From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I-Harnessing theory

A practical approach for extracting the photosystem II (PSII) contribution to near-infrared solar-induced chlorophyll fluorescence

Recent studies have indicated a good potential for using solar-induced chlorophyll fluorescence (SIF) to estimate photosynthetic CO2 assimilation. SIF can be emitted by both Photosystem I (PSI) and Photosystem II (PSII), but it is the SIF signals from PSII which are related to photosynthetic carbon fixation. However, since top-of-canopy SIF observations (SIFTOC) always contain contributions from both photosystems, to mechanistically estimate gross primary productivity (GPP) from SIF, it is essential to extract PSII SIF from SIFTOC. Based on the differences in the relative contribution of PSII across different wavelengths, we propose a practical approach for extracting PSII contribution to SIFTOC at the near-infrared (NIR) band (fPSII_760) using measurements of SIFTOC in the red and NIR spectral regions. A leaf-scale concurrent instrument was developed to assess the response of fPSII_760 under varying environments. For winter-wheat leaves, as light intensity increased from 0 to 400 μmol m-2 s-1, fPSII_760 rose from 0.6 to 0.8; with further increase in light intensity to 1800 μmol m-2 s-1, fPSII_760 consistently decreased to 0.65. There was a slight decreasing trend in fPSII_760 with rising temperatures, with values dropping from 0.65 at 15 °C to 0.61 at 40 °C. We found that variations in fPSII_760 are due to changes in the fluorescence yield of PSII, with the two having a positively proportional relationship. We also estimated canopy-scale fPSII_760 for a winter-wheat study site: fPSII_760 varied from 0.61 to 0.83, with a mean value of 0.78 during the peak growing season. A comparison with eddy covariance-derived GPP reveals that GPP estimated with dynamic fPSII_760 was more accurate than that calculated using fixed fPSII_760, with R2 increasing from 0.6 to 0.84. This study contributes to a deeper understanding of the link between SIF and photosynthetic CO2 assimilation, paving the way for more effective use of SIF to estimate GPP.

Modern phenomics to empower holistic crop science, agronomy, and breeding research

Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.

Relationship between self-psychological adjustment and post-traumatic growth in patients with lung cancer undergoing chemotherapy: a cross-sectional study

OBJECTIVES

This study aimed to determine the potential profiles of self-psychological adjustment in patients with lung cancer undergoing chemotherapy, including sense of coherence (SOC) and positive cognitive emotion regulation (PCER). The relationship between these profiles with post-traumatic growth (PTG) and the relevant factors of self-psychological adjustment in different profiles was analysed.

DESIGN

Cross-sectional study.

SETTING

Patients with lung cancer undergoing chemotherapy in China.

PARTICIPANTS

A total of 330 patients with lung cancer undergoing chemotherapy were recruited out of which 321 completed the questionnaires effectively.

METHODS

Latent profile analysis was used to identify self-psychological adjustment classes based on the two subscales of the Sense of Coherence Scale and Cognitive Emotion Regulation Questionnaire. One-way analysis of variance and multinomial logistic regression were performed to examine the subgroup association with characteristics and PTG.

RESULTS

Three latent profiles of self-psychological adjustment were identified: low level (54.5%), high SOC-low PCER (15.6%) and high PCER (29.9%). The results of univariate analysis showed a significant difference in PTG scores among different self-psychological adjustment subgroups (F=11.55, p<0.001). Patients in the high-PCER group were more likely living in urban areas (OR=2.41, 95% CI 1.17 to 4.97, p=0.02), and time since cancer diagnosis was ≥6 months and <1 year (OR=3.54, 95% CI 1.3 to 9.64, p<0.001).

CONCLUSION

This study revealed that most patients with lung cancer undergoing chemotherapy belonged to the low-level group. Three profiles are associated with PTG. There were differences in characteristics between patients treated with chemotherapy for lung cancer in the high-PCER and low-PCER groups. Thus, these profiles provide useful information for developing targeted individualised interventions based on demographic characteristics that would assist PTG in patients with lung cancer undergoing chemotherapy.

