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The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive "greenness" of these systems. We combine continuous needle‐scale chlorophyll fluorescence measurements with tower‐based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar‐induced chlorophyll fluorescence (SIF) and solar intensity‐normalized SIF (SIFrelative) under snow‐covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near‐infrared vegetation index [NIRv] under 50% snow cover). Needle‐scale fluorescence and photochemistry strongly correlated (r2 = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower‐based estimates of LUEP and SIFrelative which were well correlated across all sites (r2 = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data‐driven and process‐based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems. [ABSTRACT FROM AUTHOR]

Recently, more advanced synchronous global‐scale satellite observations, the Soil Moisture Active Passive enhanced Level 3 (SMAP L3) soil moisture product and the Orbiting Carbon Observatory 2 (OCO‐2) solar‐induced chlorophyll fluorescence (SIF) product, provide an opportunity to improve the predictive understanding of both water and carbon cycles in land surface modeling. The Simplified Simple Biosphere Model version 4 (SSiB4) was coupled with the Top‐down Representation of Interactive Foliage and Flora Including Dynamics Model (TRIFFID) and a mechanistic representation of SIF. Incorporating dynamic vegetation processes reduced global SIF root‐mean‐squared error (RMSE) by 12%. Offline experiments were conducted to understand the water and carbon cycles and their interactions using satellite data as constraints. Results indicate that soil hydraulic properties, the soil hydraulic conductivity at saturation (Ks) and the water retention curve, significantly impact soil moisture and SIF simulation, especially in the semi‐arid regions. The wilting point and maximum Rubisco carboxylation rate (Vmax) affect photosynthesis and transpiration, then soil moisture. However, without atmospheric feedback processes, their effects on soil moisture are undermined due to the compensation between soil evaporation and transpiration. With optimized parameters based on SMAP L3 and OCO‐2 data, the global RMSE of soil moisture and SIF simulations decreased by 15% and 12%, respectively. These findings highlight the importance of integrating advanced satellite data and dynamic vegetation processes to improve land surface models, enhancing understanding of terrestrial water and carbon cycles. Plain Language Summary: This study used advanced satellite observations and land surface models to learn more about the water and carbon cycles and their interactions. The soil hydraulic conductivity at saturation (Ks) and the parameter for the water retention curve were identified as important. They affect how fast water can move in soil and how much water can be held by soil, which affects the available water in the soil for plants to use and makes them particularly important in water and carbon cycle simulation in dry areas. Two vegetation parameters affecting photosynthesis can help to improve the carbon cycle simulation but cannot change the soil moisture much because of the offline model limitation. The adjustment on the two soil property parameters and the two vegetation parameters improved the global water cycle simulation by 15% and the carbon cycle simulation by 12%. The introduction of dynamic vegetation processes into our model improved the carbon cycle simulation by 12% globally and helped to better understand the water and carbon cycles and their interactions. Key Points: Key processes and parameters are identified using satellite data for advances in understanding carbon and water cycles and interactionsThe soil hydraulic properties, wilting point, and maximum Rubisco carboxylation rate are key parameters linking water‐carbon cyclesThe dynamic vegetation process plays an important role in water and carbon cycles [ABSTRACT FROM AUTHOR]

The Amazon rainforest plays a crucial role in global carbon storage, but a minor destabilization of these forests could result in considerable carbon loss. Among the external factors affecting vegetation, elevated CO 2 (eCO 2) levels have long been anticipated to have positive impacts on vegetation, including the direct enhancement of both photosynthesis and productivity and increasing water use efficiency. However, the overall impact of eCO 2 on the net carbon balance, especially concerning tree-mortality-induced carbon loss and recovery following extreme drought events, has remained elusive. Here, we use a process-based model that couples physiological CO 2 effects with demography and both drought mortality and resistance processes. The model was previously calibrated to reproduce observed drought responses of Amazon forest sites. The model results, based on factorial simulations with and without eCO 2 , reveal that eCO 2 enhances forest growth and promotes competition between trees, leading to more natural self-thinning of forest stands. This occurs following a growth–mortality trade-off response, although the growth outweighs the tree loss. Additionally, eCO 2 provides water-saving benefits, reducing the risk of tree mortality during drought episodes. However, extra carbon losses could still occur due to an eCO 2 -induced increase in background biomass density, leading to "more carbon available to lose" when severe droughts happen. Furthermore, we found that eCO 2 accelerates drought recovery and enhances drought resistance and resilience. By delving into the less-explored aspect of tree mortality response to eCO 2 , the model improvements advance our understanding of how carbon balance responds to eCO 2 , particularly regarding mechanisms of continuous competition-induced carbon loss vs. pulses of drought-induced carbon loss. These findings provide valuable insights into the intricate ways in which rising CO 2 influences forest carbon dynamics and vulnerability, offering a critical understanding of the Amazon rainforest's evolution amidst more frequent and intense extreme climate events. [ABSTRACT FROM AUTHOR]

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

Reliable and robust monitoring tools are crucial to assess the effectiveness of land mitigation techniques (LMTs) in enhancing carbon uptake, enabling informed decision making by policymakers. This study, addressing one of the scientific goals of the EU Horizon 2020 Land Use Based Mitigation for Resilient Climate Pathways (LANDMARC) project, examines the feasibility of using satellite solar-induced fluorescence (SIF) in combination with other satellite data as a monitoring proxy to evaluate the effects of LMTs on carbon uptake. Two distinct cases are explored: (1) instantaneous vegetation destruction caused by a 2019 eucalyptus wildfire in southeast Australia and (2) gradual forest gain resulting from reforestation efforts in northern China in 2007–2012. The cases are monitored using SIF from the TROPOspheric Monitoring Instrument (TROPOMI) and Global Ozone Monitoring Experiment-2A (GOME-2A), respectively. Comparing the temporal variability in SIF across the affected areas and nearby reference areas reveals that vegetation dynamics changed as a consequence of the land-use changes in both cases. Specifically, in the Australia case, TROPOMI demonstrated an immediate reduction in the SIF signal of 0.6 mW m -2 sr -1 nm -1 (- 72 %) over the eucalypt forest right after the fire. Exploiting the strong correspondence between TROPOMI SIF and gross primary productivity (GPP) at the nearby representative eddy covariance Tumbarumba site and through the FluxSat product, we estimate that the Australian fire led to a loss in GPP of 130–200 GgC in the first 8 months after the fire. Over the northern Chinese provinces of Gansu, Shaanxi, Sichuan, Chongqing, and Shanxi, we report an increase in GOME-2A summertime SIF of 0.1–0.2 mW m -2 sr -1 nm -1 , coinciding with reforestation efforts between 2007 and 2012. This increase in the SIF signal is likely driven by a combination of increasingly favourable natural conditions and by the reforestation effort itself. A multivariate model that takes into account growth factors such as water availability and maximum temperature as well as satellite-derived forest-cover data explains the observed variability in GOME-2A SIF in the Chinese case reasonably well (R2=0.72). The model suggests that increases in both forest cover and soil moisture have led, in step, to the observed increase in vegetation activity over northern China. In that region, for every 100 km 2 of additional forest cover, SIF increases by 0.1 mW m -2 sr -1 nm -1 between 2007 and 2012. Our study highlights that the use of satellite-based SIF, together with supporting in situ, modelled, and satellite data, allows us to monitor the impact of LMT implementation on regional carbon uptake as long as the scale of the LMT is of sufficient spatial extent. [ABSTRACT FROM AUTHOR]

Modeling leaf photosynthesis is essential for quantifying the carbon, water, and energy fluxes of the terrestrial biosphere. However, due to the lack of simultaneous measurements of leaf light absorption and gas exchange, canopy radiative transfer (RT) and photosynthesis modeling often rely on simplified assumptions about light absorption and electron transport. These assumptions ignore variations in leaf biophysical traits and environmental conditions. In this study, we utilized a next‐generation land surface model (LSM)—CliMA Land, which incorporates hyperspectral canopy RT and provides a more accurate representation of trait variations. We evaluated the potential bias in electron transport estimates introduced by the broadband RT schemes used in traditional LSMs. Additionally, we explored the impact of different leaf electron transport parameterization schemes on global‐scale photosynthesis and fluorescence modeling. We showed that (a) traditional LSMs that disregard the impacts of leaf temperature and leaf traits on electron transport tend to overestimate electron transport rates. (b) Photosynthesis and fluorescence within a grid can exhibit biases exceeding 20%, with these biases demonstrating contrasting seasonality. (c) Global estimates of integrated photosynthesis and fluorescence differ by 8.1% and 8.8%, respectively. These results underscore the importance of adopting more sophisticated and accurate modeling schemes, such as hyperspectral canopy RT, in future LSMs and Earth system modeling to enhance the reliability of modeling outcomes. Plain Language Summary: The way sunlight interacts within the forest canopy is often simulated using just two broad channels: one for light that helps plants grow (photosynthetically active radiation) and one for near‐infrared light. Unfortunately, these simulations don't take into account key things about leaves, like their color (determined by chlorophyll). These simplifications mean that the models ignore differences in how different leaves respond to light. For instance, green light is more common in the lower canopy, but the models treat it the same as red and blue light. The problem is that plants can use red and blue light more effectively for photosynthesis. So, while these simplified models are faster, they can lead to big mistakes when estimating how much light leaves can absorb and how much they can photosynthesize. To address this issue, we used a more detailed model that considers many different wavelengths of light. We looked at how much the simplified models might mess up estimates of photosynthesis and fluorescence. Our findings show that these errors can be larger than 20% for specific locations. To help make the simplified models more accurate, we've provided data and formulas that consider differences in leaf traits and light conditions throughout the canopy. Key Points: Hyperspectral canopy radiative transfer model is used to assess the biases in electron transport, photosynthesis, and fluorescenceVegetation gross primary productivity and solar‐induced fluorescence may be substantially biased in broadband radiative transfer modelsApproaches are provided for broadband radiative transfer models [ABSTRACT FROM AUTHOR]

