Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

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.

New Approaches to Plant Pathogen Detection and Disease Diagnosis

Detecting plant pathogens and diagnosing diseases are critical components of successful pest management. These key areas have undergone significant advancements driven by breakthroughs in molecular biology and remote sensing technologies within the realm of precision agriculture. Notably, nucleic acid amplification techniques, with recent emphasis on sequencing procedures, particularly next-generation sequencing, have enabled improved DNA or RNA amplification detection protocols that now enable previously unthinkable strategies aimed at dissecting plant microbiota, including the disease-causing components. Simultaneously, the domain of remote sensing has seen the emergence of cutting-edge imaging sensor technologies and the integration of powerful computational tools, such as machine learning. These innovations enable spectral analysis of foliar symptoms and specific pathogen-induced alterations, making imaging spectroscopy and thermal imaging fundamental tools for large-scale disease surveillance and monitoring. These technologies contribute significantly to understanding the temporal and spatial dynamics of plant diseases.

What to Choose for Estimating Leaf Water Status-Spectral Reflectance or In vivo Chlorophyll Fluorescence?

In the context of global climate change and the increasing need to study plant response to drought, there is a demand for easily, rapidly, and remotely measurable parameters that sensitively reflect leaf water status. Parameters with this potential include those derived from leaf spectral reflectance (R) and chlorophyll fluorescence. As each of these methods probes completely different leaf characteristics, their sensitivity to water loss may differ in different plant species and/or under different circumstances, making it difficult to choose the most appropriate method for estimating water status in a given situation. Here, we present a simple comparative analysis to facilitate this choice for leaf-level measurements. Using desiccation of tobacco (Nicotiana tabacum L. cv. Samsun) and barley (Hordeum vulgare L. cv. Bojos) leaves as a model case, we measured parameters of spectral R and chlorophyll fluorescence and then evaluated and compared their applicability by means of introduced coefficients (coefficient of reliability, sensitivity, and inaccuracy). This comparison showed that, in our case, chlorophyll fluorescence was more reliable and universal than spectral R. Nevertheless, it is most appropriate to use both methods simultaneously, as the specific ranking of their parameters according to the coefficient of reliability may indicate a specific scenario of changes in desiccating leaves.

CombiningNitellopsis obtusaautofluorescence intensity and F680/F750 ratio to discriminate responses to environmental stressors

Detection of autofluorescence parameters is a useful approach to gain insight into the physiological state of plants and algae, but the effect of reabsorption hinders unambiguous interpretation ofin vivodata. The exceptional morphological features ofNitellopsis obtusamade it possible to measure autofluorescence spectra along single internodal cells and estimate relative changes in autofluorescence intensity in selected spectral regions at room temperatures, avoiding the problems associated with thick or optically dense samples. The response of algal cells to controlled white light and DCMU herbicide was analyzed by monitoring changes in peak FL intensity at 680 nm and in F680/F750 ratio. Determining the association between the selected spectral FL parameters revealed an exponential relationship, which provides a quantitative description of photoinduced changes. The ability to discern the effect of DCMU not only in the autofluorescence spectra of dark-adapted cells, but also in the case of light-adapted cells, and even after certain doses of excess light, suggests that the proposed autofluorescence analysis ofN. obtusamay be useful for detecting external stressors in the field.

Response of solar-induced chlorophyll fluorescence-based spatial and temporal evolution of vegetation in Xinjiang to multiscale drought

Climate change and human activities have increased droughts, especially overgrazing and deforestation, which seriously threaten the balance of terrestrial ecosystems. The ecological carrying capacity and vegetation cover in the arid zone of Xinjiang, China, are generally low, necessitating research on vegetation response to drought in such arid regions. In this study, we analyzed the spatial and temporal characteristics of drought in Xinjiang from 2001 to 2020 and revealed the response mechanism of SIF to multi-timescale drought in different vegetation types using standardized precipitation evapotranspiration index (SPEI), solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) data. We employed trend analysis, standardized anomaly index (SAI), Pearson correlation, and trend prediction techniques. Our investigation focused on the correlations between GOSIF (a new SIF product based on the Global Orbital Carbon Observatory-2), NDVI, and EVI with SPEI12 for different vegetation types over the past two decades. Additionally, we examined the sensitivities of vegetation GOSIF to various scales of SPEI in a typical drought year and predicted future drought trends in Xinjiang. The results revealed that the spatial distribution characteristics of GOSIF, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) were consistent, with mean correlations with SPEI at 0.197, 0.156, and 0.128, respectively. GOSIF exhibited the strongest correlation with SPEI, reflecting the impact of drought stress on vegetation photosynthesis. Therefore, GOSIF proves advantageous for drought monitoring purposes. Most vegetation types showed a robust response of GOSIF to SPEI at a 9-month scale during a typical drought year, with grassland GOSIF being particularly sensitive to drought. Our trend predictions indicate a decreasing trend in GOSIF vegetation in Xinjiang, coupled with an increasing trend in drought. This study found that compared with that of the traditional greenness vegetation index, GOSIF has obvious advantages in monitoring drought in the arid zone of Xinjiang. Furthermore, it makes up for the lack of research on the mechanism of vegetation GOSIF response to drought on multiple timescales in the arid zone. These results provide strong theoretical support for investigating the monitoring, assessment, and prediction of vegetation response to drought in Xinjiang, which is vital for comprehending the mechanisms of carbon and water cycles in terrestrial ecosystems.

Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS

To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). The results showed that kernel NDVI (kNDVI) and near-infrared reflectance (NIRv) slightly outperform normalized difference vegetation index (NDVI) in yield prediction. And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). The model combining LSTM algorithm and NIRv had the best prediction effect and relatively stable performance in single year. The optimal model was then used to generate 30 m resolution wheat yield maps in the past 20 years, with higher overall accuracy. In addition, we can define the optimum prediction time at April, which can consider simultaneously the performance and lead time. In general, we expect that this prediction model can provide important information to understand and ensure food security.

Hyperspectral Indices Developed from Fractional-Order Derivative Spectra Improved Estimation of Leaf Chlorophyll Fluorescence Parameters

Chlorophyll fluorescence (ChlF) parameters offer valuable insights into quantifying energy transfer and allocation at the photosystem level. However, tracking their variation based on reflectance spectral information remains challenging for large-scale remote sensing applications and ecological modeling. Spectral preprocessing methods, such as fractional-order derivatives (FODs), have been demonstrated to have advantages in highlighting spectral features. In this study, we developed and assessed the ability of novel spectral indices derived from FOD spectra and other spectral transformations to retrieve the ChlF parameters of various species and leaf groups. The results obtained showed that the empirical spectral indices were of low reliability in estimating the ChlF parameters. In contrast, the indices developed from low-order FOD spectra demonstrated a significant improvement in estimation. Furthermore, the incorporation of species specificity enhanced the tracking of the non-photochemical quenching (NPQ) of sunlit leaves (R2 = 0.61, r = 0.79, RMSE = 0.15, MAE = 0.13), the fraction of PSII open centers (qL) of shaded leaves (R2 = 0.50, r = 0.71, RMSE = 0.09, MAE = 0.08), and the fluorescence quantum yield (ΦF) of shaded leaves (R2 = 0.71, r = 0.85, RMSE = 0.002, MAE = 0.001). Our study demonstrates the potential of FOD spectra in capturing variations in ChlF parameters. Nevertheless, given the complexity and sensitivity of ChlF parameters, it is prudent to exercise caution when utilizing spectral indices for tracking them.

