Gross Primary Production Models Research Articles

Ground Measurements and Remote Sensing Modeling of Gross Primary Productivity and Water Use Efficiency in Almond Agroecosystems

Ground Measurements and Remote Sensing Modeling of Gross Primary Productivity and Water Use Efficiency in Almond Agroecosystems

Effects of slow temperature acclimation of photosynthesis on gross primary production estimation

The slow temperature acclimation of photosynthesis has been confirmed through early field experiments and studies. However, this effect is difficult to characterize and quantify with some simple and easily accessible indicators. As a result, the impact of slow temperature acclimation of photosynthesis on gross primary production (GPP) estimation has often been overlooked or not integrated into most GPP models. In this study, we used a theorical variable-state of acclimation (S), to characterize the slow temperature acclimation. This variable represents the temperature to which the photosynthetic machinery adapts and is defined as a function of air temperature (Ta) and time constant (τ) required for vegetation to respond to temperature, to discuss its impact on GPP simulation. We used FLUXNET2015 dataset to calculate S and established a GPP model using S and shortwave radiation (SW) based on random forest algorithm (S model). As a comparison, we directly used Ta and SW to build the other GPP model (Ta model). Moreover, the divergent temperature acclimation capacities of plants are crucial to predict and make preparations for likely temperature stress in the future. Therefore, the spatial distribution of τ values was also mapped using satellite sun induced chlorophyll fluorescence (SIF) and Ta datasets. The results indicated that: (1) taking into account the slow temperature acclimation of photosynthesis led to a more precise estimation of GPP which mainly reflected in reduction of excessive fluctuations in GPP predictions; (2) considering the slow temperature acclimation of photosynthesis can reduce the sensitivity of vegetation to temperature; (3) the improvement of S model in GPP estimations was different in different vegetation growth stages which was more significant in the springtime recovery stage; (4) τ values had significant spatial distribution which was strongly affected by the determinants of vegetation growth and seasonal variations in temperature.

Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions

Elevation gradients significantly influence net primary productivity (NPP), but the relationship between elevation, climate variables, and vegetation productivity remains underexplored, particularly in diverse ecological zones. This study quantifies the impact of elevation and climatic variables on NPP in northern Pakistan, hypothesizing that elevation modulates NPP through its influence on temperature and precipitation patterns. Using remote sensing data (MODIS ERA5) and advanced ecological models like the Eddy Covariance-Light Use Efficiency (EC-LUE) model and the Thornthwaite Memorial Model (TMM), we analyzed Gross Primary Productivity (GPP) dynamics across various vegetation types and elevations from 2001 to 2023. Our findings show a mean annual NPP of 323.46 g C m-2 a-1, with an annual increase of 5.73 g C m-2 a-1. Significant elevation-dependent variations were observed, especially in mid-elevation zones (401 to 1600 meters), where NPP increased at a rate of 0.174 g C m-2 a-1 per meter (R² = 0.808, p < 0.01). In contrast, higher elevations (2800-5200 meters) exhibited a decline in NPP, decreasing by -0.171 g C m-2 a-1 per meter (R² = 0.905, p < 0.001). Temperature and precipitation were key drivers, with precipitation positively correlating with NPP across all vegetation types, particularly in Evergreen Needleleaf and Broadleaf Trees. The EC-LUE model's GPP estimates closely matched MODIS data (R² = 0.82), demonstrating the model's reliability. These findings highlight the critical role of elevation and climatic factors in vegetation productivity and underscore the need for targeted ecological management and conservation strategies. The insights from this research are vital for global climate adaptation policies and sustainable development goals, contributing to ecological resilience and carbon sequestration efforts worldwide.

