Chlorophyll Fluorescence Data Research Articles

Unraveling non-linear dynamics of biomass, photosynthesis efficiency, and lipid accumulation in Chlorella vulgaris under mixotrophic cultivation

In recent years, the potential of microalgae-based biofuels as a renewable energy source has garnered significant attention due to their inherent ability to sequester carbon dioxide (CO2) efficiently. This study investigates Chlorella vulgaris FACHB-8's growth, photosynthesis, and lipid accumulation under different carbon source and CO2 concentration conditions with Central composite design. Results reveal that a 10% CO2 concentration enhances biomass and cell count, yielding 0.94 g L−1 and 106.8 × 106 cells·ml−1, respectively, after 14 days of autotrophic cultivation. However, excessive CO2 inhibits growth, notably observed with 20% CO2 yielding 0.86 g L−1 biomass. Pigment content positively correlates with biomass under autotrophic conditions, reaching 42.55 mg g−1 in the 10% CO2 group. Under photosynthetic mixotrophy, carbonic anhydrase activity decreases with higher CO2 concentrations, suggesting reduced enzyme demand. Glucose exhibits the highest lipid content (31.87%), surpassing autotrophic conditions (24.80%). Glycerol presents favorable lipid content (31.78%) and settling efficiency (85.07%), indicating potential for self-flocculation harvesting. Chlorophyll fluorescence data highlight active photosynthesis in mixotrophic conditions, with glucose and sucrose affecting energy conversion. This highlights the critical role of microalgae biofuels in the pursuit of cleaner and more sustainable energy solutions.

Global ecosystem responses to flash droughts are modulated by background climate and vegetation conditions

AbstractFlash droughts and their physical processes have received increasing attention in recent years due to concerns about the potential of flash droughts to affect water resources and ecosystems. Yet to date, the response of ecosystems during flash drought events, particularly on a large scale, and the determinants of the ecosystem responses to flash droughts have been underexplored. Here we analyse temporal variations in vegetation anomalies during flash drought events at a global scale between 2001 and 2020 using observation-based leaf area index, gross primary productivity, and solar-induced chlorophyll fluorescence data. We identify divergent ecosystem responses in terms of the timing and intensification of drought-induced vegetation stress across different regions around the world. Furthermore, we find that these regional differences are largely modulated by background climate and vegetation conditions, rather than meteorological conditions, with ecosystems being subjected to more rapidly developing and greater degrees of vegetation stress in arid and short vegetation-dominated regions as compared to humid forests. Our results highlight the spatially heterogeneous ecological impacts of flash droughts, implying the need to comprehensively integrate aspects of both atmospheric and bioclimatic properties in flash drought monitoring and forecasting systems to improve our ability to track their evolution and impacts.

Subsurface chlorophyll maxima reduce the performance of non-photochemical quenching corrections in the Southern Ocean

Non-photochemical quenching (NPQ) within phytoplankton cells often causes the daytime suppression of chlorophyll fluorescence in the Southern Ocean. This is problematic and requires accurate correction when chlorophyll fluorescence is used as a proxy for chlorophyll-a concentration or phytoplankton abundance. In this study, we reveal that Southern Ocean subsurface chlorophyll maxima (SCMs) are the largest source of uncertainty when correcting for NPQ of chlorophyll fluorescence profiles. A detailed assessment of NPQ correction methods supports this claim by taking advantage of coincident chlorophyll fluorescence and chlorophyll concentration profiles. The best performing NPQ correction methods are conditional methods that consider the mixed layer depth (MLD), subsurface fluorescence maximum (SFM) and depth of 20% surface light. Compared to existing methods, the conditional methods proposed halve the bias in corrected chlorophyll fluorescence profiles and improve the success of replicating a SFM relative to chlorophyll concentration profiles. Of existing methods, the X12 and P18 methods, perform best overall, even when considering methods supplemented by beam attenuation or backscatter data. The widely-used S08 method, is more varied in its performance between profiles and its application introduced on average up to 2% more surface bias. Despite the significant improvement of the conditional method, it still underperformed in the presence of an SCM due to 1) changes in optical properties at the SCM and 2) large gradients of chlorophyll fluorescence across the pycnocline. Additionally, we highlight that conditional methods are best applied when uncertainty in chlorophyll fluorescence yields is within 50%. This highlights the need to better characterize the bio-optics of SCMs and chlorophyll fluorescence yields in the Southern Ocean, so that chlorophyll fluorescence data can be accurately converted to chlorophyll concentration in the absence of in situ water sampling.

