Gross Primary Production Products Research Articles

An Improved Gross Primary Production Model Considering Atmospheric CO2 Fertilization: The Qinghai–Tibet Plateau as a Case Study

Involving the effect of atmospheric CO2 fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie–Ames–Stanford Approach (CASA) model, scarcely considers the effects of atmospheric CO2 fertilization, which causes GPP estimation uncertainties. Therefore, this study proposed an improved method for estimating GPP by integrating the atmospheric CO2 concentration into the CASA model and generated a long time series GPP dataset with high precision for the Qinghai–Tibet Plateau. The CASA model was improved by considering the impact of atmospheric CO2 on vegetation productivity and discerning variations in CO2 gradients within the canopy and leaves. A 500 m monthly GPP dataset for the Qinghai–Tibet Plateau from 2003 to 2020 was generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R2 = 0.68, RMSE = 406 g C/m2/year) than the original model (R2 = 0.67, RMSE = 499.32 g C/m2/year) and MODIS GPP products (R2 = 0.49, RMSE = 522.56 g C/m2/year). The GPP on the Qinghai–Tibet Plateau increased significantly with the increase in atmospheric CO2 concentration and the gradual accumulation of dry matter. The improved method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on the Qinghai–Tibet Plateau.

Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity

The maximum Rubisco carboxylation rate normalized to 25 °C (Vcmax25) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of Vcmax25 have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four Vcmax25 datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing Vcmax25 fixed by plant functional types (PFT) derived from field measurements, incorporating Vcmax25 derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %–25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %–27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived Vcmax25 increases slightly (2.2 %) compared to that simulated using PFT-fixed Vcmax25. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using Vcmax25 with seasonal variations (i.e., LCC Vcmax25 and SIF + LCC Vcmax25) yields a 4.3 %–7.3 % decrease compared to that simulated using PFT-fixed Vcmax25. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC Vcmax25 and LCC Vcmax25 demonstrates better consistency (R2 = 0.91–0.93, RMSE = 314.2–376.6 g C m−2 yr−1). This study underscores the importance of accurately characterizing the spatiotemporal variations in Vcmax25 for the accurate simulation of global vegetation productivity.

Spatiotemporal patterns and driving factors of gross primary productivity over the Mongolian Plateau steppe in the past 20 years

The Eurasian steppe is the largest temperate grassland in the world. The grassland of the Mongolian Plateau (MP) represents an important part of the Eurasian steppe with high climatic sensitivity. Gross primary productivity (GPP) is a key indicator of the grassland's production, status and dynamic on the MP. In this study, we calibrated and evaluated the grassland-specific light use efficiency model (GRASS-LUE) against the observed GPP collected from nine eddy covariance flux sites on the MP, and compared the performance with other four GPP products (MOD17, VPM, GLASS and GOSIF). GRASS-LUE with higher R2 (0.91) and lower root mean square error (RMSE = 0.99 gC m−2 day−1) showed a better performance compared to the four GPP products in terms of model accuracy and dynamic consistency, especially in typical and desert steppe. The parameters of the GRASS-LUE are more suitable for water-limited grassland could be the reason for its outstanding performance in typical and desert steppe. Mean grassland GPP derived from GRASS-LUE was higher in the east and lower in the west of the MP. Grassland GPP was on average 205 gC m−2 over the MP between 2001 and 2020 with mean annual total GPP of 322 TgC yr−1. 30 % of the MP steppe showed a significant GPP increase. Growing season precipitation is the main factor affecting GPP of the MP steppe across regions. Anthropogenic factors (livestock density and population density) had greater effect on GPP than growing season temperature in pastoral counties in IM that take grazing as one of main industries. These findings can inform the status and trend of the productivity of MP steppe and help government and scientific research institutions to understand the drivers for spatial pattern of grassland GPP on the MP.

