Vegetation Gross Primary Production Research Articles

Solar radiation variation weakened the boost of gross primary production by vegetation restoration in China’s most forestry engineering areas during 2001–2020

Over the past decades, ecological restoration initiatives in China have made great progress in restoring degraded forests and increasing vegetation coverage, yet the carbon sequestration effects of these initiatives in the context of climate change are not clear. In this study, we assessed the effects of vegetation restoration on gross primary production (GPP) in China’s forestry engineering areas, where large-scale vegetation restoration programmes were launched, during 2001–2020 by disentangling the respective roles of land cover change (LCC), CO2 fertilization, and climate changes using a two-leaf light use efficiency model. We found that LCC attributed by the vegetation restoration dominantly accelerated the increase of GPP in seven out of the eight areas, and CO2 fertilization played a near-equivalent role in all areas. By contrast, the changes in different climate factors contributed to GPP variations diversely. The solar radiation variation greatly inhibited the vegetation GPP over time in seven out of these areas, and the changes in air temperature and vapor pressure deficit regulated GPP inter-annual variations without clear trends in all areas. This study advances our understanding of the contribution of China’s afforestation on its forest GPP in a changing climate, which may help to better manage forests to tackle the challenge of the climate crisis in the future.

Asymmetric response of vegetation GPP to impervious surface expansion: Case studies in the Yellow and Yangtze River Basins

Terrestrial gross primary production (GPP) changes due to impervious surfaces significantly impact ecosystem services in watersheds. Understanding the asymmetric response of vegetation GPP to impervious surface expansion is essential for regional development planning and ecosystem management. However, the asymmetric response of vegetation GPP to the impacts of impervious surface expansion is unknown in different watersheds. This paper selected the Yellow River and Yangtze River basins as case studies. We characterized the overall change in GPP based on changes in impervious surface ratio (ISR), determined impervious surface expansion's direct and indirect impacts on GPP in the two watersheds, and further analyzed the asymmetric response of the compensatory effects of indirect influences on the impervious surface expansion in different watersheds. The results showed that: (1) The vegetation GPP decreased with increasing ISR in the Yangtze River Basin, while that in the Yellow River Basin first increased and then reduced. (2) The direct impacts of increased ISR reduced vegetation GPP, while the indirect impacts both had a growth-compensating effect. Growth compensation stabilized at approximately 0.40 and 0.30 in the Yellow and Yangtze River Basins. (3) When the ISR was 0.34–0.56, the growth compensation could offset the reduction of GPP due to direct impact and ensure that the background vegetation GPP was not damaged in the Yellow River Basin. In contrast, the background vegetation GPP was inevitably impaired with increased ISR in the Yangtze River Basin. Therefore, this study suggests that the ISR should be ensured to be between 0.34 and 0.56 to maximize the impervious surface of the Yellow River Basin without compromising the background vegetation GPP. While pursuing impervious surface expansion in the Yangtze River Basin, other programs should be sought to compensate for the loss to GPP.

Characterizing the effect of scaling errors on the spatial downscaling of mountain vegetation gross primary productivity

ABSTRACT High spatial resolution Gross Primary Productivity (GPP) estimation makes it feasible to better understand the spatial heterogeneity of mountain vegetation photosynthesis. Spatial downscaling is a practical approach to obtaining high resolution GPP estimates, whereas there is almost no attention given to the downscaled biases resulted from the widely reported scaling errors in medium or coarse resolution GPP estimates. To fill the above gap, this study adopted an eco-hydrological model to obtain 960 m resolution distributed GPP estimates (not including scaling errors) and lumped GPP estimates (including scaling errors) over four mountainous watersheds. Then, the distributed and lumped estimates were downscaled from 960 m to 30 m, respectively. Finally, the contribution of reducing scaling errors was characterized by the agreement index (d), BIAS and Root-Mean-Square-Error (RMSE) values between downscaled GPP and referenced GPP (directly generated at the spatial resolution of 30 m). Results showed that a large difference existed between lumped and distributed GPP, with d, BIAS, and RMSE of 0.79, 212, and 334 gC m−2 year−1, demonstrating that the scaling errors should be given enough attention to current coarse resolution GPP estimates. Before considering the scaling errors, large uncertainties were observed in the GPP downscaled from lumped values, with d, BIAS, and RMSE of 0.68, 220, and 480 gC m−2 year−1. After considering the scaling errors, a significant improvement was achieved in the GPP downscaled from distributed values, with an increased d value of 0.81, a decreased BIAS value of 10 gC m−2 year−1, and a decreased RMSE value of 388 gC m−2 year−1, indicating that reducing the medium or coarse resolution scaling errors would effectively improve the spatial downscaling of mountain vegetation GPP. Our study highlights the effect of scaling errors on the spatial downscaling of mountain vegetation productivity, which should be given more attention in the future carbon modeling.

