The terrestrial biosphere and atmosphere interact through a series of feedback loops. Variability in terrestrial vegetation growth and phenology can modulate fluxes of water and energy to the atmosphere, and thus affect the climatic conditions that in turn regulate vegetation dynamics. Here we analyse satellite observations of solar-induced fluorescence, precipitation, and radiation using a multivariate statistical technique. We find that biosphere-atmosphere feedbacks are globally widespread and regionally strong: they explain up to 30% of precipitation and surface radiation variance in regions where feedbacks occur. Substantial biosphere-precipitation feedbacks are often found in regions that are transitional between energy and water limitation, such as semi-arid or monsoonal regions. Substantial biosphere-radiation feedbacks are often present in several moderately wet regions and in the Mediterranean, where precipitation and radiation increase vegetation growth. Enhancement of latent and sensible heat transfer from vegetation accompanies this growth, which increases boundary layer height and convection, affecting cloudiness, and consequently incident surface radiation. Enhanced evapotranspiration can increase moist convection, leading to increased precipitation. Earth system models underestimate these precipitation and radiation feedbacks mainly because they underestimate the biosphere response to radiation and water availability. We conclude that biosphere-atmosphere feedbacks cluster in specific climatic regions that help determine the net CO2 balance of the biosphere.

Unprecedented droughts hit southern Amazonia in 2005 and 2010, causing a sharp increase in tree mortality and carbon loss. To better predict the rainforest's response to future droughts, it is necessary to understand its behavior during past events. Satellite observations provide a practical source of continuous observations of Amazonian forest. Here we used a passive microwave-based vegetation water content record (i.e., vegetation optical depth, VOD), together with multiple hydrometeorological observations as well as conventional satellite vegetation measures, to investigate the rainforest canopy dynamics during the 2005 and 2010 droughts. During the onset of droughts in the wet-to-dry season (May July) of both years, we found large-scale positive anomalies in VOD, leaf area index (LAI) and enhanced vegetation index (EVI) over the southern Amazonia. These observations are very likely caused by enhanced canopy growth. Concurrent below-average rainfall and above average radiation during the wet-to-dry season can be interpreted as an early arrival of normal dry season conditions, leading to enhanced new leaf development and ecosystem photosynthesis, as supported by field observations. Our results suggest that further rainfall deficit into the subsequent dry season caused water and heat stress during the peak of 2005 and 2010 droughts (August October) that exceeded the tolerance limits of the rainforest, leading to widespread negative VOD anomalies over the southern Amazonia. Significant VOD anomalies were observed mainly over the western part in 2005 and mainly over central and eastern parts in 2010. The total area with significant negative VOD anomalies was comparable between these two drought years, though the average magnitude of significant negative VOD anomalies was greater in 2005. This finding broadly agrees with the field observations indicating that the reduction in biomass carbon uptake was stronger in 2005 than 2010. The enhanced canopy growth preceding drought-induced senescence should be taken into account when interpreting the ecological impacts of Amazonian droughts.

The Amazon rainforest is disproportionately important for global carbon storage and biodiversity. The system couples the atmosphere and land, with moist forest that depends on convection to sustain gross primary productivity and growth. Earth system models that estimate future climate and vegetation show little agreement in Amazon simulations. Here we show that biases in internally generated climate, primarily precipitation, explain most of the uncertainty in Earth system model results; models, empirical data and theory converge when precipitation biases are accounted for. Gross primary productivity, above-ground biomass and tree cover align on a hydrological relationship with a breakpoint at similar to 2000mm annual precipitation, where the system transitions between water and radiation limitation of evapotranspiration. The breakpoint appears to be fairly stable in the future, suggesting resilience of the Amazon to climate change. Changes in precipitation and land use are therefore more likely to govern biomass and vegetation structure in Amazonia.

