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A significant challenge in global change research is understanding how vegetation interacts with the environment to influence ecosystem gross primary productivity (GPP) through carbon assimilation. One emerging objective is to consistently predict GPP fluctuations worldwide by establishing a robust scaling relationship between GPP measured at flux towers and satellite spectral reflectance data. However, a major hurdle in achieving this goal is the discrepancy in spatial resolution between early satellite measurements and eddy flux measurements. By using a large set of growing season data covering 100 site-years in North and Central America, we explored the potential of transforming incident and reflected shortwave (Rg) and photosynthetically active radiation (PAR) measurements into a broadband normalized difference vegetation index (NDVI) and near-infrared (NIR) reflectance of vegetation (NIRv) which simultaneously explains the GPP variability. We found that the broadband NDVI and NIRv derived from Rg and PAR measurements at the daily time scale were highly correlated with Planet Fusion, Landsat-8/9, and Sentinel-2 narrowband NDVI and NIRv across a wide range of climate and ecological gradients. The differences between satellite and broadband NDVI and NIRv were found to be significantly associated with soil background variations, phenological stages, water stress and signal saturation of broadband NIR reflectance at high biomass. The seasonal variability of broadband NDVI and NIRv remarkably captured the seasonality of vegetation phenology, evaporative fraction, GPP and rainfall in different ecosystems. Although saturation of GPP at high NDVI was evident, a linear relationship between broadband NIRv times incident PAR versus GPP indicated the effectiveness of NIRv-based approach to capture the hidden light use efficiency impacts on GPP. Our study concludes that inexpensive measurement of Rg and PAR components can provide reliable information on NDVI, NIRv, and GPP uninterruptedly. This enhances the sensing capability of flux tower sites without requiring additional spectrometer measurements. The proposed in-situ vegetation indices make a compelling case on using radiation signals for handshaking between ecosystem-scale measurements and remote sensing observables relevant to carbon uptake.

Polar‐orbiting satellites have significantly improved our understanding of the terrestrial carbon cycle, yet they are not designed to observe sub‐daily dynamics that can provide unique insight into carbon cycle processes. Geostationary satellites offer remote sensing capabilities at temporal resolutions of 5‐min, or even less. This study explores the use of geostationary satellite data acquired by the Geostationary Operational Environmental Satellite—R Series (GOES‐R) to estimate terrestrial gross primary productivity (GPP) and ecosystem respiration (RECO) using machine learning. We collected and processed data from 126 AmeriFlux eddy covariance towers in the Contiguous United States synchronized with imagery from the GOES‐R Advanced Baseline Imager (ABI) from 2017 to 2022 to develop ML models and assess their performance. Tree‐based ensemble regressions showed promising performance for predicting GPP (R2 of 0.70 ± 0.11 and RMSE of 4.04 ± 1.65 μmol m−2 s−1) and RECO (R2 of 0.77 ± 0.10 and RMSE of 0.90 ± 0.49 μmol m−2 s−1) on a half‐hourly time step using GOES‐R surface products and top‐of‐atmosphere observations. Our findings align with global efforts to utilize geostationary satellites to improve carbon flux estimation and provide insight into how to estimate terrestrial carbon dioxide fluxes in near‐real time. Plain Language Summary: Fighting climate change requires an understanding of how ecosystems absorb and release carbon dioxide. While most Earth‐orbiting satellites provide limited snapshots, this study explores how more frequent imagery—every 5 min—from geostationary satellites, also known as weather satellites, can be used to estimate ecosystem carbon dioxide flux. By combining this data with machine learning techniques, we successfully estimated carbon uptake and release at over 100 US sites at half‐hourly intervals. This paves the way for near‐real‐time global monitoring of carbon exchange, offering a powerful tool for scientists and policymakers tackling climate change. Key Points: Advanced Baseline Imager (ABI) observations can estimate sub‐daily ecosystem carbon uptake and respiration at 126 AmeriFlux sitesIntegration of top‐of‐atmosphere products enhances the accuracy of monitoring land surface functionsABI observations can fill eddy covariance data gaps: up to 1 week (high accuracy), 6 months (low accuracy) [ABSTRACT FROM AUTHOR]

Due to the unique climatic characteristics and vegetation features of tropical regions, the correlation R2 of gross primary productivity (GPP) estimation in tropical regions using remote sensing models was generally lower than 0.3. Therefore, for the cloudy and rainy tropical regions, the influence brought by clouds on remote sensing images needed to be considered in GPP estimation. This paper developed a corrected vegetation photosynthesis model (VPM) for estimating GPP under cloudy conditions. It mainly corrected the two parameters, Wscalar and Enhanced Vegetation Index (EVI), which were obtained from remote sensing images and were therefore greatly influenced by clouds in the model. First, the water stress factor Wscalar was replaced by Evaporation Fraction (EF). Secondly, using the good correlation between near surface temperature and EVI, the conversion coefficient between near surface temperature and EVI was fitted to achieve the effective reconstruction of EVI contaminated by clouds. The correction of the two factors improved the estimation accuracy of the VPM model, and the comparison with the observed values of the GPP site in 4 years showed that the correction of EVI had a better improvement, with an increase of 0.22 in R2 compared with the pre-correction, and the correction of Wscalar was increased by 0.11 in R2. To verify the proposed method, the in-situ observation data of Xishuangbanna flux site from 2007 to 2010 were used. The results showed that the proposed method effectively improved the accuracy of GPP estimation by VPM model, especially in 2007 it was strongly influenced by clouds, and the improvement was significant, with R2 increasing from 0.2 to 0.82. In general, the accuracy of GPP estimation by the proposed method had been significantly improved, with RMSE (gC·m−2·8 day−1) decreasing from 15,14.4, 18.1, 14.2 to 8.07, 6.56, 10.33, 11.44, respectively. Therefore, the proposed method can be used to estimate the GPP for tropical seasonal rain forests in Xishuangbanna. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems' responses to climate change. However, the impact of future climate changes on GPP in the Tibetan Plateau, an ecologically important and climatically sensitive region, remains underexplored. This study aimed to develop a data-driven approach to predict the seasonal and annual variations in GPP in the Tibetan Plateau up to the year 2100 under changing climatic conditions. A convolutional neural network (CNN) was employed to investigate the relationships between GPP and various environmental factors, including climate variables, CO2 concentrations, and terrain attributes. This study analyzed the projected seasonal and annual GPP from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four future scenarios: SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The results suggest that the annual GPP is expected to significantly increase throughout the 21st century under all future climate scenarios. By 2100, the annual GPP is projected to reach 1011.98 Tg C, 1032.67 Tg C, 1044.35 Tg C, and 1055.50 Tg C under the four scenarios, representing changes of 0.36%, 4.02%, 5.55%, and 5.67% relative to 2021. A seasonal analysis indicates that the GPP in spring and autumn shows more pronounced growth under the SSP3–7.0 and SSP5–8.5 scenarios due to the extended growing season. Furthermore, the study identified an elevation band between 3000 and 4500 m that is particularly sensitive to climate change in terms of the GPP response. Significant GPP increases would occur in the east of the Tibetan Plateau, including the Qilian Mountains and the upper reaches of the Yellow and Yangtze Rivers. These findings highlight the pivotal role of climate change in driving future GPP dynamics in this region. These insights not only bridge existing knowledge gaps regarding the impact of future climate change on the GPP of the Tibetan Plateau over the coming decades but also provide valuable guidance for the formulation of climate adaptation strategies aimed at ecological conservation and carbon management. [ABSTRACT FROM AUTHOR]

Headwater streams influence the carbon cycle, but their productivity estimation remains challenging. We propose the use of dissolved oxygen data (% saturation, DOsat) and on‐site photosynthetically active radiation (PAR) data to develop DOsat~PAR curves as an analogy to the well‐known photosynthesis–irradiance (P–E) curves. The premise of our research is that although these curves are simple, they provide detailed information of stream ecosystem productivity dynamics. We used data from two streams in the Oregon Coast Range to investigate daily gross primary productivity (GPP). We used properties of the light‐limited portion of the DOsat~PAR regression curves to produce a model to estimate GPP. We found that the slope of the DO–PAR relation varied widely between 1.6 × 10−4 and 0.045 and had strong correlations (r2 > 0.78). The data from one of the two study sites (Oak Creek) was used for model development while the data from the other site (South Fork Mill Creek) was used for model validation. The model's ability to quantify the effects of a discrete storm event on stream productivity was tested by comparing GPP estimates calculated through a Bayesian framework (streamMetabolizer) and our raw data‐driven estimates of GPP which were based on the variability of the DOsat~PAR regression curves. The proposed methodology was successful in estimating GPP in headwaters. We foresee that this method may be used to assess disturbances and construct a baseline understanding of productivity dynamics in other headwater ecosystems that is independent of the methodological challenges of the current stream metabolism models. [ABSTRACT FROM AUTHOR]