Vegetation Optical Depth (VOD) has emerged as a valuable metric to quantify water stress on vegetation's carbon uptake from a remote sensing perspective. However, existing spaceborne microwave remote sensing platforms face limitations in capturing the diurnal VOD variations and global products lack site‐level validation against plant physiology. To address these challenges, we leveraged the Global Navigation Satellite System (GNSS) L‐band microwave signal, measuring its attenuation by the canopy of a temperate broadleaf forest using a pair of GNSS receivers. This approach allowed us to collect continuous VOD observations at a sub‐hourly scale. We found a significant seasonal‐scale correlation between VOD and leaf water potential. The VOD diurnal amplitude is affected by soil moisture, plant transpiration and leaf surface water. Additionally, VOD can help independently estimate plant transpiration. Our findings pave the way for a deeper understanding of response of the vegetation to water stress at finer temporal scales. Plain Language Summary: Microwave satellite measurements offer valuable insights into Vegetation Optical Depth (VOD), which reflects the amount of water present in plants. However, most VOD products currently available only offer observations at two times of the day, which fail to capture the variations of VOD over the course of the day. In this study, we installed Global Navigation Satellite System (GNSS) signal receivers at the top and bottom of a temperate broadleaf forest canopy located at a flux tower in Missouri, USA. This setup allowed us to obtain continuous observations of VOD at sub‐hourly scale. The strong correlation observed between GNSS VOD and local leaf water potential suggests that this robust and cost‐effective technique can complement other labor‐intensive measurements. The VOD diurnal amplitude is influenced by soil water content, evapotranspiration, and leaf surface water in the form of dew and interception. GNSS VOD also exhibits the potential to aid in estimating plant transpiration, which can be further used for partitioning evapotranspiration. The investigation of plant water content dynamics in this study will enhance our understanding of the carbon‐water coupling at a finer temporal scale. Key Points: Sub‐hourly Vegetation Optical Depth (VOD) retrieved from Global Navigation Satellite Systems attenuation relates to soil and leaf water dynamicsDaily maximum VOD correlates well with field measurements of predawn leaf water potentialPlant transpiration can be reconstructed from VOD principal components [ABSTRACT FROM AUTHOR]

Accurate and efficient modeling of stomatal conductance (gs) has been a key challenge in vegetation models across scales. Current practice of most land surface models (LSMs) assumes steady-state gs and predicts stomatal responses to environmental cues as immediate jumps between stationary regimes. However, the response of stomata can be orders of magnitude slower than that of photosynthesis and often cannot reach a steady state before the next model time step, even on half-hourly timescales. Here, we implemented a simple dynamic gs model in the vegetation module of an LSM developed within the Climate Modeling Alliance and investigated the potential biases caused by the steady-state assumption from leaf to canopy scales. In comparison with steady-state models, the dynamic model better predicted the coupled temporal response of photosynthesis and stomatal conductance to changes in light intensity using leaf measurements. In ecosystem flux simulations, while the impact of gs hysteresis response may not be substantial in terms of monthly integrated fluxes, our results highlight the importance of considering this effect when quantifying fluxes in the mornings and evenings, as well as interpreting diurnal hysteresis patterns observed in ecosystem fluxes. Simulations also indicate that the biases in the integrated fluxes are more significant when stomata exhibit different speeds for opening and closure. Furthermore, prognostic modeling can bypass the A - Ci iterations required for steady-state simulations and can be robustly run with comparable computational costs. Overall, our study demonstrates the implications of dynamic gs modeling for improving the accuracy and efficiency of LSMs and for advancing our understanding of plant–environment interactions. [ABSTRACT FROM AUTHOR]

The Amazon rainforest plays a crucial role in global carbon storage, but a minor destabilization of these forests could result in considerable carbon loss. Among the external factors affecting vegetation, elevated CO2 (eCO2) levels have long been anticipated to have positive impacts on vegetation, including direct photosynthesis / productivity enhancement and increasing water use efficiency. However, the overall impact of eCO2 on the net carbon balance, especially concerning tree mortality-induced carbon loss and recovery following extreme drought events, has remained elusive. Here, we use a processbased model that couples physiological CO2 effects with demography and drought mortality / resistance processes. The model was previously calibrated to reproduce observed drought responses of Amazon forest sites. The model results, based on factorial simulations with and without eCO2, reveal that eCO2 enhances forest growth and promotes competition between trees, leading to more natural self-thinning of the forest stands, following a growth-mortality trade-off response although the growth outweighs the tree loss. Additionally, eCO2 provides water-saving benefits, reducing the risk of tree mortality during drought episodes, although extra carbon losses still could occur due to eCO2 induced increase in background biomass density, thus 'more carbon available to lose' when severe droughts happen. Furthermore, we found that eCO2 accelerates the drought recovery and enhances drought resistance and resilience. These findings illuminate the intricate ways in which increasing CO2 concentrations shape forest carbon dynamics, offering valuable insights into the evolution of the Amazon rainforest. [ABSTRACT FROM AUTHOR]

Vegetation water content (VWC) plays a key role in transpiration, plant mortality, and wildfire risk. Although land surface models now often contain plant hydraulics schemes, there are few direct VWC measurements to constrain these models at global scale. One proposed solution to this data gap is passive microwave remote sensing, which is sensitive to temporal changes in VWC. Here, we test that approach by using synthetic microwave observations to constrain VWC and surface soil moisture within the Climate Modeling Alliance Land model. We further investigate the possible utility of sub‐daily observations of VWC, which could be obtained through a satellite in geostationary orbit or combinations of multiple satellites. These high‐temporal‐resolution observations could allow for improved determination of ecosystem parameters, carbon and water fluxes, and subsurface hydraulics, relative to the currently available twice‐daily sun‐synchronous observational patterns. We find that incorporating observations at four different times in the diurnal cycle (such as could be available from two sun‐synchronous satellites) provides a significantly better constraint on water and carbon fluxes than twice‐daily observations do. For example, the root mean square error of projected evapotranspiration and gross primary productivity during drought periods was reduced by approximately 40%, when using four‐times‐daily relative to twice‐daily observations. Adding hourly observations of the entire diurnal cycle did not further improve the inferred parameters and fluxes. Our comparison of observational strategies may be informative in the design of future satellite missions to study plant hydraulics, as well as when using existing remotely sensed data to study vegetation water stress response. Plain Language Summary: The amount of water contained within the tissues of plants influences how much water plants transpire from the soil to the atmosphere and how much carbon they take in. However, it is difficult to estimate how much water is in plants around the world at any given time, due to the diversity of plants storing and releasing water in different ways. Certain earth‐orbiting satellites carry sensors that can indicate plant water content, but they provide only snapshots of data at two points in time per day due to their orbit shapes. Here, we simulated what information could be gleaned from different combinations of satellites in different orbits when they are combined with computer models of water flow in plants. We found that using data from two satellites in different orbits, instead of just one, could greatly increase the accuracy of plant water content estimates, almost as much as if we had a large fleet of satellites observing around the clock. Our work should be useful to scientists studying plant water content with existing data sets as well as those planning future satellite missions. Key Points: We demonstrate that ecohydrological parameters and variables can be inferred from microwave radiometry via model‐data fusionWe compare scenarios that use synthetic observations at different times of day, corresponding to current and proposed satellite orbitsFor inferring land surface variables, using observations from just four times of day proves to be as useful as using data from every hour [ABSTRACT FROM AUTHOR]

The water table depth (WTD) in peatlands determines the soil carbon decomposition rate and influences vegetation growth, hence the above‐ground carbon assimilation. Here, we used satellite‐observed Solar‐Induced chlorophyll Fluorescence (SIF) as a proxy of Gross Primary Production (GPP) to investigate water‐related vegetation stress over northern peatlands. A linear model with interaction effects was used to relate short‐ and long‐term anomalies in SIF with WTD anomalies and the absolute WTD. Most locations showed the occurrence of drought and waterlogging stress though regions with exclusively waterlogging or drought stress were also detected. As a spatial median, minimal water‐related vegetation stress was found for a WTD of −0.22 m (short‐term) and −0.20 m (long‐term) (±0.01 m, 95% confidence interval of statistical uncertainty). The stress response observed with SIF is supported by an analysis of in situ GPP data. Our findings provide insight into how changes in WTD of northern peatlands could affect GPP under climate change. Plain Language Summary: Water table depth is an important variable influencing the carbon cycle and vegetation growth in northern peatlands. In this paper, the impact of changing wetness conditions on vegetation growth over peatlands was studied through satellite measurements of solar‐induced fluorescence (SIF), which is a radiation signal emitted by vegetation during photosynthesis. Previous studies over ecosystems on mineral soil, that is, not over peatland, suggested a response of SIF to drought conditions. In our study, it was shown that peatland vegetation experiences moisture‐related growth stress under both very wet and very dry conditions, which might reduce the photosynthesis efficiency and the ability to capture and store CO2. Stress due to drought conditions was detected for peatlands in the south of the Western Siberian Lowlands and the Boreal Plains. Stress due to prolonged wet conditions occurred for example, in the north of the Western Siberian Lowlands and the north of the Hudson Bay Lowlands. Key Points: Spaceborne Solar‐Induced Fluorescence (SIF) data was used to analyze soil moisture‐related vegetation stress regimes in northern peatlandsFor most locations, waterlogging as well as drought stress regimes occurred and alternated depending on peatland water level dynamicsThe SIF‐based stress response observations are supported by in situ data of Gross Primary Production [ABSTRACT FROM AUTHOR]