Global-scale improvement of the estimation of terrestrial gross primary productivity by integrating optical and microwave remote sensing with meteorological data

Photosynthesis (a key ecological process) is measured based on gross primary productivity (GPP), emphasizing the criticality of accurate GPP estimation to climate change research. The extant remote sensing-based approaches for GPP estimation were typically based on optical remote sensing data, neglecting the potential supplementary information from microwave remote sensing data. Thus, based on the random forest algorithm, we developed a GPP model through the integration of optical and microwave remote sensing with meteorological data (OMM-GPP). The software and tools used for data processing, modeling, and analysis are the standard and third-party libraries based on the Python Language. Our OMM-GPP model was trained and validated using GPP data (referred to as “observed GPP”) retrieved from carbon dioxide fluxes measured at 137 flux towers. The results indicated that GPP estimation by integrating optical and microwave data with meteorological data was more accurate than GPP estimation by integrating single-source remote sensing data (optical or microwave data) with meteorological data across eight vegetation types. The model performed well on daily and monthly scales, with determination coefficients (R2) (root-mean-square errors, RMSE) of 0.85 (1.52 gC m−2 d−1) and 0.83 (1.49 gC m−2 d−1), respectively, which increased (decreased) by 0.17–0.03 (0.58–0.12 gC m−2 d−1) and 0.11–0.02 (0.39–0.10 gC m−2 d−1) compared with the R2 (RMSE) values obtained by integrating single-source remote sensing data with meteorological variables. Further, the OMM-GPP model effectively captured the seasonal variations in daily and monthly GPP across most vegetation types. The eight-day-scale comparison of the model with the VODCA2GPP dataset revealed its enhanced performances, increasing R2 by 0.33 and decreasing RMSE by 0.97 gC m−2 d−1. Overall, the integration of microwave and optical remote sensing data with meteorological data can enhance GPP estimation accuracy, as demonstrated by the established OMM-GPP, across different vegetation types.

Embedding a novel phenology model into the Common Land Model for improving the modeling of land-atmosphere fluxes

Land surface models (LSMs) are essential tools for simulating the energy and momentum exchange between the land surface and the atmosphere. The vegetation phenology module, a pivotal component of LSMs, reflects the feedback of vegetation to climate variations. However, the vegetation phenology module in several LSMs, such as the Common Land Model (CoLM), face challenges concerning its rationality. Currently, the original satellite phenology mode in CoLM, which uses satellite-based leaf area index (LAI) data directly and statically, constrains the model's ability to accurately represent complex and dynamic ecosystem processes. In this study, we embedded a dynamic vegetation phenology model into CoLM, serving as a novel phenology module. We conducted an extensive comparison of the effects of various phenology modules on CoLM, focusing on their performance in the modeling of gross primary productivity (GPP) and evapotranspiration (ET) at both site and global scales. Results demonstrate an improvement in CoLM for modeling GPP and ET following the integration of the novel phenology module. Specifically, the Pearson's correlation coefficient for GPP increased by 1.29 % to 33.74 %, and for ET by 0.32 % to 7.47 % on a site scale. Furthermore, on a global scale, when compared with global reference datasets, the CoLM embedded with the novel phenology module successfully captures the spatiotemporal distribution of GPP and ET, and its performance is closer to the reference data than that of the CoLM with the original satellite phenology mode. This study highlights the importance of vegetation phenology in LSMs and its potential to improve the modeling of dynamic interactions between vegetation processes and environmental changes within LSMs.

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.

Beyond the visible: Accounting for ultraviolet and far-red radiation in vegetation productivity and surface energy budgets.