Identifying the stability and sensitivity of land ecosystems to climate change is vital for exploring nature-based solutions. However, the underlying mechanisms governing ecosystem stability and sensitivity, especially in regions with overlapping ecological projects, remain unclear. based on Mann-Kendall, stability analysis method, and multiple regression method, this study quantified the stability and sensitivity of gross primary productivity (GPP) to climate variables [temperature, vapor pressure deficit (VPD), soil moisture, and radiation] in China from 1982 to 2019. Our findings revealed the following: (1) GPP demonstrated an increased trend with lower stability in Eastern regions, whereas a decreasing trend with higher stability was observed in Western and Southwest China. Notably, the stability of GPP was highest (74.58%) in areas with five overlapping ecological projects: Grain to Green, Natural Forest Resource Protection Project, Three-River Ecological Conservation and Restoration Project, Return Grazing to Grassland Project, and Three-North Shelter Forestation Project. (2) In regions with minimal or no overlapping ecological projects, temperature and radiation jointly dominated GPP variations. In contrast, water-related factors (VPD and soil moisture) significantly affected GPP in areas with multiple overlapping ecological projects. (3) In the southwestern and northeastern regions, GPP exhibited the highest sensitivity to climate change, whereas, in the eastern coastal areas and Tibet, GPP showed low sensitivity to climate change. In the Loess Plateau, where five ecological projects overlap extensively, carbon sinks primarily demonstrate a monotonic increasing trend, high stability, and low sensitivity to climate change. This study aimed to assess the stability of the land ecosystems and delineate their sensitivity to climate changes, thereby laying the groundwork for understanding ecosystem resilience. [ABSTRACT FROM AUTHOR]

The gross primary productivity (GPP) of vegetation stores atmospheric carbon dioxide as organic compounds through photosynthesis. Its spatial heterogeneity is primarily influenced by the carbon uptake period (CUP) and maximum photosynthetic productivity (GPPmax). Grassland, cropland, and forest are crucial components of China's terrestrial ecosystems and are strongly influenced by the seasonal climate. However, it remains unclear whether the evolutionary characteristics of GPP are attributable to physiology or phenology. In this study, terrestrial ecosystem models and remote sensing observations of multi-source GPP data were utilized to quantitatively analyze the spatio-temporal dynamics from 1982 to 2018. We found that GPP exhibited a significant upward trend in most areas of China's terrestrial ecosystems over the past four decades. Over 60% of Chinese grassland and over 50% of its cropland and forest exhibited a positive growth trend. The average annual GPP growth rates were 0.23 to 3.16 g C m−2 year−1 for grassland, 0.40 to 7.32 g C m−2 year−1 for cropland, and 0.67 to 7.81 g C m−2 year−1 for forest. GPPmax also indicated that the overall growth rate was above 1 g C m−2 year−1 in most regions of China. The spatial trend pattern of GPPmax closely mirrored that of GPP, although local vegetation dynamics remain uncertain. The partial correlation analysis results indicated that GPPmax controlled the interannual GPP changes in most of the terrestrial ecosystems in China. This is particularly evident in grassland, where more than 99% of the interannual variation in GPP is controlled by GPPmax. In the context of rapid global change, our study provides an accurate assessment of the long-term dynamics of GPP and the factors that regulate interannual variability across China's terrestrial ecosystems. This is helpful for estimating and predicting the carbon budget of China's terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

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Climate warming has been suggested to impact high latitude grasslands severely, potentially causing considerable carbon (C) losses from soil. Warming can also stimulate nitrogen (N) turnover, but it is largely unclear whether and how altered N availability impacts belowground C dynamics. Even less is known about the individual and interactive effects of warming and N availability on the fate of recently photosynthesized C in soil. On a 10-year geothermal warming gradient in Iceland, we studied the effects of soil warming and N addition on CO2 fluxes and the fate of recently photosynthesized C through CO2 flux measurements and a 13 CO2 pulse-labeling experiment. Under warming, ecosystem respiration exceeded maximum gross primary productivity, causing increased net CO2 emissions. N addition treatments revealed that, surprisingly, the plants in the warmed soil were N limited, which constrained primary productivity and decreased recently assimilated C in shoots and roots. In soil, microbes were increasingly C limited under warming and increased microbial uptake of recent C. Soil respiration was increased by warming and was fueled by increased belowground inputs and turnover of recently photosynthesized C. Our findings suggest that a decade of warming seemed to have induced a N limitation in plants and a C limitation by soil microbes. This caused a decrease in net ecosystem CO2 uptake and accelerated the respiratory release of photosynthesized C, which decreased the C sequestration potential of the grassland. Our study highlights the importance of belowground C allocation and C-N interactions in the C dynamics of subarctic ecosystems in a warmer world.

Ecosystem‐scale photosynthetic efficiency (EPE) is proposed as an effective indicator to quantify gross primary productivity (GPP), but how the coupling between EPE and GPP varies as vegetation resilience decreases has not been evaluated. Here, we quantified forest resilience with optimized Bayesian models. With the use of multisource satellite and modeling data, our study revealed that forests on the Loess Plateau and in the Qinba Mountains in China are experiencing rapid resilience loss and are already facing mortality warnings after 2010. Reductions in resilience also drove the marked decoupling of GPP from EPE. Notably, the decline in resilience was accompanied by a decrease in EPE in about 74% of the forests while GPP increased. The mechanism underlying this decoupling could be attributed to enhanced atmospheric water demand and soil water constraints. The dynamic relationships found here could help to improve forest mortality models and enhance photosynthesis‐based GPP evaluation. Plain Language Summary: The persistence and functionality of forest ecosystems highly depend on their resilience. Low‐resilience forests may be unable to absorb changes and achieve system variable stabilization under perturbations in external drivers and may undergo irreversible system shifts. Reduced resilience is often caused by impaired physiological functions, which inevitably affects forest photosynthetic processes and carbon sinks. Our study on fragile ecosystems in China revealed that forests are rapidly losing resilience and are already facing mortality warnings after 2010. This phenomenon has also led to significant decoupling of carbon sinks from photosynthesis, driven by an elevated atmospheric water demand and soil water deficits. These findings demonstrate that forests will exhibit greater risk of mortality across broad scales under global warming and that photosynthetic proxies cannot be consistently used for evaluating carbon sequestration in ecosystems. Key Points: Early mortality warning signals have been detected for different forest types since 2010A reduction in forest resilience weakens the relationship between the gross primary productivity and photosynthetic efficiencyThe decoupling of forest photosynthesis from carbon sinks exhibits different response patterns to soil water and atmospheric drying [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm ( S I F T O C _ 760 ) was downscaled to the broadband photosystem-level SIF for photosystem II ( S I F T O T _ F U L L _ P S I I ). Our results show that GPP can be accurately estimated using a multi-linear model with S I F T O T _ F U L L _ P S I I and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method ( G P P E C ) to GPP estimated using S I F T O T _ F U L L _ P S I I and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R2 = 0.21) and daily scales (R2 = 0.25). The GPP estimates using S I F T O T _ F U L L _ P S I I and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R2 improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 μ mol•m−2•s−1; for the daily data, R2 improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 μ mol•m−2•s−1). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using S I F T O T _ F U L L _ P S I I and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions. [ABSTRACT FROM AUTHOR]

Rice–wheat rotation cropland is one of the most important agroecosystems in South China, the escalation of conflict between food demand augment and water supply shortage increased with climate change. Water use efficiency plays a more significant role in optimising water and carbon management. Thus, the diurnal and seasonal variations of water use efficiency were assessed by the 3‐year eddy covariance observations in the Shouxian National Observatory, a typical rice–wheat rotation station. The results revealed a 'U'‐shaped diurnal pattern of water use efficiency for winter wheat (Triticum aestivum L.) and rice (Oryza sativa L.). Seasonal water use efficiency had two peaks with the highest in the winter wheat‐growing season. The average water use efficiency for the rice–wheat rotation cropland was 2.85 g C kg−1 H2O over the whole year with 2.62 and 3.11 g C kg−1 H2O for winter wheat and rice, respectively. However, gross primary productivity and evapotranspiration of rice were higher than those of winter wheat. Temperature, photosynthetically active radiation were the principal impact factors of water use efficiency in the rice‐growing season. Comparatively, soil water and vapour pressure deficit dominated the water use efficiency changes in the winter wheat‐growing season. Our analyses can help understand the water use requirements for carbon assimilation on rice–wheat rotation cropland on the field scale. [ABSTRACT FROM AUTHOR]