Reliable and robust monitoring tools are crucial to assess the effectiveness of land mitigation techniques (LMTs) in enhancing carbon uptake, enabling informed decision-making by policymakers. This study, addressing one of the scientific goals of the EU H2020 LANDMARC project, examines the feasibility of using satellite solar-induced fluorescence (SIF), in combination with other satellite data, as a monitoring proxy to evaluate the effects of LMTs on carbon uptake. Two distinct cases are explored: (1) instantaneous vegetation destruction caused by a 2019 Eucalyptus wildfire in south-east Australia, and, (2) gradual forest gain resulting from reforestation efforts in northern China over 2007–2012. The cases are monitored using TROPOMI and GOME-2A SIF, respectively. Comparing the temporal variability in SIF across the affected and nearby reference areas reveals that vegetation dynamics changed as a consequence of the land use changes in both cases. Specifically, in the Australia case, TROPOMI demonstrated an immediate reduction in SIF signal of 0.6 mW m−2 sr−1 nm−1 (−72 %) over the Eucalypt Forest right after the fire. Exploiting the strong correspondence between TROPOMI SIF and gross primary productivity (GPP) at the nearby eddy-covariance Tumbarumba site and through the FluxSat product, we estimate that the fire led to a loss in GPP of 130–200 GgC in the first eight months after the fire. Over the northern Chinese provinces of Gansu, Shaanxi, Sichuan, Chongqing and Shanxi, we report an increase in GOME-2A summertime SIF of 0.1–0.2 mW m−2 sr−1 nm−1 coinciding with reforestation efforts between 2007 and 2012. This increase in SIF signal is likely driven by a combination of increasingly favourable natural conditions and the reforestation effort itself. A multivariate model that takes into account growth factors such as water availability and maximum temperature as well as satellite-derived forest cover data explains the observed variability in GOME-2A SIF in the Chinese case reasonably well (R2=0.72). The model suggests that both increases in forest cover as well as in soil moisture have led, in step, to the observed increase in vegetation activity over northern China. In that region, for every 100 km2 of additional forest cover, SIF increases by 0.1 mW m−2 sr−1 nm−1 between 2007 and 2012. Our study highlights that the combined use of satellite-based SIF, together with supporting in situ, modelled and satellite-data, allows to monitor the impact of LMT implementation on regional carbon uptake as long as the scale of the LMT is of sufficient spatial extent. [ABSTRACT FROM AUTHOR]

Tropical forests play a pivotal role in regulating the global carbon cycle. However, the response of these forests to changes in absorbed solar energy and water supply under the changing climate is highly uncertain. Three‐year (2018–2021) spaceborne high‐resolution measurements of solar‐induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) provide a new opportunity to study the response of gross primary production (GPP) and more broadly tropical forest carbon dynamics to differences in climate. SIF has been shown to be a good proxy for GPP on monthly and regional scales. Combining tropical climate reanalysis records and other contemporary satellite products, we find that on the seasonal timescale, the dependence of GPP on climate variables is highly heterogeneous. Following the principal component analyses and correlation comparisons, two regimes are identified: water limited and energy limited. GPP variations over tropical Africa are more correlated with water‐related factors such as vapor pressure deficit (VPD) and soil moisture, while in tropical Southeast Asia, GPP is more correlated with energy‐related factors such as photosynthetically active radiation (PAR) and surface temperature. Amazonia is itself heterogeneous: with an energy‐limited regime in the north and water‐limited regime in the south. The correlations of GPP with climate variables are supported by other observation‐based products, such as Orbiting Carbon Observatory‐2 (OCO2) SIF and FluxSat GPP. In each tropical continent, the coupling between SIF and VPD increases with the mean VPD. Even on the interannual timescale, the correlation of GPP with VPD is still discernable, but the sensitivity is smaller than the intra‐annual correlation. By and large, the dynamic global vegetation models in the TRENDY v8 project do not capture the high GPP seasonal sensitivity to VPD in dry tropics. The complex interactions between carbon and water cycles in the tropics illustrated in this study and the poor representation of this coupling in the current suite of vegetation models suggest that projections of future changes in carbon dynamics based on these models may not be robust. [ABSTRACT FROM AUTHOR]

The response of vegetation physiology to drought at large spatial scales is poorly understood due to a lack of direct observations. Here, we study vegetation drought responses related to photosynthesis, evaporation, and vegetation water content using remotely sensed data, and we isolate physiological responses using a machine learning technique. We find that vegetation functional decreases are largely driven by the downregulation of vegetation physiology such as stomatal conductance and light use efficiency, with the strongest downregulation in water-limited regions. Vegetation physiological decreases in wet regions also result in a discrepancy between functional and structural changes under severe drought. We find similar patterns of physiological drought response using simulations from a soil–plant–atmosphere continuum model coupled with a radiative transfer model. Observation-derived vegetation physiological responses to drought across space are mainly controlled by aridity and additionally modulated by abnormal hydro-meteorological conditions and vegetation types. Hence, isolating and quantifying vegetation physiological responses to drought enables a better understanding of ecosystem biogeochemical and biophysical feedback in modulating climate change. Vegetation resilience to drought is underlain by plant physiological responses. Here, the authors combine remote sensing data, explainable machine learning and model simulations to map global vegetation responses to drought linked to physiological processes such as stomatal regulation and light use efficiency. [ABSTRACT FROM AUTHOR]

Accurate and efficient modeling of stomatal conductance (g s) has been a key challenge in vegetation models across scales. Current practice of most land surface models (LSMs) assumes steady-state g s and predicts stomatal responses to environmental cues as immediate jumps between stationary regimes. However, the response of stomata can be orders of magnitude slower than that of photosynthesis, and often cannot reach a steady state before the next model time-step, even on half-hourly time scales. Here, we implemented a simple dynamic g s model in the vegetation module of an LSM developed within the Climate Modeling Alliance, and investigated the potential biases caused by the steady-state assumption from leaf to canopy scales. In comparison with steady-state models, the dynamic model better predicted the coupled temporal response of photosynthesis and stomatal conductance to changes in light intensity using leaf measurements. In ecosystem flux simulations, while the impact of g s hysteresis response may not be substantial in terms of daily or monthly integrated canopy fluxes, our results suggested the importance of considering this effect when quantifying fluxes in the mornings and evenings, and interpreting diurnal hysteresis patterns observed in ecosystem fluxes. Furthermore, prognostic modeling can bypass the A-Ci iterations required for steady-state simulations and can be robustly run with comparable computational costs. Overall, our study highlights the implications of dynamic g s modeling in improving the accuracy and efficiency of LSMs, and for advancing our understanding of plant-environment interactions. [ABSTRACT FROM AUTHOR]

Accurate and efficient modeling of stomatal conductance (gs) has been a key challenge in vegetation models across scales. Current practice of most land surface models (LSMs) assumes steady-state gs and predicts stomatal responses to environmental cues as immediate jumps between stationary regimes. However, the response of stomata can be orders of magnitude slower than that of photosynthesis, and often cannot reach a steady state before the next model time-step, even on half-hourly time scales. Here, we implemented a simple dynamic gs model in the vegetation module of an LSM developed within the Climate Modeling Alliance, and investigated the potential biases caused by the steady state assumption from leaf to canopy scales. In comparison with steady-state models, the dynamic model better predicted the coupled temporal response of photosynthesis and stomatal conductance to changes in light intensity using leaf measurements. In ecosystem flux simulations, while the impact of gs hysteresis response may not be substantial in terms of daily or monthly integrated canopy fluxes, our results sug gested the importance of considering this effect when quantifying fluxes in the mornings and evenings, and interpreting diurnal hysteresis patterns observed in ecosystem fluxes. Furthermore, prognostic modeling can bypass the A-Ci iterations required for steady-state simulations and can be robustly run with comparable computational costs. Overall, our study highlights the implications of dynamic gs modeling in improving the accuracy and efficiency of LSMs, and for advancing our understanding of plant-environment interactions. [ABSTRACT FROM AUTHOR]

The risks harmful algal blooms (HAB) pose to aquatic ecosystems, public health, and coastal economies necessitate supplementation of current observation strategies. Herein, we explore the use of red solar induced fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI), a remote sensing measurement retrievable in variable cloud conditions, for Karenia brevis detection. Along the West Florida Shelf from 2018 to 2020, we compare red SIF with normalized fluorescence line height (nFLH) from MODIS‐Aqua, a standard remote sensing HAB indicator limited to clear sky days. A strong positive linear relationship is found between nFLH and red SIF during severe bloom conditions in 2018 (N = 33,376, r‐value = 0.79, R2 = 0.63) permitting direct comparison. Red SIF provided nearly double the amount of spatiotemporal fluorescence information than nFLH. This work presents the first application of TROPOMI's red SIF for HAB monitoring and illuminates an approach for bolstering early warning systems for HABs beyond clear sky conditions. Plain Language Summary: When microscopic algae receive their favorable amount of light and nutrients, they can rapidly multiply and some types of algae have harmful impacts on the environment and humans. As algae photosynthesize, they emit a faint glow in the red wavelengths and this glow, known as fluorescence, is detectable by satellite instruments. We compared two satellite instruments capable of measuring fluorescence, Sentinel 5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) and MODIS‐Aqua, during massive harmful algal blooms along the West Florida Shelf from 2018 to 2020. We found that the two instruments were strongly correlated during severe harmful algal blooms and found that TROPOMI was able to gather more fluorescence information in space and time than MODIS‐Aqua because it can retrieve fluorescence in optically thin cloud conditions. Key Points: First application of red solar induced fluorescence (SIF) for harmful algal bloom (HAB) monitoringDuring severe HAB conditions, red SIF was consistent with existing monitoring toolsRed SIF provides nearly double the amount of HAB information [ABSTRACT FROM AUTHOR]