Long-term dynamics of peak photosynthesis timing and environmental controls in the Tibetan Plateau monitored by satellite solar-induced chlorophyll fluorescence

ABSTRACT Vegetation phenological metrics have attracted increasing attentions during recent years. However, compared with the peak greenness timing (PGT), the peak photosynthesis timing (PPT) has been overlooked until now. As the world's third pole, the Tibetan Plateau is one of the most sensitive areas to global climatic change. In this study, long-term time-series solar-induced chlorophyll fluorescence (SIF) data were used to identify the PPT patterns, trends, and responses in the Tibetan Plateau over the past two decades. Our findings indicated that the SIF-derived PPT can better capture the characteristics from flux-measured gross primary productivity compared with the MODIS EVI-based PGT, exhibiting great potential to characterize large-scale vegetation PPT dynamics. The multi-year mean PPT mainly appeared in late July and early August with a gradually delayed trend from southeast to northwest. Meanwhile, early PPT occurred in many areas of the Tibetan Plateau (67.36%), with the overall trend advanced by about 0.89 day decade−1. Interestingly, opposite trends of PPT were found with elevation below and above 2500 m asl. Preseason temperature, shortwave radiation, and precipitation jointly explained the strong spatial heterogeneity of PPT trend. All analyses help clarify the role of the ‘third pole’ in responding to environmental change and regional carbon cycles.

Assessment of terrestrial ecosystem sensitivity to climate change in arid, semi-arid, sub-humid, and humid regions using EVI, LAI, and SIF products

Arid and humid ecosystems are undergoing significant changes over the past decades due to the warming temperature and frequent occurrence of climate extremes. Quantifying climatic vegetation sensitivity in regions with different climate classifications is crucial for understanding the interaction mechanisms between vegetation and ongoing climate change. However, such knowledge about the spatiotemporal variations of vegetation sensitivity and its climate driving factors in arid and humid regions is still unclear. Based on this consideration, a moving-window-based Vegetation Sensitivity Index (VSI) scheme was applied to Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Solar-induced Chlorophyll Fluorescence (GOSIF) data to evaluate the spatiotemporal variations of terrestrial ecosystem in response to climate changes and explore its driving climatic factors from 2003 to 2020. Results indicated that high VSI (>60) was observed for tropical rainforests in humid regions and central Eurasia in semi-arid regions. By contrast, low VSI (<40) was typically observed in arid regions. Spatially, we found VSI exhibited a nonlinear parabolic variation along the gradient of aridity index in the arid and semi-arid regions but generally presented a linear variation with an upward trend in the humid regions. Temporally, VSI showed a significant declining trend over the past two decades but displayed a significantly increasing trend in arid areas. Temperature was the dominant climatic factor to explain the spatial variations of VSI globally, while precipitation mainly dominated the temporal variations of VSI. The dominant climatic factor of VSI varied across different aridity regions as temperature and precipitation were the main climate factor to drive spatiotemporal variations of VSI in humid and arid classifications, respectively. Our results provide unique insights into the vegetation sensitivity in response to ongoing and future climate change in regions with different climate classifications.

Identifying Crop Growth Stages from Solar-Induced Chlorophyll Fluorescence Data in Maize and Winter Wheat from Ground and Satellite Measurements

