Gross Primary Production Estimates Research Articles

Gross primary productivity estimation through remote sensing and machine learning techniques in the high Andean Region of Ecuador.

Accurately estimating gross primary productivity (GPP) is crucial for simulating the carbon cycle and addressing the challenges of climate change. However, estimating GPP is challenging due to the absence of direct measurements at scales larger than the leaf level. To overcome this challenge, researchers have developed indirect methods such as remote sensing and modeling approaches. This study estimated GPP in a humid páramo ecosystem in the Andean Mountains using machine learning models (ML), specifically Random Forest (RF) and Support Vector Regression (SVR), and compared them with traditional models. The study's objective was to analyze the strength and complex nonlinear relationships that govern GPP and to perform an uncertainty analysis for future climate projections. The methodology used to estimate GPP showed that ML-based models outperformed traditional models. The performance of ML models varied significantly among seasons, with the correlation coefficient (R) ranging from 0.24 to 0.86. The RF model performed better in capturing the temporal changes and magnitude of GPP in the less humid season, displaying the highest R (0.86), lowest root mean squared error (0.37g C*m-2), and percentage bias (-3%). Additionally, the analysis indicates that solar radiation is the primary predictor of GPP in the páramo biome, rather than water. The study presents a method for deriving daily GPP fluxes and evaluates the impact of various variables on GPP estimates. This information can be employed in the development of vegetation prediction models.

Solar-Induced Chlorophyll Fluorescence-Based GPP Estimation and Analysis of Influencing Factors for Xinjiang Vegetation

With climate change and the intensification of human activity, drought event frequency has increased, affecting the Gross Primary Production (GPP) of terrestrial ecosystems. Accurate estimation of the GPP and in-depth exploration of its response mechanisms to drought are essential for understanding ecosystem stability and developing strategies for climate change adaptation. Combining remote sensing technology and machine learning is currently the mainstream method for estimating the GPP in terrestrial ecosystems, which can eliminate the uncertainty of model parameters and errors in input data. This study employed extreme gradient boosting, random forest (RF), and light use efficiency models. Additionally, we integrated solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation, and the leaf area index (LAI) to construct various GPP estimation models. The standardised precipitation evapotranspiration index (SPEI) was utilised at various timescales to analyse the relationship between the GPP and SPEI during dry years. Moreover, the potential pathways and coefficients of environmental factors that influence GPP were explored using structural equation modelling. Our key findings include the following: (1) the model combining the SIF and RF algorithms exhibits higher accuracy and applicability in estimating vegetation GPP in the arid zone of Xinjiang, with an overall accuracy (MODIS R2) of 0.775; (2) the vegetation in Xinjiang had different response characteristics to different timescales of drought, in which the optimal timescale for GPP to respond to drought was 9 months, with a mean correlation coefficient of 0.244 between grass land GPP and SPEI09, indicating high sensitivity; (3) using structural equation modelling, we found that temperature and precipitation can affect GPP both directly and indirectly through LAI. This study provides a reliable tool for estimating the GPP in Xinjiang, and its methodology and conclusions are important references for similar environments. In addition, this study bridges the research gap in drought response to GPP at different timescales, and the potential influence mechanism of natural factors on GPP provides a scientific basis for early warning of drought and ecosystem management. Further validation using a longer time series is required to confirm the robustness of the model.

Mitigating the directional retrieval error of solar-induced chlorophyll fluorescence in the red band