Terrestrial gross primary productivity (GPP) is the largest carbon flux in the global carbon cycle and plays a crucial role in terrestrial carbon sequestration. However, historical and future global GPP estimates still vary markedly. In this study, we reduced uncertainties in global GPP estimates by employing an innovative emergent constraint method on remote sensing‐based GPP datasets (RS‐GPP), using ground‐based estimates of GPP from flux towers as the observational constraint. Using this approach, the global GPP in 2001–2014 was estimated to be 126.8 ± 6.4 PgC year−1, compared to the original RS‐GPP ensemble mean of 120.9 ± 10.6 PgC year−1, which reduced the uncertainty range by 39.6%. Independent space‐ and time‐based (different latitudinal zones, different vegetation types, and individual year) constraints further confirmed the robustness of the global GPP estimate. Building on these insights, we extended our constraints to project global GPP estimates in 2081–2100 under various Shared Socioeconomic Pathway (SSP) scenarios: SSP126 (140.6 ± 9.3 PgC year−1), SSP245 (153.5 ± 13.4 PgC year−1), SSP370 (170.7 ± 16.9 PgC year−1), and SSP585 (194.1 ± 23.2 PgC year−1). These findings have important implications for understanding and projecting climate change, helping to develop more effective climate policies and carbon reduction strategies. [ABSTRACT FROM AUTHOR]

Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one‐third of terrestrial gross primary productivity (GPP). However, the continental‐scale leaf phenology‐photosynthesis nexus over TEFs is still poorly understood to date. This knowledge gap hinders most light use efficiency (LUE) models from accurately simulating the GPP seasonality in TEFs. Leaf age is the crucial plant trait to link the dynamics of leaf phenology with GPP seasonality. Thus, here we incorporated the seasonal leaf area index of different leaf age cohorts into a widely used LUE model (i.e., EC‐LUE) and proposed a novel leaf age‐dependent LUE model (denoted as LA‐LUE model). At the site level, the LA‐LUE model (average R2 =.59, average root‐mean‐square error [RMSE] = 1.23 gC m−2 day−1) performs better than the EC‐LUE model in simulating the GPP seasonality across the nine TEFs sites (average R2 =.18; average RMSE = 1.87 gC m−2 day−1). At the continental scale, the monthly GPP estimates from the LA‐LUE model are consistent with FLUXCOM GPP data (R2 =.80; average RMSE = 1.74 gC m−2 day−1), and satellite‐based GPP data retrieved from the global Orbiting Carbon Observatory‐2 (OCO‐2) based solar‐induced chlorophyll fluorescence (SIF) product (GOSIF) (R2 =.64; average RMSE = 1.90 gC m−2 day−1) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R2 =.78; average RMSE = 1.88 gC m−2 day−1). Typically, the estimated monthly GPP not only successfully represents the unimodal GPP seasonality near the Tropics of Cancer and Capricorn, but also captures well the bimodal GPP seasonality near the Equator. Overall, this study for the first time integrates the leaf age information into the satellite‐based LUE model and provides a feasible implementation for mapping the continental‐scale GPP seasonality over the entire TEFs. [ABSTRACT FROM AUTHOR]

As a potential carbon sink, mangroves play an important role in climate mitigation. India houses several major global mangrove patches, which remain vulnerable to climate change. The ecosystem‐atmosphere CO2 exchange is most accurately measured by the eddy covariance method, whereas satellites provide the biophysical parameters for a wider area. In the present study, the Sentinel‐2 satellite data is used to map the land cover types in the Pichavaram mangrove forest and identify two major dominant species (Rhizophora spp. and Avicennia marina), which indicated more than 95% classification accuracy. We used 2 years (2017 and 2018) of in situ gross primary productivity (GPP) and leaf area index (LAI) measurements and rectified the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and LAI products from 2010 to 2018. The modified MODIS GPP and LAI products were used to develop machine learning models, that is, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to study the climate influence on mangrove productivity. The RF model (R2 = 0.85 and root mean square error (RMSE) = 0.2) outperformed the XGBoost model (R2 = 0.75 and RMSE = 0.26) and was used to project the impact of climate change on the mangrove GPP for two extreme climate change scenarios, namely SSP1‐1.26 and SSP5‐8.5. The GPP increases and decreases in future during wet and dry periods, respectively. Overall, the projected GPP indicated a reduction of 3.73%–20.3% from 2050 to 2060 and of 4.82%–28.15% from 2090 to 2100, compared to its current average (from 2010 to 2018). Plain Language Summary: Mangroves are natural ecosystems found at the confluence of land and water. Their productivity is regulated by the complex environmental conditions prevailing at the interface of terrestrial, marine, and freshwater realms. Geographically, they mostly occur between 25°N and 25°S with a significant presence in the Indian subcontinent. The climate and ecosystem models used for predicting the changes in land and aquatic ecosystems have limited predictive capability for mangroves. Machine learning (ML) models are capable of capturing the coupled non‐linear relationships among various processes and thus can improve such predictions. The present study uses in situ and satellite data to develop long‐term productivity and canopy growth in the Pichavaram mangrove in India, which are subsequently used to develop ML models. These ML models are applied to identify the dominant mangrove species in this ecosystem and predict the changes in its photosynthetic carbon uptake in the middle‐term (2050–2060) and long‐term (2090–2100) climate change. The future carbon uptake increases (decreases) during wet (dry) periods. Overall, the projected decrease in annual photosynthesis ranges from 3.73% to 28.15%, compared to its current average. The seasonal variation in air temperature plays a key role in regulating the mangrove carbon uptake. [ABSTRACT FROM AUTHOR]

As the largest carbon reservoir within terrestrial ecosystems, forest ecosystems play a major role as carbon sinks in the global carbon cycle. There are still some uncertainties regarding the responses of different carbon fluxes to environmental changes in cold temperate climate forest ecosystems. Here, 14 cold temperate forest flux sites for at least ten years were investigated, including carbon fluxes and environmental variables such as temperature, precipitation, shortwave radiation, and vapor pressure deficit. By calculating the Spearman correlation coefficient, there was a congruence between photosynthetic productivity (i.e., gross primary productivity, GPP) and carbon sequestration (i.e., net ecosystem productivity, NEP) at thirteen forest sites, and at one forest site, GPP and NEP were decoupled. Annual GPP and NEP displayed a consistent trend when temperature and precipitation had significantly opposite trends and when temperature had a significantly positive correlation with VPD. But when VPD was significantly negatively correlated with both temperature and SW in spring and when temperature was negatively correlated with both SW and VPD in summer, a decoupling of GPP and NEP occurred. The impacts of various environmental factors on the annual carbon fluxes were calculated for each year and season using the path analysis method. At forest sites with consistent trends in GPP and NEP, annual, spring, and summer temperatures had significant positive correlations with GPP and ecosystem respiration (RE). While at the decoupled forest site, environmental factors had a stronger effect on RE, which then contributed to the observed decoupling of GPP and NEP. Finally, the Partial Least Squares method was used to analyze the relative contribution of each environmental factor to annual carbon fluxes. The results revealed that temperature and summer precipitation were the key environmental factors affecting forest ecosystems. This study provides important insights into the different responses of carbon fluxes in forest ecosystems undergoing environmental changes. [ABSTRACT FROM AUTHOR]