Contrasting responses of relationship between solar-induced fluorescence and gross primary production to drought across aridity gradients

Satellite solar-induced chlorophyll fluorescence (SIF) can provide valuable insights on terrestrial gross primary production (GPP) and its response to climate from region to global scales. However, the relationship between SIF and GPP under different climate conditions (such as drought) is not yet fully understood and remains a topic of debate in the literature. Here we examined the responses of the SIF–GPP relationship (i.e., the slope k of the linear SIF–GPP relationship, GPP = k × SIF) to drought along the aridity gradient using seven satellite SIF datasets, three GPP products, and 62 flux towers. The results revealed divergent SIF–GPP relationships under different aridity and drought conditions based on different SIF and GPP datasets. Overall, we found that with increasing aridity index (AI) (more humid), k derived from SIF with afternoon overpass (dailySIFPM) decreased, while k from SIF with morning overpass (dailySIFAM) increased. When there is a drought, for arid and semi-arid regions with AI from 0 to 0.4, kPM (the slope of dailySIFPM–GPP) increased, but kAM (the slope of dailySIFAM–GPP) decreased. The dailySIFPM also showed larger decreases than dailySIFAM and GPP during the drought. The contrasting responses among two groups of SIF may result from different SIF overpass time which detected different levels of water stresses on photosynthesis. The dryland ecosystems may exhibit strong midday and afternoon depression in photosynthesis under stress, which led to underestimation of daily SIF that was converted from midday/afternoon-observed SIF by assuming ideal symmetrical diurnal cycle of solar radiation. Our study highlights that in addition to multiple previously reported factors, different observation time of SIF may also affect the SIF–GPP relationship.

Joint improvement on absorbed photosynthetically active radiation and intrinsic quantum yield efficiency algorithms in the P model betters the estimate of terrestrial gross primary productivity

Terrestrial gross primary productivity (GPP) plays a crucial role in global carbon cycle and budget. A range of light use efficiency (LUE) models have been developed to estimate GPP at different spatial scales. However, large uncertainties still remain in GPP output from such models, mainly owing to the difficulty in the proper determination of maximum light use efficiency (LUEmax). The recently developed P model directly quantifies actual LUE based on environmental and physiological factors, reducing the uncertainty in GPP estimation caused by the ambiguous LUEmax. However, the existing P models still suffer from a potential underestimation in global validation, which might be related to the calculation of absorbed photosynthetically active radiation (APAR) and intrinsic quantum yield efficiency (φ0) in the P model. In this study, we improved the P model by differentiating sunlit and shaded leaves for APAR calculation and by considering the spatial variability of the optimal temperature for φ0 (Topt_phi0). The roles of modified APAR and φ0 calculations in reducing the uncertainty of simulated GPP were assessed through model simulation experiments with monthly tower-based GPP from 120 flux sites globally distributed as the benchmark. The validation indicates that simultaneous improvements on APAR and φ0 algorithms are able to better the performance of the P model. Monthly GPP simulated with this improved P model (i.e., IP model) is in good agreement with tower-based values, with a linear R2, root mean squared error, mean error, mean absolute error, and DISO of 0.76, 1.88 g C m−2d−1, -0.14 g C m−2d−1, 1.77 g C m−2d−1 and 0.60. The total global terrestrial GPP output from the IP model (IPGPP) averaged 134.3 Pg C yr−1 during 1981—2021, in the upper range of existing GPP products.

A new global time-series GPP production: DFRF-GPP

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

Mango‐GPP: A Process‐Based Model for Simulating Gross Primary Productivity of Mangrove Ecosystems

AbstractMangrove ecosystems are becoming increasingly important in global climate mitigation. However, large gaps still exist in evaluating mangroves' gross primary productivity (GPP) due to reasons such as the specific influences, for example, temperature and salt stresses are poorly described in Earth System Models (ESMs). This study developed a process‐based biogeochemical model (Mango‐GPP) to improve the GPP simulation in natural and restored mangroves. The model integrates mangrove‐specific physiological processes, including the response to salt and temperature stresses, as well as the light‐use efficiency at different growing stages. Eddy covariance flux measurements at two natural sites and one restored site in China were used to calibrate and validate Mango‐GPP. The model was calibrated by inverse analysis approach based on two cases and independently validated against the other cases. The validation results showed that it was generally capable of simulating the seasonal and interannual GPP variations at different sites. The simulated daily and annual GPPs agreed well with the observations and yielded R2 of 0.67 and 0.96, with model efficiency of 0.64 and 0.93, respectively. In comparison, Mango‐GPP showed better performances than many current satellite‐based GPP products and ESMs. The model was more sensitive to solar radiation, carbon dioxide concentration, and leaf traits. Future improvements should focus on enhancing Mango‐GPP's descriptive power of key processes, and further simulating other carbon fluxes at regional scales. This work provides a model foundation for further simulating carbon exchanges between the atmosphere, mangrove, and ocean for studying the coastal wetland restoration on regional carbon neutrality.