Crop growth stages are integral components of plant phenology and are of significant ecological and agricultural importance. While the use of remote sensing methods for phenology identification in cropland ecosystems has been extensively explored in previous studies, the focus has often been on land surface phenology, primarily related to the start and end of the growing season. In contrast, the monitoring of crop growth within an agronomic framework has been limited, particularly in the context of recently developed solar-induced chlorophyll fluorescence (SIF) data. Additionally, some critical growth stages have not received adequate attention or evaluation. This study aims to assess the utility of SIF data, collected from both ground and satellite measurements, for identifying critical crop growth stages within the realm of remote sensing phenological estimation. A comparative analysis was conducted using enhanced vegetation index (EVI) data at the Shangqiu site in the North China Plain from 2018 to 2022. Both SIF and EVI time-series data, obtained from ground and satellite sources, undergo a comprehensive phenological estimation framework encompassing pre-processing, modeling, and transition characterization. This approach involves reconciling time-series phenological patterns with crop growth stages, revealing the necessity of redefining the mapping relationship between these two fundamental concepts. After preprocessing the time-series data, the framework incorporates the phenological modeling process employing two double logistic models and a spline model for comparison. Additionally, it includes phenological transition characterization using four different methods. Consequently, each input dataset undergoes an assessment, resulting in 12 sets of estimations, which are compared to select the ideal estimation portfolio for identifying the growth stages of maize and winter wheat. Our findings highlight the efficacy of SIF data in accurately identifying the growth stages of maize and winter wheat, achieving remarkable results with an R-square exceeding 0.9 and an RMSE of less than 1 week for key growth stages (KGSs). Notably, SIF data demonstrate superior accuracy, robustness, and sensitivity to phenological events when compared to EVI data. This study establishes an estimation portfolio utilizing SIF data, involving the Gu model, a double logistic model, as the preferred phenological modelling method together with various compositing methods and transition characterization methods, suitable for most KGSs. These findings create opportunities for future research aimed at enhancing and standardizing crop growth stage identification using remote sensing data for a wide range of KGSs.

Urban-rural gradient in vegetation phenology changes of over 1500 cities across China jointly regulated by urbanization and climate change

A comprehensive investigation on the long-term changes of urban vegetation phenology (△UVP) is critical for a better understanding of changes in urban ecosystems. However, the urban–rural gradient of the △UVP across cities with diverse climate zones and city sizes, as well as the associated drivers, remains largely unclear. Here, using multi-source remote sensing data from 2001 to 2019, we investigated the urban–rural gradient (including six urban–rural subzones, i.e., the old urban core, new urban core, new urban area, suburban area, exurban area, and rural area) of the △UVP across 1500 + cities in China and analyzed the impacts of urbanization level and climate change on the △UVP. First, over the past two decades, the △UVP during spring (i.e., the changes of the start of growing season, termed △SOS) is generally negative (advanced SOS; 73 % ± 4 %) and that during autumn (i.e., changes of the end of growing season, termed △EOS) is mainly positive (delayed EOS; 63 % ± 3 %) in all urban–rural subzones. The spatial distribution of the △UVP based on enhanced vegetation index (EVI) data is relatively consistent with that based on solar-induced chlorophyll fluorescence (SIF) data. Along the urban–rural gradient, both the SOS advance and EOS delay exhibit an increasing first, decreasing later, and finally steady curve, with the maximum occurring in the new urban area at − 12.4 ± 18.5 days (for △SOS) and 3.8 ± 9.9 days (for △EOS). Second, the SOS changes from an advance to a delay from the middle temperate to southern subtropical zones, while the EOS changes from a delay to an advance. The magnitudes of both △SOS and △EOS show a general trend of increasing first and then decreasing with decreasing latitude. Third, the SOS advance and EOS delay generally decrease with city size for the old urban core, increase initially and decrease later with city size for the three suburban subzones, while they show little deviations with city size for the new urban core and new urban area. Finally, in the three urban subzones, the mean △UVP across all the cities is contributed more by climate change (mean: △SOS: 73 %; △EOS: 78 %) than by urbanization level (mean: △SOS: 27 %; △EOS: 22 %). Our findings can improve the understanding of urban vegetation changes in the context of rapid urbanization and climate change.

Spring photosynthetic phenology of Chinese vegetation in response to climate change and its impact on net primary productivity

In the context of global warming, the advancement of spring phenology in northern and temperate regions due to increased temperatures has been widely reported. Early and delayed start of the photosynthetic period (SOP) directly affects the vegetation net primary productivity (NPP). However, the interrelationship between climate change, the SOP, and the NPP is unclear. In this paper, we use the dynamics of decadal daily solar-induced chlorophyll fluorescence data to calculate the response of Chinese vegetation photosynthetic phenology to climate change and its impact on the NPP over the last 20 years. The results found that over the last 20 years, the average SOP in China was on the 123rd day of the year, and the SOP has advanced at an average rate of 4.3 d (10 a)−1, with a faster trend of SOP advancement in highland and high-altitude areas. 64% of SOP in China is controlled by temperature; 36% of the SOP in China is controlled by precipitation, and the relative importance of temperature and precipitation was reversed as the precipitation gradient decreased, with SOP dominated by temperature when pre-season precipitation ≧300 mm, and SOP dominated by precipitation when pre-season precipitation ≦300 mm. Finally, we find that climate change indirectly increases vegetation NPP by advancing SOP. Our study emphasizes the importance of precipitation on phenology. It provides a scientific basis for understanding and predicting the response of spring photosynthetic phenology to climate change and the contribution of spring phenology to carbon estimation in terrestrial ecosystems.