Photosynthetically active radiation (PAR) is typically defined as light with a wavelength within 400-700 nm. However, ultra-violet (UV) radiation within 280-400 nm and far-red (FR) radiation within 700-750 nm can also excite photosystems, though not as efficiently as PAR. Vegetation and land surface models (LSMs) typically do not explicitly account for UV's contribution to energy budgets or photosynthesis, nor FR's contribution to photosynthesis. However, whether neglecting UV and FR has significant impacts remains unknown. We explored how canopy radiative transfer (RT) and photosynthesis are impacted when explicitly implementing UV in the canopy RT model and accounting for UV and FR in the photosynthesis models within a next-generation LSM that can simulate hyperspectral canopy RT. We validated our improvements using photosynthesis measurements from plants under different light sources and intensities and surface reflection from an eddy-covariance tower. Our model simulations suggested that at the whole plant level, after accounting for UV and FR explicitly, chlorophyll content, leaf area index (LAI), clumping index, and solar radiation all impact the modeling of gross primary productivity (GPP). At the global scale, mean annual GPP within a grid would increase by up to 7.3% and the increase is proportional to LAI; globally integrated GPP increases by 4.6 PgC year-1 (3.8% of the GPP without accounting for UV + FR). Further, using PAR to proxy UV could overestimate surface albedo by more than 0.1, particularly in the boreal forests. Our results highlight the importance of improving UV and FR in canopy RT and photosynthesis modeling and the necessity to implement hyperspectral or multispectral canopy RT schemes in future vegetation and LSMs.

Spatiotemporal variation and response of gross primary productivity to climate factors in forests in Qiannan state from 2000 to 2020

Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in properly determining the maximum LUE (LUEmax) and downregulation of LUEmax into actual LUE result in uncertainties in the LUE-estimated GPP. The recently developed P model, a new LUE model, captures the adaptability of vegetation to the environment and simplifies parameterization. Site-level studies have proven the superior performance of the P model over LUE models. As a representative karst region with significant changes in forest cover in Southwest China, Qiannan is useful for exploring the spatiotemporal variation in forest GPP and its response to climate change for formulating forest management policies to address climate changes, e.g., global warming. Based on remote sensing and meteorological data, this study estimated the forest ecosystem GPP in Qiannan from 2000–2020 via the P model. This study explored the spatiotemporal changes in GPP in the study region over the past 20 years, used the Hurst index to predict future development trends from a time series perspective, and used partial correlation analysis to analyse the spatiotemporal GPP changes over the past 20 years in response to three factors: temperature, precipitation, and vapor pressure deficit (VPD). Our results showed that (1) the total amount of GPP and average GPP in Qiannan over the past 21 years (2000–2020) were 1.9 × 104 ± 2.0 × 103 MgC ha−1 year−1 and 1238.9 ± 107.9 gC m−2 year−1, respectively. The forest GPP generally increased at a rate of 6.1 gC m−2 year−1 from 2000 to 2020 in Qiannan, and this increase mainly occurred in the nongrowing season. (2) From 2000 to 2020, the forest GPP in Qiannan was higher in the southeast and lower in the northwest, indicating significant spatial heterogeneity. In the future, more than 70% of regional forest GPP will experience a weak increase in nonsustainability. (3) In Qiannan, forest GPP was positively correlated with both temperature and precipitation, with partial correlation coefficients of 0.10 and 0.11, respectively. However, the positive response of GPP to precipitation was approximately 70.47%, while that to temperature was 64.05%. Precipitation had a stronger restrictive effect on GPP than did temperature in this region, and GPP exhibited a negative correlation with VPD. The results showed that an increase in VPD inhibits GPP to some extent. Under rapid global change, the P model GPP provides new GPP data for global ecology studies, and the comparison of various stress factors allows for improvement of the GPP model in the future. The results of this study will aid in understanding the dynamic processes of terrestrial carbon. These findings are helpful for estimating and predicting the carbon budget of forest ecosystems in karst regions, clarifying the regional carbon absorption capacity, clarifying the main factors limiting vegetation growth in these regions, promoting sustainable regional forestry development and serving the “dual carbon goal.” This work has important guiding significance for policy formulation to mitigate climate change.