Satellite microwave remote sensing techniques can be used to monitor vegetation optical depth (VOD), a metric which is directly linked to vegetation biomass and water content. However, these large-scale measurements are still difficult to reference against either rare or not directly comparable field observations. So far, in situ estimates of canopy biomass or water status often rely on infrequent and time-consuming destructive samples, which are not necessarily representative of the canopy scale. Here, we present a simple technique based on Global Navigation Satellite Systems (GNSS) with the potential to bridge this persisting scale gap. Because GNSS microwave signals are attenuated and scattered by vegetation and liquid water, placing a GNSS sensor under a vegetated canopy and measuring changes in signal strength over time can provide continuous information about VOD and thus on vegetation biomass and water content. We test this technique at a forested site in southern California for a period of 8 months. We show that variations in GNSS signal-to-noise ratios reflect the overall distribution of biomass density in the canopy and can be monitored continuously. For the first time, we show that this technique can resolve diurnal variations in VOD and canopy water content at hourly to sub-hourly time steps. Using a model of canopy transmissivity to assess these diurnal signals, we find that temperature effects on the vegetation dielectric constant, and thus on VOD, may be non-negligible at the diurnal scale or during extreme events like heat waves. Sensitivity to rainfall and dew deposition events also suggests that canopy water interception can be monitored with this approach. The technique presented here has the potential to resolve two important knowledge gaps, namely the lack of ground truth observations for satellite-based VOD and the need for a reliable proxy to extrapolate isolated and labor-intensive in situ measurements of biomass, canopy water content, or leaf water potential. We provide recommendations for deploying such off-the-shelf and easy-to-use systems at existing ecohydrological monitoring networks such as FluxNet or SapfluxNet. [ABSTRACT FROM AUTHOR]

Soil and atmospheric droughts increasingly threaten plant survival and productivity around the world. Yet, conceptual gaps constrain our ability to predict ecosystem‐scale drought impacts under climate change. Here, we introduce the ecosystem wilting point (ΨEWP), a property that integrates the drought response of an ecosystem's plant community across the soil–plant–atmosphere continuum. Specifically, ΨEWP defines a threshold below which the capacity of the root system to extract soil water and the ability of the leaves to maintain stomatal function are strongly diminished. We combined ecosystem flux and leaf water potential measurements to derive the ΨEWP of a Quercus‐Carya forest from an "ecosystem pressure–volume (PV) curve," which is analogous to the tissue‐level technique. When community predawn leaf water potential (Ψpd) was above ΨEWP (=−2.0 MPa), the forest was highly responsive to environmental dynamics. When Ψpd fell below ΨEWP, the forest became insensitive to environmental variation and was a net source of carbon dioxide for nearly 2 months. Thus, ΨEWP is a threshold defining marked shifts in ecosystem functional state. Though there was rainfall‐induced recovery of ecosystem gas exchange following soaking rains, a legacy of structural and physiological damage inhibited canopy photosynthetic capacity. Although over 16 growing seasons, only 10% of Ψpd observations fell below ΨEWP, the forest is commonly only 2–4 weeks of intense drought away from reaching ΨEWP, and thus highly reliant on frequent rainfall to replenish the soil water supply. We propose, based on a bottom‐up analysis of root density profiles and soil moisture characteristic curves, that soil water acquisition capacity is the major determinant of ΨEWP, and species in an ecosystem require compatible leaf‐level traits such as turgor loss point so that leaf wilting is coordinated with the inability to extract further water from the soil. [ABSTRACT FROM AUTHOR]

Plant growth and crop harvest are impacted by both climate change and air pollution. However, their relative importance in crop yields remains elusive, especially in heavily polluted regions. Here we develop crop yield prediction models, based on a large volume of historical crop data, as well as climate and pollution records in China since 1980. A long‐term surface ozone concentration data set is developed from a machine‐learning model and various observations. An assessment of four climate and pollution factors reveals the critical role of particulate and ozone pollution in regulating interannual variations of crop yields in China. During 2010–2018, we find that the particulate pollution mitigation outweighs the negative impacts of concurrent climate change, resulting in 0.5%–1.9% net yield increases nationwide, despite of the ozone increases in the North China Plain. Looking to the future, the impacts of climate change, particularly from surface temperature increase, will dominate over pollution factors and profoundly reduce future maize and rice yields by 0.6 to 2.8% 10 yr−1 by 2050. Our findings call for attention on the threat to future global food security from the absence of pollution mitigation and the persistence of global warming. Plain Language Summary: We capitalize on 39‐year historical records of crop yields, climate variables, and pollution data to develop a robust crop yield prediction model. Utilizing this model, we show a critical role of particulate pollution in determining annual crop yields. Moreover, our analyses reveal that the recent abatement of anthropogenic emission in China results in a net crop gain, which masks the crop loss due to the increased temperature and precipitation. In the next 30 years, temperature dominant climate effects will emerge to steer the future trend of crop yields. The present study demonstrates the co‐benefit of the recent air pollution control policy from agriculture and food perspectives. However, this benefit will eventually be diminished after the air pollution becomes alleviated in the full scale, while persisting or even exacerbated global warming will pose larger threat on the future food security. Key Points: Robust crop yield prediction models are developed to assess climate change and air pollution impacts on agricultureDuring 2010–2018, the particulate pollution mitigation outweighs the negative impacts of concurrent climate change in ChinaExacerbated global warming will lead to crop yield reduction in spite of pollution alleviation in the future scenarios [ABSTRACT FROM AUTHOR]

Plant hydraulics gains increasing interest in plant ecophysiology and vegetation modeling. However, the hydraulic properties and profiles are often improperly represented, thus leading to biased results and simulations, e.g., the neglection of gravitational pressure drop results in overestimated water flux. We highlight the commonly seen ambiguities and/or misunderstandings in plant hydraulics, including (1) the distinction between water potential and pressure, (2) differences among hydraulic conductance and conductivity, (3) xylem vulnerability curve formulations, (4) model complexity, (5) stomatal-model representations, (6) bias from analytic estimations, (7) whole-plant vulnerability, and (8) neglected temperature dependencies. We recommend careful thinking before using or modifying existing definitions, methods, and models. [ABSTRACT FROM AUTHOR]

Modeling leaf photosynthesis is crucial for Earth system modeling. However, as photosystem light absorption and gas exchange are not typically simultaneously measured, photosynthetic capacity estimation and thus photosynthesis modeling are subject to inaccurate light absorption representation in photosynthesis models. We analyzed how leaf absorption features and light source may impact photosynthesis modeling at various settings. We found that (1) estimated photosynthetic capacity can be over- or under-estimated depending on model assumption, and the bias increases with higher mismatch in leaf light absorption parameters and higher true capacity; and (2) modeled photosynthetic rate can also be over- or under-estimated depending on model assumption, and the bias increases with higher leaf internal CO2, and increases and then decreases with increasing light intensity. We recommend researchers not to mix and match results or models with inconsistent assumptions when modeling photosynthesis. [ABSTRACT FROM AUTHOR]

An increase in the seasonal cycle amplitude (SCA) of atmospheric CO2 since the 1960s has been observed in the Northern Hemisphere (NH). However, the underlying dominant drivers are still debated. The peak season CO2 uptake by vegetation is critical in shaping the CO2 seasonality. Using satellite‐upscaled gross primary production (GPP) from FLUXCOM and near‐infrared reflectance of vegetation (NIRV), we demonstrate that peak GPP has increased across the NH over the last two decades. We relate this productivity increase to changes in the CO2 SCA using an atmospheric transport model. The increased photosynthesis has strongly contributed to CO2 SCA trends, but with substantial latitudinal and longitudinal variations. Despite a general increase in the CO2 SCA, there are distinct regional differences. These differences are mainly controlled by regional biosphere carbon fluxes, with the remainder explained by non‐biome factors, including large‐scale atmospheric transport, changes in fossil fuel combustion, biomass burning and oceanic fluxes. Using the global flask and in situ CO2 measurement sites, we find that SCA trends at high latitude are mainly driven by increasingly productive natural ecosystems, whereas mid latitude sites around the Midwest United States are mainly impacted by intensified agriculture and atmospheric transport. Averaging across the 15 long‐term surface sites, forests contribute 26% (7%) to the SCA trends, while crops contribute 17% (24%) and the combined shrubland, grassland and wetland regions contribute 23% (37%) for simulations driven by FLUXCOM (NIRv) ecosystem fluxes. Our findings demonstrate that satellite inferred trends of ecosystem fluxes can capture the observed CO2 SCA trend. Plain Language Summary: An increase in the seasonal cycle amplitude (SCA) of atmospheric carbon dioxide (CO2) since the 1960s has been observed in the Northern Hemisphere (NH). However, dominant drivers of the amplified CO2 seasonality are still debated. In this study, we employ satellite‐based remote sensing observations to track spatial and temporal changes in global gross primary production (GPP) of different vegetation types. Then, we use a state‐of‐art atmospheric transport model to examine whether our bottom‐up estimates of ecosystem fluxes can capture the magnitude of CO2 SCA trends at multiple surface sites. Further, we explore dominant drivers of the observed CO2 SCA trends across different locations of sites. To our best knowledge, this paper makes the first effort to link long‐term satellite remote sensing observations with ground atmospheric CO2 measurements across the NH to explore how terrestrial carbon fluxes shape and change spatiotemporal pattern of atmospheric CO2. Key Points: Peak growing season gross primary production has increased across the northern extratropics over the last two decades based on satellite observationsThe remote sensing‐based global terrestrial carbon fluxes well capture the observed CO2 seasonal cycle amplitude trends at surface sitesMajor factors driving the observed increase in the atmospheric CO2 seasonal amplitude trends vary across the location of sites [ABSTRACT FROM AUTHOR]