Differences in drought characteristics, progression, and recession across ecosystem types in the pantropical region of the Lancang-Mekong River Basin

Exploring the development and impacts of drought across different ecosystems can offer new insights for mitigating the adverse effects of drought events. Using the pantropical Lancang-Mekong River Basin as the study region, we investigated the agricultural, ecological, and hydrological drought characteristics and explored their drought progression and recession rates across four vegetation ecosystem types: tropical forests, subtropical forests, shrubs, and crops. We utilized newly developed drought indices based on the ERA5-Land reanalysis dataset, GOSIF chlorophyll fluorescence data, and modified Moderate Resolution Imaging Spectroradiometer (MODIS) land cover data. The results showed that agricultural and hydrological droughts exhibited increasing trends from 2001 to 2021, whereas ecological drought displayed a decreasing trend over the same period. The cropland region experienced the fewest drought events, shortest drought durations, slowest progression rates, and lowest recession rates. By contrast, the two evergreen, broadleaf forest ecosystems (subtropical and tropical forests) experienced the highest number of drought events and fastest progression and recession rates. The findings suggest a trade-off relationship between vegetation resistance and recovery, where faster drought onset is associated with faster drought recession for ecological drought. Given the more severe challenges posed by agricultural and hydrological droughts, the riparian countries in the Lancang-Mekong River Basin should adopt proactive financial and management measures to mitigate the adverse impacts of these drought types. The insights gained from this study can inform the development of targeted strategies for drought monitoring, preparedness, and response across diverse ecosystems.

Amazon forest biogeography predicts resilience and vulnerability to drought

Amazonia contains the most extensive tropical forests on Earth, but Amazon carbon sinks of atmospheric CO2 are declining, as deforestation and climate-change-associated droughts1-4 threaten to push these forests past a tipping point towards collapse5-8. Forests exhibit complex drought responses, indicating both resilience (photosynthetic greening) and vulnerability (browning and tree mortality), that are difficult to explain by climate variation alone9-17. Here we combine remotely sensed photosynthetic indices with ground-measured tree demography to identify mechanisms underlying drought resilience/vulnerability in different intact forest ecotopes18,19 (defined by water-table depth, soil fertility and texture, and vegetation characteristics). In higher-fertility southern Amazonia, drought response was structured by water-table depth, with resilient greening in shallow-water-table forests (where greater water availability heightened response to excess sunlight), contrasting with vulnerability (browning and excess tree mortality) over deeper water tables. Notably, the resilience of shallow-water-table forest weakened as drought lengthened. By contrast, lower-fertility northern Amazonia, with slower-growing but hardier trees (or, alternatively, tall forests, with deep-rooted water access), supported more-drought-resilient forests independent of water-table depth. This functional biogeography of drought response provides a framework for conservation decisions and improved predictions of heterogeneous forest responses to future climate changes, warning that Amazonia's most productive forests are also at greatest risk, and that longer/more frequent droughts are undermining multiple ecohydrological strategies and capacities for Amazon forest resilience.

Temporal and spatial evolution simulation and attribution analysis of vegetation photosynthesis over the past 21 years based on satellite SIF data: a case study from Asia

Photosynthesis in vegetation is one of the key processes in maintaining regional ecological balance and climate stability, and it is of significant importance for understanding the health of regional ecosystems and addressing climate change. Based on 2001-2021 Global OCO-2 Solar-Induced Fluorescence (GOSIF) dataset, this study analyzed spatiotemporal variations in Asian vegetation photosynthesis and its response to climate and human activities. Results show the following: (1) From 2001 to 2021, the overall photosynthetic activity of vegetation in the Asian region has shown an upward trend, exhibiting a stable distribution pattern with higher values in the eastern and southern regions and lower values in the central, western, and northern regions. In specific regions such as the Turgen Plateau in northwestern Kazakhstan, Cambodia, Laos, and northeastern Syria, photosynthesis significantly declined. (2) Meteorological factors influencing photosynthesis exhibit differences based on latitude and vertical zones. In low-latitude regions, temperature is the primary driver, while in mid-latitude areas, solar radiation and precipitation are crucial. High-latitude regions are primarily influenced by temperature, and high-altitude areas depend on precipitation and solar radiation. (3) Human activities (56.44%) have a slightly greater impact on the dynamics of Asian vegetation photosynthesis compared to climate change (43.56%). This research deepens our comprehension of the mechanisms behind the fluctuations in Asian vegetation photosynthesis, offering valuable perspectives for initiatives in environmental conservation, sustainability, and climate research.

Effects of chlorophyll fluorescence on environment and gross primary productivity of moso bamboo during the leaf-expansion stage

Chlorophyll fluorescence is the long-wave light released by the residual energy absorbed by vegetation after photosynthesis and dissipation, which can directly and non-destructively reflect the photosynthetic state of plants from the perspective of the mechanism of photosynthetic process. Moso bamboo has a substantial carbon sequestration ability, and leaf-expansion stage is an important phenological period for carbon sequestration. Gross primary production (GPP) is a key parameter reflecting vegetation carbon sequestration process. However, the ability of chlorophyll fluorescence in moso bamboo to explain GPP changes is unclear. The research area of this study is located in the bamboo forest near the flux station of Anji County, Zhejiang Province, where an observation tower is built to monitor the carbon flux and meteorological change of bamboo forest. The chlorophyll fluorescence physiological parameters (Fp) and fluorescence yield (Fy) indices were measured and calculated for the leaves of newborn moso bamboo (I Du bamboo) and the old leaves of 4- to 5-year-old moso bamboo (Ⅲ Du bamboo) during the leaf-expansion stage. The chlorophyll fluorescence in response to the environment and its effect on carbon flux were analyzed. The results showed that: Fv/Fm, Y(II) and α of Ⅰ Du bamboo gradually increased, while Ⅲ Du bamboo gradually decreased, and FYint and FY687/FY738 of Ⅰ Du bamboo were higher than those of Ⅲ Du bamboo; moso bamboo was sensitive to changes in air temperature(Ta), relative humidity(RH), water vapor pressure(E), soil temperature(ST) and soil water content (SWC), the Fy indices of the upper, middle and lower layers were significantly correlated with Ta, E and ST; single or multiple vegetation indices were able to estimate the fluorescence yield indices well (all with R2 greater than 0.77); chlorophyll fluorescence (Fp and Fy indices) of Ⅰ Du bamboo and Ⅲ Du bamboo could explain 74.4% and 72.7% of the GPP variation, respectively; chlorophyll fluorescence and normalized differential vegetation index of the canopy (NDVIc) could estimate GPP well using random forest (Ⅰ Du bamboo: r = 0.929, RMSE = 0.069 g C·m-2; Ⅲ Du bamboo: r = 0.899, RMSE = 0.134 g C·m-2). The results of this study show that chlorophyll fluorescence can provide a basis for judging the response of moso bamboo to environmental changes and can well explain GPP. This study has important scientific significance for evaluating the potential mechanisms of growth, stress feedback and photosynthetic carbon sequestration of bamboo.

