Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands?

Ground solar-induced chlorophyll fluorescence (SIF) is important for the mechanistic understanding of the dynamics of vegetation gross primary production (GPP) at fine spatiotemporal scales. However, eddy covariance (EC) observations generally cover larger footprint areas than ground SIF observations (a bare fiber with nadir), and this footprint mismatch between nadir SIF and GPP could complicate the canopy SIF-GPP relationships. Here, we upscaled nadir SIF observations to EC footprint and investigated the change in SIF-GPP relationships after the upscaling in cropland. We included 13 site-years data in our study, with seven site-years corn, four siteyears soybeans, and two site-years miscanthus, all located in the US Corn Belt. All sites’ crop nadir SIF observations collected from the automated FluoSpec2 system (a hemispheric-nadir system) were upscaled to the GPP footprint-based SIF using vegetation indices (VIs) calculated from high spatiotemporal satellite reflectance data. We found that SIF-GPP relationships were not substantially changed after upscaling nadir SIF to GPP footprint at our crop sites planted with corn, soybean, and miscanthus, with R2 change after the upscaling ranging from -0.007 to 0.051 and root mean square error (RMSE) difference from -0.658 to 0.095 umol m-2 s-1 relative to original nadir SIF-GPP relationships across all the site-years. The variation of the SIF-GPP relationship within each species across different site-years was similar between the original nadir SIF and upscaled SIF. Different VIs, EC footprint models, and satellite data led to marginal differences in the SIF-GPP relationships when upscaling nadir SIF to EC footprint. Our study provided a methodological framework to correct this spatial mismatch between ground nadir SIF and GPP observations for croplands and potentially for other ecosystems. Our results also demonstrated that the spatial mismatch between ground nadir SIF and GPP might not significantly affect the SIF-GPP relationship in cropland that are largely homogeneous

Attributing differences of solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationships between two C4 crops: corn and miscanthus

There remains limited information to characterize the solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationship in C4 cropping systems. The annual C4 crop corn and perennial C4 crop miscanthus differ in phenology, canopy structure and leaf physiology. Investigating the SIF-GPP relationships in these species could deepen our understanding of SIF-GPP relationships within C4 crops. Using in situ canopy SIF and GPP measurements for both species along with leaf-level measurements, we found considerable differences in the SIF-GPP relationships between corn and miscanthus, with a stronger SIF-GPP relationship and higher slope of SIF-GPP observed in corn compared to miscanthus. These differences were mainly caused by leaf physiology. For miscanthus, high non-photochemical quenching (NPQ) under high light, temperature and water vapor deficit (VPD) conditions caused a large decline of fluorescence yield (ΦF), which further led to a SIF midday depression and weakened the SIF-GPP relationship. The larger slope in corn than miscanthus was mainly due to its higher GPP in mid-summer, largely attributed to the higher leaf photosynthesis and less NPQ. Our results demonstrated variation of the SIF-GPP relationship within C4 crops and highlighted the importance of leaf physiology in determining canopy SIF behaviors and SIF-GPP relationships

Remote sensing and environmental assessment of wetland ecological degradation in the Small Sanjiang Plain, Northeast China

IntroductionThe plain marsh wetland ecosystems are sensitive to changes in the natural environment and the intensity of human activities. The Sanjiang Plain is China’s largest area of concentrated marsh wetland, the Small Sanjiang Plain is the most important component of the Sanjiang Plain. However, with the acceleration of the urbanization and development of large-scale agricultural reclamation activities in the Small Sanjiang Plain in Northeast China, the wetland has been seriously damaged. In light of this degradation this study examines the Small Sanjiang Plain.MethodsFrom the four aspects of area, structure, function, and human activities, we try to construct a wetland degradation comprehensive index (WDCI) in cold region with expert scoring methods and analytic hierarchy process (AHP), coupled with network and administrative unit. The objective was to reveal the degradation of wetlands in Northeast China over three decades at a regional scale.ResultsThe results showed that (1) the overall wetland area decreased between 1990 and 2020 by 39.26×103 hm2. Within this period a significant decrease of 336.56×103 hm2 occurred between 1990 and 200 and a significant increase of 214.62×103 hm2 occurred between 2010 and 2020. (2) In terms of structural changes, the fractal dimension (FRAC) has the same trend as the Landscape Fragmentation Index (LFI) with little change. (3) In terms of functional changes, the average above-ground biomass (AGB) increased from 1029.73 kg/hm2 to 1405.38 kg/hm2 between 1990 and 2020 in the study area. (4) In terms of human activities, the average human disturbance was 0.52, 0.46, 0.57 and 0.53 in 1990, 2000, 2010 and 2020, with the highest in 2010. (5) The composite wetland degradation index shows that the most severe wetland degradation was 49.61% in 2010 occurred between 1990 and 2020. (6) Among the severely deteriorated trajectory types in 2010–2020, mild degradation → serious degradation accounted for the largest area of 240.23×103 hm2, and the significant improvement trajectory type in 1990–2000 accounted for the largest area of 238.50×103 hm2.DiscussionIn brief, we conclude that the degradation of the Small Sanjiang Plain wetland was caused mainly by construction, overgrazing, deforestation, and farmland reclamation. This study can also provide new views for monitoring and managing wetland degradation by remote sensing in cold regions