Ecological Environment Quality Assessment of Arid Areas Based on Improved Remote Sensing Ecological Index—A Case Study of the Loess Plateau

Ecosystems in arid and semi-arid areas are delicate and prone to different erosive effects. Monitoring and evaluating the environmental ecological condition in such areas contribute to the governance and restoration of the ecosystem. Remote sensing ecological indices (RSEIs) are widely used as a method for environmental monitoring and have been extensively applied in various regions. This study selects the arid and semi-arid Loess Plateau as the research area, in response to existing research on ecological monitoring that predominantly uses vegetation indices as monitoring indicators for greenness factors. A fluorescence remote sensing ecological index (SRSEI) is constructed by using monthly synthesized sun-induced chlorophyll fluorescence data during the vegetation growth period as a new component for greenness and combining it with MODIS product data. The study generates the RSEI and SRSEI for the research area spanning from 2001 to 2021. The study compares and analyzes the differences between the two indices and explores the evolution patterns of the ecosystem quality in the Loess Plateau over a 21-year period. The results indicate consistent and positively correlated linear fitting trend changes in the RSEI and SRSEI for the research area between 2001 and 2021. The newly constructed ecological index exhibits a higher correlation with rainfall data, and it shows a more significant decrease in magnitude during drought occurrences, indicating a faster and stronger response of the new index to drought in the research area. The largest proportions are found in the research area’s regions with both substantial and minor improvements, pointing to an upward tendency in the Loess Plateau’s ecosystem development. The newly constructed environmental index can effectively evaluate the quality of the ecosystem in the research area.

Global enhanced vegetation photosynthesis in urban environment and its drivers revealed by satellite solar-induced chlorophyll fluorescence data

Investigation on the future impacts of climatic and environmental change on vegetation photosynthesis has been largely restricted to controlled field experiments, which can hardly be extended to global scale due to limited spatial, species and ecosystem coverages. However, in urban areas plants experience altered environments that mimic potential future conditions, with higher air temperature, atmospheric carbon dioxide (CO2) concentration and pollution levels. Cities can therefore be used as global, unplanned experiments for assessing the photosynthetic response to multiple climatic and environmental drivers. Following this logic, here we investigate the urbanization impact on vegetation primary productivity and its drivers at global 160 mega-cities, using high-spatial resolution satellite solar-induced chlorophyll fluorescence (SIF) data as the proxy of photosynthesis. SIF enhancements were observed across most of the urban-rural gradients, accounting for more than 85% of the investigated land pixels. More importantly, SIF enhancements due to indirect urbanization impact (i.e., the impacts of climatic and environmental factors on vegetation growth) offset approximately 47% of the SIF reductions due to land cover change, a value significantly higher than that observed for a greenness spectral index (Enhanced Vegetation Index, EVI) (30%). Atmospheric CO2, air temperature, radiation and atmospheric nitrogen dioxide (NO2) were found to be the main drivers accounting for the enhanced SIF in urban areas. These results prove a dominant and global enhancement of vegetation photosynthesis in urban conditions, and reveal the specific contribution of climatic and environmental factors. Our findings can help to forecast the impacts of future environmental conditions on vegetation photosynthesis, and enhance our knowledge on the overall response of terrestrial biomes to climate change.