A water stress factor based on normalized difference water index substantially improved the accuracy of light use efficiency model for arid and semi-arid grasslands

High-accuracy simulation of gross primary productivity (GPP) is crucial for the monitoring and evaluation of the ecosystem services and the adaptive management of grassland. The light use efficiency (LUE) model is one of the most widely-used methods to simulate GPP, given its simple structure and low input requirements. Current LUE models are less applicable to grasslands than other vegetation types and have lower overall estimation accuracy in arid and semi-arid regions. A grassland-specific light use efficiency model (GRASS-LUE), which optimizes three important parameters (the fraction of absorbed photosynthetically active radiation FPAR, optimum temperature Topt and water stress factor f(W)), has been developed to improve the accuracy of GPP simulation for grasslands along aridity gradients. GPP simulated by the GRASS-LUE agreed well with the eddy covariance (EC) GPP estimates for grasslands along the aridity gradient at 8-day (coefficient of determination (R2) = 0.85, Bias = −0.67 gC m−2 day−1), monthly (R2 = 0.88, Bias = −22.33 gC m−2 month−1) and annual time scales (R2 = 0.95, Bias = −118.91 gC m−2 year−1). Compared with five state-of-the-art GPP products (PML, MOD17, rEC-LUE, VPM and BESS), GRASS-LUE had the best and most stable performance in reproducing EC GPP, especially for semi-arid grassland, with the highest global performance indicator (GPI) value. Sensitivity tests further revealed that: 1) modifying f(W) to be based on the Normalized Difference Water Index (NDWI) substantially improved the model accuracy for arid and semi-arid grasslands and 2) using the minimum of temperature and water stress factors (i.e., min⁡(f(W),f(T))) to represent environmental stress in GRASS-LUE was better than that from the multiplication of temperature and water stress factors (i.e., f(W)×f(T)).

Large Divergence of Satellite Monitoring of Diffuse Radiation Effect on Ecosystem Water‐Use Efficiency

AbstractWater‐use efficiency (WUE), defined as the ratio of gross primary productivity (GPP) and evapotranspiration (ET), is a key indicator of the coupling of carbon and water cycles in terrestrial ecosystems. Diffuse radiation significantly affects WUE as diffuse radiation has stronger fertilization effects on GPP than ET. Remote sensing data‐driven (RS) models were widely used to estimate WUE. However, the performance of RS models in simulating diffuse radiation effect on WUE is poorly understood. We used GPP and ET products from MODIS, PML, BESS, and GLASS to explore the response of WUE to diffuse radiation across different vegetation types. We found that there was a large divergence among the RS models in estimating the response of WUE to FDP. PML model performed better than other RS models in simulating diffuse radiation effect on WUE. This research has significant implications for improving model simulations and understanding global carbon and water cycles.

Machine learning algorithms improve MODIS GPP estimates in United States croplands

Introduction: Machine learning methods combined with satellite imagery have the potential to improve estimates of carbon uptake of terrestrial ecosystems, including croplands. Studying carbon uptake patterns across the U.S. using research networks, like the Long-Term Agroecosystem Research (LTAR) network, can allow for the study of broader trends in crop productivity and sustainability.Methods: In this study, gross primary productivity (GPP) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) for three LTAR cropland sites were integrated for use in a machine learning modeling effort. They are Kellogg Biological Station (KBS, 2 towers and 20 site-years), Upper Mississippi River Basin (UMRB - Rosemount, 1 tower and 12 site-years), and Platte River High Plains Aquifer (PRHPA, 3 towers and 52 site-years). All sites were planted to maize (Zea mays L.) and soybean (Glycine max L.). The MODIS GPP product was initially compared to in-situ measurements from Eddy Covariance (EC) instruments at each site and then to all sites combined. Next, machine learning algorithms were used to create refined GPP estimates using air temperature, precipitation, crop type (maize or soybean), agroecosystem, and the MODIS GPP product as inputs. The AutoML program in the h2o package tested a variety of individual and combined algorithms, including Gradient Boosting Machines (GBM), eXtreme Gradient Boosting Models (XGBoost), and Stacked Ensemble.Results and discussion: The coefficient of determination (r2) of the raw comparison (MODIS GPP to EC GPP) was 0.38, prior to machine learning model incorporation. The optimal model for simulating GPP across all sites was a Stacked Ensemble type with a validated r2 value of 0.87, RMSE of 2.62 units, and MAE of 1.59. The machine learning methodology was able to successfully simulate GPP across three agroecosystems and two crops.