A physiological signal derived from sun-induced chlorophyll fluorescence quantifies crop physiological response to environmental stresses in the U.S. Corn Belt

Sun-induced chlorophyll fluorescence (SIF) measurements have shown unique potential for quantifying plant physiological stress. However, recent investigations found canopy structure and radiation largely control SIF, and physiological relevance of SIF remains yet to be fully understood. This study aims to evaluate whether the SIF-derived physiological signal improves quantification of crop responses to environmental stresses, by analyzing data at three different spatial scales within the U.S. Corn Belt, i.e. experiment plot, field, and regional scales, where ground-based portable, stationary and space-borne hyperspectral sensing systems are used, respectively. We found that, when controlling for variations in incoming radiation and canopy structure, crop SIF signals can be decomposed into non-physiological (i.e. canopy structure and radiation, 60% ∼ 82%) and physiological information (i.e. physiological SIF yield, ΦF, 17% ∼ 31%), which confirms the contribution of physiological variation to SIF. We further evaluated whether ΦF indicated plant responses under high-temperature and high vapor pressure deficit (VPD) stresses. The plot-scale data showed that ΦF responded to the proxy for physiological stress (partial correlation coefficient, r p= 0.40, p\u3c 0.001) while non-physiological signals of SIF did not respond (p\u3e 0.1). The field-scale ΦF data showed water deficit stress from the comparison between irrigated and rainfed fields, and ΦF was positively correlated with canopy-scale stomatal conductance, a reliable indicator of plant physiological condition (correlation coefficient r= 0.60 and 0.56 for an irrigated and rainfed sites, respectively). The regional-scale data showed ΦF was more strongly correlated spatially with air temperature and VPD (r= 0.23 and 0.39) than SIF (r= 0.11 and 0.34) for the U.S. Corn Belt. The lines of evidence suggested that ΦF reflects crop physiological responses to environmental stresses with greater sensitivity to stress factors than SIF, and the stress quantification capability of ΦF is spatially scalable. Utilizing ΦF for physiological investigations will contribute to improve our understanding of vegetation responses to high-temperature and high-VPD stresses

Higher Risk of Stroke Is Correlated With Increased Opportunistic Pathogen Load and Reduced Levels of Butyrate-Producing Bacteria in the Gut

Objective: Gut microbiota is a newly identified risk factor for stroke, and there are no large prospective studies linking the baseline gut microbiome to long-term risk of stroke. We present here the correlation between the gut microbiota and stroke risk in people with no prior stroke history.Methods: A total of 141 participants aged ≥60 years without prior history of stroke were recruited and divided into low-risk, medium-risk, and high-risk groups based on known risk factors and whether they were suffering from chronic diseases. The composition of their gut microbiomes was compared using 16S rRNA gene amplicon next-generation-sequencing and Quantitative Insights into Microbial Ecology (QIIME) analysis. Levels of fecal short-chain fatty acids were measured using gas chromatography.Results: We found that opportunistic pathogens (e.g., Enterobacteriaceae and Veillonellaceae) and lactate-producing bacteria (e.g., Bifidobacterium and Lactobacillus) were enriched, while butyrate-producing bacteria (e.g., Lachnospiraceae and Ruminococcaceae) were depleted, in the high-risk group compared to the low-risk group. Butyrate concentrations were also lower in the fecal samples obtained from the high-risk group than from the low-risk group. The concentrations of other short-chain fatty acids (e.g., acetate, propionate, isobutyrate, isovalerate, and valerate) in the gut were comparable among the three groups.Conclusion: Participants at high risk of stroke were characterized by the enrichment of opportunistic pathogens, low abundance of butyrate-producing bacteria, and reduced concentrations of fecal butyrate. More researches into the gut microbiota as a risk factor in stroke should be carried out in the near future

