands for gross primary production estimation in two crops were in near infrared and red edge regions. Vegetation indices with red edge and near infrared reflectance were generic for maize and soybean.

Annual and seasonal variations in gross primary productivity across the agro-climatic regions in India.

Gross primary productivity (GPP) is a vital ecosystem variable that is used as a proxy to study the functional behaviour of a terrestrial ecosystem and its ability to regulate atmospheric CO2 by working as a carbon pool. India, having the potential terrestrial ecosystem dynamics to absorb the atmospheric carbon dioxide to some extent, is one of the least-explored regions in terms of carbon monitoring studies. The current study evaluates the applicability of a newly developed, quantum yield-based, remote sensing data-driven diagnostic model called the Southampton Carbon Flux (SCARF). This model was used to estimate the annual and seasonal variability of the terrestrial GPP over the Indian region with a spatial resolution of 1 km during 2008. This modified version of the conventional production efficiency model successfully predicted GPP using meteorological variables (PAR, air temperature and dew point temperature), the fraction of photosynthetically active radiation and quantum yield of C3 and C4 plants as the key input parameters. The annual GPP values were in the range from 0 to 4147.55 g C m-2 year-1, with a mean value of 1507.32 g C m-2 year-1. The maximum and minimum GPP were during the summer monsoon and pre-monsoon, respectively. The seasonal and annual distributions of GPP over the study area obtained using the SCARF model, and the MODIS GPP product (MOD17A2H) were similar. However, MODIS was found to underestimate the GPP in all regions and an overestimation in eastern Himalaya region. The study reveals that environmental scalars, specifically water stress, are the pivotal controlling variables responsible for the variation of GPP in India. The estimates of the GPP in different regions of the study area were made using SCARF, and an eddy covariance technique was similar. The SCARF model can be used to estimate GPP on a global scale. SCARF appears to be a better model in terms of the simplicity of the algorithm, performance and resolution. Thus, it may give higher accuracy in carbon monitoring studies.

Simulating emission and scattering of solar-induced chlorophyll fluorescence at far-red band in global vegetation with different canopy structures

Recent satellite retrieval of solar-induced fluorescence (SIF) has provided promising information for constraining carbon flux simulation with terrestrial biosphere models (TBMs). For this purpose, it is necessary to implement a SIF model in TBMs. SIF emerging from leaves generally undergoes multiple scattering processes within a canopy. However, it remains unclear how canopy scattering impacts observed SIF, which is used to constrain regional and global GPP modeling. Here, we developed an efficient scheme to account for the canopy scattering in a SIF model. Then we coupled this mechanistic representation of SIF to Boreal Ecosystem Productivity Simulator (BEPS) and evaluated the new model (BEPS-SIF) simulations with ground and satellite-based SIF measurements. The results showed that BEPS-SIF reproduced global patterns of SIF observed by a satellite sensor and captured the seasonality of SIF reasonably well over different regions. The canopy scattering effects on the relationship between SIF and gross primary productivity (GPP) were also examined at the global scale using the BEPS-SIF model. We confirmed that SIF emitted from leaves was more directly linked to GPP globally than the observed canopy-leaving SIF, with higher improvements of the coefficient of determination (R2) in dense vegetated regions and lower improvements of R2 in sparse vegetated regions. This study highlights the importance of implementing the canopy scattering effect in a fluorescence model of TBMs and the need to account for this effect in the use of satellite SIF data for constraining global GPP modeling.

The 2012 Flash Drought Threatened US Midwest Agroecosystems

In the summer of 2012, the US Midwest, the most productive agricultural region in the world, experienced the most intense and widespread drought on record for the past hundred years. The 2012 drought, characterized as ‘flash drought’, developed in May with a rapid intensification afterwards, and peaked in mid-July. ~76% of crop region and 60% of grassland and pasture regions have been under moderate to severe dry conditions. This study used multiple lines of evidences, i.e., in-situ AmeriFlux measurements, spatial satellite observations, and scaled ecosystem modeling, to provide independent and complementary analysis on the impact of 2012 flash drought on the US Midwest vegetation greenness and photosynthesis carbon uptake. Three datasets consistently showed that 1) phenological activities of all biomes advanced 1–2 weeks earlier in 2012 compared to the other years of 2010-2014; 2) the drought had a more severe impact on agroecosystems (crop and grassland) than on forests; 3) the growth of crop and grassland was suppressed from June with significant reduction of vegetation index, sun-induced fluorescence (SIF) and gross primary production (GPP), and did not recover until the end of growing season. The modeling results showed that regional total GPP in 2012 was the lowest (1.76 Pg C/yr) during 2010–2014, and decreased by 63 Tg C compared with the other-year mean. Agroecosystems, accounting for 84% of regional GPP assimilation, were the most impacted by 2012 drought with total GPP reduction of 9%, 7%, 6%, and 29% for maize, soybean, cropland, and grassland, respectively. The frequency and severity of droughts have been predicted to increase in future. The results imply the importance to investigate the influences of flash droughts on vegetation productivity and terrestrial carbon cycling.