A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest

Process-based models are widely used to predict the agroecosystem dynamics, but such modeled results often contain considerable uncertainty due to the imperfect model structure, biased model parameters, and inaccurate or inaccessible model inputs. Data assimilation (DA) techniques are widely adopted to reduce prediction uncertainty by calibrating model parameters or dynamically updating the model state variables using observations. However, high computational cost, difficulties in mitigating model structural error, and low flexibility in framework development hinder its applications in large-scale agroecosystem predictions. In this study, we addressed these challenges by proposing a novel DA framework that integrates a Knowledge-Guided Machine Learning (KGML)-based surrogate with tensorized ensemble Kalman filter (EnKF) and parallelized particle swarm optimization (PSO) to effectively assimilate historical and in-season multi-source remote sensing data. Specifically, we incorporate knowledge from a process-based model, ecosys, into a Gated Recurrent Unit (GRU)-based hierarchical neural network. The hierarchical architecture of KGML-DA mimics key processes of ecosys and builds a causal relationship between target variables. Using carbon budget quantification in the US Corn-Belt as a context, we evaluated KGML-DA's performance in predicting key processes of the carbon cycle at three agricultural sites (US-Ne1, US-Ne2, US-Ne3), along with county-level (627 counties) and 30-m pixel-level (Champaign County, IL) grain yield. The site experiments show that updating the upstream variable, e.g., gross primary production (GPP), improved the prediction of downstream variables such as ecosystem respiration, net ecosystem exchange, biomass, and leaf area index (LAI), with RMSE reductions ranging from 9.2% to 30.5% for corn and 4.8% to 24.6% for soybean. Uncertainty in downstream variables was automatically constrained after correcting the upstream variables, demonstrating the effectiveness of the causality linkages in the hierarchical surrogate. We found joint use of in-season GPP and evapotranspiration (ET) products along with historical GPP and surveyed yields achieved the best prediction for county-level yields, while assimilating in-season LAI observations benefitted the prediction in extreme years. Uncertainty and error analysis of regional yield estimation demonstrated that KGML-DA could reduce prediction error by 26.5% for corn and 36.2% for soybean. Remarkably, the GPU-based tensor operation design makes this DA framework more than 7000 times faster than the PB model with a High-Performance Computing system, indicating the high potential of the proposed framework for in-season, high-resolution agroecosystem predictions.

Constraining the budget of atmospheric carbonyl sulfide using a 3-D chemical transport model