Ground far-red sun-induced chlorophyll fluorescence and vegetation indices in the US Midwestern agroecosystems

Abstract Sun-induced chlorophyll fluorescence (SIF) provides an opportunity to study terrestrial ecosystem photosynthesis dynamics. However, the current coarse spatiotemporal satellite SIF products are challenging for mechanistic interpretations of SIF signals. Long-term ground SIF and vegetation indices (VIs) are important for satellite SIF validation and mechanistic understanding of the relationship between SIF and photosynthesis when combined with leaf- and canopy-level auxiliary measurements. In this study, we present and analyze a total of 15 site-years of ground far-red SIF (SIF at 760 nm, SIF760) and VIs datasets from soybean, corn, and miscanthus grown in the U.S. Corn Belt from 2016 to 2021. We introduce a comprehensive data processing protocol, including different retrieval methods, calibration coefficient adjustment, and nadir SIF footprint upscaling to match the eddy covariance footprint. This long-term ground far-red SIF and VIs dataset provides important and first-hand data for far-red SIF interpretation and understanding the mechanistic relationship between far-red SIF and canopy photosynthesis across various crop species and environmental conditions

Assessing Different Plant-Centric Water Stress Metrics for Irrigation Efficacy Using Soil-Plant-Atmosphere-Continuum Simulation

Understanding plant water stress (PWS) in the soil-plant-atmosphere-continuum (SPAC) that connects water supply from soil, water demand from atmosphere, and plant self-regulation is a prerequisite for efficient irrigation in response to water scarcity. Currently, PWS can be defined in various ways, for example, based on environmental factors and/or plant-centric metrics. The environment-based metrics usually do not take plants into consideration. Regarding the existing plant-centric metrics, their interconnections and abilities to capture the physical water constraints from both soil water supply and atmospheric water demand are still unclear. This research investigates the theoretical foundations behind different PWS metrics, and assesses their efficacy and potentials for irrigation scheduling. This study first investigated the interconnections among different PWS metrics and the co-regulation of soil moisture and vapor pressure deficit (VPD) on the plant-centric metrics through an advanced process-based model, ecosys. We then use ecosys to test different PWS metrics’ performance in guiding irrigation in terms of water use, maize yield, and economic profits. The case study was conducted at sites across a dramatic rainfall gradient in Nebraska, the largest irrigation state in the United States Corn Belt. The ecosys simulation indicates that canopy water potential and stomatal conductance (gs) are the most effective plant-centric metrics in the SPAC system in indicating PWS. In addition, our findings show that using the plant-centric metrics-based irrigation schemes, which capture the co-regulation of soil moisture and VPD, can improve producers’ economic profits through water savings

Quantifying high‐temperature stress on soybean canopy photosynthesis: The unique role of sun‐induced chlorophyll fluorescence

High temperature and accompanying high vapor pressure deficit often stress plants without causing distinctive changes in plant canopy structure and consequential spectral signatures. Sun-induced chlorophyll fluorescence (SIF), because of its mechanistic link with photosynthesis, may better detect such stress than remote sensing techniques relying on spectral reflectance signatures of canopy structural changes. However, our understanding about physiological mechanisms of SIF and its unique potential for physiological stress detection remains less clear. In this study, we measured SIF at a high-temperature experiment, Temperature Free-Air Controlled Enhancement, to explore the potential of SIF for physiological investigations. The experiment provided a gradient of soybean canopy temperature with 1.5, 3.0, 4.5, and 6.0°C above the ambient canopy temperature in the open field environments. SIF yield, which is normalized by incident radiation and the fraction of absorbed photosynthetically active radiation, showed a high correlation with photosynthetic light use efficiency (r = 0.89) and captured dynamic plant responses to high-temperature conditions. SIF yield was affected by canopy structural and plant physiological changes associated with high-temperature stress (partial correlation r = 0.60 and −0.23). Near-infrared reflectance of vegetation, only affected by canopy structural changes, was used to minimize the canopy structural impact on SIF yield and to retrieve physiological SIF yield (ΦF) signals. ΦF further excludes the canopy structural impact than SIF yield and indicates plant physiological variability, and we found that ΦF outperformed SIF yield in responding to physiological stress (r = −0.37). Our findings highlight that ΦF sensitively responded to the physiological downregulation of soybean gross primary productivity under high temperature. ΦF, if reliably derived from satellite SIF, can support monitoring regional crop growth and different ecosystems\u27 vegetation productivity under environmental stress and climate change