The future resilience of coast redwoods (Sequoia sempervirens) is now of critical concern due to the detection of a 33% decline in California coastal fog over the 20th century. However, ecosystem-scale measurements of photosynthesis and stomatal conductance are challenging in coast redwood forests, making it difficult to anticipate the impacts of future changes in fog. To address this methodological problem, we explore coastal variations in atmospheric carbonyl sulfide (COS or OCS), which could potentially be used as a tracer of these ecosystem processes. We conducted atmospheric flask campaigns in coast redwood sites, sampling at surface heights and in the canopy (similar to 70 m), at the University of California Landels-Hill Big Creek Reserve and Big Basin State Park. We simulated COS atmosphere-biosphere exchange with a high-resolution 3-D model to interpret these data. Flask measurements indicated a persistent daytime drawdown between the coast and the downwind forest (45 +/- 6 ppt COS) that is consistent with the expected relationship between COS plant uptake, stomatal conductance, and gross primary production. Other sources and sinks of COS that could introduce noise to the COS tracer technique (soils, anthropogenic activity, nocturnal plant uptake, and surface hydrolysis on leaves) are likely to be small relative to daytime COS plant uptake. These results suggest that COS measurements may be useful for making ecosystem-scale estimates of carbon, water, and energy exchange in coast redwood forests.

Sun-induced chlorophyll fluorescence (SiF) is increasingly used as a proxy for vegetation canopy photosynthesis. While ground-based, airborne, and satellite observations have demonstrated a strong linear relationship between SiF and gross primary production (GPP) at seasonal scales, their relationships at high temporal resolution across diurnal to seasonal scales remain unclear. In this study, far-red canopy SiF, GPP, and absorbed photosynthetically active radiation (APAR) were continuously monitored using automated spectral systems and an eddy flux tower over an entire growing season in a rice paddy. At half-hourly resolution, strong linear relationships between SiF and GPP (R-2 = 0.76) and APAR and GPP (R-2 = 0.76) for the whole growing season were observed. We found that relative humidity, diffuse PAR fraction, and growth stage influenced the relationships between SiF and GPP, and APAR and GPP, and incorporating those factors into multiple regression analysis led to improvements up to R-2 = 0.83 and R-2 = 0.88, respectively. Relationships between LUEp ( = GPP/APAR) and LUEf ( = SiF/APAR) were inconsistent at half-hourly and weak at daily resolutions (R 2 = 0.24). Both at diurnal and seasonal time scales with half-hourly resolution, we found considerably stronger linear relationships between SiF and APAR than between either SiF and GPP or APAR and GPP. Overall, our results indicate that for subdiurnal temporal resolution, canopy SiF in the rice paddy is above all a very good proxy for APAR at diurnal and seasonal time scales and that therefore SiF-based GPP estimation needs to take into account relevant environmental information to model LUEp. These findings can help develop mechanistic links between canopy SiF and GPP across multiple temporal scales.

Earth system models (ESMs) rely on the calculation of canopy conductance in land surface models (LSMs) to quantify the partitioning of land surface energy, water, and CO2 fluxes. This is achieved by scaling stomatal conductance, g(w), determined from physiological models developed for leaves. Traditionally, models for g(w) have been semi-empirical, combining physiological functions with empirically determined calibration constants. More recently, optimization theory has been applied to model g(w) in LSMs under the premise that it has a stronger grounding in physiological theory and might ultimately lead to improved predictive accuracy. However, this premise has not been thoroughly tested. Using original field data from contrasting forest systems, we compare a widely used empirical type and a more recently developed optimization-type g(w) model, termed BB and MED, respectively. Overall, we find no difference between the two models when used to simulate g(w) from photosynthesis data, or leaf gas exchange from a coupled photosynthesis-conductance model, or gross primary productivity and evapotranspiration for a FLUXNET tower site with the CLM5 community LSM. Field measurements reveal that the key fitted parameters for BB and MED, g(1B) and g(1M,) exhibit strong species specificity in magnitude and sensitivity to CO2, and CLM5 simulations reveal that failure to include this sensitivity can result in significant overestimates of evapotranspiration for high-CO2 scenarios. Further, we show that g(1B) and g(1M) can be determined from mean c(i)/c(a) (ratio of leaf intercellular to ambient CO2 concentration). Applying this relationship with c(i)/c(a) values derived from a leaf delta C-13 database, we obtain a global distribution of g(1B) and g(1M), and these values correlate significantly with mean annual precipitation. This provides a new methodology for global parameterization of the BB and MED models in LSMs, tied directly to leaf physiology but unconstrained by spatial boundaries separating designated biomes or plant functional types.

