Daily Gross Primary Production Research Articles

Using SAR-based products to calculate potato carbon uptake in a tropical Andean region

ABSTRACT Gross primary productivity (GPP) is an essential parameter to estimate the efficiency of carbon transfer in terrestrial ecosystems. The daily GPP has been monitored in the past using mainly optical satellite imagery. However, GPP has never been monitored using satellite Synthetic Aperture Radar (SAR) imagery. We evaluate the possibility of using Sentinel-1 SAR data to estimate GPP and compare with field GPP measurements using eddy covariance (EC) systems in three commercial potato fields, located in the Andean region of Colombia, using three different water irrigation levels. Raw and processed radar data from Sentinel-1 were compared with the daily and accumulated GPP from the EC. Results indicate that SAR data has a high correlation with the accumulated GPP measured by the EC in the field. The normalized radar backscatter and radar brilliance coefficients with VH polarization show a good correlation with the EC accumulated GPP in irrigated potato fields (R2: 0.77–0.81), while radar vegetation indices show a good correlation with the EC accumulated GPP in potato fields with no irrigation (R2≈0.82). In particular, the accumulated GPP during the whole potato crop cycle was estimated with good accuracy (~5% error with irrigation and ~10% error with no irrigation).

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.

Quantitative assessment of the scale conversion from instantaneous to daily GPP under various sky conditions based on MODIS local overpassing time

ABSTRACT With the increasing frequency of extreme events, daily gross primary productivity (GPP) is necessary to be accurately assessed to determine when and how much it is affected. Fluctuating environmental conditions contribute to diverse diurnal photosynthetic patterns influenced by changing atmospheric factors, including solar radiation, CO2 concentrations, and leaf temperature. This complexity underscores the challenge of accurately estimating daily GPP. We quantitatively assessed three temporal upscaling approaches – cosine of the solar zenith angle, extraterrestrial solar irradiance, and photosynthetic active radiation(PAR) – under varying sky conditions. Additionally, we introduced a novel correction method for universal temporal upscaling ratios. Instantaneous GPP values from the FLUXNET2015 dataset, acquired around the MODIS overpassing time, were utilized. The upscaled daily GPP exhibited minimal deviation from the half-hourly GPPs collected between 11 am and 1 pm, with increased discrepancies for GPPs later in the afternoon. The PAR-based approach demonstrated superior accuracy in the afternoon, effectively capturing incoming radiation changes due to clouds. Instant environmental variables-GPP relationships were weak under clear skies but exhibited moderate-to-high positive correlations under cloudy conditions. Analyzing the impact of the diffuse ratio of incoming photosynthetic active radiation on instantaneous GPP revealed limited enhancement at short time scales. On a daily scale, all three temporal upscaling approaches, under different ranges of clearness index, consistently underestimated daily GPP. We proposed a novel correction method, normalizing the difference between maximum and minimum air temperature in a day, notably reduced errors by approximately 10.69 and 21.07 gC m−2 d−1 for cosine of the solar zenith angle and extraterrestrial solar irradiance-based temporal upscaling approaches. We recommend adopting the corrected temporal upscaling factor globally due to its simplicity and improved accuracy.

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

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

Divergent impacts of VPD and SWC on ecosystem carbon-water coupling under different dryness conditions

Ecosystem water use efficiency (WUE) is an indicator of carbon-water interactions and is defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). However, it is currently unclear how WUE responds to atmospheric and soil drought events in terrestrial ecosystems with different dryness conditions. Additionally, the contributions of GPP and ET to the WUE response remain poorly understood. Based on measurements from 26 flux tower sites distributed worldwide, the binning method and random forest model were employed to separate the sensitivities of daily ecosystem WUE, GPP, and ET to vapor pressure deficit (VPD) and soil water content (SWC) under different dryness conditions (dryness index = potential evapotranspiration/precipitation, DI). Results showed that the sensitivity of WUE to VPD was negative at humid sites (DI < 1), while the sensitivity of WUE to SWC was positive at arid sites (DI > 2). Furthermore, the contribution of GPP to VPD-induced WUE variability was 63 % at humid sites, and the contribution of ET to SWC-induced WUE variability was 68 % when SWC was less than the 60th percentile at arid sites. Consequently, one increasing VPD-induced decrease in GPP was generally linked to a decrease in WUE at humid sites, and one drying soil moisture-caused decrease in ET was linked to a WUE increase under low SWC conditions at arid sites. Finally, VPD had a stronger effect on WUE than SWC when VPD was less than the 90th percentile or SWC was greater than the 50th percentile. Our findings underscore the importance of considering ecosystem dryness when investigating the impacts of VPD and SWC on ecosystem carbon-water coupling.