Stronger Cumulative than Lagged Effects of Drought on Vegetation in Central Asia

In the context of global warming, the strength and frequency of drought events are projected to grow in the future, and the onset of drought can have dramatic effects on vegetation growth in terrestrial ecosystems. Central Asia is the largest non-territorial drought area in the world, and the response of vegetation to drought events is extremely sensitive in the area. However, few studies have quantified and compared the vegetation gross primary productivity (GPP) response to the lagged and cumulative effects of drought. In this research, the solar-induced chlorophyll fluorescence GPP and Standardized Precipitation Evaporation Index (SPEI) were used to analyze the time and space patterns of vegetation GPP and the SPEI in Central Asia and to quantify and compare the lagged and cumulative effects of drought on the GPP of various vegetation types. During the period from 2000 to 2018, the general trends of vegetation GPP showed a slight increase in Central Asia, with the ratio of variation being 1.35 g C m−2 y−1 and a spatially decreasing distribution from north to south. SPEI showed a trend of decreasing and then increasing over a period of 19 years, with a slight decreasing (drying) trend and a rate of change of −0.02 y−1, and the overall spatial pattern was drying out from north to south. In 13 months, 72.44% of regional droughts had lagged impacts on vegetation. The maximum correlation coefficients of vegetation and the lagged effectiveness of drought were concentrated in the range of 0.15–0.35, and the high correlation was distributed in southern and northwestern Kazakhstan, which are prairie regions. Of the regions in Central Asia, 75.86% showed cumulative drought effects concentrated at 9–12 months. The maximum correlation coefficients were concentrated in the range of 0.20–0.50, and the high correlation regions were primarily situated in south Kazakhstan and east Uzbekistan. Comparing the correlation coefficients of the lagged effect of vegetation GPP and SPEI with the cumulative effect shows that the cumulative rather than lagged impacts of drought on vegetation cover were found in 86.75% of the regions in Central Asia. This research enhances our comprehension of the influence of drought events on ecosystems in arid regions and has a certain reference value for helping arid region ecosystems to cope with global climate change.

Maximum Gross Primary Productivity Dominates the Trend in Gross Primary Productivity in China’s Deciduous Forest Ecosystems

The terrestrial gross primary productivity (GPP) has increased over the past two decades. However, the climatic attribution and the physiological and phenological processes that control the trends in the GPP are still unclear. Here, we used remote-sensing-based vegetation GPP and phenology datasets, analyzed the spatial and temporal variation in the GPP, investigated the influence of the growing season length (GSL) and the maximum value of gross primary productivity (GPPmax) on the annual GPP, and quantified the effect of climate variables on the annual GPP. Our results identified a significant increase in the annual GPP (11.97 gC/m2/yr) during 2001–2020 in China’s deciduous forest. The GPPmax trend dominated the trends in the GPP, when compared with the GSL. Moreover, climate warming in summer contributes to the increase in the GPP and the GPPmax, while the extension of the GSL is primarily due to the temperature rise in spring. The annual GPP of the planted forest showed a higher increasing rate than the natural forest, due to the significant enhancement of the GPPmax and the high sensitivity of the GSL to climatic factors in the planted forest. Our findings provide a new perspective on the phenological and physiological causes of the trends in the GPP, and emphasize the importance of capturing the variability in the GPPmax when modeling the GPP.