Solar-induced chlorophyll fluorescence (SIF) is a promising tool to estimate gross primary production (GPP), but the retrieval of SIF is commonly noisy and highly sensitive to various interference factors. Particularly, the retrieval of SIF in the red band (RSIF) is more challenging than in the far-red SIF (FRSIF) due to the weaker fluorescence signal and the weaker absorption depth of oxygen at the red band compared with the far-red band. A comprehensive evaluation of all factors will allow a reproducible interpretation of SIF signals and advance the estimation of GPP from SIF. Recent studies have assessed the sensitivity of SIF retrieval to sensor characteristics, retrieval methods, and hardware specifications. However, none of these studies have systematically investigated the directional retrieval error of SIF resulting from the mismatch between irradiance measured above the canopy and the true irradiance reaching the canopy components viewed by a sensor. This study illustrated the effect of mismatched irradiance on the retrieval of RSIF using the commonly used standard 3FLD method based on SCOPE model simulations. The retrieval accuracy was highest in the hotspot direction, but it decreased as the observation direction was away from the hotspot. The relative root mean square error (RRMSE) was generally higher than 20 % in the forward directions. To reduce the retrieval error due to the mismatch effect, we proposed a modified 3FLD method (MFLD) by calculating the true irradiance reaching the canopy in a given direction based on geometric optical theory. The results showed that MFLD clearly improved the retrieval accuracy for RSIF, especially in the forward directions where RRMSE decreased by 10 % in most cases. For example, the RRMSE was reduced from 19.26 % to 5.50 % after mitigating the mismatch between the measured and actual solar irradiance, when the solar zenith angle was 40° and viewing zenith angle was 30° in the forward solar principal plane. Even at the nadir observation, the RRMSE was also reduced from 12.84 % to 5.64 %. In summary, MFLD can effectively mitigate the irradiance mismatch effect on the retrieval of RSIF. These results will improve our interpretation of the relationship between GPP and RSIF at different observation directions.

An improved instantaneous gross primary productivity model considering the difference in contributions of sunlit and shaded leaves to canopy sun-induced chlorophyll fluorescence

Sun-induced chlorophyll fluorescence (SIF) from plants offers an effective proxy for estimating gross primary productivity (GPP) by modeling SIF-GPP relationships, a widely used method to evaluate the global carbon sink. However, most SIF-GPP models ignore SIF differences between shaded and sunlit leaves, resulting in GPP underestimation, particularly in dense vegetation. This study aims to partition the contributions of sunlit and shaded leaves to canopy SIF and GPP to refine the SIF-GPP estimation model. Data from 40 eddy covariance (EC) sites representing eight major biomes and TROPOMI SIF satellite data were used for site-specific and global-scale analyses. Our results showed that the contributions of sunlit and shaded leaves to canopy SIF were 80 % and 20 %, and to canopy GPP were 55 % and 45 %, respectively. For site-specific or satellite data, the SIF-GPP relationships were the strongest for sunlit leaves (R2 > 0.51, RMSE = 4.03 μmol m−2 s−1, p < 0.001). The new SIF-GPP model, including sunlit-shaded SIF separation, can improve the accuracy of GPP estimation (R2 = 0.53, RMSE = 4.38 μmol m−2 s−1, p < 0.001). Compared with the model established with observed data, R2 was increased by 0.1, and RMSE decreased by 13.26 μmol m−2 s−1, indicating that the ‘two-leaf’ model could notably improve the SIF-GPP model. This study confirms the different contributions of sunlit and shaded leaves to canopy SIF and GPP, and ignoring this disparity would induce systematic bias in GPP estimation. Our methods and findings on sunlit-shaded SIF separation can be referenced by other studies to enhance GPP estimation accuracy.

Harnessing Information From Shortwave Infrared Reflectance Bands to Enhance Satellite‐Based Estimates of Gross Primary Productivity