Vegetation phenology is a biological factor that directly or indirectly affects the dynamic equilibrium between water and carbon fluxes in ecosystems. Quantitative evaluations of the regulatory mechanisms of vegetation phenology on water–carbon coupling are of great significance for carbon neutrality and sustainable development. In this study, the interannual variation and partial correlation between vegetation phenology (the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS)) and ET (evapotranspiration), GPP (gross primary productivity), WUE (water use efficiency; water–carbon coupling index) in the Huang-Huai-Hai and Yangtze River Basins in China from 2001 to 2019 were systematically quantified. The response patterns of spring (autumn) and growing season WUE to SOS, EOS, and LOS, as well as the interpretation rate of interannual changes, were evaluated. Further analysis was conducted on the differences in vegetation phenology in response to WUE across different river basins. The results showed that during the vegetation growth season, ET and GPP were greatly influenced by phenology. Due to the different increases in ET and GPP caused by extending LOS, WUE showed differences in different basins. For example, an extended LOS in the Huang-Huai-Hai basins reduced WUE, while in the Yangtze River Basin, it increased WUE. After extending the growing season for 1 day, ET and GPP increased by 3.01–4.79 mm and 4.22–6.07 gC/m2, respectively, while WUE decreased by 0.002–0.008 gC/kgH2O. Further analysis of WUE response patterns indicates that compared to ET, early SOS (longer LOS) in the Yellow River and Hai River basins led to a greater increase in vegetation GPP, therefore weakening WUE. This suggests that phenological changes may increase ineffective water use in arid, semi-arid, and semi-humid areas and may further exacerbate drought. For the humid areas dominated by the Yangtze River Basin, changes in phenology improved local water use efficiency. [ABSTRACT FROM AUTHOR]

Global changes cause widespread forest fragmentation, which, in turn, has given rise to many ecological problems; this is especially true if the forest carbon stock is profoundly impacted by fragmentation levels. However, the way in which forest carbon uptake changes with different fragmentation levels and the main pathway through which fragmentation affects forest carbon uptake are still unclear. Remote sensing data, vegetation photosynthesis models, and fragmentation models were employed to generate a time series GPP (gross primary productivity) dataset, as well as forest fragmentation levels for forest landscapes in Zhejiang province, China. We analyzed GPP variation with forest fragmentation levels and identified the relative importance of the phenology (carbon uptake period—CUP) and physiology (maximum daily GPP—GPPmax) control pathways of GPP under different fragmentation levels. The results showed that the normalized mean annual GPP data of highly fragmented forests during the period from 2000 to 2018 were significantly higher than those of other fragmentation levels, while there was almost no significant difference in the annual GPP trend of forest landscapes with all fragmentation levels. Moreover, the percentage area of the control variable, GPPmax, gradually increased with fragmentation levels; the mean GPPmax between 2000 and 2018 of high-level fragmentation was higher than that of other fragmentation levels. Our results demonstrate that the carbon uptake capacity per unit area was enhanced in highly fragmented forest areas, and the maximum photosynthetic capacity (physiology-based process) played an important role in controlling carbon uptake, especially in highly fragmented forest landscapes. Our study calls for a better and deeper understanding of the potential of forest carbon uptake, and it is necessary to explore the mechanism by which forest fragmentation changes the vegetation photosynthetic process. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SIF) holds enormous potential for accurately estimating terrestrial gross primary productivity (GPP). However, current studies often overlook the spatial representativeness of satellite SIF and GPP observations. This research downscaled TROPOMI SIF (TROPOSIF) and its enhanced product (eSIF) in China's Saihanba Forest Region to obtain high-resolution SIF data. SIF was simulated using the SCOPE model, and the downscaled SIF's reliability was validated at two forest eddy covariance (EC) sites (SHB1 and SHB2) in the study area. Subsequently, the downscaled SIF data were matched to the EC footprint of the two forest sites, and the relationship between SIF and GPP was compared at various observational scales. Additionally, the ability of downscaled TROPOSIF and eSIF to track GPP was compared, along with the correlations among several vegetation indices (VIs) and GPP. The findings reveal the following: (1) Downscaled TROPOSIF and eSIF showed a strong linear relationship with SCOPE-modeled SIF (R2 ≥ 0.86). The eSIF closely matched the SCOPE simulation (RMSE: 0.06 mw m−2 nm−1 sr−1) and displayed a more consistent seasonal variation pattern with GPP. (2) Comparisons among coarse-resolution SIF, EC footprint-averaged SIF (SIFECA), and EC footprint-weighted SIF (SIFECW) demonstrated significant improvements in the linear relationship between downscaled SIF and GPP (the R2 increased from the range of 0.47–0.65 to 0.78–0.85). SIFECW exhibited the strongest relationship with GPP, indicating that matching SIF to flux footprints improves their relationship. (3) As the distance from the flux tower increased, the relationship between SIF and GPP weakened, reaching its lowest point beyond 1 km from the tower. Moreover, in the highly heterogeneous landscape of the SHB2 site, the relationship between VIs and GPP was poor, with no clear pattern as distance from the flux tower increased. In conclusion, the strong spatial dependency of SIF and tower-based GPP emphasizes the importance of using high-resolution SIF to accurately quantify their relationship. [ABSTRACT FROM AUTHOR]

Drought is the most destructive phenomenon that distresses the terrestrial carbon cycle balance and crop production. The variation in evapotranspiration (ET) and gross primary productivity (GPP) is a significant cause of agricultural drought effects on water use efficiency. This study aims to evaluate the impact of agricultural drought on WUE and it's anomalies in different climate regions. The standard vegetation index was used to measure the extent of agricultural drought. WUE was calculated using the ratio of ET, GPP, and climate classification using the De Martonne method. This study was conducted over the last 22 years, from 2001 to 2022. Meanwhile, 2001, 2002, 2014, and 2018 were considered high drought years based on a 22‐year analysis. According to remote sensing analysis of ET and GPP, the WUE increased throughout drought in all climate regions and more strongly in the arid zone than in humid regions. Humid areas were more vital due to drought than arid ones. WUE anomalies badge with increased drought severity across all climates except the very humid zone. Humid climates saw faster recovery times than arid ones, and areas experienced severe droughts. The findings of this research are essential for understanding the carbon and water cycles for agriculture management. The analysis of drought severity and WUE across arid climate regions helped analyse the varying impact of drought on WUE due to climate change. The significance of this study includes informing agricultural, water resource, and drought management planning in the Punjab Province, an agricultural region vulnerable to climate change. This study holds important learnings for agricultural regions worldwide. It has practical and scientific importance regarding agricultural systems' specific stresses and responses to droughts. [ABSTRACT FROM AUTHOR]

We investigated the spatiotemporal variability of remotely sensed gross primary productivity (GPP) over Türkiye based on MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2 GPP products. The differences in various GPP products were assessed using Kruskal–Wallis and Mann–Whitney U methods, and long-term trends were analyzed using Modified Mann–Kendall (MMK), innovative trend analysis (ITA), and empirical mode decomposition (EMD). Our results show that at least one GPP product significantly differs from the others over the seven geographic regions of Türkiye (χ2 values of 50.8, 21.9, 76.9, 42.6, 149, 34.5, and 168; p < 0.05), and trend analyses reveal a significant increase in GPP from all satellite-based products over the latter half of the study period. Throughout the year, the average number of months in which each dataset showed significant increases across all study regions are 6.7, 8.1, 5.9, 9.6, and 8.7 for MODIS, TL-LUE, GOSIF, MuSyQ, and PMLV2, respectively. The ITA and EMD methods provided additional insight into the MMK test in both visualizing and detecting trends due to their graphical techniques. Overall, the GPP products investigated here suggest 'greening' for Türkiye, consistent with the findings from global studies, but the use of different statistical approaches and satellite-based GPP estimates creates different interpretations of how these trends have emerged. Ground stations, such as eddy covariance towers, can help further improve our understanding of the carbon cycle across the diverse ecosystem of Türkiye. [ABSTRACT FROM AUTHOR]

