Dynamics Of Gross Primary Production Research Articles

Comparison between Satellite Derived Solar-Induced Chlorophyll Fluorescence, NDVI and kNDVI in Detecting Water Stress for Dense Vegetation across Southern China

In the context of global climate change and the increase in drought frequency, monitoring and accurately assessing the impact of hydrological process limitations on vegetation growth is of paramount importance. Our study undertakes a comprehensive evaluation of the efficacy of satellite remote sensing vegetation indices—Normalized Difference Vegetation Index (MODIS NDVI product), kernel NDVI (kNDVI), and Solar-Induced chlorophyll Fluorescence (GOSIF product) in this regard. Initially, we applied the LightGBM-Shapley additive explanation framework to assess the influencing factors on the three vegetation indices. We found that Vapor Pressure Deficit (VPD) is the primary factor affecting vegetation in southern China (18°–30°N). Subsequently, using Gross Primary Productivity (GPP) estimates from flux tower sites as a performance benchmark, we evaluated the ability of these vegetation indices to accurately reflect vegetation GPP changes during drought conditions. Our findings indicate that SIF serves as the most effective surrogate for GPP, capturing the variability of GPP during drought periods with minimal time lag. Additionally, our study reveals that the performance of kNDVI significantly varies depending on the estimation of different kernel parameters. The application of a time-heuristic estimation method could potentially enhance kNDVI’s capacity to capture GPP dynamics more effectively during drought periods. Overall, this study demonstrates that satellite-based SIF data are more adept at monitoring vegetation responses to water stress and accurately tracking GPP anomalies caused by droughts. These findings not only provide critical insights into the selection and optimization of remote sensing vegetation product but also offer a valuable framework for future research aimed at improving our monitoring and understanding of vegetation growth status under climatic changes.

Simulation of gross primary productivity and impact of drought in Liulin watershed of Taihang mountains over 2000–2020

As the largest flux in the global terrestrial carbon cycle, Gross Primary Productivity (GPP) is a key factor for the terrestrial carbon budget. Based on the eddy flux tower data, remote sensing data and the meteorological precipitation data, we calibrated three parameters of maximum light use efficiency, optimum temperature, and maximum temperature in the Vegetation Photosynthesis Model (VPM) to reproduce the GPP spatiotemporal characteristic in Liulin watershed of Taihang Mountains. We further analyzed the time lag effect and cumulative effect of the standard precipitation index (SPI) on GPP by the Pearson correlation coefficients. Results show that VPM captures the GPP characteristics of eddy flux tower observation in Liulin watershed. During the calibration period (R2 = 0.94, RMSE = 0.47 g C m-2·d-1) and validation period (R2 = 0.91, RMSE = 0.60 g C m-2 d-1), the adjusted VPM model successfully reproduced the temporal dynamics of site-observed GPP. From 2000 to 2020, VPM model can effectively reproduce the long-term variations in GPP (R2 = 0.69 vs. NIRv_GPP), with the GPP ranging from 446.4 to 828.1 g C m-2 y-1 and an average value of 545.6 g C m-2 y-1 in Liulin watershed. The GPP exhibited an increasing trend from 2000 to 2020. A mutation in the GPP occurred in 2010, with an average annual increase of 13.9 g C m-2 y-1 from 2000 to 2010 (p < 0.05), followed by an average annual increase of 10.22 g C m-2 y-1 from 2011 to 2020 (p > 0.05). The SPI effectively monitored drought events in the Liulin watershed. With a cumulative effect of 9 months and a lag effect of 6–7 months, the time lag and cumulative effects of drought had a long-term effect on GPP. It is imperative to have long-term eddy flux tower data and meteorological data to better evaluate the spatiotemporal dynamics of GPP and the impact of drought.