Abstract. Carbonyl sulfide (OCS) has emerged as a valuable proxy for photosynthetic uptake of carbon dioxide (CO2) and is known to be important in the formation of aerosols in the stratosphere. However, uncertainties in the global OCS budget remain large. This is mainly due to the following three flux terms: vegetation uptake, soil uptake and oceanic emissions. Bottom-up estimates do not yield a closed budget, which is thought to be due to tropical emissions of OCS that are not accounted for. Here we present a simulation of atmospheric OCS over the period 2004–2018 using the TOMCAT 3-D chemical transport model that is aimed at better constraining some terms in the OCS budget. Vegetative uptake of OCS is estimated by scaling gross primary productivity (GPP) output from the Joint UK Land Environment Simulator (JULES) using the leaf relative uptake (LRU) approach. The remaining surface budget terms are taken from available literature flux inventories and adequately scaled to bring the budget into balance. The model is compared with limb-sounding satellite observations made by the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) and surface flask measurements from 14 National Oceanic and Atmospheric Administration – Earth System Research Laboratory (NOAA-ESRL) sites worldwide. We find that calculating vegetative uptake using the LRU underestimates the surface seasonal cycle amplitude (SCA) in the Northern Hemisphere (NH) mid-latitudes and high latitudes by approximately 37 ppt (35 %). The inclusion of a large tropical source is able to balance the global budget, but further improvement to the SCA and phasing would likely require a flux inversion scheme. Compared to co-located ACE-FTS OCS profiles between 5 and 30 km, TOMCAT remains within 25 ppt (approximately 5 % of mean tropospheric concentration) of the measurements throughout the majority of this region and lies within the standard deviation of these measurements. This provides confidence in the representation of atmospheric loss and surface fluxes of OCS in the model. Atmospheric sinks account for 154 Gg S of the annual budget, which is 10 %–50 % larger than previous studies. Comparing the surface monthly anomalies from the NOAA-ESRL flask data to the model simulations shows a root-mean-square error range of 3.3–25.8 ppt. We estimate the total biosphere uptake to be 951 Gg S, which is in the range of recent inversion studies (893–1053 Gg S), but our terrestrial vegetation flux accounts for 629 Gg S of the annual budget, which is lower than other recent studies (657–756 Gg S). However, to close the budget, we compensate for this with a large annual oceanic emission term of 689 Gg S focused over the tropics, which is much larger than bottom-up estimates (285 Gg S). Hence, we agree with recent findings that missing OCS sources likely originate from the tropical region. This work shows that satellite OCS profiles offer a good constraint on atmospheric sinks of OCS through the troposphere and stratosphere and are therefore useful for helping to improve surface budget terms. This work also shows that the LRU approach is an adequate representation of the OCS vegetative uptake, but this method could be improved by various means, such as using a higher-resolution GPP product or plant-functional-type-dependent LRU. Future work will utilise TOMCAT in a formal inversion scheme to better quantify the OCS budget.

Improving the ability of solar-induced chlorophyll fluorescence to track gross primary production through differentiating sunlit and shaded leaves

Recently, solar-induced chlorophyll fluorescence (SIF) is a promising tool to estimate gross primary production (GPP). Photosynthesis gradually saturates with the increasing light, but fluorescence tends to keep increasing, leading to a nonlinear SIF-GPP relationship. This nonlinearity occurs for sunlit leaves but not for shaded leaves for which photosynthesis is light-limited. However, the separation of sunlit and shaded SIF has not been systematically investigated when estimating GPP from SIF. Therefore, it is promising to develop a model for GPP estimation considering such differences. This study proposed an approach to separate the total canopy SIF emission (SIFtotal) from TROPOspheric Monitoring Instrument (TROPOMI) SIF into their sunlit and shaded components (SIFsun and SIFshade). The nonlinearity and linearity in SIF-GPP relationships for sunlit and shaded leaves were incorporated into a two-leaf hybrid model, which was fitted using flux tower data and then evaluated using leave-one-site-out crossing validation. We also elucidated the distinct SIF-GPP relationships between sunlit and shaded leaves using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model simulation. Compared to previously used linear (R2 = 0.68, RMSE = 2.13 gC·m−2·d−1) or hyperbolic (R2 = 0.72, RMSE = 2.01 gC·m−2·d−1) model based on the big-leaf assumption, our proposed two-leaf hybrid model has the best performance on GPP estimation (R2 = 0.77, RMSE = 1.79 gC·m−2·d−1). We also applied this two-leaf hybrid model to estimate the global GPP during the main growing season in Northern Hemisphere, which were highly correlated with several existing GPP products, with R2 ranging from 0.79 to 0.88. These results will improve our understanding of the relationship between SIF and GPP for sunlit and shaded leaves and will advance application of satellite SIF data to GPP estimation.

NIRvP as a remote sensing proxy for measuring gross primary production across different biomes and climate zones: Performance and limitations