Dynamics of solar-induced chlorophyll fluorescence (SIF) and its response to meteorological drought in the Yellow River Basin

Solar-induced chlorophyll fluorescence (SIF) has been used since its discovery to characterize vegetation photosynthesis and is an effective tool for monitoring vegetation dynamics. Its response to meteorological drought enhances our comprehension of the ecological consequences and adaptive mechanisms of plants facing water scarcity, informing more efficient resource management and efforts in mitigating climate change. This study investigates the spatial and temporal patterns of SIF and examines how vegetation SIF in the Yellow River Basin (YRB) responds to meteorological drought. The findings reveal a gradual southeast-to-northwest decline in SIF across the Yellow River Basin, with an overall increase-from 0.1083 W m-2μm-1sr-1 in 2001 to 0.1468 W m-2μm-1sr-1 in 2019. Approximately 96% of the YRB manifests an upward SIF trend, with 75% of these areas reaching statistical significance. The Standardized Precipitation Evapotranspiration Index (SPEI) at a time scale of 4 months (The SPEI-4), based on the Liang-Kleeman information flow method, is identified as the most suitable drought index, adeptly characterizing the causal relationship influencing SIF variations. As drought intensified, the SPEI-4 index markedly deviated from the baseline, resulting in a decrease in SIF values to their lowest value; subsequently, as drought lessened, it gravitated towards the baseline, and SIF values began to gradually increase, eventually recovering to near their annual maximum. The key finding is that the variability of SIF with SPEI is relatively pronounced in the early growing season, with forests demonstrating superior resilience compared to grasslands and croplands. The responsiveness of vegetation SIF to SPEI can facilitate the establishment of effective drought early warning systems and promote the rational planning of water resources, thereby mitigating the impacts of climate change.

Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications

Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth's status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth's surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements.

Global eight drought types: Spatio-temporal characteristics and vegetation response

The traditional classification of drought events into seasonal and flash types oversimplified the complexity and variability of global drought phenomena, limiting a deeper understanding of drought characteristics and their impacts on vegetation. To address this issue, soil moisture percentile methods and the Soil Moisture Anomaly Percentage Index (SMAPI) were employed to create time series for flash drought (FD) and seasonal drought (SD) events globally from 1981 to 2020. A novel categorization framework was proposed to subdivide the two basic drought categories into eight distinct drought types using a set relationship identification method. The results showed fluctuating trends in the frequencies of Independent FD and Inclusion FD, which declined rapidly after 2011 at rates of 0.05 and 0.04 times/year, respectively. Independent FD frequency was highest in humid areas and decreased with increasing aridity. The spatial distributions of Inclusion FD and SD were similar, with both frequencies highest in extremely arid areas and decreasing with increasing humidity. The frequency of Independent SD, which peaked in semi-arid areas, increased significantly after 2011 at a rate of 0.01 times/year. The occurrence of FD evolving into SD or emerging at the end of SD was rare, with a global average of 0.46 events/decade and little spatial variation. Between 1981 and 2020, FD showed a U-shaped trend in drought duration, while SD showed no clear pattern. The duration of FD showed little difference across arid and humid zones, but the duration of SD decreased significantly with increasing humidity. Vegetation responses to drought varied, with arid regions showing longer response time compared to humid regions. A positive correlation between temperature and solar-induced chlorophyll fluorescence (SIF) during droughts was observed, while precipitation generally showed a negative correlation with SIF. Radiation had a minimal effect on SIF during droughts. The study offered a comprehensive categorization of drought events, enhancing our understanding of their spatiotemporal characteristics and vegetation responses on a global scale.

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

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.

Air quality improvements can strengthen China's food security

Air pollution exerts crucial influence on crop yields and impacts regional and global food supplies. Here we employ a statistical model using satellite-based observations and flexible functional forms to analyse the synergistic effects of reductions in ozone and aerosols on China's food security. The model consistently shows that ozone is detrimental to crops, whereas aerosol has variable effects. China's maize, rice and wheat yields are projected to increase by 7.84%, 4.10% and 3.43%, respectively, upon reaching two air quality targets (60 μg m-3 for peak-season ozone and 35 μg m-3 for annual fine particulate matter). Average calories produced from these crops would surge by 4.51%, potentially allowing China to attain grain self-sufficiency 2 years earlier than previously estimated. These results show that ozone pollution control should be a high priority to increase staple crop edible calories, and future stringent air pollution regulations would enhance China's food security.

Azadirachta indica (AI)leaf extract coated ZnO-AInanocore-shell particles for enhanced antibacterial activity against methicillin-resistantStaphylococcus aureus(MRSA)

With the rise in microbial resistance to traditional antibiotics and disinfectants, there is a pressing need for the development of novel and effective antibacterial agents. Two major approaches being adopted worldwide to overcome antimicrobial resistance are the use of plant leaf extracts and metallic nanoparticles (NPs). However, there are no reports on the antibacterial potential of NPs coated with plant extracts, which may lead to novel ways of treating infections. This study presents an innovative approach to engineer antibacterial NPs by leveraging the inherent antibacterial properties of zinc oxide NPs (ZnO NPs) in combination withAzadirachta indica(AI) leaf extract, resulting in enhanced antibacterial efficacy. ZnO NPs were synthesised by the precipitation method and subsequently coated withAIleaf extract to produce ZnO-AInanocore-shell structures. The structural and morphological characteristics of the bare and leaf extract coated ZnO NPs were analysed by x-ray diffraction and field emission scanning electron microscopy, respectively. The presence of anAIleaf extract coating on ZnO NPs and subsequent formation of ZnO-AInanocore-shell structures was verified through Fourier transform infrared spectroscopy and photoluminescence techniques. The antibacterial efficacy of both ZnO NPs and ZnO-AInanocore-shell particles was evaluated against methicillin-resistantStaphylococcus aureususing a zone of inhibition assay. The results showed an NP concentration-dependent increase in the diameter of the inhibition zone, with ZnO-AInanocore-shell particles exhibiting superior antibacterial properties, owing to the combined effect of ZnO NPs and the poly phenols present inAIleaf extract. These findings suggest that ZnO-AInanocore-shell structures hold promise for the development of novel antibacterial creams and hydrogels for various biomedical applications.

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.

Solar-induced chlorophyll fluorescence captures the effects of elevated ozone on canopy structure and acceleration of senescence in soybean

Solar-induced chlorophyll fluorescence (SIF) provides an opportunity to rapidly and non-destructively investigate how plants respond to stress. Here, we explored the potential of SIF to detect the effects of elevated O3 on soybean in the field where soybean was subjected to ambient and elevated O3 throughout the growing season in 2021. Exposure to elevated O3 resulted in a significant decrease in canopy SIF at 760 nm (SIF760), with a larger decrease in the late growing season (36%) compared with the middle growing season (13%). Elevated O3 significantly decreased the fraction of absorbed photosynthetically active radiation by 8-15% in the middle growing season and by 35% in the late growing stage. SIF760 escape ratio (fesc) was significantly increased under elevated O3 by 5-12% in the late growth stage due to a decrease of leaf chlorophyll content and leaf area index. Fluorescence yield of the canopy was reduced by 5-11% in the late growing season depending on the fesc estimation method, during which leaf maximum carboxylation rate and maximum electron transport were significantly reduced by 29% and 20% under elevated O3. These results demonstrated that SIF could capture the elevated O3 effect on canopy structure and acceleration of senescence in soybean and provide empirical support for using SIF for soybean stress detection and phenotyping.