Winter wheat yield prediction in the conterminous United States using solar-induced chlorophyll fluorescence data and XGBoost and random forest algorithm

Predicting crop yield before harvest and understanding the factors determining yield at a regional scale is vital for global food security, supply chain management in agribusiness, crop and insurance pricing and optimising crop production. Often satellite remote sensing data, environmental data or their combinations are used to model crop yield at a regional scale. However, their contribution, including that of recently developed remote sensing data like solar-induced chlorophyll fluorescence (SIF) and near-infrared reflectance of vegetation (NIRv), are not explored sufficiently. This study aims to assess the contribution of weather, soil and remote sensing data to estimate wheat yield prediction at a regional scale. For this, we employed four types of remote sensing data, thirteen climatic variables, four soil variables, and nationwide yield data of 14 years combined with statistical learning methods to predict winter wheat yield in the Conterminous United States (CONUS) and access the role of predicting variables. Machine-learning algorithms were used to build yield prediction models in different experimental settings, and predictive performance was evaluated. Further, the relative importance of predictor variables for the models was assessed to gain insight into the model's behaviour. NIRv and SIF data are found to be promising for crop yield prediction. The model with only NIRv data explained up to 64% of the variability in yield, and adding SIF data improved it to 69%. We also found that vegetation indices, SIF, climate and soil data all contribute unique and overlapping information to crop yield prediction. The study also identified important variables and the time of the growing period when these variables have higher explanatory power for winter wheat yield prediction. This study enhanced our knowledge of yield-predicting variables, which will contribute to optimising the yield and developing better yield prediction models.

Evaluating ecosystem stability based on the dynamic time warping algorithm: A case study in the Minjiang river Basin, China

With the ongoing impacts of global climate change, the cumulative stress effects of external disturbances on ecosystems are gradually strengthening. Therefore, it is very important to scientifically evaluate the stability of ecosystems. In the terrestrial ecosystems, vegetation serves as a link between the water, energy, and carbon cycles, and is an important index which allows for the measurement of ecosystem stability. In this study, two vegetation indices (normalized difference vegetation index and enhanced vegetation index), along with solar-induced chlorophyll fluorescence data from 2001 to 2021, were used to determine the external disturbance events in the Minjiang river basin using a dynamic time warping (DTW) algorithm. Analyses of the disturbance characteristics during this period, and quantification of the vegetation resistance, resilience, and vegetation regime shift rate were conducted. The results indicated the following. (1) The disturbance results monitored by the DTW algorithm were highly consistent with typical events, and it was feasible to monitor the disturbance of the Minjiang river basin using the DTW algorithm. (2) Disturbance intensity in the river basin increased slightly, while in urban areas it decreased. (3) The resistance of different types of vegetation varied greatly, with the resistance strongest in forests, next in croplands, and weakest in grasslands. In contrast, resilience was the strongest in croplands, next in grasslands, and weakest in forests. (4) The vegetation growth state of the river basin was more significantly affected by external disturbance in 2001–2021, with 58.75% of forest, 54.37% of grassland, and 37.21% of cropland having regime shift rates < 1. Lastly, (5) the response of vegetation to external disturbance was diverse under different disturbance scenarios, with the vegetation recovery rate under gradual disturbance being greater than the rate under abrupt disturbance.

Improved monitoring of cryptogam gas-exchange and volatile emissions during desiccation-rehydration cycles with a within-chamber hydration method

Desiccation-rehydration studies in cryptogams constitute an important tool to understand the relation of key physiological traits with species stress tolerance and environmental adaptability. Real-time monitoring of responses has been limited by the design of commercial or custom measuring cuvettes and difficulties in experimental manipulation. We developed a within-chamber rehydration method that allows to rewater the samples rapidly, without the need to open the chamber and take out the sample for manual rehydration by the investigator. Data is collected in real-time and simultaneously with an infrared gas-analyzer (LICOR-7000), a chlorophyll fluorometer (Maxi Imaging-PAM) and a proton transfer reaction time-of-flight mass-spectrometer (PTR-TOF-MS) for volatile organic compound emissions. The system was tested on four cryptogam species with contrasting ecological distributions. No major errors or kinetics disruptions were found during system testing and measurements. Our within-chamber rehydration method improved accuracy, as measurement periods were not lacking, and repeatability of the protocol by reducing error variance in sample manipulation. This method provides an improved technique to conduct desiccation-rehydration measurements, contributing to the standardization and accuracy of current existing methodologies. A close real-time and simultaneous monitoring of photosynthesis, chlorophyll fluorescence and volatile organic compound emission data, offers a novel perspective in the analysis of the cryptogam stress responses that is yet to be fully explored.

Chlorophyll fluorescence variation in two Mediterranean forest species over a 21-year drought treatment period.