Extracting subpixel vegetation NDVI time series for evaluating the mixed pixel effect on GPP estimation in urban areas

ABSTRACT The normalized difference vegetation index (NDVI) is the most widely used vegetation index for monitoring vegetation vigor and cover. As NDVI time series are usually derived at coarse or medium spatial resolutions, pixel size often represents a mixture of vegetated and non-vegetated surfaces. In heterogeneous urban areas, mixed pixels impede the accurate estimation of gross primary productivity (GPP). To address the mixed pixel effect on NDVI time series and GPP estimation, we proposed a framework to extract subpixel vegetation NDVI (NDVIvege) from Landsat OLI images in urban areas, using endmember fractions, mixed NDVI (NDVImix), and NDVI of non-vegetation endmembers. Results demonstrated that the NDVIvege extracted by this framework agreed well with the true NDVIvege cross seasons and vegetation fractions, with R2 ranging from 0.74 to 0.82 and RMSE ranging from 0.03 to 0.04. The NDVIvege time series was applied to evaluate vegetation GPP in Wuhan, China. The total annual GPP estimated with NDVIvege was 28-35% higher than the total annual GPP estimated with NDVImix, implying uncertainty in the GPP estimations caused by mixed pixels. This study showed the potential of the proposed framework to resolve NDVIvege for characterizing vegetation dynamics in heterogeneous areas.

Identifying the Responses of Vegetation Gross Primary Productivity and Water Use Efficiency to Climate Change under Different Aridity Gradients across China

Despite the fact that gross primary productivity (GPP) and water use efficiency (WUE) have been widely used as indicators to evaluate the water-carbon cycle, uncertainties exist in the patterns of GPP and WUE responses to climate variability along different aridity gradients. In this study, the aridity index was used to divide China into four arid-humid zones. The spatiotemporal variability of multiple vegetation types GPP and WUE in response to climate change under different arid-humid zones were investigated based on remote sensing data. The results indicated that the increasing trend of WUE in the four arid-humid zones of China was less pronounced than GPP from 2001 to 2021. The GPP value decreased gradually from the humid to the arid zone, and the WUE value in the arid zone was slightly higher than in the semi-arid zone. The GPP of all vegetation types in China showed a tendency to increase, while shrubland and wetland WUE tended to decrease. The major vegetation types (e.g., forest, cropland and grassland) in each aridity gradient contributed to the changes in local GPP and WUE. However, in individual arid-humid zones, wetland and shrubland also exhibited high GPP and WUE values that were not inferior to forest and cropland. Temperature and precipitation were the main climatic factors responsible for the increase in vegetation GPP in different aridity gradients, with a higher positive correlation for temperature than precipitation. WUE showed a distinct positive and negative correlation with the thermal factors (temperature and net radiation) and the moisture factors (precipitation and relative humidity); this pattern was more pronounced in the humid and semi-humid zones. Net radiation and precipitation may be the main climatic factors causing a slight upward trend in WUE across the arid-humid zones, while the decrease in shrubland and wetland WUE may be related to relative humidity and precipitation.

Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data

Accurate modeling of Gross Primary Productivity (GPP) in terrestrial ecosystems is a major challenge in quantifying the carbon cycle. Many light use efficiency (LUE) models have been developed, but the variables and algorithms used for environmental constraints in different models vary importantly. It is still unclear whether the models can be further improved by machine learning methods and the combination of different variables. Here, we have developed a series of RFR-LUE models, which used the random forest regression (RFR) algorithm based on variables of LUE models, to explore the potential of estimating site-level GPP. Based on remote sensing indices, eddy covariance and meteorological data, we applied RFR-LUE models to evaluate the effects of different variables combined on GPP on daily, 8-day, 16-day and monthly scales, respectively. Cross-validation analyses revealed performances of RFR-LUE models varied significantly among sites with R2 of 0.52–0.97. Slopes of the regression relationship between simulated and observed GPP ranged from 0.59 to 0.95. Most models performed better in capturing the temporal changes and magnitude of GPP in mixed forests and evergreen needle-leaf forests than in evergreen broadleaf forests and grasslands. Performances were improved at the longer temporal scale, with the average R2 for four-time resolutions of 0.81, 0.87, 0.88, and 0.90, respectively. Additionally, the importance of the variables showed that temperature and vegetation indices were critical variables for RFR-LUE models, followed by radiation and moisture variables. The importance of moisture variables was higher in non-forests than in forests. A comparison with four GPP products indicated that RFR-LUE model predicted GPP better matcher observed GPP across sites. The study provided an approach to deriving GPP fluxes and evaluating the extent to which variables affect GPP estimation. It may be used for predicting vegetation GPP at the regional scales and for calibration and evaluation of land surface process models.