The role of satellite remote sensing in mitigating and adapting to global climate change

Climate change has critical adverse impacts on human society and poses severe challenges to global sustainable development. Information on essential climate variables (ECVs) that reflects the substantial changes that have occurred on Earth is critical for assessing the influence of climate change. Satellite remote sensing (SRS) technology has led to a new era of observations and provides multiscale information on ECVs that is independent of in situ measurements and model simulations. This enhances our understanding of climate change from space and supports policy-making in combating climate change. However, it remains challenging to remotely retrieve ECVs due to the complexity of the climate system. We provide an update on the studies on the role of SRS in climate change research, specifically in monitoring and quantifying ECVs in the atmosphere (greenhouse gases, clouds and aerosols), ocean (sea surface temperature, sea ice melt and sea level rise, ocean currents and mesoscale eddies, phytoplankton and ocean productivity), and terrestrial ecosystems (land use and land cover change and carbon flux, water resource and hydrological hazards, solar-induced chlorophyll fluorescence and terrestrial gross primary production). The benefits and challenges of applying SRS in climate change studies are also examined and discussed. This work will help us apply SRS and recommend future SRS studies to mitigate and adapt to global climate change.

Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm

Soil salinization greatly restricts crop production in arid areas for salinity stress can inhibit crop photosynthesis and growth. Chlorophyll fluorescence and photosynthetic gas exchange (CFPGE) parameters are important indicators of crop photosynthesis and have been widely used to evaluate the impacts of salinity stress on crop photosynthesis and growth. Remote sensing technology can quickly and non-destructively obtain crop information under salinity stress, however, at present, the distribution of spectral features of CFPGE parameters in different regions is still unclear. In this study (2019-2020), under salinity stress conditions, the spectral data of rapeseed leaves were acquired and the CFPGE parameters were simultaneously determined. Then, continuous wavelet transformation (CWT) and standard normal variate (SNV) transformation were utilized to preprocess the raw spectral data. After that, a CFPGE parameter estimation model was constructed by using the partial least squares regression (PLSR) algorithm and the support vector machines (SVM) algorithm based on the spectral features in the red region (600-800 nm) and those in the red, blue-green (350-600 nm), and near-infrared (800-2500 nm) regions. The results showed that the spectral features of CFPGE parameters could be extracted by successive projections algorithm (SPA) based on the CWT preprocessing. The CFPGE parameter estimation model constructed based on the spectral features in the red region (675 nm, 680 nm, 688 nm, 749 nm, and 782 nm) had the highest Fv/Fm estimation accuracy on day 30, with R2c, R2p, and RPD of 0.723, 0.585, and 1.68, respectively. Based on this, the spectral features (578 nm, 976 nm, 1088 nm, 1476 nm, and 2250 nm) in the blue-green and near-infrared regions were added in the variables for modeling, which significantly improved the accuracy and stability of the model, with R2c, R2p, and RPD of 0.886, 0.815, and 2.58, respectively. Therefore, the fusion of the spectral features in the red, blue-green, and near-infrared regions could improve the estimation accuracy of rapeseed leaf CFPGE parameters. This study will provide technical reference for rapid estimation of photosynthetic performance of crops under salinity stress in arid and semi-arid areas.

Assessing the responses of different vegetation types to drought with satellite solar-induced chlorophyll fluorescence over the Yunnan-Guizhou Plateau

The Yunnan-Guizhou Plateau (YGP) is an important ecological region in southwestern China with frequent and severe droughts affecting its vegetation and ecosystem. Many studies have used vegetation indices to monitor drought effects on vegetation across the entire ecosystem. However, the drought response of different vegetation types in the YGP is unclear. This study used solar-induced chlorophyll fluorescence (SIF) and normalized difference vegetation Index (NDVI) data to monitor different vegetation types. The results showed that cropland was most sensitive and woody savanna was most resistant to drought. SIF had a stronger correlation with drought than NDVI, indicating its potential for vegetation monitoring.

Vegetation as the catalyst for water circulation on global terrestrial ecosystem

Global climate change is expected to further intensify the global water cycle, leading to more rapid evaporation and more intense precipitation. At the same time, the growth and expansion of natural vegetation caused by climate change and human activities create potential conflicts between ecosystems and humans over available water resources. Clarifying how terrestrial ecosystem evapotranspiration responds to global precipitation and vegetation facilitates a better understanding of and prediction for the responses of global ecosystem energy, water, and carbon budgets under climate change. Relying on the spatial and temporal distribution of evapotranspiration, precipitation, and solar-induced chlorophyll fluorescence (SIF) from remote sensing platforms, we decouple the interaction mechanism of evapotranspiration, precipitation, and vegetation in linear and nonlinear scenarios using correlation and partial correlation analysis, multiple linear regression analysis, and binning. Major conclusions are as follows: (1) As a natural catalyst of the global water cycle, vegetation plays a crucial role in regulating the relationship between climate change and the water‑carbon-energy cycle. (2) Vegetation, a key parameter affecting the water cycle, participates in the entire water cycle process. (3) The increase in vegetation productivity and photosynthesis plays a dominant role in promoting evapotranspiration in vegetated areas, while the increase in precipitation dominates the promotion of evapotranspiration in non-vegetated areas.

Tracking canopy chlorophyll fluorescence with a low-cost light emitting diode platform

Chlorophyll fluorescence measured at the leaf scale through pulse amplitude modulation (PAM) has provided valuable insight into photosynthesis. At the canopy- and satellite-scale, solar-induced fluorescence (SIF) provides a method to estimate the photosynthetic activity of plants across spatiotemporal scales. However, retrieving SIF signal remotely requires instruments with high spectral resolution, making it difficult and often expensive to measure canopy-level steady-state chlorophyll fluorescence under natural sunlight. Considering this, we built a novel low-cost photodiode system that retrieves far-red chlorophyll fluorescence emission induced by a blue light emitting diode (LED) light source, for 2 h at night, above the canopy. Our objective was to determine if an active remote sensing-based night-time photodiode method could track changes in canopy-scale LED-induced chlorophyll fluorescence (LEDIF) during an imposed drought on a broadleaf evergreen shrub, Polygala myrtifolia. Far-red LEDIF (720-740 nm) was retrieved using low-cost photodiodes (LEDIFphotodiode) and validated against measurements from a hyperspectral spectroradiometer (LEDIFhyperspectral). To link the LEDIF signal with physiological drought response, we tracked stomatal conductance (gsw) using a porometer, two leaf-level vegetation indices-photochemical reflectance index and normalized difference vegetation index-to represent xanthophyll and chlorophyll pigment dynamics, respectively, and a PAM fluorimeter to measure photochemical and non-photochemical dynamics. Our results demonstrate a similar performance between the photodiode and hyperspectral retrievals of LEDIF (R2 = 0.77). Furthermore, LEDIFphotodiode closely tracked drought responses associated with a decrease in photochemical quenching (R2 = 0.69), Fv/Fm (R2 = 0.59) and leaf-level photochemical reflectance index (R2 = 0.59). Therefore, the low-cost LEDIFphotodiode approach has the potential to be a meaningful indicator of photosynthetic activity at spatial scales greater than an individual leaf and over time.