AbstractMonitoring gross primary productivity (GPP), the rate at which terrestrial ecosystems fix atmospheric carbon dioxide, is crucial for understanding global carbon cycling. Remote sensing offers a powerful tool for monitoring GPP using vegetation indices (VIs) derived from visible and near‐infrared reflectance (NIRv). While promising, these VIs often suffer from sensitivity to soil background, moisture, and variations in solar and view zenith angle (SZA and VZA). This study investigates the potential of incorporating shortwave infrared (SWIR) reflectance from MODIS and GOES‐R advanced baseline imager (ABI) sensors to improve GPP estimation. We evaluated various formulations for creating SWIR‐enhanced Near‐InfraRed reflectance of Vegetation (sNIRv) by integrating SWIR information into established VIs across 96 Ameriflux and NEON research sites. Our findings reveal that sNIRv improves correlation with GPP for ABI data by up to 0.19 on a half‐hourly basis for normalized difference vegetation index (NDVI) values below 0.25, with diminishing gains as NDVI values rise. Using MODIS data, sNIRv matches r values of NIRv for NDVI above 0.25, with a slight 0.05 increase for NDVI below 0.25. Analyses using SCOPE model simulations further support the ability of sNIRv to capture fractional photosynthetically active radiation, a proxy for GPP, especially for ecosystems with low leaf area index. Results highlight that sNIRv‐based VIs are less sensitive to soil background, SZA, and VZA compared to NIRv. SHapley Additive exPlanations (SHAP) value analysis also identifies sNIRv as the best feature for GPP estimation using machine learning modeling across different land covers, NDVI ranges, and soil water content levels.

Accurate Estimation of Gross Primary Production of Paddy Rice Cropland with UAV Imagery-Driven Leaf Biochemical Model

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy (Vcmax,025), was quantified using various spatial information representation methods, including mean (μref) and standard deviation (σref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of Vcmax,025 and GPP estimation. CNN methods achieved the best Vcmax,025 estimation, with an R of 0.94, an RMSE of 19.44 μmol m−2 s−1, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m−2 s−1, and an MdAPE of 23%. The μref-GLCM texture features and μref-LBPH joint-driven models also gave promising results. However, σref contributed less to Vcmax,025 estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μref-LBPH joint-driven model mainly prioritized reflectance information. The μref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle.

Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset

Remote sensing and process-coupled ecological models are widely used for the simulation of GPP, which plays a key role in estimating and monitoring terrestrial ecosystem productivity. However, most such models do not differentiate the C3 and C4 photosynthetic pathways and neglect the effect of nitrogen content on Vmax and Jmax, leading to considerable bias in the estimation of gross primary productivity (GPP). Here, we developed a model driven by the leaf area index, climate, and atmospheric CO2 concentration to estimate global GPP with a spatial resolution of 0.1° and a temporal interval of 1 day from 2000 to 2022. We validated our model with ground-based GPP measurements at 128 flux tower sites, which yielded an accuracy of 72.3%. We found that the global GPP ranged from 116.4 PgCyear−1 to 133.94 PgCyear−1 from 2000 to 2022, with an average of 125.93 PgCyear−1. We also found that the global GPP showed an increasing trend of 0.548 PgCyear−1 during the study period. Further analyses using the structure equation model showed that atmospheric CO2 concentration and air temperature were the main drivers of the global GPP changes, total associations of 0.853 and 0.75, respectively, while precipitation represented a minor but negative contribution to global GPP.

A 2001–2022 global gross primary productivity dataset using an ensemble model based on the random forest method

Abstract. Advancements in remote sensing technology have significantly contributed to the improvement of models for estimating terrestrial gross primary productivity (GPP). However, discrepancies in the spatial distribution and interannual variability within GPP datasets pose challenges to a comprehensive understanding of the terrestrial carbon cycle. In contrast to previous models that rely on remote sensing and environmental variables, we developed an ensemble model based on the random forest method (denoted ERF model). This model used GPP outputs from established models: Eddy Covariance Light Use Efficiency (EC-LUE), GPP estimate model based on Kernel Normalized Difference Vegetation Index (GPP-kNDVI), GPP estimate model based on Near-Infrared Reflectance of Vegetation (GPP-NIRv), Revised-EC-LUE, Vegetation Photosynthesis Model (VPM), and GPP estimate model based on the Moderate Resolution Imaging Spectroradiometer (MODIS). These outputs were used as inputs to estimate GPP. The ERF model demonstrated superior performance, explaining 85.1 % of the monthly GPP variations at 170 sites and surpassing the performance of selected GPP estimate models (67.7 %–77.5 %) and an independent random forest model using remote sensing and environmental variables (81.5 %). Additionally, the ERF model improved accuracy across each month and with various subranges, mitigating the issue of “high-value underestimation and low-value overestimation” in GPP estimates. Over the period from 2001 to 2022, the global GPP estimated by the ERF model was 132.7 PgC yr−1, with an increasing trend of 0.42 PgC yr−2, which is comparable to or slightly better than the accuracy of other mainstream GPP datasets in terms of validation results of GPP observations independent of FLUXNET (i.e., ChinaFLUX). Importantly, for a growing number of GPP datasets, our study provides a way to integrate these GPP datasets, which may lead to a more reliable estimate of global GPP.