Global prediction of gross primary productivity under future climate change

The ecosystem gross primary productivity (GPP) is crucial to land-atmosphere carbon exchanges, and changes in global GPP as well as its influencing factors have been well studied in recent years. However, identifying the spatio-temporal variations of global GPP under future climate changes is still a challenging issue. This study aims to develop data-driven approach for predicting the global GPP as well as its monthly and annual variations up to the year 2100 under changing climate. Specifically, Catboost was employed to examine the potential relationship between the GPP and environmental factors, with climate variables, CO2 concentration and terrain attributes being selected as environmental factors. The predicted monthly and annual GPP from Coupled Model Intercomparison Project phase 6 (CMIP6) under future SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5 scenarios were analyzed. The results indicate that the global GPP is predicted to increase under the future climate change in the 21st century. The annual GPP is expected to be 115.122 Pg C, 116.537 Pg C, 117.626 Pg C, and 120.097 Pg C in 2100 under four future scenarios, and the predicted monthly GPP shows seasonal difference. Meanwhile, GPP tends to increase in the northern mid–high latitude regions and decrease in the equatorial regions. For the climate zones form Köppen-Geiger classification, the arid, cold, and polar zones present increased GPP, while GPP in the tropical zone will decrease in the future. Moreover, the high importance of climate variables in GPP prediction illustrates that the future climate change is the main driver of the global GPP dynamics. This study provides a basis for predicting how global GPP responds to future climate change in the coming decades, which contribute to understanding the interactions between vegetation and climate.

A comparison of moderate and high spatial resolution satellite data for modeling gross primary production and transpiration of native prairie, alfalfa, and winter wheat

Although agroecosystems have a significant potential to offset carbon dioxide (CO2), the amount of CO2 captured can vary significantly depending on management practices. Accurate estimation of gross primary production (GPP) and transpiration (T) of agroecosystems at the field scale are essential for the study of food security and water resource management. To date, the carbon and water fluxes data products for commercial agroecosystems are limited, mostly at the moderate spatial resolution (MSR, hundreds of meters), which cannot be used to assess the temporal dynamics of GPP and T at the field scale. This study used the vegetation photosynthesis model (VPM) and vegetation transpiration model (VTM) to estimate field-level daily GPP (GPPVPM) and T (TVTM), respectively, in native prairie, alfalfa (Medicago sativa L.), and winter wheat (Triticum aestivum L.) in central Oklahoma, USA. We evaluated the reliability and advantages of vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI) in monitoring the land surface phenology using moderate spatial resolution data from Moderate Resolution Imaging Spectroradiometer (MODIS) and high spatial resolution (HSR, tens of meters) data from Landsat and Sentinel-2. The accuracy of GPPVPM and TVTM estimates at different spatial scales was evaluated using GPP (GPPEC) and evapotranspiration (ETEC) from the eddy flux tower sites, respectively. Results demonstrate the capacity of VPM and VTM to estimate the field-level carbon and water flux dynamics and their responses to weather conditions. The use of HSR vegetation indices helped to address certain challenges faced by MSR indices, especially in capturing the crop phenology in smaller areas with conservation measures or disturbances. The findings highlight the importance of using HSR GPP estimates to reduce uncertainty in quantifying CO2 fluxes for croplands and grasslands. The findings also demonstrate the ability of the models to track field-level vegetation phenology, carbon uptake, and water use in agroecosystems under different management practices.

Non-stomatal processes are responsible for the decrease in gross primary production of a potato crop during edaphic drought

Soil water stress is one of the main constraints on agrosystem functioning, causing a reduction in gross primary production (GPP). It is a key factor for the selection of drought-tolerant plant varieties and the adaptation of irrigation management strategies. Therefore, identifying the physiological factors limiting GPP is crucial. Land surface models commonly implement a beta (β) stress function to reproduce the effects of drought on GPP. It is still unclear whether GPP limitations originate from a direct stomatal response (SOL) or from other non-stomatal causes (NSOL). Moreover, the shape and thresholds of the β function are a major cause of uncertainty in LSMs. This study investigated the effects of edaphic drought on GPP limitations of a potato crop by using eddy covariance data from the Lonzée ICOS station (BE-Lon) in Belgium for four years (2006, 2010, 2014 and 2018) and by calibrating the β function on NSOL and SOL.The decrease in the relative extractable water in the soil (REW) induced a decline in the apparent maximum carboxylation rate (NSOL) from similar REW thresholds for all growing seasons of potato crops. A sensitivity analysis showed that the non-inclusion of REW decrease on NSOL in the modeling of GPP led to important overestimations of carbon sequestration from a threshold corresponding to 46 % of the maximum available soil water. The stomatal sensitivity to photosynthesis (SOL) remained constant or even increased in 2010 and 2018. The carbon and water fluxes were decoupled when REW decreased, exhibiting a strict control of NSOL on hourly dynamics of GPP. This study provides REW thresholds to identify drought stress episodes and to help for designing irrigation management strategies. Our results advocate for a better representation of the influence of drought on photosynthesis processes of potato crops to improve the accuracy of model predictions during drought.