The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO2 fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO2 fertilization effect from other long‐term confounding drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO2 fertilization effect on photosynthetic carbon uptake using eddy covariance (EC) records across 38 extratropical forest sites. We find the median stimulation rate of GPP to be 3.2 ± 0.9 gC m−2 yr−1 ppm−1 (or 16.4 ± 4.2% per 100 ppm) under increasing atmospheric CO2 across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process‐based land surface models. We address additional factors, including nitrogen deposition and land management, that may impact plant productivity, potentially confounding the attribution to the CO2 fertilization effect. Assuming these site‐specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO2 fertilization effect. However, disentanglement of these long‐term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long‐term CO2 fertilization across multiple EC sites. Such observation‐based quantification is key to constraining the long‐standing uncertainties in the land carbon cycle under rising CO2 concentrations. Plain Language Summary: Through photosynthesis, plants convert CO2 and water into sugars and oxygen using solar energy, one of the most important chemical reactions on Earth. Human‐made carbon emissions are increasing atmospheric CO2 levels, impacting global photosynthesis. The additional carbon is believed to have a fertilizing effect on photosynthesis, causing vegetation to absorb a significant portion of the emitted CO2. However, the strength of this CO2 fertilization effect on photosynthesis is uncertain, but is a crucial factor in determining the future trajectory of atmospheric CO2 concentrations. In this study, we introduce a new statistical method to quantify the increase in photosynthetic carbon uptake, stimulated by rising CO2, based on measurements from 38 forest sites. Our results reveal that a 100 ppm increase in CO2 enhances photosynthesis by approximately 16%. Testing the statistical method with artificial model experiments supports the robustness of our findings. Our study improves the understanding of the impacts of human‐made CO2 emissions on the global carbon cycle. Key Points: We present a novel statistical method to disentangle the variability of photosynthetic rates related to climate and non‐climate driversThe analysis from 38 eddy covariance sites reveals a 3.2 ± 0.9 gC m−2 yr−1 increase in plant productivity per ppm rise in CO2Our statistical method is successfully tested against idealized model simulations with and without increasing CO2 [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP), representing organic carbon fixation through photosynthesis, is crucial for developing science-based strategies for sustainable development. Given that the tropical region harbors nearly half of all species, it plays a pivotal role in safeguarding the global environment against climate change and preserving global biodiversity. Thus, investigating changes in vegetation productivity within this region holds substantial practical importance for estimating global vegetation productivity. In this study, we employed an enhanced P model to estimate vegetation GPP in the tropical region from 2001 to 2020, based on which we quantified the spatiotemporal changes and associated mechanisms. The results reveal that the annual mean GPP in the tropical region ranged from 2603.9 to 2757.1 g·cm−2 a−1, demonstrating an overall apparent increasing trend. Inland areas were mainly influenced by precipitation, while coastal areas were primarily influenced by temperature. Land cover changes, especially conversion to cropland, significantly influence GPP, with deciduous—evergreen forest transitions causing notable decreases. Climate change emerges as the dominant factor affecting GPP, as indicated by the contribution rate analysis. This research interprets the spatiotemporal pattern and mechanisms of GPP in the tropics, offering valuable insights for sustainable ecosystem management. [ABSTRACT FROM AUTHOR]

The aim of our study is to understand the patterns of variation in water use efficiency (WUE) in coniferous and broad-leaved mixed forest ecosystems across multiple scales and to identify its main controlling factors. We employ the eddy covariance method to gather data from 2017, 2018, and 2020, which we use to calculate the gross primary productivity and evapotranspiration of these forests in East China and to determine WUE at the ecosystem level. The mean daily variation in WUE ranges from 4.84 to 7.88 gC kg−1 H2O, with a mean value of 6.12 gC kg−1 H2O. We use ridge regression analysis to ascertain the independent effect of environmental factors on WUE variation. We find that WUE responds differently to environmental factors at different time scales. In mixed conifer ecosystems, temperature and relative humidity emerge as the most significant environmental factors influencing WUE variability. Especially at the seasonal scale, temperature and relative humidity can explain more than 51% of the WUE variation. Our results underscore the varied effects of environmental factors on WUE variation across different time scales and aid in predicting the response of WUE to climate change in coniferous and broad-leaved mixed forest ecosystems. [ABSTRACT FROM AUTHOR]

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Water level fluctuations are among the main factors affecting the development of wetland vegetation communities, carbon sinks, and ecological processes. Hongze Lake is a typical seasonal lake wetland in the Huaihe River Basin. Its water levels have experienced substantial fluctuations because of climate change, as well as gate and dam regulations. In this study, long-term cloud-free remote sensing images of water body area, net plant productivity (NPP), gross primary productivity (GPP), and Fractional vegetation cover (FVC) of the wetlands of Hongze Lake were obtained from multiple satellites by Google Earth Engine (GEE) from 2006 to 2023. The trends in FVC were analyzed using a combined Theil-Sen estimator and Mann-Kendall (MK) test. Linear regression was employed to analyze the correlation between the area of water bodies and that of different degrees of FVC. Additionally, annual frequencies of various water levels were constructed to explore their association with GPP, NPP, and FVC.The results showed that water level fluctuations significantly influence the spatial and temporal patterns of wetland vegetation cover and carbon sinks, with a significant correlation (P

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.

Gross primary productivity (GPP) is a key parameter in research on the global carbon cycle and changes. Understanding the spatiotemporal dynamics and influencing factors of GPP on the Loess Plateau (LP) helps identify the health status of ecosystems, thereby enabling the implementation of effective conservation and restoration measures. In this study, we used a modified vegetation photosynthesis model (VPM) to simulate a long-term series of GPP in the LP from 2001 to 2022, and the impacts of different land-use patterns and meteorological factors on GPP were investigated using a transition matrix, linear regression, and partial correlation analyses. The findings suggested that the modified simulation yielded a more reliable performance (coefficient of determination (R2) = 0.89, root mean square error (RMSE) = 143.47 gC·m−2·yr−1) and was suitable for further research endeavors. (1) The GPP on the LP significantly increased by 232.65 TgC from 2001 to 2022. The southeastern region contributed more than the northwestern region, and the GPP exhibited higher multi-year averages and growth rates below 1000 m elevation. (2) Forests in the southeastern region of the LP, characterized by a heightened growth rate, will influence future spatial variations in GPP increases across the LP. Despite the decline in grassland and cultivated land areas, substantial land coverage has significantly contributed to the overall GPP growth. Urbanization encroaching on cultivated land has emerged as a key contributor to the decline in GPP in low-altitude regions. (3) Air temperature was the main physical driving force for GPP change in the LP. Additionally, the GPP in forested regions exhibited a negative correlation with rainfall, whereas the GPP in areas undergoing the return of cropland to forest–grassland and cropland reclamation correlated negatively with solar radiation. (4) The attribution analysis indicated that the surge in vegetation GPP on the LP was collectively driven by human activities and meteorological changes, with human activities dominating these changes by 61.41 %. This study deepens the understanding of terrestrial ecology in semi-humid regions and provides scientific insights for implementing ecological governance strategies in the LP.

Abstract Polar‐orbiting satellites have significantly improved our understanding of the terrestrial carbon cycle, yet they are not designed to observe sub‐daily dynamics that can provide unique insight into carbon cycle processes. Geostationary satellites offer remote sensing capabilities at temporal resolutions of 5‐min, or even less. This study explores the use of geostationary satellite data acquired by the Geostationary Operational Environmental Satellite—R Series (GOES‐R) to estimate terrestrial gross primary productivity (GPP) and ecosystem respiration (RECO) using machine learning. We collected and processed data from 126 AmeriFlux eddy covariance towers in the Contiguous United States synchronized with imagery from the GOES‐R Advanced Baseline Imager (ABI) from 2017 to 2022 to develop ML models and assess their performance. Tree‐based ensemble regressions showed promising performance for predicting GPP (R2 of 0.70 ± 0.11 and RMSE of 4.04 ± 1.65 μmol m−2 s−1) and RECO (R2 of 0.77 ± 0.10 and RMSE of 0.90 ± 0.49 μmol m−2 s−1) on a half‐hourly time step using GOES‐R surface products and top‐of‐atmosphere observations. Our findings align with global efforts to utilize geostationary satellites to improve carbon flux estimation and provide insight into how to estimate terrestrial carbon dioxide fluxes in near‐real time.

Wetland ecosystems play a pivotal role in terrestrial carbon and water cycles, thereby possessing great potential to regulate terrestrial water use efficiency (WUE), which is calculated as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). However, it remains unclear the possible changes in wetland WUE under present and future climate conditions. In this research, WUE variations in a Phragmites australis-dominated freshwater wetland were determined by the eddy covariance method during 2020–2023. Further, we projected future GPP, ET, and WUE under four Representative Concentration Pathway (RCP) scenarios based on five Earth System Models. The 3-year average field observation suggested that the P. australis marsh exhibited high GPP (1149 g C m−2), but consumed large amounts of water through ET (611 mm H2O), resulting in relatively low WUE (1.89 g C mm−1 H2O). During wet years, the studied marsh consumed much more water through evaporation than through transpiration, thus exhibiting lower WUE. Contrarily, large amounts of water were utilized to maintain high primary productivity through transpiration in 2021–2022 dry year, leading to higher WUE. In future scenarios, GPP in the P. australis marsh shows consistently faster uptrends compared to ET from 2020 to 2100, consequently yielding persistent growths of WUE. Future growths of WUE indicate that P. australis marsh tends consume more water for maintaining productivity levels rather than loss via evaporation under future climate conditions, thereby intensifying the carbon–water interaction. Driven by the most rapid growth of environmental drivers, the RCP8.5 scenario shows the fastest increasing trends in GPP, ET, and WUE among four RCP scenarios, whereas the reverse ocurres under RCP2.6 scenario. Overall, our study advocates for more comprehensive research encompassing field observation and model simulation to address the current knowledge gap regarding the response of wetland WUE to contemporary and future climate change.