The Mediterranean is among the regions predicted to be most affected by climate change due to rising temperatures and increasing frequency as well as intensity of extreme weather events, such as drought. These shifts in climatic conditions might lead to changes in species community composition by enabling the increase of drought-tolerant species at the expense of less tolerant ones. This hypothesis was tested in the current study using chlorophyll fluorescence data from a 21-year precipitation exclusion experiment in a Mediterranean forest for two co-dominant Quercus ilex and Phillyrea latifolia species with contrasting low and high levels of drought tolerance, respectively. The maximum potential quantum efficiency of photosystem II (PSII) (Fv/Fm), photochemical efficiency of PSII (yield), and non-photochemical quenching (NPQ) varied seasonally. Fv/Fm and NPQ levels were positively correlated with air temperature and Standardized Precipitation-Evapotranspiration Index (SPEI), whereas yield, which was greater under drought treatment was negatively associated with vapor pressure deficit and SPEI. Fv/Fm values showed similar increase in the two species over the 21-year study period regardless of treatment and in parallel to progressive warming. In contrast, yield values were higher in Q. ilex than in P. latifolia, while NPQ values were higher in P. latifolia than in Q. ilex. Notably, high yield values were also observed in the drought treated plots. Throughout the study, plants in the drought treated plots exhibited decreased basal area, leaf biomass, and aerial cover due to high stem mortality. In addition, a continuous increase in temperature was detected in summer and autumn, which might explain the observed increase in Fv/Fm values over the study period. Higher yield and lower NPQ detected in Q. ilex could be attributed to less competition for resources in the drought treated plots and acclimation of Q. ilex plants over the study period. Our results indicate that reduction in stem density could improve forest resilience to climate change-induced drought conditions.

Evaluating the durability and performance of polyoxometalate-ionic liquid coatings on calcareous stones: Preventing biocolonisation in outdoor environments

Rock-based materials exposed to outdoor environments are naturally colonised by an array of microorganisms, which can cause dissolution and fracturing of the natural stone. Biocolonisation of monuments and architectures of important cultural heritage therefore represents an expensive and recurring problem for local authorities and private owners alike. In this area, preventive strategies to mitigate biocolonisation are generally preferred to curative approaches, such as mechanical cleaning by brush or high-pressure cleaning, to remove pre-existing patina. The aim of this work was to study the interaction between biocidal polyoxometalate-ionic liquid (POM-IL) coatings and calcareous stones and evaluate the capacity of these coatings to prevent biocolonisation through a series of accelerated ageing studies in climate chambers, carried out in parallel with a two-year period of outdoor exposure in north-eastern France. Our experiments show that POM-IL coatings did not affect water vapour transfer nor significantly alter the total porosity of the calcareous stones. Simulated weathering studies replicating harsh (hot and wet) climatic weather conditions demonstrated that the colour variation of POM-IL-coated stones did not vary significantly with respect to the natural uncoated stones. Accelerated biocolonisation studies performed on the weathered POM-IL-coated stones proved that the coatings were still capable of preventing colonisation by an algal biofilm. However, a combination of colour measurements, chlorophyll fluorescence data, and scanning electron microscopy imaging of stones aged outdoors in northern France for two years showed that coated and uncoated stone samples showed signs of colonisation by fungal mycelium and phototrophs. Altogether, our results demonstrate that POM-ILs are suitable as preventative biocidal coatings for calcareous stones, but the correct concentrations must be chosen to achieve a balance between porosity of the stone, the resulting colour variation and the desired duration of the biocidal effect over longer periods of time, particularly in outdoor environments.

Spatiotemporal Variations in the Sensitivity of Vegetation Growth to Typical Climate Factors on the Qinghai–Tibet Plateau