A novel hybrid modelling framework for GPP estimation: Integrating a multispectral surface reflectance based [formula omitted] simulator into the process-based model

Terrestrial gross primary productivity (GPP) is the key element in the carbon cycle process. Accurate GPP estimation hinges on the maximum carboxylation rate (Vcmax,025). The high uncertainty in deriving ecosystem-level Vcmax,025 has long hampered efforts toward the performance of the GPP model. Recently studies suggest the strong relationship between spectral reflectance and Vcmax,025. We proposed the multispectral surface reflectance-driven Vcmax,025 simulator using the fully connected deep neural network and built the hybrid modelling framework for GPP estimation by integrating the data-driven Vcmax,025 simulator in the process-based model. The performance of hybrid GPP model was evaluated at 95 flux sites. The result shows that the multispectral surface reflectance-driven Vcmax,025 simulator acquires the satisfactory estimation, with correlation coefficient (R), root mean square error (RMSE) and median absolute percentage error (MdAPE) ranging from 0.34 to 0.80, 14 to 43 μmol m−2 s−1 and 21 % to 66 % across different land cover types, respectively. The hybrid framework generates good GPP estimates with R, RMSE and MdAPE varying from 0.76 to 0.89, 1.79 to 6.16 μmol m−2 s−1 and 27 % to 90 %, respectively. Compared with EVI-driven method, the multispectral surface reflectance significantly improves the Vcmax,025 and GPP estimates, with MdAPE declining by 0.6 %–18 % and 1 % to 21 %, respectively. The Shapley value analysis reveals that red (620–670 nm), near-infrared (841–876 nm) and shortwave infrared (1628–1652 nm and 2105–2155 nm) are the key bands for Vcmax,025 estimation. This study highlights the potential of multispectral surface reflectance for quantifying ecosystem-level Vcmax,025. The new hybrid framework fully extracts the information of all available spectral bands using deep learning to reduce parameter uncertainty while maintains the description of photosynthetic process to ensure its physical reasonability. It can serve as a powerful tool for accurate global GPP estimation.

Vegetation Index‐Based Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity

AbstractRecently developed solar‐induced chlorophyll fluorescence‐related vegetation indices (e.g., near infrared reflectance of vegetation (NIRv) and kernel normalized difference vegetation index (kNDVI)) have been reported to be appropriate proxies for vegetation photosynthesis. These vegetation indices can be used to estimate gross primary productivity (GPP) without considering meteorological constraints. However, it is not clear whether such a statement holds true under various environmental conditions. In this study, we explored whether these vegetation indices require meteorological constraints to better characterize GPP under extreme drought conditions using three extreme drought cases in Europe in 2003, 2010, and 2018. According to the long‐term series of observations, vegetation indices (NIRv and kNDVI) alone explained 60% and 57%, respectively, of the weekly GPP variation across the 66 flux sites. The explained variation increased to 69% and 64%, respectively, for the models that take into account radiative effects (NIRv and kNDVI multiplied by radiation). However, without considering meteorological constraints, these vegetation index‐based estimations severely underestimated negative GPP anomalies under drought stress, especially in models that incorporate radiative effects. After incorporating vapor pressure deficit (VPD)‐based meteorological constraints, the GPP estimations exhibited more pronounced negative anomalies during drought periods while maintaining model accuracy (at 70% and 65%, respectively). In addition, the GPP models based on site observations were applied at the regional scale (Europe). Our results indicated that the models without meteorological constraints again underestimated the impact of drought on GPP. This study emphasizes the importance of meteorological constraints in the estimation of GPP, especially under extreme drought conditions.

Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images

Sugarcane croplands account for ~70% of global sugar production and ~60% of global ethanol production. Monitoring and predicting gross primary production (GPP) and transpiration (T) in these fields is crucial to improve crop yield estimation and management. While moderate-spatial-resolution (MSR, hundreds of meters) satellite images have been employed in several models to estimate GPP and T, the potential of high-spatial-resolution (HSR, tens of meters) imagery has been considered in only a few publications, and it is underexplored in sugarcane fields. Our study evaluated the efficacy of MSR and HSR satellite images in predicting daily GPP and T for sugarcane plantations at two sites equipped with eddy flux towers: Louisiana, USA (subtropical climate) and Sao Paulo, Brazil (tropical climate). We employed the Vegetation Photosynthesis Model (VPM) and Vegetation Transpiration Model (VTM) with C4 photosynthesis pathway, integrating vegetation index data derived from satellite images and on-ground weather data, to calculate daily GPP and T. The seasonal dynamics of vegetation indices from both MSR images (MODIS sensor, 500 m) and HSR images (Landsat, 30 m; Sentinel-2, 10 m) tracked well with the GPP seasonality from the EC flux towers. The enhanced vegetation index (EVI) from the HSR images had a stronger correlation with the tower-based GPP. Our findings underscored the potential of HSR imagery for estimating GPP and T in smaller sugarcane plantations.

Integrating leaf functional traits improves modelled estimates of carbon and water fluxes at a subtropical evergreen conifer forest

Simulations of gross primary productivity (GPP) and evapotranspiration (ET) by terrestrial biosphere models (TBMs) are subject to significant uncertainty, in part due to the spatiotemporal variability in leaf photosynthetic capacity, which is not well represented in models. Recent studies have shown the potential for using leaf chlorophyll content (Chlleaf) to constrain GPP and ET modeling in deciduous vegetation with a strong seasonal phenology. However, little is known about how integrating physiological trait information affects modelled GPP and ET in evergreen plants. In this study, we investigated the feasibility of incorporating Chlleaf and leaf age into a TBM, as a proxy for leaf maximum carboxylation rate at 25 °C (Vcmax25) for improving GPP and ET simulations. Measurements of Chlleaf and Vcmax25 from different leaf age classes (current-year and 1-year-old) for Masson pine and Slash pine species, and leaf area index (LAI) were made in a subtropical Evergreen Needleleaf Forest (ENF) eddy covariance flux tower site. The parameterization of Vcmax25 using combined information on Chlleaf and leaf age considerably reduced the biases in simulated GPP and ET, relative to the cases of i) constant Vcmax25 and ii) Chlleaf based Vcmax25. The largest improvements in GPP and ET simulations were found in growing season (May to August), when monthly absolute errors (AEs) of modeled GPP were ∼40 % reduced, from 120.5 to 71.2 g C m−2 mon−1, with a 25 % decrease of monthly AEs of modeled ET from 52.3 to 39.1 mm mon−1. Chlleaf plays a different role in modelled photosynthesis and transpiration between sunlit and shaded leaves. The modeled water use efficiency (WUE) and light use efficiency (LUE) of the shaded leaves were both higher than those of sunlit leaves. This study presents the newly use of Chlleaf and leaf age as a proxy for improving Vcmax25 modeling at an ENF stand, which highlights the importance of using plant physiological traits and leaf age for improving ecosystem carbon-water coupling simulations.

Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers

Abstract. The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. It is modulated by hydrometeorological drivers (i.e. short-wave radiation, air temperature, vapour pressure deficit and soil moisture) and the vegetation state (i.e. canopy greenness, leaf area index) at instantaneous to interannual timescales. In this study, we set out to evaluate the ability of GPP models to capture this variability. Eleven models were considered, which rely purely on remote sensing data (RS-driven), meteorological data (meteo-driven, e.g. dynamic global vegetation models; DGVMs) or a combination of both (hybrid, e.g. light-use efficiency, LUE, models). They were evaluated using in situ observations at 61 eddy covariance sites, covering a broad range of herbaceous and forest biomes. The results illustrated how the determinant of temporal variability shifts from meteorological variables at sub-seasonal timescales to biophysical variables at seasonal and interannual timescales. RS-driven models lacked the sensitivity to the dominant drivers at short timescales (i.e. short-wave radiation and vapour pressure deficit) and failed to capture the decoupling of photosynthesis and canopy greenness (e.g. in evergreen forests). Conversely, meteo-driven models accurately captured the variability across timescales, despite the challenges in the prognostic simulation of the vegetation state. The largest errors were found in water-limited sites, where the accuracy of the soil moisture dynamics determines the quality of the GPP estimates. In arid herbaceous sites, canopy greenness and photosynthesis were more tightly coupled, resulting in improved results with RS-driven models. Hybrid models capitalized on the combination of RS observations and meteorological information. LUE models were among the most accurate models to monitor GPP across all biomes, despite their simple architecture. Overall, we conclude that the combination of meteorological drivers and remote sensing observations is required to yield an accurate reproduction of the spatio-temporal variability of GPP. To further advance the performance of DGVMs, improvements in the soil moisture dynamics and vegetation evolution are needed.