The gross primary production (GPP) asymmetry (AGPP) is an essential indicator for predicting carbon sink stability in grassland ecosystems. However, little is known about the response patterns and potential driving mechanisms of asymmetry over long-time series at the biome-scale. Here, we used three satellite-based GPPs, combined with climate-vegetation-soil factors to calculate the AGPP, and then explored the transition characteristics and attributions of AGPP in the past 35 years. Our results showed that the temporal dynamics of AGPP followed a three-stage non-linear pattern (slow-declining stabilization period, fast-declining change period, and slow-increasing stabilization period) in the grasslands of Northern China. During the two stabilization periods, significant regional variations were observed among different ecological functional areas. I: humid and semi-humid meadow grassland and meadow area and II: semi-arid grassland area shifted from negative to positive asymmetry, with a decreasing contribution ratio of mean annual precipitation (MAP). Conversely, III: warm shrub-grassland area, V: alpine meadow area, and VI: alpine grassland area shifted from positive to negative asymmetry, with mean annual temperature (MAT) being the dominant driving factor. We further showed that the overall AGPP of the grasslands in Northern China continues to exhibit a positive asymmetry under future climate scenarios, which means that interannual changes in AGPP favor the promotion of the future grassland carbon sink. These results contribute to predicting interannual variations in grassland carbon sink and formulating grassland management measures.

Climate change is one of the foremost challenges confronting the contemporary world. Carbon and water cycles and their interconnections of ecosystems are playing a pivotal role in assessing the impacts of climate change on ecosystems. The Qinling Mountains of China represent a focal area for research on global climate change and regional adaptation strategies. Based on historical and CMIP6 (Coupled Model Intercomparison Project Phase 6) future climatic data, the Biome-BGC model, which was sufficiently optimized with the Biome-BGC-PEST package, was used to simulate the historical and future trends of GPP (Gross primary productivity), ET (Evapotranspiration), and WUE (Ecosystem water use efficiency) in the Qinling Mountains, so as to elucidate the dynamics and spatiotemporal variations of carbon and water fluxes amidst climate change. The results showed that the GPP, ET, and WUE of the Qinling Mountains heterogeneously distributed in space, due to the dramatic disparities in carbon and water fluxes among different vegetation types in the Qinling Mountains. Among the various vegetation types, evergreen needle-leaved forest had the highest annual mean value of WUE. In the historical period of 1979-2018, deciduous broad-leaved forest had the highest annual mean value of GPP and ET, while evergreen needle-leaved forest would dominate in the future (2021-2100). For the annual GPP and ET flux variations, different vegetation cover types followed varying peak patterns in the Qinling Mountains. Deciduous broad-leaved forest, evergreen needle-leaved forest, and shrub meadow showed ’single-peak’ pattern annually, whereas the crop area displayed ’double-peak’ pattern due to the different growing seasons of crops. There also were temporal variations for the carbon and water fluxes within the same vegetation type. Future projections suggested a continuous increase in GPP and ET fluxes, underscoring the consistent role of the Qinling Mountains in water resource conservation and carbon sink amid evolving climatic conditions.

Solar-induced chlorophyll fluorescence (SIF) is strongly correlated with gross primary productivity (GPP). Satellite-observed canopy SIF (SIFobs) captures only a part of the total leaf-emitted SIF (SIFtotal); therefore, SIFobs may hinder the interpretation of the physiological mechanism for GPP estimation. Furthermore, there are still significant discrepancies in the estimated SIFobs escape ratio (fesc) from the canopy to the leaf level with current methods. Here, we selected several vegetation canopy variables and downscaled SIFobs based on the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model from the canopy to the leaf level using machine learning (ML) algorithms and then applied our method to the TROPOspheric Monitoring Instrument (TROPOMI) near-infrared (NIR) SIFobs. The results showed that simulating the fesc with SIFobs, TROPOMI NIR reflectance, and the fraction of photosynthetically active radiation (FPAR) avoided the effects of different sun-sensor geometry conditions introduced by different sensors and was more suitable for satellite-observed SIFobs downscaling. Our downscaled SIFtotal also correlated well with the flux site GPP in areas with sparse vegetation types. SIFtotal better reflected the photosynthetic differences among vegetation types and showed an enhanced relationship with absorbed photosynthetically active radiation (APAR) compared with SIFobs. We provide an efficient canopy-to-leaf SIFobs downscaling method improved SIFtotal and GPP estimation, and our results also demonstrated the potential for using SIFobs as vegetation information in sparse coverage areas.

The present research evaluated the prospects of utilizing rainfall and temperature combined with Landsat-8 derived HANTS (Harmonic Analysis of Time Series) reconstructed NDVI for estimating the metrics of the mangrove phenology. The selected period of the study was from 2013 to 2020 for the Pichavaram mangroves of Tamil Nadu. The NDVI and ERA5 (ECMWF Re-Analysis) datasets of rainfall and temperature were the input datasets for developing the new algorithm. The ‘z-score sum’ provided a measure of the cumulative impact of rainfall and temperature, displaying its most negative value coinciding with the peak positive value of the NDVI time series datasets. The algorithm developed for phenological metrics estimation identified the common inflection points of the z-score sum and NDVI curves. The temporal analysis of metrics revealed the average Length of Season (LoS) as 230 days. The metrics also identified the drought year 2016 with the shortest LoS and the least Gross Primary Productivity (GPP) values. The analysis showed the influences of the preceding year’s monsoon rainfall on the GPP values of the later part of the phenological cycle. The temperatures during the days of PoS were found to be the optimum temperature for the growth of mangroves. HIGHLIGHTS The combined influence of rainfall and temperature on mangrove phenology is more significant than individual influences.; The z-score sum of rainfall and temperature exhibited an inverse relationship with NDVI values.; GPP values of mangroves and estimated phenological metrics showed good correspondence.; The estimated phenological metrics captured the influences of drought and abundant rainfall on mangrove phenology.;

The interchange of water vapor between the land and the atmosphere is influenced by actual evapotranspiration (AET). A nonlinear model (AET-SWC-PET-GPP, ASPG) was developed in this study to combine potential evapotranspiration (PET), soil water content (SWC), and gross primary productivity (GPP) in order to quantitatively estimate AET. The Fluxnet Network 2015 global flux station dataset was used to compare the AET models (AET-SWC, AS; AET-SWC-PET, ASP and AET-SWC-PET2, ASP2) with various combinations of influencing factors. The results show that the simulation accuracy of the ASPG model is higher than that of AS, ASP, and ASP2, with improvements in a coefficient of determination (R2) of 45.3, 8.1, and 5.7%, respectively.The ASPG performed well for various vegetation types, geographical regions, and time scales. It was also discovered that the fitting coefficients vary depending on the type of vegetation, each with its own range of values. The ASPG model put forth in this study can be used to more effectively estimate AET quantitatively on a global scale and can serve as a theoretical foundation for the accurate calculation of global evapotranspiration and the wise use of water resources. HIGHLIGHTS Introduction of a new model (ASPG) that integrates potential evapotranspiration (PET), soil water content (SWC), and gross primary productivity (GPP) to quantitatively estimate actual evapotranspiration (AET).; Additionally, the study emphasizes the variability of fitting coefficients based on vegetation type, suggesting the model's adaptability to different ecosystems.;

Accurate evaluation of terrestrial carbon balance is essential for designing climate change mitigation policies, and capabilities of remote sensing techniques in monitoring carbon fluxes are widely recognized for their great contributions to regional and global carbon budget accounting. In this review, we synthesized satellite-based data and methodologies to estimate the main flux components of terrestrial carbon balance and their uncertainties over the past two decades. The global gross primary production (GPP) during the period 2001–2022 is 134 ± 14 PgC yr−1, and nearly half of them occurs in tropical forest regions such as South America and Africa. Less than 2% of global GPP is converted into a net carbon sink of 2.28 ± 1.12 PgC yr−1 using satellite-based atmospheric inversion during 2015–2020, and this sink is comparable to the stock change-based estimate (2.49 PgC yr−1) but twice as large as model-based estimate (1.08 ± 0.78 PgC yr−1). By decomposing satellite-derived net carbon balance into different terms including satellite-derived carbon emissions from land-use change and wildfires (3.55 PgC yr−1), we inferred that ~ 43% of global GPP would be respired through soil microbes (57.1 PgC yr−1), but which is higher than the previous bottom-up estimate (39–46 PgC yr−1). We then propose that an accurate remote sensing of terrestrial carbon balance requires to enhance representations of photosynthetic responses to rising CO2 and disturbances, develop satellite-constrained belowground carbon dynamics and separate natural fluxes from anthropogenic CO2 emissions, by integrating multi-source satellite sensors in orbit, revolutionized remote sensing capabilities with focused field campaigns in data-scarce regions.