Gaining knowledge about vegetation sensitivity in response to climate change is a current research priority in the context of accelerated shifts generated by global warming, especially for the Qinghai–Tibet Plateau (QTP), where vegetation is known to be highly sensitive to ongoing climate change. However, the temporal variability of vegetation sensitivity in response to climate change is still poorly understood on the QTP. Here, we articulate the interannual variability of the vegetation sensitivity in response to typical climate factors, including temperature, solar radiation, and water availability, on the QTP during 2000–2021, using a variety of indicators characterizing vegetation dynamics, including the Leaf Area Index (LAI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and solar-induced chlorophyll fluorescence (SIF) data. The results indicate that temperature exerted positive impacts on forests, grasslands, and barren or sparsely vegetated areas (BSVs). However, all the land-cover types showed decreasing sensitivity to temperature variability. Solar radiation had a positive impact on forests, while it had a negative impact on grasslands and BSVs. An increasing trend was observed for forests, while a decreasing trend was found for grasslands and BSVs regarding their sensitivity to solar radiation. Water availability exerted a positive impact on grasslands and BSVs, and no obvious impact direction could be determined for forests. Over the last two decades, forests and BSVs exhibited increasing sensitivity to water availability, and no obvious trend was observed for grasslands. Overall, temperature was the most important climate factor, followed by solar radiation and water availability, regarding the regulation of vegetation sensitivity on the QTP. Spatially, temperature and solar radiation jointly dominated the vegetation sensitivity in the central to eastern QTP. Conversely, water availability dominated the sensitivity of forests in the southeastern QTP and grasslands in the northeastern and southwestern QTP. This study provides theoretical support for the ecological conservation and management of the QTP in the context of ongoing climate change.

Increasing temperature regulates the advance of peak photosynthesis timing in the boreal ecosystem

The shift in vegetation phenology is an essential indicator of global climate change. Numerous researches based on reflectance-based vegetation index data have explored the changes in the start (SOS) and end (EOS) of vegetation life events at long time scales, while a huge discrepancy existed between the phenological metrics of vegetation structure and function. The peak photosynthesis timing (PPT), which is crucial in regulating terrestrial ecosystem carbon balance, has not received much attention. Using two global reconstructed solar-induced chlorophyll fluorescence data (CSIF and GOSIF) directly associated with vegetation photosynthesis, the spatio-temporal dynamics in PPT as well as the key environmental controls across the boreal ecosystem during 2001–2019 were systematically explored. Multi-year mean pattern showed that PPT mainly appeared in the first half of July. Compared to the northern Eurasia, later PPT appeared in the northern North America continent for about 4–5 days. Meanwhile, spatial trend in PPT exhibited an advanced trend during the last two decades. Especially, shrubland and grassland were obvious among all biomes. Spatial partial correlation analysis revealed that preseason temperature was the dominant environmental driver of PPT trends, occupying 81.32% and 78.04% of the total pixels of PPTCSIF and PPTGOSIF, respectively. Attribution analysis by ridge regression again emphasized the largest contribution of temperature to PPT dynamics in the boreal ecosystem by 52.22% (PPTCSIF) and 46.59% (PPTGOSIF), followed by radiation (PPTCSIF: 24.44%; PPTGOSIF: 28.66%) and precipitation (PPTCSIF: 23.34%; PPTGOSIF: 24.75%). These results have significant implications for deepening our understanding between vegetation photosynthetic phenology and carbon cycling with respect to future climate change in the boreal ecosystem.

Spatiotemporal dynamics of vegetation net ecosystem productivity and its response to drought in Northwest China

ABSTRACT Net ecosystem productivity (NEP) quantifies magnitude of the terrestrial vegetation carbon sinks. Drought is one of the most important stressors affecting vegetation NEP. At present, the spatiotemporal dynamics of vegetation NEP in drought-prone of Northwest China (NWC) lack discussion under different climatic zones and land cover types, and the response of vegetation NEP to drought remains unclear. Hence, we estimated the vegetation NEP in NWC using ground and remote sensing data and quantified the spatiotemporal differentiation of NEP under different climatic zones and land cover types. The drought fluorescence monitoring index (DFMI) was developed to examine the relationship between vegetation NEP and drought response based on the solar-induced chlorophyll fluorescence (SIF) data. Our results suggested that vegetation carbon sinks increased significantly at 7.09 g C m−2 yr−1 in NWC during 2000–2019, mainly in northern Shaanxi, eastern and southern Gansu, and southern Ningxia. NEP showed increasing trends under different climatic zones and land cover types, but there were differences in carbon sink capacity. The strongest carbon sink capacity was in humid regions and forests, while the weakest was in arid regions and grasslands. The vegetation carbon sinks showed a non-linear relationship with the drought degree reflecting multiple trend differences, especially in forests and grasslands. The response to drought was faster and more significant in semi-arid and semi-humid transition zones and extreme humid regions when vegetation carbon sinks decreased. DFMI was a good indicator to monitor drought conditions in NWC. NEP and DFMI were an 8–20-month periodic positive correlation and showed a high correlation with high–high and low–low clustering spatially. Drought significantly weakened vegetation carbon sinks in NWC. This study emphasizes the demand to rapidly identify climatic conditions that lead to decrease significantly in vegetation carbon sinks and to formulate adaptation strategies aimed at reducing drought risk under global warming.