We review the capability of current and scheduled satellite observations of atmospheric methane in the shortwave infrared (SWIR) to quantify methane emissions from the global scale down to point sources. We cover retrieval methods, precision and accuracy requirements, inverse and mass balance methods for inferring emissions, source detection thresholds, and observing system completeness. We classify satellite instruments as area flux mappers and point source imagers, with complementary attributes. Area flux mappers are high-precision (<1 %) instruments with 0.1–10 km pixel size designed to quantify total methane emissions on regional to global scales. Point source imagers are fine-pixel (<60 m) instruments designed to quantify individual point sources by imaging of the plumes. Current area flux mappers include GOSAT (2009–present), which provides a high-quality record for interpretation of long-term methane trends, and TROPOMI (2018–present), which provides global continuous daily mapping to quantify emissions on regional scales. These instruments already provide a powerful resource to quantify national methane emissions in support of the Paris Agreement. Current point source imagers include the GHGSat constellation and several hyperspectral and multispectral land imaging sensors (PRISMA, Sentinel-2, Landsat-8/9, WorldView-3), with detection thresholds in the 100–10 000 kg h -1 range that enable monitoring of large point sources. Future area flux mappers, including MethaneSAT, GOSAT-GW, Sentinel-5, GeoCarb, and CO2M, will increase the capability to quantify emissions at high resolution, and the MERLIN lidar will improve observation of the Arctic. The averaging times required by area flux mappers to quantify regional emissions depend on pixel size, retrieval precision, observation density, fraction of successful retrievals, and return times in a way that varies with the spatial resolution desired. A similar interplay applies to point source imagers between detection threshold, spatial coverage, and return time, defining an observing system completeness. Expanding constellations of point source imagers including GHGSat and Carbon Mapper over the coming years will greatly improve observing system completeness for point sources through dense spatial coverage and frequent return times. [ABSTRACT FROM AUTHOR]

Land and Earth system modeling is moving towards more explicit biophysical representations, requiring increasing variety of datasets for initialization and benchmarking. However, researchers often have difficulties in identifying and integrating non-standardized datasets from various sources. We aim towards a standardized database and one-stop distribution method of global datasets. Here, we present the GriddingMachine as (1) a database of global-scale datasets commonly used to parameterize or benchmark the models, from plant traits to vegetation indices and geophysical information and (2) a cross-platform open source software to download and request a subset of datasets with only a few lines of code. The GriddingMachine datasets can be accessed either manually through traditional HTTP, or automatically using modern programming languages including Julia, Matlab, Octave, Python, and R. The GriddingMachine collections can be used for any land and Earth modeling framework and ecological research at the regional and global scales, and the number of datasets will continue to grow to meet the increasing needs of research communities. [ABSTRACT FROM AUTHOR]

Plant hydraulics gains increasing interest in plant eco-physiology and vegetation modeling. However, the hydraulic properties and profiles are often improperly represented thus leading to biased results and simulations, e.g., the neglection of gravitational pressure drop results in overestimated water flux. We highlight the commonly seen ambiguities and/or misunderstandings in plant hydraulics, including (1) distinction between water potential and pressure, (2) differences among hydraulic 5 conductance and conductivity, (3) xylem vulnerability curve formulations, (4) stomatal model representations, (5) bias from analytic estimations, (6) whole plant vulnerability, and (7) neglected temperature dependencies. We recommend careful thinking before using or modifying existing definitions, methods, and models. [ABSTRACT FROM AUTHOR]

We review the capability of current and scheduled satellite observations of atmospheric methane in the shortwave infrared (SWIR) to quantify methane emissions from the global scale down to point sources. We cover retrieval methods, precision and accuracy requirements, inverse and mass balance methods to infer emissions, source detection thresholds, and observing system completeness. We classify satellite instruments as area flux mappers and point source imagers, with complementary attributes. Area flux mappers are high-precision (<1%) instruments with 0.1-10 km pixel size designed to quantify total methane emissions on regional to global scales. Point source imagers are fine-pixel (<60 m) instruments designed to quantify individual point sources by imaging of the plumes. Current area flux mappers include GOSAT (2009-present), which provides a high-quality record for interpretation of long-term methane trends, and TROPOMI (2018-present) which provides global continuous daily mapping to quantify emissions on regional scales. Current point source imagers include the GHGSat constellation and several hyperspectral and multispectral land imaging sensors (PRISMA, Sentinel-2, Landsat-8/9, WorldView-3), with detection thresholds in the 100-10000 kg h-1 range. Future area flux mappers including MethaneSAT, GOSAT-GW, MicroCarb, GeoCarb, and CO2M will increase the capability to quantify emissions from source regions, and the MERLIN lidar will improve observation of the Arctic. The future constellation of Carbon Mapper point source imagers will achieve high observing system completeness for point sources through high spatial coverage and frequent return times. [ABSTRACT FROM AUTHOR]

The retrieval of solar-induced chlorophyll fluorescence (SIF) from space is a relatively new advance in Earth observation science, having only become feasible within the last decade. Interest in SIF data has grown exponentially, and the retrieval of SIF and the provision of SIF data products has become an important and formal component of spaceborne Earth observation missions. Here, we describe the global Level 2 SIF Lite data products for the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory-2 (OCO-2), and Orbiting Carbon Observatory-3 (OCO-3) platforms, which are provided for each platform in daily netCDF files (Frankenberg, 2022, 10.22002/D1.8771; OCO-2 Science Team et al., 2020, 10.5067/XO2LBBNPO010; OCO-3 Science Team et al., 2020, 10.5067/NOD1DPPBCXSO). We also outline the methods used to retrieve SIF and estimate uncertainty, describe all the data fields, and provide users with the background information necessary for the proper use and interpretation of the data, such as considerations of retrieval noise, sun sensor geometry, the indirect relationship between SIF and photosynthesis, and differences among the three platforms and their respective data products. OCO-2 and OCO-3 have the highest spatial resolution of spaceborne SIF retrievals to date, and the target and snapshot area mode observation modes of OCO-2 and OCO-3 are unique. These modes provide hundreds to thousands of SIF retrievals at biologically diverse global target sites during a single overpass, and provide an opportunity to better inform our understanding of canopy-scale vegetation SIF emission across biomes. [ABSTRACT FROM AUTHOR]

Satellite microwave remote sensing techniques can be used to monitor vegetation optical depth (VOD), a metric which is directly linked to vegetation biomass and water content. However, these large- scale measurements are still difficult to reference against either rare or not directly comparable field observations. So far, in-situ estimates of biomass or water status often rely on infrequent and time- consuming samplings, which are not necessarily representative of the canopy scale. Here, we present a simple technique based on Global Navigation Satellite Systems (GNSS) with the potential to bridge this persisting scale gap. Because GNSS microwave signals are attenuated and scattered by vegetation and liquid water, placing a GNSS sensor under a vegetated canopy and measuring changes in signal quality over time can provide continuous information on VOD, and thus on vegetation biomass and water content. We test this technique at forested site in Southern California for a period of 8 months. We show that variations in GNSS signal to noise ratios reflect the overall distribution of biomass density in the canopy and can be monitored continuously. For the first time, we show that this technique can resolve diurnal variations in VOD and canopy water content at hourly to sub-hourly time steps. Using a model of canopy transmissivity to assess these diurnal signals, we find that temperature effects on the vegetation dielectric constant, and thus on VOD, may be non-negligible at the diurnal scale or during extreme events like heatwaves. The rainfall and dew deposition events also suggest that canopy water interception can be monitored with this approach. The technique presented here has the potential to resolve two important knowledge gaps, namely the lack of ground truth observations for satellite-based VOD, as well as the need of a reliable proxy to extrapolate isolated and labour-intensive in-situ measurements of biomass, canopy water content, or leaf water potential. We provide recommendations for deploying such off-the- shelf and easy-to-use radar systems at existing ecohydrological monitoring networks such as FluxNet or SapfluxNet. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is the sum of leaf photosynthesis and represents a crucial component of the global carbon cycle. Space-borne estimates of GPP typically rely on observable quantities that co-vary with GPP such as vegetation indices using reflectance measurements (e.g., normalized difference vegetation index, NDVI, near-infrared reflectance of terrestrial vegetation, NIR v , and kernel normalized difference vegetation index, kNDVI). Recent work has also utilized measurements of solar-induced chlorophyll fluorescence (SIF) as a proxy for GPP. However, these SIF measurements are typically coarse resolution, while many processes influencing GPP occur at fine spatial scales. Here, we develop a convolutional neural network (CNN), named SIFnet, that increases the resolution of SIF from the TROPOspheric Monitoring Instrument (TROPOMI) on board of the satellite Sentinel-5P by a factor of 10 to a spatial resolution of 500 m. SIFnet utilizes coarse SIF observations together with high-resolution auxiliary data. The auxiliary data used here may carry information related to GPP and SIF. We use training data from non-US regions between April 2018 until March 2021 and evaluate our CNN over the conterminous United States (CONUS). We show that SIFnet is able to increase the resolution of TROPOMI SIF by a factor of 10 with a r2 and RMSE metrics of 0.92 and 0.17 mW m -2 sr -1 nm -1 , respectively. We further compare SIFnet against a recently developed downscaling approach and evaluate both methods against independent SIF measurements from Orbiting Carbon Observatory 2 and 3 (together OCO-2/3). SIFnet performs systematically better than the downscaling approach (r=0.78 for SIFnet, r=0.72 for downscaling), indicating that it is picking up on key features related to SIF and GPP. Examination of the feature importance in the neural network indicates a few key parameters and the spatial regions in which these parameters matter. Namely, the CNN finds low-resolution SIF data to be the most significant parameter with the NIR v vegetation index as the second most important parameter. NIR v consistently outperforms the recently proposed kNDVI vegetation index. Advantages and limitations of SIFnet are investigated and presented through a series of case studies across the United States. SIFnet represents a robust method to infer continuous, high-spatial-resolution SIF data. [ABSTRACT FROM AUTHOR]