Climate change significantly impacts vegetation gross primary productivity (GPP), yet uncertainties persist in the carbon cycle of tropical terrestrial ecosystems due to incomplete consideration of productivity drivers and lag effects. To address this, we developed a remote sensing-based process model by integrating high-resolution vegetation indices and multi-layer soil hydrological module, to simulate monthly GPP at a 30 m resolution across Hainan Island from 2000 to 2020. The finer GPP can capture more spatial details and show higher accuracy at site scales (R = 0.79 and NRMSE = 14.79 %). Trend analysis and Hurst exponent were used to reveal spatiotemporal dynamics and sustainability of GPP. Meanwhile, nonlinear Granger causality tests quantified both concurrent and lagged correlations between various environmental factors and GPP. The results indicated significant GPP increases across 98.5 % of vegetated areas, with an annual rise of 437.02 g C/m2, and a marked improvement in trends around 2011. Future projections suggest sustained high GPP sustainability (Hurst = 0.53), and reducing “positive-inconsistent” areas in the northeast and southwest is crucial for enhancing local carbon sinks. Furthermore, water availability, temperature, and radiation were primary drivers of GPP changes, affecting 53.55 %, 27.77 %, and 14.43 % of vegetated areas, respectively, with their compounded effects enhancing explanatory power by 35.84 %. Relative humidity dominated water availability impacts on GPP (10.02 % to 79.98 % variation), surpassing precipitation and soil moisture impacts. Lag effects were observed in 68.83 % of vegetated areas, with 1 to 4-month delays in responses to net solar radiation and surface temperature, especially in forest and shrubland ecosystems. This study provides deeper insights into fine-scale GPP simulations and analysis of climate interactions, which are crucial for effective carbon cycle management in tropical ecosystems.

Accurately quantifying terrestrial gross primary productivity (GPP) can provide insights into the dynamics of global ecosystems. Light Use Efficiency (LUE) models with simple and effective structures have been widely used to estimate GPP. However, GPP estimation using LUE models was biased due to inaccurately representing the nonlinear effect of solar radiation on photosynthesis. To address this issue, we developed a hybrid LUE model, termed LUE-MLP, which integrates a multilayer perceptron (MLP) neural network simulating the relationship between GPP and solar radiation with a big-leaf LUE model. LUE-MLP was optimized and tested for GPP simulation on data from 166 flux sites from the FLUXNET2015 dataset, which covers 10 biome types. Results show that LUE-MLP exhibited a reliable performance (R2 = 0.76 and RMSE = 4.17 μmol m−2 s−1) in estimating (half-)hourly GPP on test data. Compared to the original big-leaf and two-leaf LUE models, LUE-MLP's R² (RMSE) for (half-)hourly GPP simulation across biomes rose (fell) by – 0.01-0.13 (−7.6%−16.8%) and 0.01-0.15 (0.39%−29.1%), respectively. Additionally, LUE-MLP well captured the seasonal variations in tower-based GPP. The proposed model can improve GPP estimation on a global scale, enhancing our understanding of global ecosystem dynamics.

ABSTRACTGross 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).

Carbon uptake of vegetation is controlled by phenology and photosynthetic carbon uptake capacity. However, our knowledge of the seasonal responses of vegetation productivity to phenological and physiological changes in alpine ecosystems is still weak. In this study, we quantified the spatio-temporal variations of vegetation phenology and gross primary productivity (GPP) across the source region of the Yellow River (SRYR) by analyzing MODIS-derived vegetation phenology and GPP from 2001 to 2019, and explored how vegetation phenology and maximum carbon uptake capacity (GPPmax) affected seasonal GPP over the region. Our results showed that the SRYR experienced significantly advanced trends (p < 0.05) for both start (SOS) and peak (POS) of the growing season from 2001 to 2019. Spring GPP (GPPspr) had a significantly increasing trend (p < 0.01), and the earlier SOS had obvious positive effects on GPPspr. Summer GPP (GPPsum) was significantly and negatively correlated to POS (p < 0.05). In addition, GPPmax had a significant and positive correlation with GPPsum and GPPann (p < 0.01), respectively. It was found that an earlier spring-summer phenology and higher photosynthetic peak enhanced the photosynthetic efficiency of vegetation in spring and summer and altered the seasonal patterns of vegetation productivity in the SRYR under warming and wetting climates. This study indicated that not only spring and autumn phenology but also summer phenology and maximum carbon uptake capacity should be regarded as crucial indicators regulating the carbon uptake process in alpine ecosystems. This research provides important information about how changes in phenology affect vegetation productivity in alpine ecosystems under global climate warming. [ABSTRACT FROM AUTHOR]

Carbon sinks provided by land ecosystems play a crucial role in achieving carbon neutrality. However, the future potential of carbon sequestration remains highly uncertain. The impact of pollutant emission reduction (PER) introduced by the proposed synergistic approach to air pollution control and carbon neutrality on carbon sinks in China has not yet been fully evaluated. In this study, we analyzed the effects of regional carbon-neutral PER policies, global climate change, and their coupled effects on China's terrestrial gross primary productivity (GPP) by conducting numerical experiments using the weather research and forecasting model coupled with chemistry (WRF-Chem) and the moderate resolution imaging spectroradiometer photosynthesis algorithm (MODIS-PSN). We found that carbon-neutral PER policies could promote GPP growth in most regions of China in 2060, particularly during April and October, resulting in a total increase of at least 21.84 TgC compared to that in 2016, which offset the adverse effects of global climate change up to fourfold. The aerosol radiative effects drive GPP growth under carbon-neutral PER policies, primarily through an increase in daily minimum temperature during winter and an increase in shortwave radiation during other seasons. Our research highlights that reducing pollutant emissions enhances future potential for carbon sequestration, revealing positive feedback towards achieving the target of carbon neutrality. [ABSTRACT FROM AUTHOR]