Improving ecological indicators of arid zone deserts through simulation

Ecological indicators, such as soil moisture content and gross primary production (GPP), play important roles in the management of ecosystems and water resources and within climate change research. However, studies on monitoring and simulation of ecological indicators of arid zones remain limited. This study simulated soil moisture (SM), latent heat (LH), and gross primary productivity (GPP) for the arid Yarkant River Basin in China using the High-Resolution Land Data Assimilation System and Deep learning. Simulation of hydrology over a large spatial scale is difficult due to a lack of observed data and the difficulty in parameterizing models to represent complicated ecological mechanisms. A comparison of Noah Multi-Parameterization (Noah-MP) simulations to multiple datasets at an annual scale obtained correlation coefficients exceeding 0.8. The model was able to replicate the broad temporal dynamics of GPP and LH over the Yarkant River Basin. Point- and regional-scale assessments across the Yarkant River Basin obtained the optimal model performance. The temporal trends in simulated SM anomalies were consistent with observations across the different sub-basins. The simulations of the Noah-MP model driven by Global Land Data Assimilation System (GLDAS) and ERA5 data provided accurate representations of most hydrological variables, except for in the upper reaches of Yarkant River Basin, likely due to the extensive freeze-thaw activity in this transitional region. The results of this study can fill hydrological data gaps in arid zones areas.

Revisiting the main driving factors influencing the dynamics of gross primary productivity in the Mongolian Plateau

By contributing to what is considered as the world's largest carbon flux, global climate change has been significantly altering terrestrial vegetation gross primary production (GPP) at a global level. Thus, a robust understanding of the main driving factors influencing GPP change is neccessary for increasing terrestrial carbon sequestration potential of a region. In this study, we conduct an analysis of the main driving factors that have influenced GPP change in the Mongolian Plateau in the 1982–2018 period. The results of our analysis indicate that the GPP of the plateau's total vegetated area has witnessed both growth and decline (of 51.16% and 48.48%, respectively) over the past 37 years, with the latter occurring mainly across northern Mongolia and northeastern Inner Mongolia. Water availability, temperature, and solar radiation are deemed the most crucial climatic factors behind GPP change in 51.21%, 15.51%, and 18.63%, respectively, of the plateau's total vegetated area, according to a nonlinear Granger causality test. These three factors also explain up to 31%, 25.20%, and 17.21% of GPP change in 82.78%, 64.23%, and 69.96%, respectively, of the plateau's total vegetated area, as 34.52%, 31.57%, and 48.62% of the total vegetated area affected by the abovementioned driving factors concomitantly exhibit time-lag effects. The average intensity and extent of the effects of extreme climate indices on GPP change are lesser than the effect of general climate change; up to 21.31% and 22.35% of the GPP change can be explained by the extreme precipitation and temperature in 47.41% and 34.33%, respectively, of the plateau's total vegetated area. Moreover, the standardized total effects of this study's structural equation model (SEM) show that precipitation has the greatest impact on the GPP change (0.42), followed by snow water equivalent (SWE) (–0.11), solar radiation (0.08), and soil moisture or near surface temperature (0.07). Overall, our study provides new insights into the effects of the aforementioned driving factors on GPP change in the Mongolian Plateau, which will be helpful in mitigating the adverse effects of future climate change in the region.

The Impacts of Climate and Human Activities on Grassland Productivity Variation in China

Grassland degradation is widespread and increasing globally, which is closely related to the sustainable development of the ecosystems and the well-being of human life in pastoral areas. Quantifying the factors influencing grassland ecosystems, specifically climate change and human activities, is of great significance for grassland restoration. However, due to the unpredictability of human activities, further research is still needed to distinguish and identify the factors affecting grasslands. In this study, we examined the changes in the gross primary productivity (GPP) of grassland cover in 10 provinces (autonomous regions) of China from 2000 to 2018 and selected three representative climate factors (temperature, precipitation, solar radiation) and six factors covering socioeconomic (primary industry production and population), animal husbandry (large livestock and sheep populations), and national policies (grazing areas, rodent, and pest control) to characterize human activities; then, we quantified the effects and contribution of climate and human factors using three analysis methods (partial correlation analysis, geographical and temporal weighted regression model, and Lindeman Merenda Gold method). The results indicated that the GPP of grassland presented an obvious uptrend (4.75 g C m−2 yr−1, p &lt; 0.05). Among the nine factors, sheep, precipitation, and temperature were the primary factors affecting grassland dynamics. Additionally, the GPP dynamics of grassland were mainly dominated by human activities in seven provinces (autonomous regions). These findings provide decision support for protecting grassland ecosystems and implementing ecological restoration policies in China.