SIF-based GPP modeling for evergreen forests considering the seasonal variation in maximum photochemical efficiency

Solar-induced chlorophyll fluorescence (SIF) has shown great potential in estimating gross primary production (GPP). However, their quantitative relationship is not invariant, which undermines the reliability of empirical SIF-based GPP estimation at fine spatiotemporal scales, especially under extreme conditions. In this study, we developed a parsimonious mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) by employing the Mechanistic Light Response framework and Eco-Evolutionary theory to describe the light and dark reactions during photosynthesis, respectively. Specifically, we found that considering the seasonal variation in a key parameter of the MLR framework, the maximum photochemical efficiency of photosystem II (ΦPSIImax), can avoid the GPP overestimation in winter and early spring due to the relatively low environmental sensitivity of SIF. Compared to the estimates from other benchmark models, our GPP estimates were closer to the 1: 1 line and had higher accuracy (average R2 = 0.86, RMSE=1.99 μmol m−2 s−1) across sites. Furthermore, the changes in the relationship between SIF and J (refers to the electron transport rate) contribute a lot to the dynamic SIF–GPP relationship in this study, while the J–GPP relationship is less variant when the temperature drops. The seasonal variation in the SIF–J relationship, especially the reduction in its slope at low temperatures, is found largely explained by the ΦPSIImax. These results indicate the importance of the uncertainty caused by the variation in the SIF–J relationship for SIF-based GPP estimation, and the consideration of changes in ΦPSIImax under extreme conditions (such as severe winter in this study) is crucial for the improvement of GPP estimation via SIF.

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.

Proximal remote sensing and gross primary productivity in a temperate salt marsh

Salt marshes are highly productive ecosystems relevant for Blue Carbon assessments, but information for estimating gross primary productivity (GPP) from proximal remote sensing (PRS) is limited. Temperate salt marshes have seasonal canopy structure and metabolism changes, defining different canopy phenological phases, GPP rates, and spectral reflectance. We combined multi-annual PRS data (i.e., PhenoCam, discrete hyperspectral measurements, and automated spectral reflectance sensors) with GPP derived from eddy covariance. We tested the performance of empirical models to predict GPP from 12 common vegetation indices (VIs; e.g., NDVI, EVI, PSRI, GCC), Sun-Induced Fluorescence (SIF), and reflectance from different areas of the electromagnetic spectrum (i.e., VIS-IR, RedEdge, IR, and SIF) across the annual cycle and canopy phenological phases (i.e., Greenup, Maturity, Senescence, and Dormancy). Plant Senescence Reflectance Index (PSRI) from hyperspectral data and the Greenness Index (GCC) from PhenoCam, showed the strongest relationship with daily GPP across the annual cycle and within phenological phases (r2=0.30–0.92). Information from the visible-infrared electromagnetic region (VIS-IR) coupled with a partial least square approach (PLSR) showed the highest data-model agreement with GPP, mainly because of its relevance to respond to physiological and structural changes in the canopy, compared with indices (e.g., GCC) that particularly react to changes in the greenness of the canopy. The most relevant electromagnetic regions to model GPP were ∼550 nm and ∼710 nm. Canopy phenological phases impose challenges for modeling GPP with VIs and the PLSR approach, particularly during Maturity, Senescence, and Dormancy. As more eddy covariance sites are established in salt marshes, the application of PRS can be widely tested. Our results highlight the potential to use canopy reflectance from the visible spectrum region for modeling annual GPP in salt marshes as an example of advances within the AmeriFlux network.