Relationship between Photosynthetic CO2 Assimilation and Chlorophyll Fluorescence for Winter Wheat under Water Stress

Solar-induced chlorophyll fluorescence (SIF) has a high correlation with Gross Primary Production (GPP). However, studies focusing on the impact of drought on the SIF-GPP relationship have had mixed results at various scales, and the mechanisms controlling the dynamics between photosynthesis and fluorescence emission under water stress are not well understood. We developed a leaf-scale measurement system to perform concurrent measurements of active and passive fluorescence, and gas-exchange rates for winter wheat experiencing a one-month progressive drought. Our results confirmed that: (1) shifts in light energy allocation towards decreasing photochemistry (the quantum yields of photochemical quenching in PSII decreased from 0.42 to 0.21 under intermediate light conditions) and increasing fluorescence emissions (the quantum yields of fluorescence increased to 0.062 from 0.024) as drought progressed enhance the degree of nonlinearity of the SIF-GPP relationship, and (2) SIF alone has a limited capacity to track changes in the photosynthetic status of plants under drought conditions. However, by incorporating the water stress factor into a SIF-based mechanistic photosynthesis model, we show that drought-induced variations in a variety of key photosynthetic parameters, including stomatal conductance and photosynthetic CO2 assimilation, can be accurately estimated using measurements of SIF, photosynthetically active radiation, air temperature, and soil moisture as inputs. Our findings provide the experimental and theoretical foundations necessary for employing SIF mechanistically to estimate plant photosynthetic activity during periods of drought stress.

Timely estimation of leaf chlorophyll fluorescence parameters under varying light regimes by coupling light drivers to leaf traits

Unveiling informative chlorophyll a fluorescence (ChlF) parameters and leaf morphological/biochemical traits under varying light conditions is important in ecological studies but has less been investigated. In this study, the trait-ChlF relationship and regressive estimation of ChlF parameters from leaf traits under varying light conditions were investigated using a dataset of synchronous measurements of ChlF parameters and leaf morphological/biochemical traits in Mangifera indica L. The results showed that the relationships between ChlF parameters and leaf traits varied across light intensities, as indicated by different slopes and intercepts, highlighting the limitations of using leaf traits alone to capture the dynamics of ChlF parameters. Light drivers, on the other hand, showed a better predictive ability for light-dependent ChlF parameters compared to leaf traits, with light intensity having a large effect on light-dependent ChlF parameters. Furthermore, the responses of ФF and NPQ to light drivers differed between leaf types, with light intensity having an effect on ФF in shaded leaves, whereas it had a primary effect on NPQ in sunlit leaves. These results facilitate and deepen our understanding of how the light environment affects leaf structure and function and, therefore, provide the theoretical basis for understanding plant ecological strategies in response to the light environment.

Affordable method for measuring fluorescence using Gaussian distributions and bounded MESE

We present an accurate and low-cost method for measuring fluorescence in materials. Our method outputs an estimate of the material's Donaldson matrix, which is a commonly used two-dimensional spectral characterization of its fluorescence and reflectance properties. To find the estimate, only a few measurements of the material's reflectance under a few illuminants are needed, which we demonstrate using low-cost optical components. Internally, our algorithm is based on representing each Donaldson matrix with a multivariate Gaussian mixture model and its diagonal with a bounded MESE (maximum entropy spectral estimate). It parametrizes and constrains the estimate in a robust and simple way, allowing the use of gradient-descent optimization. We evaluate our algorithm on a combination of real and synthetic data, and four examples of distinct optical components. We reach significantly lower errors than the current state of the art on the exact same inputs, our estimates do not suffer from artifacts such as oscillations of the spectra, and they are stable and robust.

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.

Terrestrial CO2 exchange diagnosis using a peatland-optimized vegetation photosynthesis and respiration model (VPRM) for the Hudson Bay Lowlands

Satellite-based light use efficiency (LUE) models have been widely used to estimate gross primary production in various terrestrial ecosystems such as forests and croplands, but northern peatlands have received less attention. In particular, the Hudson Bay Lowlands (HBL) which is a massive peatland-rich region in Canada has been largely ignored in previous LUE-based studies. These peatland ecosystems have accumulated large stocks of organic carbon over many millennia, and play a vital role in the global carbon cycle. In this study, we used the satellite data-driven Vegetation Photosynthesis and Respiration Model (VPRM) to examine the suitability of LUE models for carbon flux diagnosis in the HBL. VPRM was driven alternately with the satellite-derived enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF). The model parameter values were constrained by eddy covariance (EC) tower observations from the Churchill fen and Attawapiskat River bog sites. The main objectives of the study were to (i) investigate if site-specific parameter optimization improved NEE estimates, (ii) determine which satellite-based proxy of photosynthesis produced more reliable estimates of peatland net carbon exchange, and (iii) examine how LUE and other model parameters vary within and between the study sites. The results indicate that the VPRM mean diurnal and monthly estimates of NEE had significant strong agreements with EC tower fluxes at the two study sites. A comparison of the site-optimized VPRM against a generic peatland-optimized version of the model revealed that the site-optimized VPRM provided better estimates of NEE only during the calibration period at the Churchill fen. The diurnal and seasonal cycles of peatland carbon exchange were better captured by the SIF-driven VPRM, demonstrating that SIF is a more accurate proxy for photosynthesis compared to EVI. Our study suggests that satellite-based LUE models have the potential to be applied on a larger scale to the HBL region.

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

Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5-10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question-How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.

A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence

Enhancing the photosynthetic rate is one of the effective ways to increase rice yield, given that photosynthesis is the basis of crop productivity. At the leaf level, crops' photosynthetic rate is mainly determined by photosynthetic functional traits including the maximum carboxylation rate (V_cmax) and stomatal conductance (gs). Accurate quantification of these functional traits is important to simulate and predict the growth status of rice. In recent studies, the emerging sun-induced chlorophyll fluorescence (SIF) provides us an unprecedented opportunity to estimate crops' photosynthetic traits, owing to its direct and mechanistic links to photosynthesis. Therefore, in this study, we proposed a practical semimechanistic model to estimate the seasonal V_cmax and gs time-series based on SIF. We firstly generated the coupling relationship between the open ratio of photosystem II (qL) and photosynthetically active radiation (PAR), then estimate the electron transport rate (ETR) based on the proposed mechanistic relationship between SIF and ETR. Finally, V_cmax and gs were estimated by linking to ETR based on the principle of evolutionary optimality and the photosynthetic pathway. Validation with field observations showed that our proposed model can estimate V_cmax and gs with high accuracy (R² > 0.8). Compared to simple linear regression model, the proposed model could increase the accuracy of V_cmax estimates by >40%. Therefore, the proposed method effectively enhanced the estimation accuracy of crops' functional traits, which sheds new light on developing high-throughput monitoring techniques to estimate plant functional traits, and also can improve our understating of crops' physiological response to climate change.