Alternative method for determining leaf CO2 assimilation without gas exchange measurements: Performance, comparison and sensitivity analysis

We present an alternative method to determine leaf CO2 assimilation rate (An ), eliminating the need for gas exchange measurements in proximal and remote sensing. This method combines the Farquhar-von Caemmerer-Berry photosynthesis model with mechanistic light reaction (MLR) theory and leaf energy balance (EB) analysis. The MLR theory estimates the actual electron transport rate (J) by leveraging chlorophyll fluorescence via pulse amplitude-modulated fluorometry for proximal sensing or sun-induced chlorophyll fluorescence measurements for remote sensing, along with spectral reflectance. The EB equation is used to directly estimate stomatal conductance from leaf temperature. In wheat and soybean, the MLR-EB model successfully estimated An variations, including midday depression, under various environmental and phenological conditions. Sensitivity analysis revealed that the leaf boundary layer conductance (gb ) played an equal, if not more, crucial role compared to the variables for J. This was primarily caused by the indirect influence of gb through the EB equation rather than its direct impact on convective CO2 exchange on the leaf. Although the MLR-EB model requires an accurate estimation of gb , it can potentially reduce uncertainties and enhance applicability in photosynthesis assessment when gas exchange measurements are unavailable.

Wildfire-induced increases in photosynthesis in boreal forest ecosystems of North America

Observations of the annual cycle of atmospheric CO2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape-level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat-derived land cover time series to characterize plant functional types (PFTs), and solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) as a proxy for photosynthesis. We analyze these datasets to characterize post-fire changes in plant succession and photosynthetic activity using a space-for-time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid-succession yield enhancements in SIF by 8-40% during June and July for 2- to 59-year stands relative to pre-fire controls. From the analysis of post-fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long-term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early- and mid-summer. Second, fire appears to facilitate a long-term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO2 exchange without a close coupling to long-term increases in carbon storage.

The weekly cycle of photosynthesis in Europe reveals the negative impact of particulate pollution on ecosystem productivity

Aerosols can affect photosynthesis through radiative perturbations such as scattering and absorbing solar radiation. This biophysical impact has been widely studied using field measurements, but the sign and magnitude at continental scales remain uncertain. Solar-induced fluorescence (SIF), emitted by chlorophyll, strongly correlates with photosynthesis. With recent advancements in Earth observation satellites, we leverage SIF observations from the Tropospheric Monitoring Instrument (TROPOMI) with unprecedented spatial resolution and near-daily global coverage, to investigate the impact of aerosols on photosynthesis. Our analysis reveals that on weekends when there is more plant-available sunlight due to less particulate pollution, 64% of regions across Europe show increased SIF, indicating more photosynthesis. Moreover, we find a widespread negative relationship between SIF and aerosol loading across Europe. This suggests the possible reduction in photosynthesis as aerosol levels increase, particularly in ecosystems limited by light availability. By considering two plausible scenarios of improved air quality-reducing aerosol levels to the weekly minimum 3-d values and levels observed during the COVID-19 period-we estimate a potential of 41 to 50 Mt net additional annual CO2 uptake by terrestrial ecosystems in Europe. This work assesses human impacts on photosynthesis via aerosol pollution at continental scales using satellite observations. Our results highlight i) the use of spatiotemporal variations in satellite SIF to estimate the human impacts on photosynthesis and ii) the potential of reducing particulate pollution to enhance ecosystem productivity.

Determinants of photochemical characteristics of the photosynthetic electron transport chain of maize

Introduction

The photosynthetic electron transport chain (ETC) is the bridge that links energy harvesting during the photophysical reactions at one end and energy consumption during the biochemical reactions at the other. Its functioning is thus fundamental for the proper balance between energy supply and demand in photosynthesis. Currently, there is a lack of understanding regarding how the structural properties of the ETC are affected by nutrient availability and plant developmental stages, which is a major roadblock to comprehensive modeling of photosynthesis.

Methods

Redox parameters reflect the structural controls of ETC on the photochemical reactions and electron transport. We conducted joint measurements of chlorophyll fluorescence (ChlF) and gas exchange under systematically varying environmental conditions and growth stages of maize and sampled foliar nutrient contents. We utilized the recently developed steady-state photochemical model to infer redox parameters of electron transport from these measurements.

Results and discussion

We found that the inferred values of these photochemical redox parameters varied with leaf macronutrient content. These variations may be caused either directly by these nutrients being components of protein complexes on the ETC or indirectly by their impacts on the structural integrity of the thylakoid and feedback from the biochemical reactions. Also, the redox parameters varied with plant morphology and developmental stage, reflecting seasonal changes in the structural properties of the ETC. Our findings will facilitate the parameterization and simulation of complete models of photosynthesis.

Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications

Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.

From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II-Harnessing data

Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in "data desert" regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.