Recent advances in satellite observations of solar‐induced chlorophyll fluorescence (SIF) provide a new opportunity to constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and its radiative transfer to remote sensing sensors is an essential prerequisite for data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf‐level to canopy‐level is usually not well‐represented. Here, we incorporate the simulation of far‐red SIF observed at nadir into the Community Land Model version 5 (CLM5). Leaf‐level fluorescence yield was simulated by a parametric simplification of the Soil Canopy‐Observation of Photosynthesis and Energy fluxes model (SCOPE). And an efficient and accurate method based on escape probability is developed to scale SIF from leaf‐level to top‐of‐canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and SCOPE agreed well at sites except one in needleleaf forest (R2 > 0.91, root‐mean‐square error <0.19 W⋅m−2⋅sr−1⋅μm−1), and captured the day‐to‐day variation of tower‐measured SIF at temperate forest sites (R2 > 0.68). At the global scale, simulated SIF generally captured the spatial and seasonal patterns of satellite‐observed SIF. Factors including the fluorescence emission model, clumping, bidirectional effect, and leaf optical properties had considerable impacts on SIF simulation, and the discrepancies between simulate d and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF toward constraining GPP simulations. Plain Language Summary: During photosynthesis, plants emit faint light referred to as solar‐induced chlorophyll fluorescence (SIF). Global observations of SIF by satellites provide a new opportunity to evaluate and constrain the simulation of terrestrial photosynthesis by models, which is highly uncertain. To achieve this, accurate simulation of observed SIF is required. As a light signal, SIF experiences complicated scattering and re‐absorption (radiative transfer) before it reaches the sensor. The radiative transfer of SIF is usually not well‐represented in the few studies that incorporated SIF into global models. Here, we incorporate simulation of SIF into one of those models with the radiative transfer processes taken into account. Simulated SIF generally captured the spatial and seasonal patterns of observed SIF, and whether the radiative transfer processes were properly considered had a considerable impact on simulated SIF. By better representing the processes involved in SIF simulation, our model allows more reasonable comparisons between simulated and observed SIF toward constraining and evaluating the simulation of terrestrial photosynthesis. Key Points: Simulation of nadir solar‐induced chlorophyll fluorescence (SIF) at 740 nm is incorporated in Community Land Model version 5 (CLM5) with canopy scattering, clumping, and bidirectional effect taken into accountCLM5 SIF simulation generally captured the spatial and seasonal patterns of observed SIFThe radiative transfer processes had considerable impact on SIF simulations [ABSTRACT FROM AUTHOR]

Remote sensing of solar‐induced chlorophyll fluorescence (SIF) provides a powerful proxy for gross primary productivity (GPP). It is particularly promising in boreal ecosystems where seasonal downregulation of photosynthesis occurs without significant changes in canopy structure or chlorophyll content. The use of SIF as a proxy for GPP is complicated by inherent non‐linearities due to both physical (illumination effects) and ecophysiological (light use efficiencies) controls at fine spatial (tower/leaf) and temporal (half‐hourly) scales. To study the SIF‐GPP relationship, we investigated the diurnal and seasonal dynamics of continuous tower‐based measurements of SIF, GPP, and common vegetation indices at the Southern Old Black Spruce Site (SOBS) in Saskatchewan, CA over the course of two years. We find that SIF outperforms other vegetation indices as a proxy for GPP at all temporal scales but shows a non‐linear relationship with GPP at a half‐hourly resolution. At small temporal scales, SIF and GPP are predominantly driven by light and non‐linearity between SIF and GPP is due to the light saturation of GPP. Averaged over daily and monthly scales, the relationship between SIF and GPP is linear due to a reduction in the observed PAR range. Seasonal changes in the light responses of SIF and GPP are driven by changes in light use efficiency which co‐vary with changes in temperature, while illumination and canopy structure partially linearize the SIF‐GPP relationship. Additionally, we find that the SIF‐GPP relationship has a seasonal dependency. Our results help clarify the utility of SIF for estimating carbon assimilation in boreal forests. Plain Language Summary: Remote sensing can help us better understand plants' role in the global carbon cycle. In particular, a small light signal emitted by plants during photosynthesis, known as solar‐induced chlorophyll fluorescence (SIF), is a promising remotely sensed proxy for carbon uptake due to its strong relationship with gross primary productivity (GPP, ecosystem carbon assimilated during photosynthesis) when observed by satellite. Recent work at the leaf and tower‐scale has highlighted differences in the relationship between SIF and GPP, thereby raising questions over the utility of SIF for this purpose. We collect tower‐based SIF and GPP measurements from the southern end of the boreal forest to clarify their connection in boreal ecosystems. We find that temporal resolution is critical for understanding the nuances between SIF and GPP and that light and temperature effects complicate their relationship. Additionally, the relationship between SIF and GPP has a seasonal dependency that should be considered when using SIF. Taken together, our results suggest that SIF is a powerful tool for estimating carbon assimilation in boreal forests, but some key considerations need to be addressed for properly interpreting this faint signal. Key Points: Tower‐based solar‐induced chlorophyll fluorescence (SIF) closely tracks gross primary productivity (GPP) over two years in a mixed‐species boreal forestLight saturation of photosynthesis drives non‐linearity between SIF and GPPThe SIF‐GPP relationship is seasonally variant due to dynamics between LUEF and LUEP [ABSTRACT FROM AUTHOR]

Lack of direct carbon, water, and energy flux observations at global scales makes it difficult to calibrate land surface models (LSMs). The increasing number of remote-sensing-based products provide an alternative way to verify or constrain land models given their global coverage and satisfactory spatial and temporal resolutions. However, these products and LSMs often differ in their assumptions and model setups, for example, the canopy model complexity. The disagreements hamper the fusion of global-scale datasets with LSMs. To evaluate how much the canopy complexity affects predicted canopy fluxes, we simulated and compared the carbon, water, and solar-induced chlorophyll fluorescence (SIF) fluxes using five different canopy complexity setups from a one-layered canopy to a multi-layered canopy with leaf angular distributions. We modeled the canopy fluxes using the recently developed land model by the Climate Modeling Alliance, CliMA Land. Our model results suggested that (1) when using the same model inputs, model-predicted carbon, water, and SIF fluxes were all higher for simpler canopy setups; (2) when accounting for vertical photosynthetic capacity heterogeneity, differences between canopy complexity levels increased compared to the scenario of a uniform canopy; and (3) SIF fluxes modeled with different canopy complexity levels changed with sun-sensor geometry. Given the different modeled canopy fluxes with different canopy complexities, we recommend (1) not misusing parameters inverted with different canopy complexities or assumptions to avoid biases in model outputs and (2) using a complex canopy model with angular distribution and a hyperspectral radiation transfer scheme when linking land processes to remotely sensed spectra. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is the sum of leaf photosynthesis and represents a crucial component of the global carbon cycle. Space-borne estimates of GPP typically rely on observable quantities that co-vary with GPP such as vegetation indices using reflectance measurements (e.g., NDVI, NIR[sub v], and kNDVI). Recent work has also utilized measurements of solar-induced chlorophyll fluorescence (SIF) as a proxy for GPP. However, these SIF measurements are typically coarse resolution while many processes influencing GPP occur at fine spatial scales. Here, we develop a Convolutional Neural Network (CNN), named SIFnet, that increases the resolution of SIF from the TROPOspheric Monitoring Instrument (TROPOMI) on board of the satellite Sentinel-5P by a factor of 10 to a spatial resolution of 500 m. SIFnet utilizes coarse SIF observations together with high resolution auxiliary data. The auxiliary data used here may carry information related to GPP and SIF. We use training data from non-US regions between April 2018 until March 2021 and evaluate our CNN over the conterminous United States (CONUS). We show that SIFnet is able to increase the resolution of TROPOMI SIF by a factor of 10 with a r² and RMSE metrics of 0.92 and 0.17 mW m-2 sr-1 nm-1, respectively. We further compare SIFnet against a recently developed downscaling approach and evaluate both methods against independent SIF measurements from Orbiting Carbon Observatory 2 and 3 (OCO-2/3). SIFnet performs systematically better than the downscaling approach (r = 0.78 for SIFnet, r = 0.72 for downscaling), indicating that it is picking up on key features related to SIF and GPP. Examination of the feature importance in the neural network indicates a few key parameters and the spatial regions these parameters matter. Namely, the CNN finds low resolution SIF data to be the most significant parameter with the NIRv vegetation index as the second most important parameter. NIR[sub v] consistently outperforms the recently proposed kNDV I vegetation index. Advantages and limitations of SIFnet are investigated and presented through a series of case studies across the United States. SIFnet represents a robust method to infer continuous, high spatial resolution SIF data. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) has previously been shown to strongly correlate with gross primary productivity (GPP); however this relationship has not yet been quantified for the recently launched TROPOspheric Monitoring Instrument (TROPOMI). Here we use a Gaussian mixture model to develop a parsimonious relationship between SIF from TROPOMI and GPP from flux towers across the conterminous United States (CONUS). The mixture model indicates the SIF–GPP relationship can be characterized by a linear model with two terms. We then estimate GPP across CONUS at 500 m spatial resolution over a 16 d moving window. We observe four extreme precipitation events that induce regional GPP anomalies: drought in western Texas, flooding in the midwestern US, drought in South Dakota, and drought in California. Taken together, these events account for 28 % of the year-to-year GPP differences across CONUS. Despite these large regional anomalies, we find that CONUS GPP varies by less than 4 % between 2018 and 2019. [ABSTRACT FROM AUTHOR]