Restored vegetation is more resistant to extreme drought events than natural vegetation in Southwest China

Large scale Ecosystem restoration projects (ERPs) have been implemented to restore vegetation and increase carbon stocks across China. However, whether restored vegetation is strongly resistant to Extreme drought events (EDEs) remains unclear, especially when compared to natural vegetation. Therefore, we used the standardized anomaly of 3-month Standard Precipitation-Evapotranspiration Index (SPEI) to characterize the spatial-temporal trends of EDEs, and figured out the capacity of restored vegetation to withstand the strongest EDE in Southwest China by analyzing their changes of Gross Primary Productivity (GPP) and Water Use Efficiency (WUE). The results showed that Southwest China had experienced six typical EDEs with increasing frequency and severity from 1982 to 2017, particularly the EDE during 2009–2010 (EDE 2009/2010) which had the longest duration and strongest severity. Overall, the EDE 2009/2010 substantially suppressed the vegetation GPP and ecosystem WUE in both restored and natural vegetation area. Compared with natural vegetation, the GPP and WUE of restored vegetation was relative higher and moreover, their GPP decreased more slowly during the EDE 2009/2010 and increased more quickly during the recovery period. This indicates that restored vegetation had a higher drought resistance to the EDE than natural vegetation. Additionally, karst landforms have a stronger negative impact on vegetation GPP and WUE during the EDE. Furthermore, the reduction in the afforestation areas was more obviously observed than that in natural forest areas. Therefore, we suggest that vegetation suitable for regional characteristics should be selected during vegetation restoration, such as afforestation in the non-karst areas.

2021 February Texas Ice Storm Induced Spring GPP Reduction Compensated by the Higher Precipitation

AbstractClimate extremes are more frequent and affect the carbon cycle. On 10–20 February, an exceptional winter storm in Texas brought severe ecological and economic effects. However, its impacts on vegetation activities and the carbon cycle are unclear. This study evaluated the early 2021 ice storm effects on spring phenology and the carbon cycle. The results showed that the ice storm led to a significant later vegetation spring phenology and a reduction of 42.32 Tg C in gross primary production (GPP) and a reduction of 34. 47 Tg C net ecosystem production (NEP) in spring. In the 2021 spring, Texas became a carbon source from the carbon sink. More precipitation in late spring and summer precipitation recharged the rootzone soil moisture, which enhanced summer vegetation GPP and NEP and compensated for the effects of the ice storm on spring carbon uptake. The summer GPP enhancement offset all the spring GPP reduction, and thus GPP was increased by 4.97% during 2021. 91.47% of the spring NEP reduction was compensated by summer NEP enhancement due to higher root zone soil moisture. Due to the NEP enhancement during autumn, NEP did not decrease as well and increased by 10.88% during 2021. This observation‐based research highlights the seasonal compensation effects on the interactions between extreme climate events and the biosphere.

