Accurate phenological information is essential for measuring ecosystem dynamics and carbon uptake.Southwest China is one of the country's largest terrestrial carbon sink regions and plays a crucial role in carbon ...Accurate phenological information is essential for measuring ecosystem dynamics and carbon uptake.Southwest China is one of the country's largest terrestrial carbon sink regions and plays a crucial role in carbon peaking and neutrality.However,its complex terrain,fragile ecosystem,and variable climate challenge carbon sink stability.Vegetation phenology significantly impacts carbon absorption and release,making accurate phenological data essential for understanding carbon sequestration dynamics.The widespread distribution of evergreen forests and their weak seasonal variation in canopy introduce significant uncertainties in extracting phenology using traditional remote sensing information in this region.These limitations can lead to inaccurate assessments of carbon sink dynamics.Therefore,precise phenology extraction and analysis are vital for improving ecosystem dynamics and the carbon cycle in Southwest China.Firstly,we employed different ways to evaluate the ability of solar-induced chlorophyll fluorescence(SIF)and traditional remote sensing information to extract phenology.Secondly,based on SIF,we analyzed the spatial and temporal changes in the start of the growing season(SOS),the end of the growing season(EOS),and the length of the growing season(LOS)from 2001 to 2020.Finally,we systematically analyzed the response of SOS and EOS to five preseason climatic factors.The results showed that(1)SIF outperformed traditional remote sensing information in extracting phenology.(2)Vegetation phenology exhibited significant spatial heterogeneity.Moreover,SOS,EOS,and LOS showed trends of advancement,delay,and extension both overall and across all vegetation types.(3)Precipitation was the main factor influencing SOS,while surface downward solar radiation and mean temperature were the main factors affecting EOS,and the phenology of different vegetation types showed a great difference in response to preseason climate factors.These findings improve our understanding of vegetation phenology and its dynamics over Southwest China.展开更多
Origanum elongatum(OE)is an aromatic,medicinal plant endemic to Morocco that is widely used in traditional medicine due to its biological properties.This study aimed to elucidate the chemical composition of the essent...Origanum elongatum(OE)is an aromatic,medicinal plant endemic to Morocco that is widely used in traditional medicine due to its biological properties.This study aimed to elucidate the chemical composition of the essential oil(EO)obtained from O.elongatum(OEEO)at three stages of its life cycle,including vegetative stage(OEEO-VS),flowering stage(OEEO-FS),and post-flowering(OEEO-PFS),as well as to evaluate its biological and antiradical characteristics.The chemical analysis of the essential oil was conducted using gas chromatography-mass spectrometry(GC-MS).The antibacterial activity was evaluated in vitro through distinct methodologies,namely,disc diffusion and microatmosphere assay;subsequently,the minimum inhibitory concentration(MIC)was then determined.The antioxidant potential was also measured by using the DPPH and FRAP assays.The GC-MS revealed the predominant of p-cymene(26.83%_31.45%),γ-terpinene(8.46%_26.95%),thymol(13%_29.54%),and carvacrol(20.25%_37.26%),in all three samples,with notable variations according to the phenological stage of the samples.The EOs extracted at three phenological stages demonstrated notable antibacterial efficacy against all the phytopathogen tested.The MICs for Erwinia amylovora exhibited a range of 6.25 and 250μg/mL.However,for Agrobacterium tumefaciens C58 and Allorhizobium vitis S4,the MICs spanned 125 and 250μg/mL.In the DPPH test,the IC50 values were 168.25±1.14,147.01±0.78,and 132.01±2.06μg/mL for EOs derived from the vegetative,flowering,and post-flowering period,respectively.In the FRAP test,the EC50 values were 164.22±1.04,215.73±1.48,and 184.06±0.95μg/mL for the same stages.The findings offer promising prospects for the phytochemical development,demonstrating how the phenological stage significantly influences the therapeutic and biotechnological potential of O.elongatum.This has the potential to open up new avenues of research in the pharmaceutical,agronomic,and environmental fields.展开更多
Vegetation phenology is commonly studied using time series of multispectral vegetation indices derived from satellite imagery.Differences in reflectance among land-cover and/or plant functional types are obscured by s...Vegetation phenology is commonly studied using time series of multispectral vegetation indices derived from satellite imagery.Differences in reflectance among land-cover and/or plant functional types are obscured by sub-pixel mixing,and so phenological analyses have typically sought to maximize the compositional purity of input satellite data by increasing spatial resolution.We present an alternative method to mitigate this‘mixed-pixel problem’and extract the phenological behavior of individual land-cover types inferentially,by inverting the linear mixture model traditionally used for sub-pixel land-cover mapping.Parameterized using genetic algorithms,the method takes advantage of the discriminating capacity of calibrated surface reflectance measurements in red,near infrared,and short-wave infrared wavelengths,as well as the Normalized Difference Vegetation Index(NDVI)and the Normalized Difference Water Index.In simulation,the unmixing procedure reproduced the reflectances and phenological signals of grass,crop,and deciduous forests with high fidelity(RMSE<0.007 NDVI);and in empirical tests,the algorithm extracted the phenological characteristics of evergreen trees and seasonal grasses in a semi-arid savannah.The approach shows potential for a wide range of ecological applications,including detection of differential responses to climate,soil,or other factors among vegetation types.展开更多