Methane (CH 4) is the second most important anthropogenic greenhouse gas with a significant impact on radiative forcing, tropospheric air quality, and stratospheric water vapor. Remote sensing observations enable the detection and quantification of local methane emissions across large geographical areas, which is a critical step for understanding local flux distributions and subsequently prioritizing mitigation strategies. Obtaining methane column concentration measurements with low noise and minimal surface interference has direct consequences for accurately determining the location and emission rates of methane sources. The quality of retrieved column enhancements depends on the choices of the instrument and retrieval parameters. Here, we studied the changes in precision error and bias as a result of different spectral resolutions, instrument optical performance, and detector exposure times by using a realistic instrument noise model. In addition, we formally analyzed the impact of spectrally complex surface albedo features on retrievals using the iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) algorithm. We built an end-to-end modeling framework that can simulate observed radiances from reflected solar irradiance through a simulated CH4 plume over several natural and artificial surfaces. Our analysis shows that complex surface features can alias into retrieved methane abundances, explaining the existence of retrieval biases in current airborne methane observations. The impact can be mitigated with higher spectral resolution and a larger polynomial degree to approximate surface albedo variations. Using a spectral resolution of 1.5 nm, an exposure time of 20 ms, and a polynomial degree of 25, a retrieval precision error below 0.007 molem-2 or 1.0 % of total atmospheric CH4 column can be achieved for high albedo cases, while minimizing the bias due to surface interference such that the noise is uncorrelated among various surfaces. At coarser spectral resolutions, it becomes increasingly harder to separate complex surface albedo features from atmospheric absorption features. Our modeling framework provides the basis for assessing tradeoffs for future remote sensing instruments and algorithmic designs. For instance, we find that improving the spectral resolution beyond 0.2 nm would actually decrease the retrieval precision, as detector readout noise will play an increasing role. Our work contributes towards building an enhanced monitoring system that can measure CH4 concentration fields to determine methane sources accurately and efficiently at scale. [ABSTRACT FROM AUTHOR]

Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil‐plant‐atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem‐scale analog of the pressure–volume curve, the non‐linear relationship between average leaf or branch water potential and water content commonly used in plant hydraulics. The sources of variability in these ecosystem‐scale pressure‐volume curves and their relationship to forest response to water stress are discussed. We further show to what extent diel, seasonal, and decadal dynamics of VWC reflect variations in different processes relating the tree response to water stress. VWC can also be used for inferring belowground conditions—which are difficult to impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne observational system for VWC, when combined with existing datasets, can capture diel and seasonal water dynamics to advance the science and applications of global forest vulnerability to future droughts. [ABSTRACT FROM AUTHOR]

In this study, the GOSAT Proxy Retrieval (v9.0) data product of column‐averaged dry‐air mole fractions of atmospheric methane (XCH4) for the period 2009–2019 was analyzed to detect methane (CH4) trends in the three western Canadian provinces where oil and gas development activities have changed significantly over the last decade. Although we found statistically significant increasing XCH4 trends in all subdomains (northeast British Columbia‐NE, Alberta‐AB, southern Saskatchewan‐SK), XCH4 trends are not higher than the background trend (7.25 ± 0.30 ppb/yr) and enhancement trends (ΔXCH4, after removing the background quantity) are not detectable at any subdomain during 2009–2019. For further insight into trends in all subdomains, we divided the whole period (2009–2019) into two shorter periods (2009–2013 and 2014–2019) and estimated trends. We found XCH4 trends are higher than background trends particularly in the AB and SK subdomains during 2009–2013, and their ΔXCH4 trends are positive and also marginally statistically significant. However, we do not find any detectable ΔXCH4 trend if we consider either long‐term (2009–2019) or the second shorter period (2014–2019), suggesting local emission sources are dominating year to year fluctuation. From the source attribution analysis, we found both wetland and oil and gas sectors are controlling the CH4 growth rate in western Canada, but the oil and gas sector is the dominant driver in NE and SK subdomains. We also found the satellite‐based average ΔXCH4 trend (15.43 ± 8.19%/yr) between 2009 and 2013 likely reflects a trend in oil and gas CH4 emissions in AB and SK for the same period. Key Points: Methane growth rate in western Canada was positive during 2009–2013, fluctuated afterward, resulting in an undetectable trend after 2013Methane growth seemed to be related to emissions from oil and gas industriesAverage growth rate (15.43 ± 8.19%/yr) between 2009 and 2013 likely reflects a trend in oil and gas CH4 emissions in western Canada [ABSTRACT FROM AUTHOR]

At the leaf level, stomata control the exchange of water and carbon across the air–leaf interface. Stomatal conductance is typically modeled empirically, based on environmental conditions at the leaf surface. Recently developed stomatal optimization models show great skills at predicting carbon and water fluxes at both the leaf and tree levels. However, how well the optimization models perform at larger scales has not been extensively evaluated. Furthermore, stomatal models are often used with simple single-leaf representations of canopy radiative transfer (RT), such as big-leaf models. Nevertheless, the single-leaf canopy RT schemes do not have the capability to model optical properties of the leaves nor the entire canopy. As a result, they are unable to directly link canopy optical properties with light distribution within the canopy to remote sensing data observed from afar. Here, we incorporated one optimization-based and two empirical stomatal models with a comprehensive RT model in the land component of a new Earth system model within CliMA, the Climate Modelling Alliance. The model allowed us to simultaneously simulate carbon and water fluxes as well as leaf and canopy reflectance and fluorescence spectra. We tested our model by comparing our modeled carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) to two flux tower observations (a gymnosperm forest and an angiosperm forest) and satellite SIF retrievals, respectively. All three stomatal models quantitatively predicted the carbon and water fluxes for both forests. The optimization model, in particular, showed increased skill in predicting the water flux given the lower error (ca. 14.2 % and 21.8 % improvement for the gymnosperm and angiosperm forests, respectively) and better 1:1 comparison (slope increases from ca. 0.34 to 0.91 for the gymnosperm forest and from ca. 0.38 to 0.62 for the angiosperm forest). Our model also predicted the SIF yield, quantitatively reproducing seasonal cycles for both forests. We found that using stomatal optimization with a comprehensive RT model showed high accuracy in simulating land surface processes. The ever-increasing number of regional and global datasets of terrestrial plants, such as leaf area index and chlorophyll contents, will help parameterize the land model and improve future Earth system modeling in general. [ABSTRACT FROM AUTHOR]

The first satellite-based global retrievals of terrestrial sun-induced chlorophyll fluorescence (SIF) were achieved in 2011. Since then, a number of global SIF datasets with different spectral, spatial, and temporal sampling characteristics have become available to the scientific community. These datasets have been useful to monitor the dynamics and productivity of a range of vegetated areas worldwide, but the coarse spatiotemporal sampling and low signal-to-noise ratio of the data hamper their application over small or fragmented ecosystems. The recent advent of the Copernicus Sentinel-5P TROPOMI mission and the high quality of its data products promise to alleviate this situation, as TROPOMI provides daily global measurements at a much denser spatial and temporal sampling than earlier satellite instruments. In this work, we present a global SIF dataset produced from TROPOMI measurements within the TROPOSIF project funded by the European Space Agency. The current version of the TROPOSIF dataset covers the time period between May 2018 and April 2021. Baseline SIF retrievals are derived from the 743–758 nm window. A secondary SIF dataset derived from an extended fitting window (735–758 nm window) is included. This provides an enhanced signal-to-noise ratio at the expense of a higher sensitivity to atmospheric effects. Spectral reflectance spectra at seven 3 nm windows devoid of atmospheric absorption within the 665–785 nm range are also included in the TROPOSIF dataset as an important ancillary variable to be used in combination with SIF. The methodology to derive SIF and ancillary data as well as results from an initial data quality assessment are presented in this work. The TROPOSIF dataset is available through the following digital object identifier (DOI): https://doi.org/10.5270/esa-s5p_innovation-sif-20180501_20210320-v2.1-202104. [ABSTRACT FROM AUTHOR]