A fine spatial resolution estimation scheme for large-scale gross primary productivity (GPP) in mountain ecosystems by integrating an eco-hydrological model with the combination of linear and non-linear downscaling processes

Accurate estimation of mountain vegetation gross primary productivity (GPP) at fine spatial resolutions offers opportunities to better understand mountain ecosystems’ feedback to the global climate system. Eco-hydrological models have great advantages in simulating mountain vegetation photosynthesis, but their large-scale applications remain challenging at fine spatial resolutions due to the computing resources. In this work, a scheme by integrating an eco-hydrological model called Boreal Ecosystem Productivity Simulator-TerrainLab (BTL) with the linear and non-linear downscaling processes, was developed to obtain large-scale mountain vegetation GPP at the 30 m resolution over four watersheds. Firstly, two coarse spatial resolution GPP were simulated by BTL at 480 m and 120 m. Then, the 30 m resolution GPP was estimated by a linear downscaling process modelled at 120 m and a non-linear downscaling process modelled from 480 m to 120 m. The 30 m resolution BTL-simulated GPP was served as reference for evaluation. Results showed that the Root-Mean-Square-Error (RMSE) after downscaling was decreased by 110 gCm-2year−1 compared to the 120 m resolution BTL-simulated GPP (500 gCm−2 year−1) at the 30 m resolution, highlighting the effectiveness of the proposed scheme in recovering the topographic variations of mountain vegetation GPP at fine spatial resolutions. Compared to the 120 m resolution BTL-simulated GPP (351 gCm−2 year−1), RMSE after downscaling was decreased by 156 gCm−2 year−1 at the 120 m resolution, indicating that the proposed scheme is feasible in correcting GPP errors at coarse spatial resolutions. More specifically, the non-linear downscaling process was observed to effectively improve GPP estimates after linear downscaling, suggesting that the spatial scaling errors in coarse estimates should be considered in the downscaling process. Our study indicates that the scheme that runs eco-hydrological models at coarse resolutions and then downscales them by surface heterogeneity is a practical approach for obtaining large-scale mountain vegetation GPP at fine spatial resolutions.

Dynamics of Vegetation Productivity in Relation to Surface Meteorological Factors in the Altay Mountains in Northwest China

Vegetation productivity, as the basis of the material cycle and energy flow in an ecosystem, directly reflects the information of vegetation change. At the ecosystem level, the gross primary productivity (GPP) refers to the amount of organic carbon fixed by plant bodies. How to accurately estimate the spatiotemporal variation of vegetation productivity of the forest ecosystem in the Altay Mountains in northwest China has become a critical issue to be addressed. The Altay Mountains, with rich forest resources, are located in a semi-arid climate zone and are sensitive to global climate changes, which will inevitably have serious impacts on the function and structure of forest ecosystems in northwest China. In this paper, to reveal the variation trends of vegetation gross primary productivity (GPP) and its response to surface meteorological factors in the Altay Mountains in northwest China, daily temperature and precipitation data from the period of 2000–2017 were collected from seven meteorological stations in Altay prefecture and its surrounding areas; the data were analyzed by using the MODIS GPP model, moving average trend analysis, linear regression analysis and the climate tendency rate method. The results show that: (1) The spatial distribution pattern of GPP in the whole year was almost the same as that in the growing season of vegetation in the Altay Mountains. In the whole mountain range, the proportion of the area which had a GPP value of 400–600 g c/m2 had the highest value; the proportion of the annual and growing season of this area was 41.10% and 40.88%, respectively, which was mainly distributed in the middle and west alpine areas of the Altay Mountains. (2) There was a big gap in the GPP value in the different stages of the vegetation growing season (April to September), which reached the highest value in July, the area with a GPP of 100–150 g c/m2 was the highest, with 36.15%. (3) The GPP of the Altay Mountains showed an overall increasing trend, but the annual fluctuation was relatively large. In 2003, 2008, 2009 and 2014, the GPP showed lower values, which were 385.18 g c/m2, 384.90 g c/m2, 384.49 g c/m2 and 393.10 g c/m2, respectively. In 2007, 2011 and 2016, the GPP showed higher values, which were 428.49 g c/m2, 428.18 g c/m2 and 446.61 g c/m2. (4) In 64.85% of the area of the Altay Mountains, the GPP was positively correlated with annual average temperature, and in 36.56% of the area, the correlation coefficient between temperature and GPP ranged from −0.2 to 0. In 71.61% of the area of the Altay Mountains, the GPP was positively correlated with annual accumulated precipitation, and in 28.39% of the area, the GPP was negatively correlated with annual accumulated precipitation. Under the scenario of global climate change, our study has quantitatively analyzed the long-term dynamics of vegetation GPP and its responses to meteorological factors in the Altay Mountains, which would be helpful for evaluating and estimating the variation trends of forest ecosystems in China, and has important guiding significance for policy formulation to protect forest resources and improve the local ecological environment.