Coupling a light use efficiency model with a machine learning-based water constraint for predicting grassland gross primary production

Light use efficiency (LUE) model was established to estimate gross primary production (GPP) for understanding the carbon-climate feedbacks of the terrestrial ecosystems. However, water constraints in LUE models can cause large uncertainties in GPP estimates, especially in semiarid grasslands where water is a key forcing factor for multiple ecosystem processes. Here, we proposed a novel LUE-Gradient Boosting Regression Trees (GBRT) model framework where water scalar is derived from five different water constraints for improving the estimates of grassland GPP over the conterminous United States (CONUS). The performance of LUE-GBRT and ten other GPP models [i.e., LUE-RF, LUE-ERT, GBRT, RF, ERT, LUE-fEF, LUE-fVPD, LUE-fLSWI, LUE-fSM, and LUE-fLST] was evaluated against data from eddy covariance (EC) observations at 25 measurement sites over the CONUS domain from 2000 to 2021. We found that LUE-GBRT improved grassland GPP estimates at all EC sites and yielded the highest Kling-Gupta efficiency (0.85) and the lowest root-mean-square error (1.4 g C m−2 d−1) when compared with the five individual GPP models. LUE-GBRT also showed a superior performance compared to LUE-RF and LUE-ERT. Compared with GBRT, the improvements were particularly from the responses to extreme surface conditions that were better characterized and estimated. An innovation of this method is that LUE-GBRT takes machine learning complementary to the physical-based LUE framework for an optimal junction between GPP physical process and model accuracy.

An Empirical Model of Gross Primary Productivity (GPP) and Relations between GPP and Its Driving Factors, Biogenic Volatile Organic Compounds in a Subtropical Conifer Plantation in China

Measurements of net ecosystem exchange (NEE), solar global radiation, photosynthetically active radiation (PAR) and meteorological parameters were carried out on a subtropical conifer plantation in China from 2013 to 2016. These observations were used to develop and evaluate an empirical model of gross primary production (GPP) (EMGPP) with 3-factor and 2-factor models. Using a 3-factor model, the simulated hourly GPP values were consistent with observations with a relative bias of 9.96% and normalized mean square error values of 0.07 mg CO2 m−2 s−1 for the scattering factor S/Q (S and Q are diffuse and global solar radiation) &lt; 0.5 and 15.52% and 0.15 mg CO2 m−2 s−1 for S/Q ≥ 0.5. Validations of the EMGPP for hourly, daily, monthly, and annual GPP values were carried out and showed that both 3-factor and 2-factor EMGPP models can accurately capture diurnal, seasonal and interannual variations in GPP, but most simulated GPP overestimated the observed value. When the scattering factor is not available, the 2-factor EMGPP can be used. The EMGPP using 3-factor and 2-factor models was applied to simulate GPP under all sky conditions from 2013–2016, and the estimated GPP were in reasonable agreement with the measured values and showed systematic overestimations of 31% and 29% for mean hourly GPP and 41% and 29% for annual amounts, respectively. The sensitivity test demonstrated that GPP values were more sensitive to changes in PAR than to changes in water vapor and scattering factor at low S/Q, but were more sensitive to changes in water vapor than to PAR and S/Q at high S/Q. The sensitivity test revealed some mechanisms of GPP and its related processes, including the relationships between GPP and scattering of PAR, GPP and water vapor, which were in good agreement with other observations and model studies. An empirical model based on PAR energy balance can better describe the multiple interactions between GPP and its driving factors (PAR, water vapor, S/Q). The ratio of the emissions of biogenic volatile organic compounds (BVOCs) to net ecosystem exchange clearly varied between forests in different climate zones.