Challenges and opportunities in remote sensing-based crop monitoring: a review

Building a more resilient food system for sustainable development and reducing uncertainty in global food markets both require concurrent and near-real-time and reliable crop information for decision making. Satellite-driven crop monitoring has become a main method to derive crop information at local, regional, and global scales by revealing the spatial and temporal dimensions of crop growth status and production. However, there is a lack of quantitative, objective, and robust methods to ensure the reliability of crop information, which reduces the applicability of crop monitoring and leads to uncertain and undesirable consequences. In this paper, we review recent progress in crop monitoring and identify the challenges and opportunities in future efforts. We find that satellite-derived metrics do not fully capture determinants of crop production and do not quantitatively interpret crop growth status; the latter can be advanced by integrating effective satellite-derived metrics and new onboard sensors. We have identified that ground data accessibility and the negative effects of knowledge-based analyses are two essential issues in crop monitoring that reduce the applicability of crop monitoring for decisions on food security. Crowdsourcing is one solution to overcome the restrictions of ground-truth data accessibility. We argue that user participation in the complete process of crop monitoring could improve the reliability of crop information. Encouraging users to obtain crop information from multiple sources could prevent unconscious biases. Finally, there is a need to avoid conflicts of interest in publishing publicly available crop information.

High spatial resolution solar-induced chlorophyll fluorescence and its relation to rainfall precipitation across Brazilian ecosystems

The detection of Solar-Induced chlorophyll Fluorescence (SIF) by remote sensing has opened new perspectives on ecosystem studies and other related aspects such as photosynthesis. In general, fluorescence high-resolution studies were limited to proximal sensors, but new approaches were developed to improve SIF resolution by combining OCO-2 with MODIS orbital observations, improving its resolution from 0.5° to 0.05 on a global scale. Using a high-resolution dataset and rainfall data some SIF characteristics of the satellite were studied based across 06 contrasting ecosystems in Brazil: Amazonia, Caatinga, Cerrado, Atlantic Forest, Pampa, and Pantanal, from years 2015-2018. SIF spatial variability in each biome presented significant spatial variability structures with high R² values (>0.6, Gaussian models) in all studied years. The rainfall maps were positively and similar related to SIF spatial distribution and were able to explain more than 40% of SIF's spatial variability. The Amazon biome presented the higher SIF values (>0.4 W m^-2 sr^-1 μm^-1) and also the higher annual rainfall precipitation (around 2000 mm), while Caatinga had the lowest SIF values and precipitations (<0.1 W m^-2 sr^-1 μm^-1, precipitation around 500 mm). The linear relationship of SIF to rainfall across biomes was mostly significant (except in Pantanal) and presented contrasting sensitivities as in Caatinga SIF was mostly affected while in the Amazon, SIF was lesser affected by precipitation events. We believe that the features presented here indicate that SIF could be highly affected by rainfall precipitation changes in some Brazilian biomes. Combining rainfall with SIF allowed us to detect the differences and similarities across Brazil's biomes improving our understanding on how these ecosystems could be affected by climate change and severe weather conditions.

Vegetation clumping modulates global photosynthesis through adjusting canopy light environment

The spatial dispersion of photoelements within a vegetation canopy, quantified by the clumping index (CI), directly regulates the within-canopy light environment and photosynthesis rate, but is not commonly implemented in terrestrial biosphere models to estimate the ecosystem carbon cycle. A few global CI products have been developed recently with remote sensing measurements, making it possible to examine the global impacts of CI. This study deployed CI in the radiative transfer scheme of the Community Land Model version 5 (CLM5) and used the revised CLM5 to quantitatively evaluate the extent to which CI can affect canopy absorbed radiation and gross primary production (GPP), and for the first time, considering the uncertainty and seasonal variation of CI with multiple remote sensing products. Compared to the results without considering the CI impact, the revised CLM5 estimated that sunlit canopy absorbed up to 9%-15% and 23%-34% less direct and diffuse radiation, respectively, while shaded canopy absorbed 3%-18% more diffuse radiation across different biome types. The CI impacts on canopy light conditions included changes in canopy light absorption, and sunlit-shaded leaf area fraction related to nitrogen distribution and thus the maximum rate of Rubisco carboxylase activity (V_cmax ), which together decreased photosynthesis in sunlit canopy by 5.9-7.2 PgC year^-1 while enhanced photosynthesis by 6.9-8.2 PgC year^-1 in shaded canopy. With higher light use efficiency of shaded leaves, shaded canopy increased photosynthesis compensated and exceeded the lost photosynthesis in sunlit canopy, resulting in 1.0 ± 0.12 PgC year^-1 net increase in GPP. The uncertainty of GPP due to the different input CI datasets was much larger than that caused by CI seasonal variations, and was up to 50% of the magnitude of GPP interannual variations in the tropical regions. This study highlights the necessity of considering the impacts of CI and its uncertainty in terrestrial biosphere models.

Early Monitoring of Forest Wood-Boring Pests with Remote Sensing

Wood-boring pests (WBPs) pose an enormous threat to global forest ecosystems because their early stage infestations show no visible symptoms and can result in rapid and widespread infestations at later stages, leading to large-scale tree death. Therefore, early-stage WBP detection is crucial for prompt management response. Early detection of WBPs requires advanced and effective methods like remote sensing. This review summarizes the applications of various remote sensing sensors, platforms, and detection methods for monitoring WBP infestations. The current capabilities, gaps in capabilities, and future potential for the accurate and rapid detection of WBPs are highlighted.

Environmental stress - what can we learn from chlorophyll a fluorescence analysis in woody plants? A review

Chlorophyll a fluorescence (ChF) signal analysis has become a widely used and rapid, non-invasive technique to study the photosynthetic process under stress conditions. It monitors plant responses to various environmental factors affecting plants under experimental and field conditions. Thus, it enables extensive research in ecology and benefits forestry, agriculture, horticulture, and arboriculture. Woody plants, especially trees, as organisms with a considerable life span, have a different life strategy than herbaceous plants and show more complex responses to stress. The range of changes in photosynthetic efficiency of trees depends on their age, ontogeny, species-specific characteristics, and acclimation ability. This review compiles the results of the most commonly used ChF techniques at the foliar scale. We describe the results of experimental studies to identify stress factors that affect photosynthetic efficiency and analyse the experience of assessing tree vigour in natural and human-modified environments. We discuss both the circumstances under which ChF can be successfully used to assess woody plant health and the ChF parameters that can be useful in field research. Finally, we summarise the advantages and limitations of the ChF method in research on trees, shrubs, and woody vines.