Can Gross Primary Productivity Products be effectively evaluated in regions with few observation data?

ABSTRACT The dynamics of Gross Primary Productivity (GPP) is key to understand the global carbon cycle. Multiple GPP products are currently available based on remote sensing, Light Use Efficiency model (LUE) or diagnostic biophysical model. However, little knowledge is available on the spatial patterns of the uncertainty of different GPP products and their potential drivers over the Central Asia (CA), a fragile environment for accurate GPP estimation. This study investigates the sensitivity of the 8-day, monthly and yearly GPP uncertainties based on the three-cornered hat (TCH) method and Shapley additive explanation (SHAP) model in terms of vegetation, energy, water, climate and terrain factors in the dryland ecosystem during the 2003–2015 period. Ten GPP products were examined, including one product (FLUXCOM) from machine learning (ML), six products (EC-LUE, FluxSat, LUEopt, MODIS, MuSyQ and VPM) based on the (LUE), two products (GOSIF and NIRv) from satellite-based direct proxies (Proxies) and one product (PML) from the diagnostic biophysical model. The results indicate that the spatial distribution of the ten GPP products in CA showed similar patterns at different time scales, but with values varied at different products and time scales. According to the eddy covariance (EC) observations and the TCH-based uncertainties, the FLUXCOM product showed smaller relative uncertainties than other products. The attribution analysis denotes that the sources of uncertainty of the GPP varied for each product, and thus the improvement strategies for different products should be implemented separately The FLUXCOM should adapt the vegetation- related module to the dryland environment of CA. The LUE model should optimize the LUE parameters for the dryland ecosystem and incorporate the water related variables in the model. The Proxies model should incorporate the water and energy variables (such as soil moisture and radiation) as input data to improve their performance in CA. The diagnostic model should consider the elevation variable as input data, which may improve the performance of the PML in CA. Our results do not only provide an important basis for the selection of GPP products in the study of the carbon cycle in CA, but also offer a new insight into the GPP model development and improvement for the dryland ecosystem.

Synergy between TROPOMI sun-induced chlorophyll fluorescence and MODIS spectral reflectance for understanding the dynamics of gross primary productivity at Integrated Carbon Observatory System (ICOS) ecosystem flux sites

Abstract. An accurate estimation of vegetation gross primary productivity (GPP), which is the amount of carbon taken up by vegetation through photosynthesis for a given time and area, is critical for understanding terrestrial–atmosphere CO2 exchange processes and ecosystem functioning, as well as ecosystem responses and adaptations to climate change. Prior studies, based on ground, airborne, and satellite sun-induced chlorophyll fluorescence (SIF) observations, have recently revealed close relationships with GPP at different spatial and temporal scales and across different plant functional types (PFTs). However, questions remain regarding whether there is a unique relationship between SIF and GPP across different sites and PFTs and how we can improve GPP estimates using solely remotely sensed data. Using concurrent measurements of daily TROPOspheric Monitoring Instrument (TROPOMI) SIF (daily SIFd); daily MODIS Terra and Aqua spectral reflectance; vegetation indices (VIs, notably normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photochemical reflectance index (PRI)); and daily tower-based GPP across eight major different PFTs, including mixed forests, deciduous broadleaf forests, croplands, evergreen broadleaf forests, evergreen needleleaf forests, grasslands, open shrubland, and wetlands, the strength of the relationships between tower-based GPP and SIFd at 40 Integrated Carbon Observation System (ICOS) flux sites was investigated. The synergy between SIFd and MODIS-based reflectance (R) and VIs to improve GPP estimates using a data-driven modeling approach was also evaluated. The results revealed that the strength of the hyperbolic relationship between GPP and SIFd was strongly site-specific and PFT-dependent. Furthermore, the generalized linear model (GLM), fitted between SIFd, GPP, and site and vegetation type as categorical variables, further supported this site- and PFT-dependent relationship between GPP and SIFd. Using random forest (RF) regression models with GPP as output and the aforementioned variables as predictors (R, SIFd, and VIs), this study also showed that the spectral reflectance bands (RF-R) and SIFd plus spectral reflectance (RF-SIF-R) models explained over 80 % of the seasonal and interannual variations in GPP, whereas the SIFd plus VI (RF-SIF-VI) model reproduced only 75 % of the tower-based GPP variance. In addition, the relative variable importance of predictors of GPP demonstrated that the spectral reflectance bands in the near-infrared, red, and SIFd appeared as the most influential and dominant factors determining GPP predictions, indicating the importance of canopy structure, biochemical properties, and vegetation functioning on GPP estimates. Overall, this study provides insights into understanding the strength of the relationships between GPP and SIF and the use of spectral reflectance and SIFd to improve estimates of GPP across sites and PFTs.