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.

Remote sensing of daily evapotranspiration and gross primary productivity of four forest ecosystems in East Asia using satellite multi-channel passive microwave measurements

Satellite multi-channel passive microwave observations provide valuable information of canopy vertical structure and vegetation water content, while their potential in the simultaneous estimation of forest evapotranspiration (ET) and gross primary productivity (GPP) has not been explored. This study combined satellite multi-channel microwave Emissivity Difference Vegetation Indices (EDVIs) from 6.9 to 36.5 GHz with the Penman-Monteith (PM), Priestley-Taylor (PT) and light use efficiency (LUE) models to estimate daily ET and GPP over four forest ecosystems in East Asia. In-situ measurements of daily ET, GPP and meteorological variables from ChinaFLUX network (2003 to 2010) were used to calculate and validate the method. An exponential relationship was found between EDVIs and canopy conductance across the forests. By inverting EDVIs-based LUE model coupled with PM and PT models when canopy carbon uptake and water content reached a maximum in growing seasons, the maximum LUE (EDVI-LUEmax) was derived and found to agree with other independent studies. Site validations showed that the EDVI-based method performed a good simultaneous estimation of daily ET and GPP at a tropical forest and three sub-tropical and temperate forests (R2 of 0.64 to 0.94 and RMSE of −11.7% to 19.6% for ET, R2 of 0.39 to 0.73 and RMSE of 31.2% to 50.4% for GPP). The modeled relationship between GPP and ET also agreed that from in-situ measurements. The results were also found to correlate with microwave frequencies. In-situ ET and GPP showed a stronger correlation with EDVI at 18.7 and 36.5 GHz than that at 10.7 and 18.7 GHz across the forests. EDVI-LUEmax of a temperate mixed forest increased as EDVI incorporated lower frequency microwave emissions from deeper canopy layers, which reduced the negative bias in GPP estimation. Overall, this study demonstrated the potential of multi-channel microwave measurements in estimating ET and GPP with the potential effects of vegetation vertical structure.

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

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

Interannual variability in the ecosystem CO2fluxes at a paludified spruce forest and ombrotrophic bog in the southern taiga

Abstract. Climate warming in high latitudes impacts CO2 sequestration of the northern peatlands through the changes in production and decomposition processes. The response of the net CO2 fluxes between ecosystems and the atmosphere to climate change and weather anomalies can vary across forest and non-forest peatlands. To better understand the differences in CO2 dynamics at forest and non-forest boreal peatlands induced by changes in environmental conditions, the estimates of interannual variability in the net ecosystem exchange (NEE), total ecosystem respiration (TER), and gross primary production (GPP) was obtained at two widespread peatland ecosystems – paludified spruce forest and the adjacent ombrotrophic bog in the southern taiga of west Russia using 6 years of paired eddy covariance flux measurements. Both positive and negative annual and growing season air temperature and precipitation anomalies were observed in the period of measurement (2015–2020). Flux measurements showed that, in spite of the lower growing season TER (332±17 … 339±15 gC m−2) and GPP (442±13 … 464± 11 gC m−2) rates, the bog had a higher CO2 uptake rates (NEE was -132±11 … -108±6) than the forest, except for the warmest and the wettest year of the period (2020), and was an atmospheric CO2 sink in the selected years, while the forest was a CO2 sink or source, depending on the environmental conditions. Growing season NEE at the forest site was between -142±48 and 28±40 gC m−2, TER between 1135±64 and 1366±58 gC m−2, and GPP between 1207±66 and 1462±107 gC m−2. Annual NEE at the forest was between -62±49 and 145±41 gC m−2, TER between 1429±87 and 1652±44 C m−2, and GPP between 1345±89 and 1566±41 gC m−2, respectively. Under the anomalously warm winter conditions with sparse and thin snow cover (2019/2020), the increased daily GPP, TER, and net CO2 uptake at the forest was observed, while at the bog, the changes in CO2 fluxes between the warm and cold winters were not significant. This study suggests that the warming in winter can increase the CO2 uptake of the paludified spruce forests of the southern taiga in non-growing seasons.

Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture

Abstract. Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We designed an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The integrated model, LPJ-PM (LPJ-PT-JPLSM Model) as the underlying model, was coupled from the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ-DGVM version 3.01) and a hydrology module (i.e., the updated Priestley–Taylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the Global LAnd and Surface Satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 ( coefficient of determination) of 0.91 and 0.81 and an ubRMSD (unbiased root mean square deviation) reduced by 40.3 % and 29.9 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The GPP and ET resulting from the assimilation demonstrated a better performance in the arid and semi-arid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m−2 d−1; ET: R2 = 0.73, ubRMSD = 0.61 mm d−1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m−2 d−1; ET: R2 = 0.66; ubRMSD = 0.67 mm d−1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS data had a slight difference, and the SMAP soil moisture data performed better than SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, and GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (μ): 3.4 mm per month; estimated standard deviation of μ: 1.91 mm per month). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.

A daily and 500 m coupled evapotranspiration and gross primary production product across China during 2000–2020

Abstract. Accurate high-resolution actual evapotranspiration (ET) and gross primary production (GPP) information is essential for understanding the large-scale water and carbon dynamics. However, substantial uncertainties exist in the current ET and GPP datasets in China because of insufficient local ground measurements used for model constraint. This study utilizes a water–carbon coupled model, Penman–Monteith–Leuning Version 2 (PML-V2), to estimate 500 m ET and GPP at a daily scale. The parameters of PML-V2(China) were well calibrated against observations of 26 eddy covariance flux towers across nine plant functional types in China, indicated by a Nash–Sutcliffe efficiency (NSE) of 0.75 and a root mean square error (RMSE) of 0.69 mm d−1 for daily ET, respectively, and a NSE of 0.82 and a RMSE of 1.71 g C m−2 d−1 for daily GPP. The model estimates get a small Bias of 6.28 % and a high NSE of 0.82 against water-balance annual ET estimates across 10 major river basins in China. Further evaluations suggest that the newly developed product is better than other typical products (MOD16A2, SEBAL, GLEAM, MOD17A2H, VPM, and EC-LUE) in estimating both ET and GPP. Moreover, PML-V2(China) accurately monitors the intra-annual variations in ET and GPP in the croplands with a dual-cropping system. The new data showed that, during 2001–2018, the annual GPP and water use efficiency experienced a significant (p&lt;0.001) increase (8.99 g C m−2 yr−2 and 0.02 g C mm−1 H2O yr−1, respectively), but annual ET showed a non-significant (p&gt;0.05) increase (0.43 mm yr−2). This indicates that vegetation in China exhibits a huge potential for carbon sequestration with little cost in water resources. The PML-V2(China) product provides a great opportunity for academic communities and various agencies for scientific studies and applications, freely available at https://doi.org/10.11888/Terre.tpdc.272389 (Zhang and He, 2022).

Comparing Different Light Use Efficiency Models to Estimate the Gross Primary Productivity of a Cork Oak Plantation in Northern China