Improving global gross primary productivity estimation using two-leaf light use efficiency model by considering various environmental factors via machine learning

Distinguishing gross primary productivity (GPP) into sunlit (GPPsu) and shaded (GPPsh) components is critical for understanding the carbon exchange between the atmosphere and terrestrial ecosystems under climate change. Recently, the two-leaf light use efficiency (TL-LUE) model has proven effective for simulating global GPPsu and GPPsh. However, no known physical method has focused on integrating the overall constraint of intricate environmental factors on photosynthetic capability, and seasonal differences in the foliage clumping index (CI), which most likely influences GPP estimation in LUE models. Here, we propose the TL-CRF model, which uses the random forest technique to integrate various environmental variables, particularly for terrestrial water storage (TWS), into the TL-LUE model. Moreover, we consider seasonal differences in CI at a global scale. Based on 267 global eddy covariance flux sites, we explored the functional response of vegetation photosynthesis to key environmental factors, and trained and evaluated the TL-CRF model. The TL-CRF model was then used to simulate global eight-day GPP, GPPsu, and GPPsh from 2002 to 2020. The results show that the relative prediction error of environmental stress factors on the maximum LUE is reduced by approximately 52 % when these factors are integrated via the RF model. Thus the accuracy of global GPP estimation (R2 = 0.87, RMSE = 0.94 g C m−2 d−1, MAE = 0.61 g C m−2 d−1) in the TL-CRF model is greater than that (R2 = 0.76, RMSE = 2.18 g C m−2 d−1, MAE = 1.50 g C m−2 d−1) in the TL-LUE model, although this accuracy awaits further investigation among the released GPP products. TWS exerts the greatest control over ecosystem photosynthesis intensity, making it a suitable water indicator. Furthermore, the results confirm an optimal minimum air temperature for photosynthesis. Overall, these findings indicate a promising method for producing a new global GPP dataset, advancing our understanding of the dynamics and interactions between photosynthesis and environmental factors.

Gross Primary Production of Antarctic Landfast Sea Ice: A Model‐Based Estimate

AbstractMuch of the Antarctic coast is covered by seasonal landfast sea ice (fast ice), which serves as an important habitat for ice algae. Fast‐ice algae provide a key early season food source for pelagic and benthic food webs, and contribute to biogeochemical cycling in Antarctic coastal ecosystems. Summertime fast ice is undergoing a decline, leading to more seasonal fast ice with unknown impacts on interconnected Earth system processes. Our understanding of the spatiotemporal variability of Antarctic fast ice, and its impact on polar ecosystems is currently limited. Evaluating the overall productivity of fast‐ice algae has historically been hampered by limitations in observations and models. By linking new fast‐ice extent maps with a one‐dimensional sea‐ice biogeochemical model, we provide the first estimate of the spatio‐seasonal variability of Antarctic fast‐ice algal gross primary production (GPP) and its annual primary production on a circum‐Antarctic scale. Experiments conducted for the 2005–2006 season provide a mean fast ice‐algal production estimate of 2.8 Tg C/y. This estimate represents about 12% of overall Southern Ocean sea‐ice algae production (estimated in a previous study), with the mean fast‐ice algal production per area being 3.3 times higher than that of pack ice. Our Antarctic fast‐ice GPP estimates are probably underestimated in the Ross Sea and Weddell Sea sectors because the sub‐ice platelet layer habitats and their high biomass are not considered.