The product of near-infrared radiation reflected by vegetation (NIRv) and PAR (NIRvP) is a promising proxy for the remote estimation of gross primary production (GPP). However, the efficiency of NIRvP in estimating the GPP and its limitations across multiple biomes and climate zones remain unclear. In this study, we aimed to evaluate the performance and limitations of NIRvP in estimating the GPP in comparison to absorbed photosynthetically active radiation (APAR), solar-induced chlorophyll fluorescence (SIF), and the MOD17A2H GPP product. Overall, the correlation between NIRvP and eddy covariance (EC) GPP was stronger than that of APAR, SIF, and MOD17A2H GPP across most biomes with usually similar seasonal variations in radiation, air temperature (TA), and precipitation. The near-​infrared (NIR) reflectance (ρNIR) and light use efficiency (LUE) exhibited a covarying relationship under these environmental conditions, which suggested that the ρNIR contributed positively to the NIRvP-GPP relationship under such climatic conditions. However, the performance of NIRvP was poor in some biomes and climate zones, which exhibited different variations in the seasonal patterns of radiation, TA, and precipitation. The resulting inconsistencies between ρNIR and LUE implied that the ρNIR contributed negatively to the NIRvP-GPP relationship in these regions. Altogether, the findings demonstrated that the NIRvP-GPP relationship was robust but attained a moderate overall relationship across ecosystems (R2 < 0.50) in the majority of biomes and climate zones. In addition, this study also elucidated the limitations of NIRvP as a GPP proxy in certain climate zones, which was attributed to the synergistic contributions of APAR and ρNIR in the NIRvP-GPP relationship.

Comparison and evaluation of evapotranspiration estimations by different approaches in the Yellow River Basin

Abstract Accurately quantifying large-scale evapotranspiration (ET) is of significant scientific importance. In this study, the Shuttleworth–Wallace (SW) model incorporates gross primary productivity (GPP) values from the near-infrared reflectance of vegetation (NIRv) GPP product (NIRvGPP). Monthly ET estimations for the Yellow River Basin (YRB) were obtained using the SW models with GPP derived from the GLASS GPP product (GLASSGPP) and NIRvGPP, as well as GLASS ET and CR ET products (SW_GLASSGPP ET, SW_NIRvGPP ET, GLASS ET, and CR ET). The study analyzes the annual spatio-temporal patterns of these products, evaluates their accuracy and uncertainty, and examines the applicability of a fused ET estimation. The results revealed: (1) differences exist in ET estimations among GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET; (2) monthly GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET align well with the monthly ETWB, with SW_GLASSGPP ET and SW_NIRvGPP ET outperforming GLASS ET and CR ET; (3) average uncertainties of GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET display different spatial variation patterns; and (4) compared to GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET, the fused ET estimation achieves the best performance within the YRB.

Temporal upscaling of MODIS instantaneous FAPAR improves forest gross primary productivity (GPP) simulation

Gross primary productivity (GPP) is a measure of carbon uptake by terrestrial ecosystems for carbon neutrality and serves as a key indicator for the Sustainable Development Goals. The instantaneous Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is applied in GPP estimation by light use efficiency (LUE) models. However, an obvious time scale mismatch exists between instantaneous FAPAR and daily accumulated GPP. In the study, we explored the potential of temporal upscaling instantaneous FAPAR in improving GPP simulation. The absorbed photosynthetically active radiation (APAR) derived from instantaneous and upscaled FAPARs were first compared. GPPs were estimated from instantaneous and upscaled FAPAR by MOD17 LUE model using default and optimized parameters. The optimized GPP was finally compared with three GPP products: GLASS, MOD17A2H and MYD17A2H GPP. The APARs and GPPs were evaluated with the Eddy Covariance (EC) GPP at 78 forest sites from the Fluxnet community. Results showed that when compared with EC GPP, the upscaled FAPAR-derived APAR held a higher R2 (0.26–0.73) than that from instantaneous FAPARs. The upscaled FAPAR-based GPPs by MOD17 LUE model with default or optimized parameters had a larger R2 and smaller RMSE reducing uncertainties by 6.82–7.8% (maximum value: 27.9–49%). The optimized upscaled FAPAR-derived GPP was far superior to the GPP products with larger R2 (0.69–0.85) and smaller RMSE (12.8–14.6 g C/m2/8d). It is concluded that temporal upscaling of MODIS instantaneous FAPAR can well improve the estimation of forest GPP and the parameters of MOD17 LUE model should be further optimized. Our study is an effort toward reducing the uncertainties from FAPAR in the GPP estimation for better assessing the achievement of carbon neutrality and Sustainable Development Goals.