Impact of physiological and phenological change on carbon uptake on the Tibetan Plateau revealed through GPP estimation based on spaceborne solar-induced fluorescence

Although gross primary production (GPP) is an essential proxy for reflecting terrestrial ecosystem function, GPP estimation at regional scale on the Tibetan Plateau (TP) is constrained by the lack of ground observations. Moreover, how climate-induced phenological and physiological change further affects carbon uptake in this region remains unclear. In this study, we first estimated GPP at 8-day intervals and a 0.5° resolution from 2007 to 2015 over the TP based on an improved approach and GOME-2 sun-induced fluorescence (SIF) retrievals. The obtained SIF-based GPP coincided well with flux observations and two state of the art GPP products, with a regional carbon uptake of 0.62 ± 0.04 PgC year−1 or 307 ± 22 gC m−2 year−1. With the SIF-based GPP, two phenological indicators (start and end date of the growing season, i.e., SGS and EGS) and one physiological indicator (maximum photosynthesis capacity, GPPmax) were identified and their relative contributions to inter-annual GPP variability were further quantitatively separated using a multiple regression model. Advanced SGS, delayed EGS, and increasing GPPmax can all enhance carbon uptake and a combination of the three indicators can explain 72 ± 20% of GPP inter-annual variability. The response of annual GPP to phenological and physiological variations has significant altitude dependence, as the decline of annual GPP in most of the area is dominated by the GPPmax decline, while the increase of annual GPP in the high-altitude area is dominated by the advanced SGS. The response of all three indicators to both temperature and precipitation variation has great spatial heterogeneity. Our study suggests that remote sensing of SIF can provide a unique opportunity to estimate GPP in regions with a lack of ground observations and that our enhanced understanding of the impact of the climate-induced phenological and physiological change on GPP variability in alpine ecosystems can improve GPP estimation in a changing climate.

The impact of spatiotemporal variability in atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; concentration on global terrestrial carbon fluxes

Abstract. Land carbon fluxes, e.g., gross primary production (GPP) and net biome production (NBP), are controlled in part by the responses of terrestrial ecosystems to atmospheric conditions near the Earth's surface. The Coupled Model Intercomparison Project Phase 6 (CMIP6) has recently proposed increased spatial and temporal resolutions for the surface CO2 concentrations used to calculate GPP, and yet a comprehensive evaluation of the consequences of this increased resolution for carbon cycle dynamics is missing. Here, using global offline simulations with a terrestrial biosphere model, the sensitivity of terrestrial carbon cycle fluxes to multiple facets of the spatiotemporal variability in atmospheric CO2 is quantified. Globally, the spatial variability in CO2 is found to increase the mean global GPP by a maximum of 0.05 Pg C year−1, as more vegetated land areas benefit from higher CO2 concentrations induced by the inter-hemispheric gradient. The temporal variability in CO2, however, compensates for this increase, acting to reduce overall global GPP; in particular, consideration of the diurnal variability in atmospheric CO2 reduces multi-year mean global annual GPP by 0.5 Pg C year−1 and net land carbon uptake by 0.1 Pg C year−1. The relative contributions of the different facets of CO2 variability to GPP are found to vary regionally and seasonally, with the seasonal variation in atmospheric CO2, for example, having a notable impact on GPP in boreal regions during fall. Overall, in terms of estimating global GPP, the magnitudes of the sensitivities found here are minor, indicating that the common practice of applying spatially uniform and annually increasing CO2 (without higher-frequency temporal variability) in offline studies is a reasonable approach – the small errors induced by ignoring CO2 variability are undoubtedly swamped by other uncertainties in the offline calculations. Still, for certain regional- and seasonal-scale GPP estimations, the proper treatment of spatiotemporal CO2 variability appears important.

Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing

Gross primary production (GPP) in forests is the most important carbon flux in terrestrial ecosystems. Forest ecosystems with high leaf area index (LAI) values have diverse species or complex forest structures with vertical stratifications that influence the carbon–water–energy cycles. In this study, we used three light use efficiency (LUE) GPP models and site-level experiment data to analyze the effects of the vertical stratification of dense forest vegetation on the estimates of remotely sensed GPP during the growing season of two forest sites in East Asia: Dinghushan (DHS) and Tomakomai (TMK). The results showed that different controlling environmental factors of the vertical layers, such as temperature and vapor pressure deficit (VPD), produce different responses for the same LUE value in the different sub-ecosystems (defined as the tree, shrub, and grass layers), which influences the GPP estimation. Air temperature and VPD play important roles in the effects of vertical stratification on the GPP estimates in dense forests, which led to differences in GPP uncertainties from −50% to 30% because of the distinct temperature responses in TMK. The unequal vertical LAI distributions in the different sub-ecosystems led to GPP variations of 1–2 gC/m2/day with uncertainties of approximately −30% to 20% because sub-ecosystems have unique absorbed fractions of photosynthetically active radiation (APAR) and LUE. A comparison with the flux tower-based GPP data indicated that the GPP estimations from the LUE and APAR values from separate vertical layers exhibited better model performance than those calculated using the single-layer method, with 10% less bias in DHS and more than 70% less bias in TMK. The precision of the estimated GPP in regions with thick understory vegetation could be effectively improved by considering the vertical variations in environmental parameters and the LAI values of different sub-ecosystems as separate factors when calculating the GPP of different components. Our results provide useful insight that can be used to improve the accuracy of remote sensing GPP estimations by considering vertical stratification parameters along with the LAI of sub-ecosystems in dense forests.

A remote sensing-based two-leaf canopy conductance model: Global optimization and applications in modeling gross primary productivity and evapotranspiration of crops

The temporal dynamics of optimum stomatal conductance (gsmax), as well differences between C3 and C4 crops, have rarely been considered in previous remote sensing (RS)-based Jarvis-type canopy conductance (Gc) models. To address this issue, a RS-based two-leaf Jarvis-type Gc model, RST-Gc, was optimized and validated for C3 and C4 crops using 19 crop flux sites across Europe, North America, and China. RST-Gc included restrictive functions for air temperature, vapor pressure deficit, and soil water deficit, and it used satellite-retrieved NDVI to formulate the temporal variation of gsmax defined at a photosynthetic photon flux density (PPFD) of 2000 μmol m−2 s−1 (gsm, 2000). Results showed that the parameters of RST-Gc differed between C3 and C4 crops. RST-Gc successfully simulated variations in Penman–Monteith (PM)-derived daytime Gc with R2 = 0.57 for both C3 and C4 crops. RST-Gc was incorporated into a revised evapotranspiration (ET) model and a new gross primary productivity (GPP) model. The two models were validated at 19 crop flux sites. Daily mean inputs were generally incorporated into a PM approach to model daily transpiration. This is inappropriate because available energy and stomatal conductance vary significantly on a diurnal basis, with both non-linearly regulating transpiration rate. The PM approach with daily mean inputs produced unreasonable transpiration rate estimates. Efforts were made in the revised ET model (denoted as RS-WBPM2), which was modified from the water balance based RS-PM (RS-WBPM) model of Bai et al. (2017), to address this issue by calculating transpiration using daytime inputs. The photosynthesis-based stomatal conductance model, developed by Ball et al. (1987a) and improved by Leuning (1995) (BBL model), was inverted to calculate GPP using canopy conductance; the inverted model was denoted as IBBL. Cross validation showed good agreement between flux tower measurements and modeled ET (R2 = 0.79, RMSE (root mean standard error) = 20.66 W m−2 for daily ET and R2 = 0.87, RMSE = 15.32 W m−2 for 16-day ET) and GPP (R2 = 0.83, RMSE = 2.49 gC m−2 d−1 for daily GPP and R2 = 0.86, RMSE = 1.96 gC m−2 d−1 for 16-day GPP) for the two models. Within-site validations demonstrated the successful performance of the two models at 18 sites (albeit with one outlier). Inter-site variations in ET and GPP were also successfully reproduced by the models. NDVI-derived gsm, 2000 outperformed the fixed gsm, 2000 in both ET and GPP estimates. The results imply that the RS-WBPM2 and IBBL models are useful tools for modeling regional and global ET and GPP.