Climate‐Induced Uncertainty in Modeling Gross Primary Productivity From the Light Use Efficiency Approach

AbstractLight use efficiency (LUE) models, along with satellite‐based vegetation maps and climatic reanalysis products as drivers, are effective tools for estimating large‐scale gross primary productivity (GPP). However, the climate‐induced uncertainty in LUE‐based GPP remains poorly understood, particularly the temporal scaling uncertainty from ignored climatic fluctuations. Here, two 1‐hourly reanalysis products, along with site‐based observations, were employed to drive a two‐leaf LUE model at 194 eddy‐covariance (EC) sites. The observation‐driven and reanalysis‐driven GPP at the 1‐hourly resolution were evaluated against EC GPP to illustrate the uncertainty from reanalysis products, with mean absolute deviation (MAD) and Nash‐Sutcliffe efficiency (NSE) as criterion. Moreover, the climate‐induced temporal scaling uncertainty was characterized by comparing distributed GPP (modeled with 1‐hourly resolution climatic drivers) and lumped GPP (modeled with 6‐hourly resolution climatic drivers). At the 1‐hourly resolution, results demonstrated that the reanalysis‐driven GPP showed a weaker relationship with EC GPP (MAD = 0.14 gC m−2h−1, NSE = 0.48) than the observation‐driven GPP (MAD = 0.12 gC m−2h−1, NSE = 0.60), confirming the nonnegligible climate‐induced uncertainty from reanalysis products. Additionally, the climate‐induced uncertainty arising from gridded radiation was found to be significantly larger than that associated with temperature and vapor pressure deficit (VPD). At the 6‐hourly resolution, both the observation‐driven and reanalysis‐driven lumped GPP exhibited a lower relationship with EC GPP (MAD = 0.63 gC m−26h−1, NSE = 0.54) than the corresponding distributed GPP (MAD = 0.57 gC m−26h−1, NSE = 0.59), demonstrating that the climate‐induced temporal scaling uncertainty in 6‐hourly GPP estimates was significantly apparent. This study emphasizes the imperative to refine reanalysis products for more precise capture of short‐term fluctuations and to reduce scaling uncertainties in coarse temporal resolution GPP estimates.

Assessing Drought Impacts on Gross Primary Productivity of Rubber Plantations Using Flux Observations and Remote Sensing in China and Thailand

Rubber (Hevea brasiliensis Muell.) plantations are vital agricultural ecosystems in tropical regions. These plantations provide key industrial raw materials and sequester large amounts of carbon dioxide, playing a vital role in the global carbon cycle. Climate change has intensified droughts in Southeast Asia, negatively affecting rubber plantation growth. Limited in situ observations and short monitoring periods hinder accurate assessment of drought impacts on the gross primary productivity (GPP) of rubber plantations. This study used GPP data from flux observations at four rubber plantation sites in China and Thailand, along with solar-induced chlorophyll fluorescence (SIF), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photosynthetically active radiation (PAR) indices, to develop a robust GPP estimation model. The model reconstructed eight-day interval GPP data from 2001 to 2020 for the four sites. Finally, the study analyzed the seasonal drought impacts on GPP in these four regions. The results indicate that the GPP prediction model developed using SIF, EVI, NDVI, NIRv, and PAR has high accuracy and robustness. The model’s predictions have a relative root mean square error (rRMSE) of 0.22 compared to flux-observed GPP, with smaller errors in annual GPP predictions than the MOD17A3HGF model, thereby better reflecting the interannual variability in the GPP of rubber plantations. Drought significantly affects rubber plantation GPP, with impacts varying by region and season. In China and northern Thailand (NR site), short-term (3 months) and long-term (12 months) droughts during cool and warm dry seasons cause GPP declines of 4% to 29%. Other influencing factors may alleviate or offset GPP reductions caused by drought. During the rainy season across all four regions and the cool dry season with adequate rainfall in southern Thailand (SR site), mild droughts have negligible effects on GPP and may even slightly increase GPP values due to enhanced PAR. Overall, the study shows that drought significantly impacts rubber the GPP of rubber plantations, with effects varying by region and season. When assessing drought’s impact on rubber plantation GPP or carbon sequestration, it is essential to consider differences in drought thresholds within the climatic context.