Quantifying effects of different types of droughts on gross primary production in China

AbstractThe increasing impact of drought stress on ecosystems with regard to climate change has received considerable attention, which has been demonstrated to have a time‐lag effect on ecosystems. However, the time‐lag effect on different types of droughts and gross primary production (GPP) at large regional scales remains unclear. Thus, using the GOSIF GPP product data of solar‐induced chlorophyll fluorescence, the meteorological drought index (standardized precipitation evapotranspiration index [SPEI]), and the agricultural drought index (soil moisture deficit index [SMDI]) based on the soil moisture data obtained from the Global Land Data Assimilation System, the time‐lag effect of regional drought and GPP and the contribution of drought to GPP variation were analysed in China from 2000 to 2018. Results showed that (1) China's annual GPP increased at an average rate of 9.26 g C m−2 y−1. With regard to the average rate for each climate zone, the largest increase of 30.44 g C m−2 y−1 was observed in the southern subtropical region, and the smallest increase (4.01 g C m−2 y−1) was observed in the Qinghai–Tibetan plateau, with about 53.11% of the regions showing a significant upward trend. In addition, the significantly declining regions were primarily concentrated in urban areas, accounting for about 2.26% of China. (2) SPEI and GPP had an average lag time of 6.28 months, whereas SMDI and GPP had an average lag time of 7.17 months. The comparison demonstrated that SMDI had a greater influence on GPP than SPEI. (3) The average contribution rate of meteorological drought and agricultural drought to the decrease of GPP was 79.37% and 87.06%, and their average contribution rate to the increase of GPP was 39.11% and 50.57%, respectively. This study will provide comprehensive understanding of the effects of drought on carbon and water cycles in terrestrial ecosystems with regard to climate change.

Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning

A reliable estimate of the gross primary productivity (GPP) is crucial for understanding the global carbon balance and accurately assessing the ability of terrestrial ecosystems to support the sustainable development of human society. However, there are inconsistencies in variations and trends in current GPP products. To improve the estimation accuracy of GPP, a deep learning method has been adopted to merge 23 CMIP6 data to generate a monthly GPP merged product with high precision and a spatial resolution of 0.25°, covering a time range of 1850–2100 under four climate scenarios. Multi-model ensemble mean and the merged GPP (CMIP6DL GPP) have been compared, taking GLASS GPP as the benchmark. Compared with the multi-model ensemble mean, the coefficient of determination between CMIP6DL GPP and GLASS GPP was increased from 0.66 to 0.86, with the RMSD being reduced from 1.77 gCm−2d−1 to 0.77 gCm−2d−1, which significantly reduced the random error. Merged GPP can better capture long-term trends, especially in regions with dense vegetation along the southeast coast. Under the climate change scenarios, the regional average annual GPP shows an upward trend over China, and the variation trend intensifies with the increase in radiation forcing levels. The results contribute to a scientific understanding of the potential impact of climate change on GPP in China.

Analysing far-red SIF directional anisotropy of three structurally contrasting forest canopies towards improved GPP estimation

Solar-induced chlorophyll fluorescence (SIF) is used to estimate terrestrial gross primary production (GPP) due to its physiological link to photosynthesis. However, strong angular variations in satellite-observed SIF (SIFobs), especially for forest canopies with a high structural heterogeneity, hampers robust estimation of GPP. Here, we use directional SIFobs datasets from OCO-2 and TROPOMI satellite sensors and the 3D discrete anisotropic radiative transfer model to investigate the directional properties of far-red SIFobs and their relationships with two GPP products obtained from MODIS and FluxCom datasets for three European forest types with contrasting canopy structures, i.e., boreal, temperate and Mediterranean forests. We found bowl-like angular distribution patterns of SIFobs in both observed and simulated boreal and temperate forests, but no generic distribution pattern of SIFobs was found for the Mediterranean forest. In our GPP estimation of boreal forest, oblique SIFobs performed better than the widely used nadir SIFobs. However, we did not find an optimal view angle suitable for all three forest types. Further, the angular dependencies and forest structure impacts present in SIFobs were found to still propagate into the estimation of total emitted SIF (SIFtotal), which generally regarded as free of these confounding effects. Finally, our results demonstrate that two commonly used variables, i.e., SIFobs with a constant viewing angle and the approximated SIFtotal, both fail to produce a robust relationship with GPP for the Mediterranean forest. These findings highlight the importance of forest canopy structural heterogeneity, including forest floor, on interpretation of directional satellite SIF observations, even when corrected and expressed as a total canopy SIF emission.

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.