Solar-induced chlorophyll fluorescence imperfectly tracks the temperature response of photosynthesis in winter wheat

Solar-induced fluorescence (SIF) is a promising proxy for photosynthesis, but it is unclear whether it performs well in tracking the gross primary productivity (GPP) under different environmental conditions. In this study, we investigated the dynamics of the two parameters from October 2020 to June 2021 in field-grown winter wheat (Triticum aestivum) and found that the ability of SIF to track GPP was weakened at low temperatures. Accounting for the coupling of light and temperature at a seasonal scale, we found that SIF yield showed a lower temperature sensitivity and had a lower but broader optimal temperature range compared with light-use efficiency (LUE), although both SIF yield and LUE decreased in low-temperature conditions. The discrepancy between the temperature responses of SIF yield and GPP caused an increase in the ratio of SIF/GPP in winter, which indicated the variation in the relationship between them during this period. The results of our study highlight the impact of low temperature on the relationship between SIF and GPP and show the necessity of reconsidering the dynamics of energy distribution inside plants under changing environments.

HyScreen: A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence

Solar-induced chlorophyll fluorescence (SIF) is used as a proxy of photosynthetic efficiency. However, interpreting top-of-canopy (TOC) SIF in relation to photosynthesis remains challenging due to the distortion introduced by the canopy's structural effects (i.e., fluorescence re-absorption, sunlit-shaded leaves, etc.) and sun-canopy-sensor geometry (i.e., direct radiation infilling). Therefore, ground-based, high-spatial-resolution data sets are needed to characterize the described effects and to be able to downscale TOC SIF to the leafs where the photosynthetic processes are taking place. We herein introduce HyScreen, a ground-based push-broom hyperspectral imaging system designed to measure red (F687) and far-red (F760) SIF and vegetation indices from TOC with single-leaf spatial resolution. This paper presents measurement protocols, the data processing chain and a case study of SIF retrieval. Raw data from two imaging sensors were processed to top-of-canopy radiance by dark-current correction, radiometric calibration, and empirical line correction. In the next step, the improved Fraunhofer line descrimination (iFLD) and spectral-fitting method (SFM) were used for SIF retrieval, and vegetation indices were calculated. With the developed protocol and data processing chain, we estimated a signal-to-noise ratio (SNR) between 50 and 200 from reference panels with reflectance from 5% to 95% and noise equivalent radiance (NER) of 0.04 (5%) to 0.18 (95%) mW m-2 sr-1 nm-1. The results from the case study showed that non-vegetation targets had SIF values close to 0 mW m-2 sr-1 nm-1, whereas vegetation targets had a mean F687 of 1.13 and F760 of 1.96 mW m-2 sr-1 nm-1 from the SFM method. HyScreen showed good performance for SIF retrievals at both F687 and F760; nevertheless, we recommend further adaptations to correct for the effects of noise, varying illumination and sensor optics. In conclusion, due to its high spatial resolution, Hyscreen is a promising tool for investigating the relationship between leafs and TOC SIF as well as their relationship with plants' photosynthetic capacity.

Solar-induced chlorophyll fluorescence imaging spectrometer: design, manufacture, and evaluation

A scientific imaging spectrometer has been presented for the observation of solar-induced chlorophyll fluorescence of vegetation in NIR waveband, which may provide a new method to scale SIF application from leaf to canopy for the research of terrestrial vegetation photosynthesis. The SIF imaging spectrometer accommodates a telescope with a medium spatial resolution (1 mrad) over a field of view of 20°, a high spectral resolution (0.3nm) to measure the fluorescence spectrum within two oxygen absorption bands (O_2A and O_2B), and a high numerical aperture (0.25) for high SNR. Both of transmission optical systems, with high etendue and dispersive prism-VPH grating (P-G) with high diffraction efficiency, have been utilized for the optical design of imaging spectrometer. The design and prototype present excellent optical performances as demonstrated by the latest simulation and calibration. The in-situ observation proves that the advanced SIF imaging spectrometer could provide precise fluorescence data. The instrument will highlight SIF signal retrieval strategies, techniques for field and airborne and satellite sensing, and applications of these capabilities in evaluation of photosynthesis and stress effects for fluorescence science.

Ultrafast laser filament-induced fluorescence for detecting uranium stress in Chlamydomonas reinhardtii

Plants and other photosynthetic organisms have been suggested as potential pervasive biosensors for nuclear nonproliferation monitoring. We demonstrate that ultrafast laser filament-induced fluorescence of chlorophyll in the green alga Chlamydomonas reinhardtii is a promising method for remote, in-field detection of stress from exposure to nuclear materials. This method holds an advantage over broad-area surveillance, such as solar-induced fluorescence monitoring, when targeting excitation of a specific plant would improve the detectability, for example when local biota density is low. After exposing C. reinhardtii to uranium, we find that the concentration of chlorophyll a, chlorophyll fluorescence lifetime, and carotenoid content increase. The increased fluorescence lifetime signifies a decrease in non-photochemical quenching. The simultaneous increase in carotenoid content implies oxidative stress, further confirmed by the production of radical oxygen species evidence in the steady-state absorption spectrum. This is potentially a unique signature of uranium, as previous work finds that heavy metal stress generally increases non-photochemical quenching. We identify the temporal profile of the chlorophyll fluorescence to be a distinguishing feature between uranium-exposed and unexposed algae. Discrimination of uranium-exposed samples is possible at a distance of [Formula: see text]35 m with a single laser shot and a modest collection system, as determined through a combination of experiment and simulation of distance-scaled uncertainty in discriminating the temporal profiles. Illustrating the potential for remote detection, detection over 125 m would require 100 laser shots, commensurate with the detection time on the order of 1 s.

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under shortterm, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.

Improved estimation of global gross primary productivity during 1981-2020 using the optimized P model

Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the net carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in proper determination of maximum LUE (LUE_max) and downregulation of LUE_max into actual LUE result in uncertainties in GPP estimated by LUE models. The recently developed P model, as a LUE-like model, captures the deep mechanism of photosynthesis and simplifies parameterization. Site level studies have proved the outperformance of P model over LUE models. However, the global application of the P model is still lacking. Thus, the effectiveness of 5 water stress factors integrated into the P model was compared. The optimal P model was used to generate a new long-term (1981-2020) global monthly GPP dataset at a spatial resolution of 0.1° × 0.1°, called PGPP. Validation at globally distributed 109 FLUXNET sites indicated that PGPP is better than three widely-used GPP products. R² between PGPP and observed GPP equals to 0.75, the corresponding root mean squared error (RMSE) and mean absolute error (MAE) equal to 1.77 g C m^-2 d^-1 and 1.28 g C m^-2 d^-1. During the period from 1981 to 2020, PGPP significantly increased in 69.02% of global vegetated regions (p < 0.05). Overall, PGPP provides a new GPP product choice for global ecology studies and the comparison of various water stress factors provides a new idea for the improvement of GPP model in the future.