Quantifying Scaling Effect on Gross Primary Productivity Estimation in the Upscaling Process of Surface Heterogeneity

AbstractAccurate estimation of gross primary productivity (GPP) is essential for understanding the terrestrial carbon budget. Current large‐scale GPP estimates are often obtained at coarse resolutions without considering the subpixel heterogeneity, leading to scaling errors in results. Here, to further characterize (a) the critical sub‐upscaling process causing the largest error and (b) the contributions of various heterogeneity factors in causing the scaling errors, a hydrology‐vegetation model was used to estimate GPP at the 30 m resolution (assumed as reality), and other coarser resolutions (60, 120, 240, 480, and 960 m, assumed as approximations) for 16 mountainous watersheds. Then, GPP scaling errors in the upscaling process of surface heterogeneity were investigated by the root mean squared error between the reality and approximations. Results showed that any surface heterogeneity aggregation from fine to coarse resolutions (e.g., 30–960 m) could cause GPP scaling errors (133 ± 40 gCm−2yr−1), and the aggregation from medium to coarse resolutions (e.g., 240–960 m) may be the largest source. More specifically, GPP scaling errors caused by the vegetation heterogeneity aggregation from fine to medium resolutions were relatively small, and the GPP errors caused by the surface topography aggregation from fine to coarse resolutions were all non‐negligible. Elevation aggregation caused larger GPP scaling error than the aggregations of land cover, leaf area index, slope, and aspect. This work highlights the need to consider surface heterogeneity (especially the elevation information) when modeling mountain vegetation GPP at coarse resolutions.

Revisiting the cumulative effects of drought on global gross primary productivity based on new long-term series data (1982-2018).

Drought has broad and deep impacts on vegetation. Studies on the effects of drought on vegetation have been conducted over years. Recently, the cumulative effect of drought is recognized as another key factor affecting plant growth. However, global-scale studies on this phenomenon are still lacking. Thus, based on new satellite based gross primary productivity (GPP) and multi-temporal scale Standardized Precipitation Evapotranspiration Index data sets, we explored the cumulative effect duration (CED) of drought on global vegetation GPP and analyzed its variability across elevations and climatic zones. The main findings were as follows: (1) The cumulative effect of drought on GPP was widespread, with an average CED of 4.89months. (2) CED of drought on GPP varied among vegetation types. Specifically, grasslands showed the longest duration, with an average value of 5.28months, followed by shrublands (5.09months), wetlands (5.03months), croplands (4.85months), savannas (4.58months), and forestlands (4.57months). (3) CED of drought on GPP changes with climate conditions. It decreased with the decrease of precipitation in the driest month (Pdry ) and mean annual precipitation in tropical and arid climate zones, respectively. In both temperate and cold climate zones, CED of drought on GPP was shorter in areas with dry winter than that in areas with dry summer. It increased with the decrease of mean annual air temperature in tropical climate zones and decreased with the increase of summer temperature in temperate and cold climatic zones. (4) With increasing elevation, CED of drought on GPP showed a pattern of increasing (0-3000m), then decreasing (3000-5000m), and increasing again (>5000m). Our findings highlight the heterogeneity of CED of drought on GPP, owing to differences in vegetation types, long-term hydrothermal conditions, elevation, etc. The results could deepen our understanding of the effects of drought on global vegetation.

How does urbanization affect vegetation productivity in the coastal cities of eastern China?