Methods developed to explore the luminescent properties of the moon facilitated the development of techniques to infer terrestrial solar‐induced chlorophyll fluorescence (SIF) from satellite instruments. While single SIF retrievals are inherently noisy, averaging many retrievals allows us to obtain highly accurate estimates. We analyzed several years of aggregated SIF data collected by the TROPOspheric Monitoring Instrument (TROPOMI) and the Orbiting Carbon Observatory‐2 (OCO‐2) over nonvegetated areas to explore the potential of SIF retrievals beyond the realm of photosynthesis. The fundamentally different retrievals at varying wavelengths in the near‐infrared reveal that about 10% of all barren surfaces are weakly luminescent, while a few areas luminesce strongly—amounts comparable to SIF from vegetation. By means of lithological maps, we attributed the strongest luminescence signals to exposed carbonate sedimentary rocks. Besides a detailed evaluation of the signal properties, we discussed implications for SIF data sets and other remote sensing products. Plain Language Summary: During the last decade, advancements in remote sensing techniques allowed us to infer solar‐induced chlorophyll fluorescence, a faint electromagnetic signal emitted by vegetation, from space‐borne measurements. Here, we explored the potential of such measurements to also observe mineral luminescence, a signal that is typically even weaker than fluorescence from vegetation, which explains why it has not been documented to be detectable from space. We identified luminescent regions by means of several years of vegetation fluorescence data derived from two satellites (OCO‐2 and Sentinel‐5P) over nonvegetated areas and analyzed the composition of luminescent surfaces. According to our analysis, about 10% of all barren areas on Earth are weakly luminescent, whereas some areas display surprisingly strong signals. Key Points: First report of mineral luminescence observed from spaceStrongest signals originate from carbonate sedimentary rocksEmission appears to be spectrally flat in the near‐infrared and Lambertian [ABSTRACT FROM AUTHOR]

Methods developed to explore the luminescent properties of the moon facilitated the development of techniques to infer terrestrial solar‐induced chlorophyll fluorescence (SIF) from satellite instruments. While single SIF retrievals are inherently noisy, averaging many retrievals allows us to obtain highly accurate estimates. We analyzed several years of aggregated SIF data collected by the TROPOspheric Monitoring Instrument (TROPOMI) and the Orbiting Carbon Observatory‐2 (OCO‐2) over nonvegetated areas to explore the potential of SIF retrievals beyond the realm of photosynthesis. The fundamentally different retrievals at varying wavelengths in the near‐infrared reveal that about 10% of all barren surfaces are weakly luminescent, while a few areas luminesce strongly—amounts comparable to SIF from vegetation. By means of lithological maps, we attributed the strongest luminescence signals to exposed carbonate sedimentary rocks. Besides a detailed evaluation of the signal properties, we discussed implications for SIF data sets and other remote sensing products. Plain Language Summary: During the last decade, advancements in remote sensing techniques allowed us to infer solar‐induced chlorophyll fluorescence, a faint electromagnetic signal emitted by vegetation, from space‐borne measurements. Here, we explored the potential of such measurements to also observe mineral luminescence, a signal that is typically even weaker than fluorescence from vegetation, which explains why it has not been documented to be detectable from space. We identified luminescent regions by means of several years of vegetation fluorescence data derived from two satellites (OCO‐2 and Sentinel‐5P) over nonvegetated areas and analyzed the composition of luminescent surfaces. According to our analysis, about 10% of all barren areas on Earth are weakly luminescent, whereas some areas display surprisingly strong signals. Key Points: First report of mineral luminescence observed from spaceStrongest signals originate from carbonate sedimentary rocksEmission appears to be spectrally flat in the near‐infrared and Lambertian [ABSTRACT FROM AUTHOR]

The retrieval of solar induced chlorophyll fluorescence (SIF) from space is a relatively new advance in Earth observation science, having only become feasible within the last decade. Interest in SIF data has grown exponentially, and the retrieval of SIF and the provision of SIF data products has become an important and formal component of spaceborne Earth observation missions. Here, we describe the global Level 2 SIF Lite data products for the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory-2 (OCO-2), and OCO-3 platforms, which are provided for each platform in daily netCDF files. We also outline the methods used to retrieve SIF and estimate uncertainty, describe all the data fields, and provide users the background information necessary for the proper use and interpretation of the data, such as considerations of retrieval noise, sun-sensor geometry, the indirect relationship between SIF and photosynthesis, and differences among the three platforms and their respective data products. OCO-2 and OCO-3 have the highest spatial resolution spaceborne SIF retrievals to date, and the target and snapshot area mode observation modes of OCO-2 and OCO-3 are unique. These modes provide hundreds to thousands of SIF retrievals at biologically diverse global target sites during a single overpass, and provide an opportunity to better inform our understanding of canopy-scale vegetation SIF emission across biomes. [ABSTRACT FROM AUTHOR]

After stagnating in the early 2000s, the atmospheric methane growth rate has been positive since 2007 with a significant acceleration starting in 2014. While the causes for previous growth rate variations are still not well determined, this recent increase can be studied with dense surface and satellite observations. Here, we use an ensemble of six multi-species atmospheric inversions that have the capacity to assimilate observations of the main species in the methane oxidation chain – namely, methane, formaldehyde, and carbon monoxide – to simultaneously optimize both the methane sources and sinks at each model grid. We show that the surge of the atmospheric growth rate between 2010–2013 and 2014–2017 is most likely explained by an increase of global CH 4 emissions by 17.5±1.5 Tg yr -1 (mean ± 1σ), while variations in the hydroxyl radicals (OH) remained small. The inferred emission increase is consistently supported by both surface and satellite observations, with leading contributions from the tropical wetlands (∼ 35 %) and anthropogenic emissions in China (∼ 20 %). Such a high consecutive atmospheric growth rate has not been observed since the 1980s and corresponds to unprecedented global total CH 4 emissions. [ABSTRACT FROM AUTHOR]

Lack of direct carbon, water, and energy flux observations at global scales makes it difficult to calibrate land surface models (LSMs). The increasing number of remote sensing based products provide an alternative way to verify or constrain land models given its global coverage and satisfactory spatial and temporal resolutions. However, these products and LSMs often differ in their assumptions and model setups, for example, the canopy model complexity. The disagreements hamper the fusion of global scale datasets with LSMs. To evaluate how much the canopy complexity affects predicted canopy fluxes, we simulated and compared the carbon, water, and solar-induced chlorophyll fluorescence (SIF) fluxes using five different canopy complexity setups from a one-layered big-leaf canopy to a multi-layered canopy with leaf angular distributions. We modeled the canopy fluxes using a recently developed Land model by the Climate Modeling Alliance. Our model results suggested that (1) when using the same model inputs, model predicted carbon, water, and SIF fluxes were all higher for simpler canopy setups; (2) when accounting for vertical photosynthetic capacity heterogeneity, differences among canopy complexity levels increased compared to the scenario of a uniform canopy; (3) SIF fluxes modeled with different canopy complexity levels changed with sun-sensor geometry. Given the different modeled canopy fluxes with different canopy complexities, we recommend (1) not misusing parameters inverted with different canopy complexities or assumptions to avoid biases in model outputs, and (2) using complex canopy model with angular distribution and hyperspectral radiation transfer scheme when linking land processes to remotely sensed spectra. [ABSTRACT FROM AUTHOR]

Methane (CH4) is the 2nd most important anthropogenic greenhouse gas with a significant impact on radiative forcing, tropospheric air quality and stratospheric water vapor. Remote-sensing observations enable the detection and quantification of local methane emissions across large geographical areas, which is a critical step for understanding local flux distributions and subsequently prioritizing mitigation strategies. Obtaining methane column concentration measurements with low noise and minimal surface interference has direct consequences for accurately determining the location and emission rates of methane sources. The quality of retrieved column enhancements depends on the choices of instrument and retrieval parameters. Here, we studied the changes in precision error and bias as a result of different spectral resolutions, instrument optical performance and detector exposure times by using a realistic instrument noise model. In addition, we formally analysed the impact of spectrally complex surface albedo features on retrievals using the Iterative Maximum a Posteriori- Differential Optical Absorption Spectroscopy (IMAP-DOAS) algorithm. We built an end-to-end modelling framework that can simulate observed radiances from reflected solar irradiance through a simulated CH4 plume over several natural and man-made surfaces. Our analysis shows that complex surface features can alias into retrieved methane abundances, explaining the existence of retrieval biases in current airborne methane observations. The impact can be mitigated with higher spectral resolution and a larger polynomial degree to approximate surface albedo variations. Using a spectral resolution of 1.5 nm, an exposure time of 20 ms, and a polynomial degree of 25, a retrieval precision error below 0.007 mole m-2 or 1.0 % of total atmospheric CH4 column can be achieved for high albedo cases, while minimizing the bias due to surface interference such that the noise is uncorrelated among various surfaces. At coarser spectral resolutions, it becomes increasingly harder to separate complex surface albedo features from atmospheric absorption features. Our modelling framework provides the basis for assessing trade-offs for future remote-sensing instruments and algorithmic designs. For instance, we find that improving the spectral resolution beyond 0.2 nm would actually decrease the retrieval precision as detector readout noise will play an increasing role. Our work contributes towards building an enhanced monitoring system that can measure CH4 concentration fields to determine methane sources accurately and efficiently at scale. [ABSTRACT FROM AUTHOR]