GPP of a Chinese Savanna Ecosystem during Different Phenological Phases Simulated from Harmonized Landsat and Sentinel-2 Data

Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China’s savanna ecosystems constitute only a small part of the world’s savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPPHLS-VPM) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPPMODIS-VPM) with GPP estimates from an eddy covariance (EC) flux tower (GPPEC) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MOD17A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide.

Carbon Flux Models in a Zonally Dry Tropical Forest Area (Caatinga)

Studies on energy exchange in ecosystems are critical for understanding carbon flows amid different vegetation patterns. In semi-arid areas, comprehending the variability in carbon absorption is essential for quantifying and anticipating the impacts of changes in the caatinga ecosystem. This study aims to calibrate and evaluate models for Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and Ecosystem Respiration (Reco) in the caatinga biome. NEE measurements were obtained and calculated at 30-minute intervals using EddyPro 3.6 software, from raw data measured at 10 Hz. GPP was estimated by partitioning NEE and Reco, all measured in micromoles of CO2 per square meter per second (μmol CO2m⁻²s⁻¹) using the eddy covariance (EC) tower, installed in a legal reserve at Embrapa Semiárido, in Petrolina, Pernambuco, within a section of the caatinga canopy. Following the measurement of carbon fluxes and ecosystem respiration, field measurements were conducted using a portableFieldSpec HandHeld spectroradiometer to obtain the reflectance of the canopy around the turbulent eddy covariance tower. These measurements were carried out in 2015 in a preserved caatinga area. Multiple linear regression models weredeveloped to estimate carbon fluxes from orbital images, enabling accurate estimation of GPP, NEE, and Reco, using the MODIS/Terra Daily Surface Reflectance product (MOD09GA). The primary results demonstrate the effectiveness of the developed models, particularly the GPP model, which exhibited the best statistical indices (R = 0.97; R² = 0.95; Root Mean Square Error = 0.20). Observations from the EC tower indicated that the models developed to estimate NEE, GPP, and Reco, using visible and near-infrared reflectance data, accurately represented the dry period and effectively captured the phenological aspects of the caatinga ecosystem.

Rooting meta-ecosystems with reciprocal lateral carbon and nitrogen flows in a Yangtze coastal marsh

Abstract The dynamics of lateral nutrient fluxes through hydrological movements are crucial for understanding ecological functions related to the flow of energy, materials, and organisms across various spatiotemporal scales. To investigate the connectivity of multiple spatial flow processes, we conducted a one-year field study to measure lateral hydrologic carbon (C) and nitrogen (N) fluxes across the continental shelf in the Yangtze estuary. We observed a significant correlation between the differences in remote sensing-based estimates of gross primary production (GPP) (ΔGPPMODIS) and the differences in eddy covariance (EC) tower-based GPP (ΔGPPEC) at both high-elevation and low-elevation sites. Our findings indicate that the saltmarsh acts as a net source of dissolved total C while serving as a net sink for dissolved total N. Furthermore, there was a significant correlation in the total dissolved stoichiometry of the C/N ratio between imports from and exports to adjacent aquatic systems. These findings highlight the importance of integrating ecological stoichiometric principles to improve our understanding of the complex relationships among physical, chemical, and ecological processes, particularly within the context of the meta-ecosystem framework. Additionally, when reciprocal hydrological lateral C and N flows are considered, a single ecosystem can function as both a source and sink within the meta-ecosystem framework.