Earlier Ecological Drought Detection by Involving the Interaction of Phenology and Eco‐Physiological Function

AbstractMeteorological drought indices like the Standardized Precipitation Evaporation Index (SPEI) are frequently used to diagnose “ecological drought,” despite the fact that they were not explicitly designed for this purpose. More recently developed indices like the Evaporative Stress Index (ESI), which is based on the degree of coupling between actual to potential evapotranspiration, may better capture dynamic plant response to moisture limitations. However, the skill of these indices at describing plant water stress is rarely evaluated at sub‐seasonal timescales over which drought evolves. Moreover, it remains unclear how variability in phenological timing impacts and complicates early drought detection. Here, we compared the ability of ESI and SPEI to reflect the dynamics of ecological drought in forests and grasslands, based on anomalies of Gross Primary Productivity (GPP), surface conductance (Gs, a proxy for stomatal conductance), soil moisture, and vapor pressure deficit. ESI performed better than SPEI in capturing the dynamics of GPP and Gs, but still missed early ecological drought signals due to biases linked to earlier onset of spring leaf development. Thus, we developed a modified variant of the ESI () that accounts for the complicating effects of phenological shifts in leaf area index (LAI). The detected drought onset up to 7–10 weeks earlier than SPEI and ESI. Additionally, drought onset dates determined from are close to (±2 weeks) the dates determined from LAI‐corrected anomalies of Gs, and GPP, as well as the onset dates of soil water deficit and atmospheric aridity.

Light use efficiency models incorporating diffuse radiation impacts for simulating terrestrial ecosystem gross primary productivity: A global comparison

Estimating dynamic changes in gross primary productivity (GPP) of terrestrial ecosystems has always been challenging. Indeed, light use efficiency (LUE) models are extensively employed to capture GPP dynamics. Improved big-leaf LUE models by introducing a cloudiness scalar and two-leaf LUE models by dividing the canopy into shaded and sunlit leaves are two improved families for addressing the widely-recognized effects of diffuse radiation on terrestrial ecosystem GPP. However, the global performance in simulating GPP dynamics between such models has not been evaluated comprehensively. Here, we assess and compare the global performance of the ten LUE models considering diffuse radiation impacts (CFLUX, DIFFUSE, CI-LUE, CCW, Wang's Model, CI-EF, TL-LUE, TL-LUEn, DTEC, and RTL-LUE) at 102 flux sites in a standardized framework. Results indicate that LUE models considering diffuse radiation impacts can explain 46.7–63.6% of daily GPP variability obtained by the eddy covariance technique. Seven of the ten models show a better performance across all ecosystems than MOD17, despite the relatively poor performance of three (i.e., DIFFUSE, Wang's Model, and DTEC). The seven improved models exhibiting a similar explanation of GPPEC perform poorly in evergreen broadleaf forests and croplands but well in deciduous broadleaf forests. Although two-leaf models with higher leaf area index (LAI) sensitivity and better model structure have greater potential to describe diffuse radiation fertilization (DRF), their performance in capturing GPP dynamics is limited by the uncertainties of remote sensing data. This study indicates the importance of accurate LAI for two-leaf models and emphasizes the necessity of incorporating diffuse radiation impacts into the global GPP estimates in the future.

Estimating local-scale forest GPP in Northern Europe using Sentinel-2: Model comparisons with LUE, APAR, the plant phenology index, and a light response function