Changes in terrestrial gross primary productivity (GPP) caused by rapid urbanization may result in negative effects on ecosystem services and ecosystem health. These impacts are of great concern in coastal zones where rapid urbanization is predominant. Knowing how urbanization affects vegetation productivity will be helpful for policymakers to make decisions on urban vegetation and ecosystem management. In this study, we chose 48 cities along the coastal zone of eastern China to evaluate the impacts of urbanization on vegetation GPP. The spatiotemporal comparison was used to identify the changes in built-up lands and vegetation GPP for multiple years (2000, 2005, 2010, and 2015). The area percentage of built-up lands was used to define the urbanization density. It was found that: (1) the actual vegetation GPP changed in different patterns with urbanization gradient from low to high intensity, including straight declining, depressed, and reversed S shapes at the city scale. The vegetation GPP change due to urbanization include both direct impact that is resulted directly from the loss of green land, and indirect impact that is induced by the change of macro-environment associated with urbanization. The slope of direct impacts change from low to high urbanization intensity were − 0.917, −0.925, −0.933, −0.938 for 2000, 2005, 2010, and 2015, respectively. The greater value means urban vegetation GPP loss faster as urbanization intensity increase. (2) A turning point on the maximum values for the indirect impacts was observed at approximately 0.8 of urbanization intensities, although it indicates both positive and negative value for the cities. However, no significant differences were observed for indirect impacts among provinces and coastal zones. The indirect impacts of urbanization on vegetation GPP were generally positive in the northern and middle coastal zones, and they were negative in the southern coastal zones. The results indicated that measures can be applied in the coastal cities in order to mitigate the negative impacts of urbanization on GPP. Our findings are helpful for policymakers to make decisions on urban planning and management.

Machine Learning‐Based Modeling of Vegetation Leaf Area Index and Gross Primary Productivity Across North America and Comparison With a Process‐Based Model

AbstractVegetation plays a key role in regulating the material and energy exchanges among the biosphere, the atmosphere, and the pedosphere. Modeling and predicting vegetation key variables such as leaf area index (LAI) and gross primary productivity (GPP) are crucial to understand and project the processes of vegetation growth in response to climate change. While a number of studies developed models to simulate vegetation GPP using satellite‐derived LAI, the requirement of satellite‐based model inputs largely limits the predicting power of these developed models. This study developed a machine learning scheme, utilizing both support vector regression (SVR) and random forests (RF), which are capable of modeling LAI and GPP time series using only meteorological variables. We first simulated the LAI time series directly using meteorological variables as inputs and then buffered its unrealistic day‐to‐day fluctuation, and further modeled the GPP time series using meteorological variables and modeled LAI time series. This scheme enhanced the interpretability of machine learning models by considering the nonnegligible coupling between LAI and GPP. We tested our methods for four main plant functional types across North America and evaluated the models using both satellite‐based and flux tower data. The results demonstrated that the machine learning models perform well on simulating the time series of both LAI and GPP. We identified that there is a need to improve the phenology representation in the Biome‐BGCMuSo model. The machine learning models provide an alternative way to predict time series of LAI and GPP using only meteorological variables across large geographic regions, and also provide benchmarking accuracies for future developments of the process‐based models.

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.

Importance of Shaded Leaf Contribution to the Total GPP of Canadian Terrestrial Ecosystems: Evaluation of MODIS GPP

AbstractTerrestrial gross primary productivity (GPP) quantifies the photosynthetic uptake by vegetation and it is the largest component of the terrestrial carbon cycle. The separation of sunlit and shaded leaves has been shown to be an effective leaf‐to‐canopy upscaling method for modeling vegetation GPP. In this study, the performance of the Integrated Carbon‐Canadian Land Surface Scheme (IC‐CLASS) based on Farquhar's photosynthetic model and the two‐leaf approach was compared against that of Moderate Resolution Imaging Spectroradiometer (MODIS) GPP algorithm using the light use efficiency (LUE) approach, validated with eddy covariance (EC) measured GPP over a variety of terrestrial ecosystems in Canada. There were systematic differences between the IC‐CLASS simulated GPP and the MODIS GPP product in spatial distribution patterns. The differences were due to inherent shortcomings of the LUE modeling approach where a constant maximum LUE value is specified for each biome type, ignoring the variation of shaded leaf contribution to total GPP. The IC‐CLASS model separates the sunlit and shaded leaves and the bias in simulating GPP was minimized. The IC‐CLASS performed better than the MODIS GPP algorithm compared with monthly and annual GPP derived from EC flux data at 13 Canadian Carbon Program sites. The differences between the IC‐CLASS and the MODIS GPP estimates were larger in more clumped canopies (i.e. forests), because of the increase in the shaded leaf fraction. Different LUEs in sunlit and shaded portions of the canopy should be considered for effective and reliable estimation of GPP at regional scale.