Optimizing the terrestrial ecosystem gross primary productivity using carbonyl sulfide (COS) within a two-leaf modeling framework

Abstract. Accurately modeling gross primary productivity (GPP) is of great importance for diagnosing terrestrial carbon–climate feedbacks. Process-based terrestrial ecosystem models are often subject to substantial uncertainties, primarily attributed to inadequately calibrated parameters. Recent research has identified carbonyl sulfide (COS) as a promising proxy of GPP due to the close linkage between leaf exchange of COS and carbon dioxide (CO2) through their shared pathway of stomatal diffusion. However, most of the current modeling approaches for COS and CO2 do not explicitly consider the vegetation structural impacts, i.e., the differences between the sunlit and shaded leaves in COS uptake. This study used ecosystem COS fluxes from seven sites to optimize GPP estimation across various ecosystems with the Biosphere-atmosphere Exchange Process Simulator (BEPS), which was further developed to simulate the canopy COS uptake under its state-of-the-art two-leaf framework. Our results demonstrated substantial improvement in GPP simulation across various ecosystems through the data assimilation of COS flux into the two-leaf model, with the ensemble mean of the root mean square error (RMSE) for simulated GPP reduced by 20.16 % to 64.12 %. Notably, we also shed light on the remarkable identifiability of key parameters within the BEPS model, including the maximum carboxylation rate of RuBisCO at 25 °C (Vcmax25), minimum stomatal conductance (bH2O), and leaf nitrogen content (Nleaf), despite intricate interactions among COS-related parameters. Furthermore, our global sensitivity analysis delineated both shared and disparate sensitivities of COS and GPP to model parameters and suggested the unique treatment of parameters for each site in COS and GPP modeling. In summary, our study deepened insights into the sensitivity, identifiability, and interactions of parameters related to COS and showcased the efficacy of COS in reducing uncertainty in GPP simulations.

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.

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.

The applicability of a SIF-based mechanistic model for estimating GPP at the canopy scale

Mechanistically linking gross primary productivity (GPP) and sun-induced chlorophyll fluorescence (SIF) is an essential step to unleash the full potential of SIF for remote sensing-based predictions of GPP across biomes, climates, and spatiotemporal scales. The latest SIF-based mechanistic light response model that includes the fraction of open photosystem II reaction centers as key parameter (qMLR-SIF model), can accurately reproduce leaf-scale photosynthesis under various conditions. However, it remains unclear to what extent the qMLR-SIF model is suitable for estimating GPP at larger scales such as the canopy scale. Therefore, canopy-scale data of tower-based far-red SIF, GPP and key environmental variables from 10 study sites were collected to analyze the SIF-GPP relationship and to compare the qMLR-SIF model with the widely used Farquhar, von Caemmerer, Berry (FvCB) model and with a light use efficiency (LUE) model for different plant functional types (PFTs), photosynthetic pathways (C3 and C4), and temporal scales (hourly, daily and 4-day). Results showed that the nonlinear SIF-GPP relationship existed in all PFTs and the degree of linearity increased at larger temporal scales. The qMLR-SIF model exhibited wide applicability to quantify canopy GPP for different PFTs (R2 = 0.55–0.80, RMSE = 2.72–11.03 μmol CO2 m-2 s-1), photosynthetic pathways (R2 = 0.70–0.78, RMSE = 5.29–9.05 μmol CO2 m-2 s-1) and temporal scales (R2 = 0.82–0.97, RMSE = 3.42–8.32 μmol CO2 m-2s-1). Compared with the two other models, the qMLR-SIF model performed best overall, which is mainly due to its simpler model structure and the mechanistic link between SIF and photosynthesis. Particularly, the qMLR-SIF model could more accurately estimate GPP in C4 species, with higher R2 (0.78) and lower RMSE (8.46 μmol CO2 m-2s-1). These findings highlight the advantages of the qMLR-SIF model in GPP estimation at the canopy scale, showing its potential in applications at regional and global scales.