The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We r...The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We retrieved the start of spring phenology(SOS)of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology.Trend analysis,partial correlation analysis,and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS.The results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of−0.473 d yr^(−1).The SOS of most forests correlated negatively with air temperature(TEMP)and positively with precipitation(PRE),suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,respectively.The dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively minor.The response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.展开更多
The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ.Although previous studies have established a stronger correlation between leaf onset and...The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ.Although previous studies have established a stronger correlation between leaf onset and diurnal temperature than between leaf onset and average temperature,current research on modeling spring phenology based on diurnal temperature indicators remains limited.In this study,we confirmed the start of the growing season(SOS)sensitivity to diurnal temperature and average temperature in boreal forest.The estimation of SOS was carried out by employing K-Nearest Neighbor Regression(KNR-TDN)model,Random Forest Regres-sion(RFR-TDN)model,eXtreme Gradient Boosting(XGB-TDN)model and Light Gradient Boosting Machine model(LightGBM-TDN)driven by diurnal temperature indicators during 1982-2015,and the SOS was projected from 2015 to 2100 based on the Coupled Model Intercomparison Project Phase 6(CMIP6)climate scenario datasets.The sensitivity of boreal forest SOS to daytime temperature is greater than that to average temperature and nighttime temperature.The LightGBM-TDN model perform best across all vegetation types,exhibiting the lowest RMSE and bias compared to the KNR-TDN model,RFR-TDN model and XGB-TDN model.By incorporating diurn-al temperature indicators instead of relying only on average temperature indicators to simulate spring phenology,an improvement in the accuracy of the model is achieved.Furthermore,the preseason accumulated daytime temperature,daytime temperature and snow cover end date emerged as significant drivers of the SOS simulation in the study area.The simulation results based on LightGBM-TDN model exhibit a trend of advancing SOS followed by stabilization under future climate scenarios.This study underscores the potential of diurn-al temperature indicators as a viable alternative to average temperature indicators in driving spring phenology models,offering a prom-ising new method for simulating spring phenology.展开更多
Accurate spatio-temporal land cover information in agricultural irrigation districts is crucial for effective agricultural management and crop production.Therefore,a spectralphenological-based land cover classificatio...Accurate spatio-temporal land cover information in agricultural irrigation districts is crucial for effective agricultural management and crop production.Therefore,a spectralphenological-based land cover classification(SPLC)method combined with a fusion model(flexible spatiotemporal data fusion,FSDAF)(abbreviated as SPLC-F)was proposed to map multi-year land cover and crop type(LC-CT)distribution in agricultural irrigated areas with complex landscapes and cropping system,using time series optical images(Landsat and MODIS).The SPLC-F method was well validated and applied in a super-large irrigated area(Hetao)of the upper Yellow River Basin(YRB).Results showed that the SPLC-F method had a satisfactory performance in producing long-term LC-CT maps in Hetao,without the requirement of field sampling.Then,the spatio-temporal variation and the driving factors of the cropping systems were further analyzed with the aid of detailed household surveys and statistics.We clarified that irrigation and salinity conditions were the main factors that had impacts on crop spatial distribution in the upper YRB.Investment costs,market demand,and crop price are the main driving factors in determining the temporal variations in cropping distribution.Overall,this study provided essential multi-year LC-CT maps for sustainable management of agriculture,eco-environments,and food security in the upper YRB.展开更多
Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms d...Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms driving climate change,phenological monitoring is essential.Traditional methods of defining phenological phases often rely on fixed thresholds.However,with the development of technology,deep learning-based classification models are now able to more accurately delineate phenological phases from images,enabling phenological monitoring.Despite the significant advancements these models have made in phenological monitoring,they still face challenges in fully capturing the complexity of biotic-environmental interactions,which can limit the fine-grained accuracy of phenological phase identification.To address this,we propose a novel deep learning model,RESformer,designed to monitor tree phenology at a fine-grained level using PhenoCam images.RESformer features a lightweight structure,making it suitable for deployment in resource-constrained environments.It incorporates a dual-branch routing mechanism that considers both global and local information,thereby improving the accuracy of phenological monitoring.To validate the effectiveness of RESformer,we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin,focusing on the phenology of Acer.The images were classified into seven distinct phenological stages,with RESformer achieving an overall monitoring accuracy of 96.02%.Furthermore,we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate(GCC)index and ten popular classification models.The results showed that RESformer excelled in fine-grained monitoring,effectively capturing and identifying changes in phenological stages.This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.展开更多