Warmer spring alleviated the impacts of 2018 European summer heatwave and drought on vegetation photosynthesis

Future projections of climate extremes are expected to become more frequent. Parts of Europe experienced an extensive heatwave and drought during 2018. However, its impacts on terrestrial carbon cycle remain elusive. Here we investigated the vegetation responses to the heatwave and drought during 2018 based on satellite solar-induced chlorophyll fluorescence (SIF) and near-infrared reflectance (NIRv) data, which were used to estimate gross primary productivity (GPP). Results showed that there were no significant (p= 0.60) reductions in GPP across most of Europe during April–August of 2018. The higher temperatures in spring enhanced vegetation GPP, largely alleviated the negative impacts of heatwave and drought on vegetation photosynthesis during the subsequent summer, which resulted in evident compensation effects. Concurrently, warmer spring also had lagged effects by diminishing soil moisture, accompanied by scarce precipitation, leading to water stress on plant growth during summer. This observation-based study highlights the need for more considerations of seasonal compensation and lagged effects on the interactions between climate extreme events and biosphere.

Integrating SIF and Clearness Index to Improve Maize GPP Estimation Using Continuous Tower-Based Observations.

Solar-induced chlorophyll fluorescence (SIF) has been proven to be well correlated with vegetation photosynthesis. Although multiple studies have found that SIF demonstrates a strong correlation with gross primary production (GPP), SIF-based GPP estimation at different temporal scales has not been well explored. In this study, we aimed to investigate the quality of GPP estimates produced using the far-red SIF retrieved at 760 nm (SIF760) based on continuous tower-based observations of a maize field made during 2017 and 2018, and to explore the responses of GPP and SIF to different meteorological conditions, such as the amount of photosynthetically active radiation (PAR), the clearness index (CI, representing the weather condition), the air temperature (AT), and the vapor pressure deficit (VPD). Firstly, our results showed that the SIF760 tracked GPP well at both diurnal and seasonal scales, and that SIF760 was more linearly correlated to PAR than GPP was. Therefore, the SIF760–GPP relationship was clearly a hyperbolic relationship. For instantaneous observations made within a period of half an hour, the R2 value was 0.66 in 2017 and 2018. Based on daily mean observations, the R2 value was 0.82 and 0.76 in 2017 and 2018, respectively. Secondly, it was found that the SIF760–GPP relationship varied with the environmental conditions, with the CI being the dominant factor. At both diurnal and seasonal scales, the ratio of GPP to SIF760 decreased noticeably as the CI increased. Finally, the SIF760-based GPP models with and without the inclusion of CI were trained using 70% of daily observations from 2017 and 2018 and the models were validated using the remaining 30% of the dataset. For both linear and non-linear models, the inclusion of the CI greatly improved the SIF760-based GPP estimates based on daily mean observations: the value of R2 increased from 0.71 to 0.82 for the linear model and from 0.82 to 0.87 for the non-linear model. The validation results confirmed that the SIF760-based GPP estimation was improved greatly by including the CI, giving a higher R2 and a lower RMSE. These values improved from R2 = 0.66 and RMSE = 7.02 mw/m2/nm/sr to R2 = 0.76 and RMSE = 6.36 mw/m2/nm/sr for the linear model, and from R2 = 0.71 and RMSE = 4.76 mw/m2/nm/sr to R2 = 0.78 and RMSE = 3.50 mw/m2/nm/sr for the non-linear model. Therefore, our results demonstrated that SIF760 is a reliable proxy for GPP and that SIF760-based GPP estimation can be greatly improved by integrating the CI with SIF760. These findings will be useful in the remote sensing of vegetation GPP using satellite, airborne, and tower-based SIF data because the CI is usually an easily accessible meteorological variable.