Light use efficiency (LUE) models have been widely used to estimate terrestrial gross primary production (GPP). However, the estimation of GPP still has large uncertainties owing to an insufficient understanding of the complex relationship between water availability and photosynthesis. The plant water stress index (PWSI), which is based on canopy temperature, is very sensitive to the plant stomatal opening and has been regarded as a good indicator for monitoring plant water status at the regional scale. In this study, we selected a cork oak plantation in northern China with an obvious seasonal drought as the research object. Using the ground-observed data, we evaluated the applicability of the LUE models with typical water stress scalars (MOD17, MODTEM, EC-LUE, ECM-LUE, SM-LUE, GLO-PEM, and Wang) in a GPP simulation of the cork oak plantation and explored whether the model’s accuracy can be improved by applying PWSI to modify the above models. The results showed that among the seven LUE models, the water stress scalar had a greater impact on the model’s performance than the temperature stress scalar. On sunny days, the daily GPP simulated by the seven LUE models was poorly matched with the measured GPP, and all models explained only 23–52% of the GPP variation in the cork oak plantation. The modified LUE models can significantly improve the prediction accuracy of the GPP and explain 49–65% of the variation in the daily GPP. On cloudy days, the performance of the modified LUE models did not improve, and the evaporative fraction was more suitable for defining the water stress scalar in the LUE models. The ECM-LUE and the modified GLO-PEM based on PWSI had optimal model structures for simulating the GPP of the cork oak plantation under cloudy and sunny days, respectively. This study provides a reference for the accurate prediction of GPP in terrestrial ecosystems in the future.

Seasonal dynamics of carbon dioxide and water fluxes in a rice-wheat rotation system in the Yangtze-Huaihe region of China

Investigating variations in the characteristics and influencing factors on carbon dioxide and water fluxes in cultivated lands are crucial for improving crop yield and water use efficiency. As a critical cropping system, rice-wheat rotations are widely distributed in South and East Asia. However, few studies have reported on the long-term changes in carbon dioxide and water fluxes in rice-wheat rotation systems using the eddy covariance method. In this study, a typical rice-wheat rotation field located in Anhui province, China, was chosen for our study. We analyzed seasonal dynamics and influencing factors of net ecosystem CO2 exchange (NEE), gross primary production (GPP), ecosystem respiration (Re), and evapotranspiration (ET) based on 5-year observations and boosted regression trees method. The main results were as follows: (1) daily NEE, GPP, Re, and ET displayed a bimodal curve during the year, with two peaks in the rice and wheat growing seasons, respectively. (2) The cumulative values of NEE were negative in the rice and wheat growing seasons, indicating the cultivated land acted as a carbon sink during the study period. Furthermore, cumulative values of NEE, GPP, Re, and ET were slightly higher in the rice-growing seasons than the wheat-growing seasons. (3) The relative importance analysis revealed the leaf area index (LAI) was the primary control factor of NEE and GPP, whereas temperature and LAI essentially controlled Re. In comparison, net radiation was the most crucial controller of ET. However, their contributions varied in different growing seasons. The findings have important implications for parameterizing crop models to improve simulating carbon and water fluxes for rice-wheat rotation systems.

Effect of diffuse fraction on gross primary productivity and light use efficiency in a warm-temperate mixed plantation.

Diffuse radiation (I f) is one of important variables determining photosynthetic rate and carbon uptake of forest ecosystems. However, the responses of gross primary productivity (GPP) and light use efficiency (LUE) to diffuse fraction (DF) are still poorly understood. We used a 6-year dataset of carbon flux at a warm-temperate mixed plantation site in North China to explore the impacts of DF on GPP and LUE. During 2011-2017, ecosystem apparent quantum yield (α) and photosynthesis at photosynthetically active radiation (PAR) of 1800 µmol m-2 s-1 (P 1800) on cloudy days were 63% and 17% higher than on clear days, respectively. Under lower vapor pressure deficit (VPD) and air temperature (T a) conditions, canopy photosynthesis was significantly higher on cloudy skies than on clear skies. On half-hourly scale, increased DF enhanced α and P 1800. Daily GPP peaked at a median DF (=0.5), while daily LUE significantly increased with DF (p<0.01). Both GPP and LUE were mainly controlled directly by DF and PAR. DF had an indirect effect on LUE and GPP mainly through PAR. At high DF levels (>0.5), the increase in LUE did not make GPP enhancement. The direct effect of DF on GPP and LUE under lower T a and VPD was more sensitive than under higher T a and VPD. When DF was incorporated into the Michaelis-Menten model, it performed well in the GPP estimation, and the determination coefficient increased by 32.61% and the root mean square error decreased by 25.74%. These findings highlight the importance of incorporating DF into carbon sequestration estimation in North China.