Northern forest ecosystems make up an important part of the global carbon cycle. Hence, monitoring local-scale gross primary production (GPP) of northern forest is essential for understanding climatic change impacts on terrestrial carbon sequestration and for assessing and planning management practices. Here we evaluate and compare four methods for estimating GPP using Sentinel-2 data in order to improve current available GPP estimates: four empirical regression models based on either the 2-band Enhanced Vegetation Index (EVI2) or the plant phenology index (PPI), an asymptotic light response function (LRF) model, and a light-use efficiency (LUE) model using the MOD17 algorithm. These approaches were based on remote sensing vegetation indices, air temperature (Tair), vapor pressure deficit (VPD), and photosynthetically active radiation (PAR). The models were parametrized and evaluated using in-situ data from eleven forest sites in North Europe, covering two common forest types, evergreen needleleaf forest and deciduous broadleaf forest. Most of the models gave good agreement with eddy covariance-derived GPP. The VI-based regression models performed well in evergreen needleleaf forest (R2 = 0.69–0.78, RMSE = 1.97–2.28 g C m−2 d−1, and NRMSE = 9–11.0%, eight sites), whereas the LRF and MOD17 performed slightly worse (R2 = 0.65 and 0.57, RMSE = 2.49 and 2.72 g C m−2 d−1, NRMSE = 12 and 13.0%, respectively). In deciduous broadleaf forest all models, except the LRF, showed close agreements with the observed GPP (R2 = 0.75–0.80, RMSE = 2.23–2.46 g C m−2 d−1, NRMSE = 11–12%, three sites). For the LRF model, R2 = 0.57, RMSE = 3.21 g C m−2 d−1, NRMSE = 16%. The results highlighted the necessity of improved models in evergreen needleleaf forest where the LUE approach gave poorer results., The simplest regression model using only PPI performed well beside more complex models, suggesting PPI to be a process indicator directly linked with GPP. All models were able to capture the seasonal dynamics of GPP well, but underestimation of the growing season peaks were a common issue. The LRF was the only model tending to overestimate GPP. Estimation of interannual variability in cumulative GPP was less accurate than the single-year models and will need further development. In general, all models performed well on local scale and demonstrated their feasibility for upscaling GPP in northern forest ecosystems using Sentinel-2 data.

Gross primary productivity by remote sensing in the Serra das Confusões National Park, Piauí, Brazil

The increase in anthropogenic CO2 emissions has raised the Earth's surface temperature in an anomalous and unprecedented way. Among the activities that contribute most to these increased emissions is the burning of fossil fuels and the change in land use. Part of these emissions are absorbed by terrestrial ecosystems, potentially productive and important carbon sinks (C) among them, we can mention the seasonally dry tropical forests (SDTF), which are found mainly in the Caatinga biome. Although little explored, the role they play in the global carbon (C) cycle is not negligible. Therefore, this research aimed to evaluate the seasonal behavior of biophysical variables and their effect on gross primary productivity (GPP) in a Conservation Unit in the Caatinga biome. The study area is located in the Conservation Unit (UC) of the Serra das Confusões National Park, located in the southwest region of the state of Piauí. Data for the years 2010–2020 were obtained from the MODIS sensor, which is inserted in the Terra satellite. Data on air temperature, precipitation, relative humidity and global solar radiation were obtained from the National Institute of Meteorology. According to the results obtained, the dynamics of GPP is modulated by the seasonal distribution of precipitation, presenting an average value of 5.53 gC m−2 day−1 in the months of higher precipitation and 1.43 gC m−2 day−1 in the period of lower precipitation. Additionally, precipitation also exerts a strong influence on vegetation status, as observed from the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) the maximum values observed during the rainy season, reflecting directly on the radiative and water components. However, even subjected to adverse environmental conditions, the potential of the area as a sink for C. Thus, in a future scenario of projected climate changes, and considering the vulnerability of these ecosystems, studies that evaluate the seasonal behavior of its structure and functioning is of fundamental importance for the knowledge of its resilience mechanisms and also as a contribution to the implementation of public policies that promote the conservation of the ecosystem.

Forests for forests: combining vegetation indices with solar-induced chlorophyll fluorescence in random forest models improves gross primary productivity prediction in the boreal forest

Remote sensing is a powerful tool for understanding and scaling measurements of plant carbon uptake via photosynthesis, gross primary productivity (GPP), across space and time. The success of remote sensing measurements can be attributed to their ability to capture valuable information on plant structure (physical) and function (physiological), both of which impact GPP. However, no single remote sensing measure provides a universal constraint on GPP and the relationships between remote sensing measurements and GPP are often site specific, thereby limiting broader usefulness and neglecting important nuances in these signals. Improvements must be made in how we connect remotely sensed measurements to GPP, particularly in boreal ecosystems which have been traditionally challenging to study with remote sensing. In this paper we improve GPP prediction by using random forest models as a quantitative framework that incorporates physical and physiological information provided by solar-induced fluorescence (SIF) and vegetation indices (VIs). We analyze 2.5 years of tower-based remote sensing data (SIF and VIs) across two field locations at the northern and southern ends of the North American boreal forest. We find (a) remotely sensed products contain information relevant for understanding GPP dynamics, (b) random forest models capture quantitative SIF, GPP, and light availability relationships, and (c) combining SIF and VIs in a random forest model outperforms traditional parameterizations of GPP based on SIF alone. Our new method for predicting GPP based on SIF and VIs improves our ability to quantify terrestrial carbon exchange in boreal ecosystems and has the potential for applications in other biomes.

