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The ecological mechanism underlying nocturnal stomatal conductance (gsn) in C3 and C4 plants remains elusive. In this study, we proposed a 'coordinated leaf trait' hypothesis to explain gsn in rice plants. We conducted an open‐field experiment by applying drought, nutrient stress and the combined drought–nutrient stress. We found that gsn was neither strongly reduced by drought nor consistently increased by nutrient stress. With the aforementioned multiple abiotic stressors considered as random effects, gsn exhibited a strong positive correlation with dark respiration (Rn). Notably, gsn primed early morning (5:00–7:00) photosynthesis through faster stomatal response time. This photosynthesis priming effect diminished after mid‐morning (9:00). Leaves were cooled by gsn‐derived transpiration. However, our results clearly suggest that evaporative cooling did not reduce dark respiration cost. Our results indicate that gsn is more closely related to carbon respiration and assimilation than water and nutrient availability, and that dark respiration can explain considerable variation of gsn. Summary Statement: The nocturnal stomatal conductance in rice plants was not strongly affected by drought or macronutrient stress. Instead, it exhibited a strong positive correlation with dark respiration and played a role in priming early morning photosynthesis. [ABSTRACT FROM AUTHOR]

Aim: Leaf traits are central to plant function, and key variables in ecosystem models. However recently published global trait maps, made by applying statistical or machine‐learning techniques to large compilations of trait and environmental data, differ substantially from one another. This paper aims to demonstrate the potential of an alternative approach, based on eco‐evolutionary optimality theory, to yield predictions of spatio‐temporal patterns in leaf traits that can be independently evaluated. Innovation: Global patterns of community‐mean specific leaf area (SLA) and photosynthetic capacity (Vcmax) are predicted from climate via existing optimality models. Then leaf nitrogen per unit area (Narea) and mass (Nmass) are inferred using their (previously derived) empirical relationships to SLA and Vcmax. Trait data are thus reserved for testing model predictions across sites. Temporal trends can also be predicted, as consequences of environmental change, and compared to those inferred from leaf‐level measurements and/or remote‐sensing methods, which are an increasingly important source of information on spatio‐temporal variation in plant traits. Main conclusions: Model predictions evaluated against site‐mean trait data from > 2,000 sites in the Plant Trait database yielded R2 = 73% for SLA, 38% for Nmass and 28% for Narea. Declining species‐level Nmass, and increasing community‐level SLA, have both been recently reported and were both correctly predicted. Leaf‐trait mapping via optimality theory holds promise for macroecological applications, including an improved understanding of community leaf‐trait responses to environmental change. [ABSTRACT FROM AUTHOR]

We test the relationship between canopy photosynthesis and reflected near‐infrared radiation from vegetation across a range of functional (photosynthetic pathway and capacity) and structural conditions (leaf area index, fraction of green and dead leaves, canopy height, reproductive stage, and leaf angle inclination), weather conditions, and years using a network of field sites from across central California. We based our analysis on direct measurements of canopy photosynthesis, with eddy covariance, and measurements of reflected near‐infrared and red radiation from vegetation, with light‐emitting diode sensors. And we interpreted the observed relationships between photosynthesis and reflected near‐infrared radiation using simulations based on the multilayer, biophysical model, CanVeg. Measurements of reflected near‐infrared radiation were highly correlated with measurements of canopy photosynthesis on half‐hourly, daily, seasonal, annual, and decadal time scales across the wide range of function and structure and weather conditions. Slopes of the regression between canopy photosynthesis and reflected near‐infrared radiation were greatest for the fertilized and irrigated C4 corn crop, intermediate for the C3 tules on nutrient‐rich organic soil and nitrogen fixing alfalfa, and least for the native annual grasslands and oak savanna on nutrient‐poor, mineral soils. Reflected near‐infrared radiation from vegetation has several advantages over other remotely sensed vegetation indices that are used to infer canopy photosynthesis; it does not saturate at high leaf area indices, it is insensitive to the presence of dead legacy vegetation, the sensors are inexpensive, and the reflectance signal is strong. Hence, information on reflected near‐infrared radiation from vegetation may have utility in monitoring carbon assimilation in carbon sequestration projects or on microsatellites orbiting Earth for precision agriculture applications. Key Points: Reflected near‐infrared radiation is a strong proxy for canopy photosynthesisThis proxy works for a wide range of ecosystem structure and functionThe method has promise to be used for a variety of applications relating to carbon and water use [ABSTRACT FROM AUTHOR]

Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions. A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and hyperspectral sensor systems were installed in parallel with an existing eddy covariance flux tower, providing spectral information of the vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based hyperspectral and multispectral sensors were compared with NDVI products derived from Sentinel-2A and Moderate-resolution Imaging Spectroradiometer (MODIS). The influence of different spatial and temporal resolutions was assessed. High correlations and similar phenological patterns between in situ and satellite-based NDVI time series demonstrated the reliability of satellite-based phenological metrics. Sentinel-2-derived metrics showed better agreement with in situ measurements than MODIS-derived metrics. Dynamic filtering with the best index slope extraction algorithm was nevertheless beneficial for Sentinel-2 NDVI time series despite the availability of quality information from the atmospheric correction procedure. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SIF) has recently become an important topic in large scale photosynthesis research. This is mainly related to the selectivity of SIF for vegetation on the one hand and the close mechanistic relationships to photosynthesis on the other hand. The latter, however, is already nonlinear at the leaf level and further complicated by the canopy scattering of far-red SIF. This scattering process, which is strongly affected by the structure of the canopy, results in only a fraction of SIF photons emitted at the leaf-level to escape the canopy. The challenge for canopy-level SIF research is therefore to disentangle the contributions related to canopy structure and leaf physiology. While it is widely assumed that the latter has the stronger relationship to photosynthesis, this has not been tested experimentally at the canopy scale so far. We used a recently developed approach to estimate the canopy escape probability of far-red SIF to analyze the contributions of canopy structure and leaf physiology separately. For this, we conducted a combined analysis for three in-situ datasets from a rice paddy, a wheat and a corn field that all had fPAR, PAR, far-red SIF, GPP and canopy reflectance observations covering most of the growing season. Our analysis focused on half-hourly observations both at the level of fluxes involving the absorbed radiation as well as at the level of light use efficiencies, which excludes the radiation component.We found that canopy structure effects dominated the seasonal component of the SIF escape probability and that this was the cause of a positive correlation of the latter to photosynthetic light use efficiency. The estimated total SIF yield of all leaves in the canopy, in contrast, was dominated by diurnal variations and was almost entirely uncorrelated to photosynthetic light use efficiency. Furthermore, our findings confirm the strong relationships between NIRv to both SIF and GPP at short time scales and the site level that were previously only reported at large spatial and long temporal scales. Thus, our findings bridge gaps between spatio-temoral scales and challenge the widely held assumption that the leaf physiology component in SIF provides most of the additional information for photosynthesis estimation that goes beyond APAR. We think that our approach has the potential to unify previous findings into a coherent picture that could enable the community to more easily identify the advantages and limitations of different methods of estimating GPP from remotely sensed observations. [ABSTRACT FROM AUTHOR]

The article explores the impact of the 2020 western U.S. heatwave on ecosystem photosynthesis, using observations from new-generation geostationary satellites. It mentions drylands show widespread midday depression in photosynthesis, shifting peak production towards mornings due to water and heat stress, revealing the potential of geostationary satellites in monitoring diurnal photosynthesis and the need to consider diurnal asymmetry in carbon-climate interactions.