A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems

Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand the global carbon budget but also plays a critical role in human welfare and development. Light-use efficiency (LUE) models have been widely applied in estimating regional and global GPP due to their simple structure and clear physical basis. However, maximum LUE (), a key photosynthetic parameter in LUE models, has generally been treated as a constant, leading to common overestimation and underestimation of low and high magnitudes of GPP, respectively. Here, we propose a parsimonious and practical two-stage LUE (TS-LUE) model to improve GPP estimates by (a) considering seasonal variations of , and (b) separately re-parameterizing in the green-up and senescence stages. The TS-LUE model is inter-compared with state-of-the-art –static moderate resolution imaging spectroradiometer-GPP, eddy-covariance-LUE, and vegetation production models. Validation results at 14 FLUXNET sites for five crop species showed that: (a) the TS-LUE model significantly reduced the large bias at high- and low-level GPP as produced by the three –static LUE models for all crop types; and (b) the TS-LUE model generated daily GPP estimates in good agreement with in-situ measurements and was found to outperform the three –static LUE models. Especially compared to the well-known moderate resolution imaging spectroradiometer-GPP, the TS-LUE model could remarkably decrease the root mean square error (in gC m−2 d−1) by 24.2% and 35.4% (from 3.84 to 2.91 and 2.48) and could increase the coefficient of determination by 14.7% and 20% (from 0.75 to 0.86 and 0.9) when the leaf area index (LAI) and infrared reflectance of vegetation (NIRv) were used to re-parameterize the , respectively. The TS-LUE model provides a brand-new perspective on the re-parameterization of and indicates great potential for improving daily agroecosystem GPP estimates at a global scale.

Sun-induced chlorophyll fluorescence is more strongly related to photosynthesis with hemispherical than nadir measurements: Evidence from field observations and model simulations

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a novel proxy for terrestrial gross primary production (GPP). A growing number of ground-based automatic SIF observation systems equipped with hemispherical-conical and bi-hemispherical observation configurations have been developed in synergy with EC flux measurements across different ecosystems. However, the difference in the canopy SIF observed by these two types of configurations has not been well studied, which poses challenges in evaluating their performance in tracking GPP. In this study, we investigated SIF from both hemispherical-conical and bi-hemispherical observation configurations for their ability to track GPP in a maize field during the 2020 growth season. We found that bi-hemispherical SIF observations (SIFHemis) showed higher correlations with GPP at both diurnal and seasonal scales, and the superiority of SIFHemis for GPP estimation was also supported by Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model simulations. In addition, we found that the SIFHemis-GPP model established at a satellite overpass time (e.g., 09:30) outperformed the corresponding SIFNadir-GPP model in estimating both the half-hourly and daily GPP. The underlying mechanism for the advantage of this SIFHemis-GPP relationship was elucidated by a simplified geometrical optical model, which showed that the diurnal patterns of the observed sunlit and shaded leaves for the SIFHemis were consistent with those of the canopy GPP. Our study recommends a bi-hemispherical configuration setup for its superiority in monitoring GPP dynamics.

Responses of grassland ecosystem carbon fluxes to precipitation and their environmental factors in the Badain Jaran Desert.