Estimating the Applicability of NDVI and SIF to Gross Primary Productivity and Grain-Yield Monitoring in China

Vegetation, a key intermediary linking water, the atmosphere, and the ground, performs extremely important functions in nature and for our existence. Although satellite-based remote-sensing technologies have become important for monitoring vegetation dynamics, selecting the correct remote-sensing vegetation indicator has become paramount for such investigations. This study investigated the consistencies between a photosynthetic activity index (the solar-induced chlorophyll fluorescence (SIF) indicator) and the traditional vegetation index (the Normalized Difference Vegetation Index (NDVI)) among different land-cover types and in different seasons and explored the applicability of NDVI and SIF in different cases by comparing their performances in gross primary production (GPP) and grain-yield-monitoring applications. The vegetation cover and photosynthesis showed decreasing trends, which were mainly concentrated in northern Xinjiang and part of the Qinghai–Tibet Plateau; a decreasing trend was also identified in a small part of Northeast China. The correlations between NDVI and SIF were strong for all land-cover types except evergreen needleleaf forests and evergreen broadleaf forests. Compared with NDVI, SIF had some advantages when monitoring the GPP and grain yields among different land-cover types. For example, SIF could capture the effects of drought on GPP and grain yields better than NDVI. To summarize, as the temporal extent of the available SIF data is extended, SIF will certainly perform increasingly wide applications in agricultural-management research that is closely related to GPP and grain-yield monitoring.

Response of vegetation ecosystems to flash drought with solar-induced chlorophyll fluorescence over the Hai River Basin, China during 2001-2019

With global climate change, frequent flash droughts have critically impacted vegetation productivity. Based on the new definition on flash drought onset and duration, the temporal and spatial evolution patterns of the flash drought over the Hai River Basin (HRB) was analysed. Among the events, the flash drought in 2019 lasted for 40 days, from the day of the year (DOY) 120 to DOY160, which was the strongest and mainly concentrated in the south-eastern part of the basin. Solar-induced chlorophyll fluorescence (SIF) and vegetation indices were used to explore the responses of different vegetation types to this flash drought. Compared to forest and grassland, the SIF and SIFyield (SIF normalized by the absorbed photosynthetically active radiation (APAR)) values of cropland were more sensitive to water losses and replenishment. By analysing different radiation conditions which would affect SIF and photosynthesis, low radiation was found altering the linear relationship between fluorescence and photosynthesis. The flash drought event caused gross primary productivity (GPP) losses in 40% of the basin and the maximum loss reached 0.16 kg C m^-2, indicating that the impact of this flash drought on vegetation productivity was quite serious. The results obtained in this work can be used to understand the mechanisms with which the vegetation photosynthetic capacity responds to flash droughts and to evaluate the impact of flash droughts on terrestrial ecosystems.

Different Responses of Solar-Induced Chlorophyll Fluorescence at the Red and Far-Red Bands and Gross Primary Productivity to Air Temperature for Winter Wheat

Solar-induced chlorophyll fluorescence (SIF) is closely related to the light-reaction process and has been recognized as a good indicator for tracking gross primary productivity (GPP). Nevertheless, it has not been widely examined how SIF and GPP respond to temperature. Here, we explored the linkage mechanisms between SIF and GPP in winter wheat based on continuous measurements of canopy SIF (cSIF), GPP, and meteorological data. To separately explore the structural and physiological mechanisms underlying the SIF–GPP relationship, we studied the temperature responses of the estimated light use efficiency (LUEp), canopy-level chlorophyll fluorescence yield (cSIFyield) and photosystem-level chlorophyll fluorescence yield (ΦF) estimated using canopy-scale remote sensing measurements. We found that GPP, red canopy SIF (cSIF688) and far-red canopy SIF (cSIF760) all exhibited a decreasing trend during overwintering periods. However, GPP and cSIF688 showed relatively more obvious changes in response to air temperature (Ta) than cSIF760 did. In addition, the LUEp responded sensitively to Ta (the correlation coefficient, r = 0.83, p-value < 0.01). The cSIFyield_688 and ΦF_688 (ΦF at 688 nm) also exhibited significantly positive correlations with Ta (r > 0.7, p-value < 0.05), while cSIFyield_760 and ΦF_760 (ΦF at 760 nm) were weakly correlated with Ta (r < 0.3, p-value > 0.05) during overwintering periods. The results also show that LUEp was more sensitive to Ta than ΦF, which caused changes in the LUEp/ΦF ratio in response to Ta. By considering the influence of Ta, the GPP estimation based on the total SIF emitted at the photosystem level (tSIF) was improved (with R2 increased by more than 0.12 for tSIF760 and more than 0.05 for tSIF688). Therefore, our results indicate that the LUEp/ΦF ratio is affected by temperature conditions and highlights that the SIF–GPP model should consider the influence of temperature.

An empirical model for estimating daily atmospheric column-averaged CO2 concentration above São Paulo state, Brazil

BACKGROUND

The recent studies of the variations in the atmospheric column-averaged CO2 concentration ([Formula: see text]) above croplands and forests show a negative correlation between [Formula: see text]and Sun Induced Chlorophyll Fluorescence (SIF) and confirmed that photosynthesis is the main regulator of the terrestrial uptake for atmospheric CO2. The remote sensing techniques in this context are very important to observe this relation, however, there is still a time gap in orbital data, since the observation is not daily. Here we analyzed the effects of several variables related to the photosynthetic capacity of vegetation on [Formula: see text] above São Paulo state during the period from 2015 to 2019 and propose a daily model to estimate the natural changes in atmospheric CO2.

RESULTS

The data retrieved from the Orbiting Carbon Observatory-2 (OCO-2), NASA-POWER and Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) show that Global Radiation (Qg), Sun Induced Chlorophyll Fluorescence (SIF) and, Relative Humidity (RH) are the most significant factors for predicting the annual [Formula: see text] cycle. The daily model of [Formula: see text] estimated from Qg and RH predicts daily [Formula: see text] with root mean squared error of 0.47 ppm (the coefficient of determination is equal to 0.44, p < 0.01).

CONCLUSION

The obtained results imply that a significant part of daily [Formula: see text] variations could be explained by meteorological factors and that further research should be done to quantify the effects of the atmospheric transport and anthropogenic emissions.

Informing Nature-based Climate Solutions for the United States with the best-available science

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point- and tree-scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.

Evaluating the Impact of Summer Drought on Vegetation Growth Using Space-Based Solar-Induced Chlorophyll Fluorescence Across Extensive Spatial Measures

Drought is the primary and dominant natural cause of stress on vegetation, and thus, it needs our full attention. Current understanding of drought across extensive spatial measures, around the world, is considerably limited. As case studies to evaluate the feasibility of utilizing space-based solar-induced chlorophyll fluorescence (SIF) across extensive spatial measures, here, we have used data from 2007 to 2017 in Heilongjiang and Jiangsu provinces of China. The onset of the 2015 drought was accompanied by a substantial response of SIF from vegetation in both the provinces; these data were associated with changes in soil moisture, standardized precipitation evapotranspiration index, and emissivity. Our findings suggest that SIF can effectively provide the spatial and temporal progress of drought, as inferred through substantial associations with SIF normalized by absorbed photosynthetically active radiation (related to Φ_F) and by photosynthetically active radiation (SIF_par). For the depiction of onset to drought, SIF, Φ_F, and SIF_par provide a significant association and a quicker response than the leaf area index and the normalized difference vegetation index. Furthermore, we found that the correlation between gross primary productivity and SIF is highly substantial in both Heilongjiang (R² = 0.85, p < 0.001) and Jiangsu (R² = 0.75, p < 0.001) during the drought period. Our results indicate that continuing evaluation from space-based SIF can indeed provide an understanding of the seasonal differences in vegetation for evaluating the impact of drought across extensive spatial measures.