Tree-ring δ13C of Pinus koraiensis is a better tracer of gross primary productivity than tree-ring width index in an old-growth temperate forest

Tree-ring carbon (C) isotope composition (δ13C) varies depending on photosynthetic capacity and stomatal conductance, and it is therefore intrinsically associated with vegetation productivity. Still, very little is known about the relationship between tree-ring δ13C values and forest gross primary productivity (GPP). Here, we investigated relationships between tree-ring δ13C, tree-ring width index (RWI) and ecosystem-level GPP in a Korean pine (Pinus koraiensis)-dominated old-growth temperate forest in northeastern China. Specifically, we generated chronologies of RWI and δ13C from early-wood (δ13CEW) and late-wood (δ13CLW) of Korean pines, and we built a 17-year continuous ecosystem-level GPP series using eddy covariance measurements, in which a GPP model was used for gap filling. RWI showed vague relationships with tree-ring δ13C and whole-forest GPP, and it was insensitive to interannual variations in climate at the study site. By contrast, both δ13CEW and δ13CLW showed significant relationships with climate variables and GPP. This suggests that isotopic signals of photosynthetic C uptake were imprinted on the formation of tree rings and that tree-ring δ13C of Korean pine is a better proxy for forest GPP than RWI. In addition, tree-ring δ13C of Korean pines was positively correlated with GPP, with an especially close relationship between δ13CEW and GPP of the current year. This implies that photosynthetic activity, rather than stomatal conductance, was the main driver of tree-ring δ13C signals of Korean pine and it was mainly influenced by the climate conditions of the current year, probably because of a limited use of previously fixed C during early-wood formation. Our findings demonstrate the great potential of tree-ring δ13C for reconstructing GPP timeseries at the centennial scale for forest ecosystems, and they could help to constrain parameters in terrestrial C cycle models to improve predictions of C fluxes.

Changes in Carbon Dioxide Balance Associated with Land Use and Land Cover in Brazilian Legal Amazon Based on Remotely Sensed Imagery

The Amazon region comprises the largest tropical forest on the planet and is responsible for absorbing huge amounts of CO2 from the atmosphere. However, changes in land use and cover have contributed to an increase in greenhouse gas emissions, especially CO2, and in endangered indigenous lands and protected areas in the region. The objective of this study was to detect changes in CO2 emissions and removals associated with land use and land cover changes in the Brazilian Legal Amazon (BLA) through the analysis of multispectral satellite images from 2009 to 2019. The Gross Primary Production (GPP) and CO2Flux variables were estimated by the MODIS sensor onboard Terra and Aqua satellite, representing carbon absorption by vegetation during the photosynthesis process. Atmospheric CO2 concentration was estimated from the GOSAT satellite. The variables GPP and CO2Flux showed the effective flux of carbon in the BLA to atmosphere, which were weakly correlated with precipitation (r = 0.191 and 0.133). The forest absorbed 211.05 TgC annually but, due to its partial conversion to other land uses, the loss of 135,922.34 km2 of forest area resulted in 5.82 TgC less carbon being absorbed. Pasture and agriculture, which comprise the main land conversions, increased by 100,340.39 km2 and absorbed 1.32 and 3.19 TgC less, and emitted close to twice more, than forest in these areas. Atmospheric CO2 concentrations increased from 2.2 to 2.8 ppm annually in BLA, with hotspots observed in the southeast Amazonia, and CO2 capture by GPP showed an increase over the years, mainly after 2013, in the north and west of the BLA. This study brings to light the carbon dynamics, by GPP and CO2Flux models, as related to the land use and land cover in one of the biggest world carbon reservoirs, the Amazon, which is also important to fulfillment of international agreements signed by Brazil to reduce greenhouse gas emissions and for biodiversity conservation and other ecosystem services in the region.