Studying the effects of precipitation on carbon exchange in grassland ecosystems is critical for revealing the mechanisms of the carbon cycle. In this study, the eddy covariance (EC) technique was used to monitor the carbon fluxes in a grassland ecosystem in the Badain Jaran Desert (BJD) during the growing season from 2018 to 2020. The responses of net ecosystem CO2 exchange (NEE), ecosystem respiration (Reco), and gross primary productivity (GPP) to precipitation were analysed, as well as the effects of environmental factors on carbon fluxes at half-hour and daily scales. The results showed that (1) during the growing seasons in 2019 and 2020, the grassland ecosystem in a lake basin in the BJD was a net CO2 sink, and the cumulative NEE was - 91.9 and - 79.2g C m-2, respectively. The greater the total precipitation in the growing season, the stronger the carbon sequestration capacity of a grassland ecosystem. (2) The precipitation intensity, frequency, and timing significantly affected the carbon fluxes in the ecosystem. Isolated minor precipitation events did not trigger obvious NEE, GPP, and Reco pulses. However, large precipitation events or continuous minor precipitation events over several days caused delayed high assimilation; in addition, the greater the precipitation intensity, the greater the carbon flux pulse and carbon assimilation. The timing and frequency of precipitation events had more important effects on carbon exchange than total precipitation. Droughts create a shift in grasslands, causing them to move from being a carbon sink to a carbon source. (3) Correlation analysis showed that NEE was significantly negatively correlated with photosynthetically active radiation (PAR). On the half-hour scale, Reco and GPP were significantly positively correlated with soil temperature at 5 cm deep (Ts5) and PAR, respectively. However, they were strongly correlated with air temperature (Ta), soil surface temperature (Ts) and (Ts5)on the daily scale. The correlations between daily NEE, Reco, GPP, and precipitation varied across years and seasons. Due to warming and humidification in northwest China, precipitation events will have a greater impact on the carbon sequestration capacity of the BJD. The results are vital for predicting the possible effects of climate change on the carbon cycle.

Environmental controls on water use efficiency in a hilly tea plantation in southeast China

Knowledge of water use efficiency (WUE) at the ecosystem level is imperative as it is a critical ecophysiological index reflecting the coupling processes between carbon sequestration and water consumption. However, the temporal patterns of WUE and their underlying regulating factors during different seasons in tea plantations, which are under intense pruning practice, remain poorly understood. Based on carbon and water vapor flux data obtained by the eddy covariance technique in a subtropical hilly tea plantation in southeast China during 2014–2018, this study analyzed the seasonal variations in gross primary productivity (GPP), evapotranspiration (ET) and ecosystem WUE, and discussed the response of GPP, ET and WUE to environmental variables during different seasons. The results showed that the mean annual GPP, ET and WUE ranged from 1426.61 ~ 1896.05 g C m−2, 607.05 ~ 805.83 mm and 2.22 ~ 2.68 g C kg−1 H2O, respectively. At the daily time scale, GPP and ET responded significantly and positively to variations in air temperature (Ta), water vapor pressure deficit (VPD), and net radiation (Rn) in both the pruning season and late growing season, but daily WUE had a significantly negative response to variations in Ta, VPD and Rn. Rn was identified as the dominant factor in regulating GPP and ET during both the pruning season and late growing season, while the dominant factor controlling daily variations in WUE was VPD during the pruning season and Ta during the late growing season. Such results were mainly due to the divergent responses of carbon assimilation and water loss to different environmental variations and management strategies. These findings highlighted the importance of understanding the coupled processes between carbon assimilation and water loss in tea plantations.

Dynamics of the stream–lake transitional zone affect littoral lake metabolism

Lake ecosystems, as integrators of watershed and climate stressors, are sentinels of change. However, there is an inherent time-lag between stressors and whole-lake response. Aquatic metabolism, including gross primary production (GPP) and respiration (R), of stream–lake transitional zones may bridge the time-lag of lake response to allochthonous inputs. In this study, we used high-frequency dissolved oxygen data and inverse modeling to estimate daily rates of summer epilimnetic GPP and R in a nutrient-limited oligotrophic lake at two littoral sites located near different major inflows and at a pelagic site. We examined the relative importance of stream variables in comparison to meteorological and in-lake predictors of GPP and R. One of the inflow streams was substantially warmer than the other and primarily entered the lake’s epilimnion, whereas the colder stream primarily mixed into the metalimnion or hypolimnion. Maximum GPP and R rates were 0.2–2.5 mg O2 L−1 day−1 (9–670%) higher at littoral sites than the pelagic site. Ensemble machine learning analyses revealed that > 30% of variability in daily littoral zone GPP and R was attributable to stream depth and stream–lake transitional zone mixing metrics. The warm-stream inflow likely stimulated littoral GPP and R, while the cold-stream inflow only stimulated littoral zone GPP and R when mixing with the epilimnion. The higher GPP and R observed near inflows in our study may provide a sentinel-of-the-sentinel signal, bridging the time-lag between stream inputs and in-lake processing, enabling an earlier indication of whole-lake response to upstream stressors.