Study on the nitrogen content estimation model of cotton leaves based on "image-spectrum-fluorescence" data fusion.

Precise monitoring of cotton leaves' nitrogen content is important for increasing yield and reducing fertilizer application. Spectra and images are used to monitor crop nitrogen information. However, the information expressed using nitrogen monitoring based on a single data source is limited and cannot consider the expression of various phenotypic and physiological parameters simultaneously, which can affect the accuracy of inversion. Introducing a multi-source data-fusion mechanism can improve the accuracy and stability of cotton nitrogen content monitoring from the perspective of information complementarity. Five nitrogen treatments were applied to the test crop, Xinluzao No. 53 cotton, grown indoors. Cotton leaf hyperspectral, chlorophyll fluorescence, and digital image data were collected and screened. A multilevel data-fusion model combining multiple machine learning and stacking integration learning was built from three dimensions: feature-level fusion, decision-level fusion, and hybrid fusion. The determination coefficients (R2) of the feature-level fusion, decision-level fusion, and hybrid-fusion models were 0.752, 0.771, and 0.848, and the root-mean-square errors (RMSE) were 3.806, 3.558, and 2.898, respectively. Compared with the nitrogen estimation models of the three single data sources, R2 increased by 5.0%, 6.8%, and 14.6%, and the RMSE decreased by 3.2%, 9.5%, and 26.3%, respectively. The multilevel fusion model can improve accuracy to varying degrees, and the accuracy and stability were highest with the hybrid-fusion model; these results provide theoretical and technical support for optimizing an accurate method of monitoring cotton leaf nitrogen content.

Monitoring of Drought Stress in Chinese Forests Based on Satellite Solar-Induced Chlorophyll Fluorescence and Multi-Source Remote Sensing Indices

Greenhouse gas emissions have largely changed the global climate, leading to an increase in the frequency and extent of droughts. Forests are essential natural resources, and they play an important role in maintaining ecological security. Effectively monitoring drought stress in forests can help promote sustainable forestry development. Solar-induced chlorophyll fluorescence is a spectral signal released by vegetation photosynthesis after light absorption. In this study, we used solar-induced chlorophyll fluorescence data (SIF), canopy fluorescence yield (SIFyield) data, vegetation indices (NDVI, EVI), leaf area index (LAI), and fraction of absorbed photosynthetically active radiation (fPAR) to study forest drought stress in the Yunnan, Fujian, Shaanxi, and Heilongjiang provinces in China, respectively. The temporal and spatial ranges of drought stress indicated by the Standardized Precipitation-Evapotranspiration Index (SPEI) values were used as a reference (SPEI ≤ −0.5 indicates the occurrence of drought). Firstly, the standardized anomalous values of SIF, SIFyield, NDVI, EVI, LAI, and fPAR were calculated. The temporal and spatial response abilities of each variable to drought stress were analyzed. Secondly, the correlation between each variable and the drought indicator SPEI was quantified. Finally, the validity and variability of SIF and other variables for drought monitoring were analyzed and verified with a random forest classification model. The results showed that on a temporal scale, SIFyield showed an earlier response to drought stress than other variables and the abnormal change of SIFyield was higher than other variables by 10% or more. Spatially, the range of drought areas indicated by SIFyield and SPEI had more coincident areas than other variables. The overall correlation between SIFyield and SPEI was also higher during the drought period, especially during late drought periods when other variables showed negative correlations. For SIFyield, the correlation coefficients of the Yunnan, Fujian, Shaanxi, and Heilongjiang provinces were 0.57, 0.43, 0.32, and 0.49, respectively. Additionally, the variable importance assessment using a random forest model also indicated that SIFyield is more sensitive to forest droughts. We concluded that SIFyield is an effective tool for monitoring forest drought stress in various regions of China and that it can provide a scientific basis for forest drought monitoring.