Satellite footprint data from OCO-2 and TROPOMI reveal significant spatio-temporal and inter-vegetation type variabilities of solar-induced fluorescence yield in the U.S. Midwest

Solar-induced chlorophyll fluorescence (SIF) measured from space has been increasingly used to quantify plant photosynthesis at regional and global scales. Apparent canopy SIF yield (SIF_(yield apparent)), determined by fluorescence yield (Φ_F) and escaping ratio (f^(esc)), together with absorbed photosynthetically active radiation (APAR), is crucial in driving spatio-temporal variability of SIF. While strong linkages between SIF_(yield apparent) and plant physiological responses and canopy structure have been suggested, spatio-temporal variability of SIF_(yield apparent) at regional scale remains largely unclear, which limits our understanding of the spatio-temporal variability of SIF and its relationship with photosynthesis. In this study, we utilized recent SIF data with high spatial resolution from two satellite instruments, OCO-2 and TROPOMI, together with multiple other datasets. We estimated SIF_(yield apparent) across space, time, and different vegetation types in the U.S. Midwest during crop growing season (May to September) from 2015 to 2018. We found that SIF_(yield apparent) of croplands was larger than non-croplands during peak season (July–August). However, SIFyield apparent between corn (C4 crop) and soybean (C3 crop) did not show a significant difference. SIF_(yield apparent) of corn, soybean, forest, and grass/pasture show clear seasonal and spatial patterns. The spatial variability of precipitation during the growing season could explain the overall spatial pattern of SIF_(yield apparent). Further analysis by decomposing SIF_(yield apparent) into Φ_F and f^(esc) using near-infrared reflectance of vegetation (NIRV) suggests that f^(esc) may be the major driver of the observed variability of SIF_(yield apparent)

Satellite footprint data from OCO-2 and TROPOMI reveal significant spatio-temporal and inter-vegetation type variabilities of solar-induced fluorescence yield in the U.S. Midwest

Solar-induced chlorophyll fluorescence (SIF) measured from space has been increasingly used to quantify plant photosynthesis at regional and global scales. Apparent canopy SIF yield (SIF_(yield apparent)), determined by fluorescence yield (Φ_F) and escaping ratio (f^(esc)), together with absorbed photosynthetically active radiation (APAR), is crucial in driving spatio-temporal variability of SIF. While strong linkages between SIF_(yield apparent) and plant physiological responses and canopy structure have been suggested, spatio-temporal variability of SIF_(yield apparent) at regional scale remains largely unclear, which limits our understanding of the spatio-temporal variability of SIF and its relationship with photosynthesis. In this study, we utilized recent SIF data with high spatial resolution from two satellite instruments, OCO-2 and TROPOMI, together with multiple other datasets. We estimated SIF_(yield apparent) across space, time, and different vegetation types in the U.S. Midwest during crop growing season (May to September) from 2015 to 2018. We found that SIF_(yield apparent) of croplands was larger than non-croplands during peak season (July–August). However, SIFyield apparent between corn (C4 crop) and soybean (C3 crop) did not show a significant difference. SIF_(yield apparent) of corn, soybean, forest, and grass/pasture show clear seasonal and spatial patterns. The spatial variability of precipitation during the growing season could explain the overall spatial pattern of SIF_(yield apparent). Further analysis by decomposing SIF_(yield apparent) into Φ_F and f^(esc) using near-infrared reflectance of vegetation (NIRV) suggests that f^(esc) may be the major driver of the observed variability of SIF_(yield apparent)