We examined the seasonality of photosynthesis in 46 evergreen needleleaf (evergreen needleleaf forests (ENF)) and deciduous broadleaf (deciduous broadleaf forests (DBF)) forests across North America and Eurasia. We quantified the onset and end (StartGPP and EndGPP) of photosynthesis in spring and autumn based on the response of net ecosystem exchange of CO2 to sunlight. To test the hypothesis that snowmelt is required for photosynthesis to begin, these were compared with end of snowmelt derived from soil temperature. ENF forests achieved 10% of summer photosynthetic capacity ∼3 weeks before end of snowmelt, while DBF forests achieved that capacity ∼4 weeks afterward. DBF forests increased photosynthetic capacity in spring faster (1.95% d−1) than ENF (1.10% d−1), and their active season length (EndGPP–StartGPP) was ∼50 days shorter. We hypothesized that warming has influenced timing of the photosynthesis season. We found minimal evidence for long‐term change in StartGPP, EndGPP, or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier StartGPP (1.3–2.5 days °C−1) or later EndGPP (1.5–1.8 days °C−1, depending on forest type and month). Finally, we tested whether existing phenological models could predict StartGPP and EndGPP. For ENF forests, air temperature‐ and daylength‐based models provided best predictions for StartGPP, while a chilling‐degree‐day model was best for EndGPP. The root mean square errors (RMSE) between predicted and observed StartGPP and EndGPP were 11.7 and 11.3 days, respectively. For DBF forests, temperature‐ and daylength‐based models yielded the best results (RMSE 6.3 and 10.5 days). Plain Language Summary: We used records of forest‐atmosphere carbon dioxide exchange and weather to determine when photosynthesis begins and ends each year in 46 northern hemisphere forests. We used observations of soil temperature to determine the timing of the end of the snowmelt period. We found that evergreen needleleaf forests began photosynthesis ∼3 weeks before snowmelt ended, while deciduous broadleaf forests (DBF) waited until ∼4 weeks after snowmelt ended. The DBF type ramped up photosynthesis in spring, and ramped down in autumn, faster than the ENF, and the length of the photosynthesis (or "growing") season was ∼50 days shorter for DBF forests. Abundant evidence suggests that spring is occurring earlier in recent decades. We checked whether these forests are starting photosynthesis earlier by looking at forests with long‐term records. We found minimal support for changes in photosynthetic phenology over time, but very strong connections between temperature and the timing of spring and autumn transitions. We tested 19 models that use weather data to predict plant phenological events. We used gridded weather data to drive the models, and the best models were able to predict the spring and autumn photosynthetic transitions to within ∼10 days. Key Points: Evergreen forests began photosynthesis in spring ∼3 weeks before end of snowmelt, deciduous forests ∼4 weeks after end of snowmeltThere is little evidence for lengthening of the photosynthetic season in the northern hemisphere forest flux tower recordInterannual variation in onset and end of photosynthesis was related to air temperature [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is an important component of the terrestrial carbon cycle in climate change research. The global GPP product derived using Moderate Resolution Imaging Spectroradiometer (MODIS) data is perhaps the most widely used. Unfortunately, many studies have indicated evident error patterns in the MODIS GPP product. One of the main reasons for this is that the applied big‐leaf (BL) MOD17 model cannot properly handle the variable relative contribution of sunlit and shaded leaves to the total canopy‐level GPP. In this study, we developed a model for correcting the errors in the MODIS GPP product by bridging BL and two‐leaf (TL) light use efficiency (LUE) models (CTL‐MOD17). With the available MODIS GPP product, which considers environmental stress factors, the CTL‐MOD17 model only needs to reuse the two inputs of the leaf area index (LAI) and incoming radiation. The CTL‐MOD17 model was calibrated and validated at 153 global FLUXNET eddy covariance (EC) sites. The results indicate that the modeled GPP obtained with the correction model matches better with the EC GPP than the original MODIS GPP product at different time scales, with an improvement of 0.07 in R2 and a reduction in root‐mean‐square error (RMSE) of 117.08 g C m−2 year−1. The improvements are more significant in the green season when the contribution of shaded leaves is larger. In terms of the global spatial pattern, the obvious underestimation in the regions with high LAI and the overestimation in the low LAI regions of the MODIS GPP product is effectively corrected by the CTL‐MOD17 model. This paper not only bridges the BL and TL LUE models, but also provides a new and simple method to obtain accurate GPP through reusing two inputs used in producing the MODIS GPP product. Plain Language Summary: Gross primary productivity (GPP) is crucial for terrestrial carbon cycle study. The Moderate Resolution Imaging Spectroradiometer (MODIS) GPP data is perhaps the most widely used product, but many studies have indicated evident error patterns in it. These errors can be mainly explained by the applied big‐leaf (BL) MOD17 model cannot properly handle the variable relative contribution of sunlit and shaded leaves to the total canopy‐level GPP. As a result, this paper developed a novel and simple model (CTL‐MOD17) to obtain accurate GPP by reusing two inputs data from the MODIS GPP product, based on a two‐leaf (TL) theory. The CTL‐MOD17 model is evaluated at 153 global FLUXNET eddy covariance (EC) sites, and compared with other TL GPP products and models. The results indicate that CTL‐MOD17 can significantly improve the accuracy of MODIS GPP products at different time scales, to be similar to other TL models but with fewer data inputs. The obvious underestimation/overestimation of MODIS GPP in the high/low leaf area index (LAI) regions are all effectively corrected. This study proves the possibility of bridging the BL and TL models for the first time, which can be migrated to other BL products and models. Key Points: A novel product‐based model (CTL‐MOD17) corrects Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product bias, achieving results similar to two‐leaf models with fewer inputsIt is the first time to prove the possibility of bridging the big‐leaf and two‐leaf models, which can be migrated to other big‐leaf GPP products and modelsThe obvious underestimation/overestimation of MODIS GPP in high/low LAI regions are all effectively corrected by the CTL‐MOD17 model [ABSTRACT FROM AUTHOR]

Changes in snow cover have an important effect on vegetation gross primary productivity (GPP), but the role of underlying surface types in this effect still remains unclear. The underlying surface refers to the surface of the Earth and is located at the bottom of the snow cover during the snow season. Here, we focus on the influence of snow cover change on GPP in Northeast China from 1982 to 2015, revealing the various dependencies of the association between snow cover and GPP on forest, grassland, and dryland. The results show that the increases in GPP in dryland and grassland are primarily influenced by the increase in snow water equivalent, while the variations in GPP in forest are primarily influenced by snow phenological indicators, with snow cover end date and snow cover days dominating GPP in the early and peak growing seasons, respectively. Additionally, snow cover can have a rather strong time‐lagged effect on GPP by changing the soil temperature in dryland. Our findings emphasize the importance of considering underlying surface types in the snow–GPP relation and provide insight into how climate change may affect ecosystems in regions with comparable underlying surface types. [ABSTRACT FROM AUTHOR]

The two phases of El‐Niño‐Southern Oscillation (ENSO) influence both regional and global terrestrial vegetation productivity on inter‐annual scales. However, the major drivers for the regional vegetation productivity and their controlling strengths during different phases of ENSO remain unclear. We herein disentangled the impacts of two phases of ENSO on regional carbon cycle using multiple data sets. We found that soil moisture predominantly accounts for ∼40% of the variability in regional vegetation productivity during ENSO events. Our results showed that the satellite‐derived vegetation productivity proxies, gross primary productivity from data‐driven models (FLUXCOM) and observation‐constrained ecosystem model (Carbon Cycle Data Assimilation System) generally agree in depicting the contribution of soil moisture and air temperature in modulating regional vegetation productivity. However, the ensemble of weakly constrained ecosystem models exhibits non‐negligible discrepancies in the roles of vapor pressure deficit and radiation over extra‐tropics. This study highlights the significance of water in regulating regional vegetation productivity during ENSO. Plain Language Summary: ENSO, a significant climate phenomenon, profoundly influences ecosystem productivity from regional to global scales. Yet, accurately pinpointing the attributions of different climate factors that control Gross Primary Productivity (GPP) during ENSO events remains challenging, partly due to large uncertainties in existing GPP data. Through extensive analysis using various data sets, we found that soil moisture plays a dominant role in controlling GPP across most continents. This implies that current terrestrial biosphere models might underestimate the importance of soil moisture in driving productivity during ENSO events. Addressing this gap in understanding is crucial for refining and properly constraining the related processes in terrestrial biosphere models. Key Points: Soil moisture predominantly governs vegetation productivity changes during El‐Niño‐Southern Oscillation (ENSO) eventsSatellite‐derived vegetation indices consistently support the predominant influence of water on vegetation productivityENSO triggers varying effects on vegetation productivity across different regions, with distinct impacts during El Niño and La Niña [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is a reliable measure of the carbon sink potential of terrestrial ecosystems and is an essential element of terrestrial carbon cycle research. This study employs the diffuse fraction-based two-leaf light-use efficiency (DTEC) model to imitate China's monthly GPP from 2001 to 2020. We studied the trend of GPP, investigated its relationship with climatic factors, and separated the contributions of climate change and human activities. The findings showed that the DTEC model was widely applicable in China. During the study period, China's average GPP increased significantly, by 9.77 g C m−2 yr−1 (p < 0.001). The detrimental effect of aerosol optical depth (AOD) on GPP was more widespread than that of total precipitation, temperature, and solar radiation. Areas that benefited from AOD, such as Northwest China, experienced significant increases in GPP. Climate change and human activities had a primary and positive influence on GPP during the study period, accounting for 28% and 72% of the increase, respectively. Human activities, particularly ecological restoration projects and the adoption of advanced agricultural technologies, played a significant role in China's GPP growth. China's afforestation plan was particularly notable, with the GPP increasing in afforestation areas at a rate greater than 10 g C m−2 yr−1. This research provides a theoretical foundation for the long-term management of China's terrestrial ecosystems and helps develop adaptive ecological restoration tactics. [ABSTRACT FROM AUTHOR]