Tracking diurnal to seasonal variations of gross primary productivity using a geostationary satellite, GK-2A advanced meteorological imager

The diurnal sampling capability of geostationary satellites provides unprecedented opportunities for monitoring canopy photosynthesis at multiple temporal scales. At the diurnal scale, only geostationary satellites can currently provide sub-daily data at regular intervals, also it can help to minimize data gaps due to clouds at the seasonal scale. However, the potential of geostationary satellites for monitoring photosynthesis has not been explored in depth. In this study, we tracked diurnal to seasonal variations in gross primary production (GPP) using the product of near-infrared reflectance of vegetation and photosynthetically active radiation (PAR) (NIRvP) over deciduous forests, mixed forests and a rice paddy during the growing season. For this purpose, we generated three levels of reflectance and PAR from Geostationary Korea Multi-Purpose Satellite-2A (GK-2A). We examined how NIRvP derived from GK-2A tracked in-situ GPP data collected from five flux tower sites in South Korea. Bi-directional Reflectance Distribution Function (BRDF) normalized NIRvP agreed well with in-situ GPP over the course of the growing season at hourly (R2 = 0.68–0.77) and daily timesteps (R2 = 0.71–0.83). Atmospheric correction and BRDF normalization improved the performance of NIRvP in tracking GPP at both the diurnal and seasonal time scales. Also, GK-2A showed a much higher percentage of available high-quality BRDF data over the whole growing season for all study sites than the Moderate Resolution Imaging Spectroradiometer (MODIS) (GK-2A: 85%; MODIS: 39%), especially during the cloudy monsoon period. Our findings demonstrated that the unique observation characteristics of geostationary satellites can contribute to large-scale monitoring of diurnal to seasonal GPP dynamics.

CROP YIELD ESTIMATION IN THE NORTH CHINA PLAIN FROM 2001 TO 2016 USING MULTI-SOURCE REMOTE SENSING DATA AND PROCESS-BASED FGM MODEL

Abstract. Gross primary productivity (GPP) is an essential indicator of vegetation growth that reflects ecosystem function. GPP is the original source of energy entering cropland ecosystem and thus could serve as a direct indicator of crop yield. In the context of increasing population, changing climate, and decreasing available resources, accurate monitoring and forecasting of food and crop yields play an essential role in sustainable human development. In this study, the process-based Farquhar GPP model (FGM) driven by multisource remote sensing data was implemented to estimate the spatial and temporal dynamics of GPP in crop-growing areas of the North China Plain from 2001 to 2016. We found that the GPP of crops in the North China Plain is relatively high in the southern provinces while lower in the northern part. The GPP values showed a significant increasing trend from 2001 to 2016 (+2.19 Mt C yr−1, P&lt;0.05). Based on crop yield statistical yearbook, we found that GPP is well correlated with crop yield (R2 = 0.98, RMSE = 10.4 Mt yr−1). Thus, we constructed an empirical regression model between GPP and crop yield (i.e., ‘GPP-yield’ empirical model). Finally, time-series GPP data and the ‘ GPP-yield ’ model were applied the crop yield in the North China Plain with spatial and temporal continuity. We found that the crop yield in the North China Plain changed in accordance with GPP, and also showed a significant increasing trend from 2001 to 2016, with a mean increasing rate of +2.84 Mt yr−1 (P&lt;0.05, R2 = 0.16, RMSE = 31.73 Mt yr−1). This study proved an example of large-scale crop yield estimation using multi-source remote sensing data.

Spatio-temporal dynamics of gross primary productivity in China from 1982 to 2017 based on different datasets

Understanding the spatio-temporal variations of gross primary productivity (GPP) of terrestrial ecosystem and its relationship with climatic factors can provide important basis for vegetation restoration and protection. Based on meteorological data and three public GPP datasets (EC-LUE GPP, GLASS GPP, and NIRv GPP), we syste-matically analyzed the spatial-temporal variations of GPP and its response to climate change in China during 1982-2017. All the results based on the three GPP datasets showed that the annual and seasonal GPP in China increased annually from 1982 to 2017, with that in 1998 and 2002 significantly being higher than the average level during the study period, and that in 1989 and 1992 significantly being lower than the average annual GPP. From 1982 to 2017, GPP showed a significant upward trend in most regions of China, with the regions with significant increases accounting for 75.7%, 73.0%, and 69.6% of the whole study area, respectively. There was a significant positive correlation between annual GPP and precipitation and temperature, but spatial heterogeneity was strong. Among them, the regions with positive correlation between GPP and temperature were mainly distributed in Northwest and Central China, while the regions with positive correlation between GPP and precipitation were mainly distributed in North China. There was obvious spatial-temporal heterogeneity in regions that GPP being affected by temperature and precipitation in different seasons. Temperature was the limiting factor of GPP in spring, autumn and winter, while summer GPP was mainly affected by precipitation.