Quantifying global terrestrial photosynthesis is essential to understanding the global carbon cycle and the climate system. Remote sensing has played a pivotal role in advancing our understanding of photosynthesis from leaf to global scale; however, substantial uncertainties still exist. In this review, we provide a historical overview of theory, modeling, and observations of photosynthesis across space and time for decadal intervals beginning in the 1950s. Then we identify the key uncertainties in global photosynthesis estimates, including evaluating light intercepted by canopies, biophysical forcings, the structure of light use efficiency models and their parameters, like photosynthetic capacity, and relationships between sun-induced chlorophyll fluorescence and canopy photosynthesis. Finally, we review new opportunities with big data and recently launched or planned satellite missions.

Recent development of sun-induced chlorophyll fluorescence (SIF) technology is stimulating studies to remotely approximate canopy photosynthesis (measured as gross primary production, GPP). While multiple applications have advanced the empirical relationship between GPP and SIF, mechanistic understanding of this relationship is still limited. GPP:SIF relationship, using the standard light use efficiency framework, is determined by absorbed photosynthetically active radiation (APAR) and the relationship between photosynthetic light use efficiency (LUE) and fluorescence yield (SIFy). While previous studies have found that APAR is the dominant factor of the GPP:SIF relationship, the LUE:SIFy relationship remains unclear. For a better understanding of the LUE:SIFy relationship, we deployed a ground-based system (FluoSpec2), with an eddy-covariance flux tower at a soybean field in the Midwestern U.S. during the 2016 growing season to collect SIF and GPP data simultaneously. With the measurements categorized by plant growth stages, light conditions, and time scales, we confirmed that a strong positive GPP:SIF relationship was dominated by an even stronger linear SIF:APAR relationship. By normalizing both GPP and SIF by APAR, we found that under sunny conditions our soybean field exhibited a clear positive SIFy:APAR relationship and a weak negative LUE:SIFy relationship, opposite to the positive LUE:SIFy relationship reported previously in other ecosystems. Our study provides a first continuous SIF record over multiple growth stages for agricultural systems and reveals a distinctive pattern related to the LUE:SIFy relationship compared with previous work. The observed positive relationship of SIFy:APAR at the soybean site provides new insights of the previous understanding on the SIF's physiological implications.

The terrestrial carbon and water cycles are coupled through a multitude of connected processes among soil, roots, leaves, and the atmosphere. The strength and sensitivity of these couplings are not yet well known at the global scale, which contributes to uncertainty in predicting the terrestrial water and carbon budgets. We now have synchronous, global-scale satellite observations of critical terrestrial carbon and water cycle components: solar-induced chlorophyll fluorescence (SIF) and soil moisture. We used these observations within the framework of a global terrestrial biosphere model (Simplified Simple Biosphere Model version 2.0, SSiB2) to investigate carbon-water coupling processes. We updated SSiB2 to include a mechanistic representation of SIF and tested the sensitivity of model parameters to improve the simulation of both SIF and soil moisture with the ultimate objective of improving the first-order terrestrial carbon component, gross primary production. Although several vegetation parameters, such as leaf area index and the green leaf fraction, improved the simulated SIF, and several soil parameters, such as hydraulic conductivity, improved simulated soil moisture, their effects were mainly limited to their respective cycles. One root-mean-square error parameter emerged as the key coupler between the carbon and water cycles: the wilting point. Updates to the wilting point significantly improved the simulations for SIF and gross primary production although substantial mismatches with the satellite data still existed. This study demonstrates the value of synchronous global measurements of the terrestrial carbon and water cycles in improving the understanding of coupled carbon-water cycles.