Spatiotemporal evolutionary and mechanism analysis of grassland GPP in China

At the ecosystem level, Gross Primary Productivity (GPP) was defined as the organic compounds formed by plants that absorb atmospheric CO2 through photosynthesis and sequester carbon in plant bodies. How to accurately assess the spatiotemporal evolution of ecosystem carbon indicators has become a critical issue to be addressed. Chinese grasslands are located in a typical arid and semi-arid climate zone and are sensitive to global changes, which will inevitably have serious impacts on the function and structure of Chinese grasslands. Based on this, our paper takes Chinese grassland ecosystems as the research area, and used multi-source GPP dataset from terrestrial ecosystem model simulations and remote sensing satellite observations to quantitatively analyze the spatiotemporal evolution patterns of GPP in Chinese grasslands over the past 40 years. We combined trend analysis and breakpoint test, and then to analyze the mechanism components of GPP space–time variability. The main results are as follows: (1) The model and remote sensing estimated GPP results showed that more than 80% of Chinese grasslands show a significant increasing trend over the past 40 years, with growth rates ranging from 0.68 to 3.13 g C/m−2 year−1. (2) GPPmax also shows that more than 80% of Chinese grasslands are growing rapidly, with an overall growth rate of more than 0.1 g C/m−2 year−1 in each region. The overall long-term trends and interannual variability of multi-source GPP and GPPmax are generally consistent, yet vegetation dynamics in local areas are still uncertain. (3) The breakpoint test showed that ‘monotonically increasing’ was the largest breakpoint type of GPP in Chinese grasslands (33.09%), and the direction of change of GPP in Chinese grasslands before and after the breakpoint was also increasing. (4) GPPmax × CUP explained 91% of the temporal variability of annual-scale GPP in Chinese grasslands from a mechanistic view, and we found that the peak photosynthetic growth and the length of the phenological period synergistically controlled the interannual variability of GPP in Chinese grasslands. Under the scenario of rapid global change, our study accurately assessed the long-term dynamics of GPP and its mechanism-driven of grassland ecosystems in China, which is helpful for estimating and predicting the carbon budget of grassland ecosystems in China, and has important guiding significance for policy formulation to mitigate climate change.

Future Land Use/Land Cover Change Has Nontrivial and Potentially Dominant Impact on Global Gross Primary Productivity

AbstractAnthropogenic land use/land cover (LULC) change alters terrestrial gross primary productivity (GPP), the largest land‐atmosphere carbon exchanges. Identifying the impacts of LULC changes on future terrestrial GPP has been challenging due to the scarcity of standardized future LULC for ecosystem models. Here, we present eight scenario‐based projections of global spatially explicit LULC at 1‐km resolution over the period 2015–2100 with a Future Land Use Simulation model—consistent with the Shared Socioeconomic Pathways and Representative Concentration Pathways. Twenty computational experiments with different LULC patterns, climate forcing, and CO2 concentrations were conducted to quantify their contributions to future GPP dynamics. Results show that the global terrestrial GPP would decline in the 21st century in most LULC scenarios due to urbanization, agricultural expansion, and deforestation. Moreover, the contribution of LULC changes to global GPP dynamics ranges from 3.43% to 10.78% when CO2 fertilization effect (CFE) is not modeled during 2000–2100 (7%–9% of the terrestrial area is dominated by LULC change). However, this value may range from 10.92% to 16.16% during 2000–2050 and 1.41%–14.57% during 2050–2100. The contribution of LULC even reached 56.08% during 2050–2100 in Southeast Asia due to deforestation. Despite the relatively important role of LULC to GPP dynamics, it becomes trivial globally when incorporating CFE into the model (i.e., LULC accounts for 1.24%–2.51% during 2000–2100). Our findings emphasize the strategic role of CFE in enhancing global GPP and highlight the quantitatively nontrivial role of LULC at the regional scale.