Climate warming is reshaping the phenology of plants in recent decades,with potential implications for forest productivity,carbon sequestration,and ecosystem functioning.While the effects of warming on secondary growt...Climate warming is reshaping the phenology of plants in recent decades,with potential implications for forest productivity,carbon sequestration,and ecosystem functioning.While the effects of warming on secondary growth phenology is becoming increasingly clear,the influenceof environmental factors on different developmental phases of xylem remains to be quantified.In this study,we investigated the temporal dynamics of xylem cell enlargement,wall-thickening,and the interval between these events in twelve temperate tree species from Northeast China over the period 2019–2024.We found that both cell enlargement and wall-thickening advanced significantlyin response to climate warming,with species-specific variations in the rate of advancement.Importantly,the advancing rate of wallthickening was greater than that of cell enlargement,leading to a shortening of the interval between these two events.Linear mixed-effects models revealed that photoperiod,forcing temperature,and precipitation were the primary environmental drivers influencingthe timing of both cell enlargement and wall-thickening,with photoperiod emerging as the most important factor.These results suggest that climate warming accelerates the heat accumulation required for the transition from xylem cell enlargement to wall-thickening,thereby shortening the time interval between these two developmental stages.Beyond contributing valuable multi-year xylem phenological data,our results provide mechanistic insights that enhance predictions of wood formation dynamics under future climate scenarios and improve the accuracy of forest carbon models.展开更多
Climate change and its resulting effects on seasonality are known to alter a variety of animal behaviors including those related to foraging,phenology,and migration.Although many studies focus on the impacts of phenol...Climate change and its resulting effects on seasonality are known to alter a variety of animal behaviors including those related to foraging,phenology,and migration.Although many studies focus on the impacts of phenological changes on physiology or ftness enhancing behaviors,fewer have investigated the relationship between variation in weather and phenology on risk assessment.Fleeing from predators is an economic decision that incurs costs and benefts.As environmental conditions change,animals may face additional stressors that affect their decision to fee and infuence their ability to effectively assess risk.Flight initiation distance(FID)—the distance at which animals move away from threats—is often used to study risk assessment.FID varies due to both internal and external biotic and physical factors as well as anthropogenic activities.We asked whether variation in weather and phenology is associated with risk-taking in a population of yellow-bellied marmots(Marmota faviventer).As the air temperature increased marmots tolerated closer approaches,suggesting that they either perceived less risk or that their response to a threat was thermally compromised.The effect of temperature was relatively small and was largely dependent upon having a larger range in the full data set that permitted us to detect it.We found no effects of either the date that snow disappeared or July precipitation on marmot FID.As global temperatures continue to rise,rainfall varies more and drought becomes more common,understanding climate-related changes in how animals assess risk should be used to inform population viability models.展开更多
Individual phenological life-history variations in the context of seasonal conditions are well documented in fshes and birds.However,amphibians,a group heavily affected by habitat loss and fragmentation,have received ...Individual phenological life-history variations in the context of seasonal conditions are well documented in fshes and birds.However,amphibians,a group heavily affected by habitat loss and fragmentation,have received relatively little attention regarding research on life-history adaptations.Here we present 3 years of data on the timing of reproductive activity in a suburban European green toad(Bufotes viridis)population.We found annually consistent patterns of reproductive activity and investigated whether these were caused by allochrony or individual attributes.Body size(a proxy for age),body condition,and sex signifcantly affected the timing of reproductive activity.However,most individuals showed considerable overlap in their reproductive timeframe,refuting the existence of allochronic subpopulations.Our fndings may indicate life-history adaptations in the direction of a faster lifestyle in response to hazardous environments.We propose to focus further research efforts on phenological variations in the context of environmental conditions,and that phenological variations should be considered more strongly in amphibian conservation efforts.展开更多
Short rotation plantation forestry(SRF)is being widely adopted to increase wood production,in order to meet global demand for wood products.However,to ensure maximum gains from SRF,optimised management regimes need to...Short rotation plantation forestry(SRF)is being widely adopted to increase wood production,in order to meet global demand for wood products.However,to ensure maximum gains from SRF,optimised management regimes need to be established by integrating robust predictions and an understanding of mechanisms underlying tree growth.Hybrid ecophysiological models,such as potentially useable light sum equation(PULSE)models,are useful tools requiring minimal input data that meet the requirements of SRF.PULSE models have been tested and calibrated for different evergreen conifers and broadleaves at both juvenile and mature stages of tree growth with coarse soil and climate data.Therefore,it is prudent to question:can adding detailed soil and climatic data reduce errors in this type of model?In addition,PULSE techniques have not been used to model deciduous species,which are a challenge for ecophysiological models due to their phenology.This study developed a PULSE model for a clonal Populus tomentosa plantation in northern China using detailed edaphic and climatic data.The results showed high precision and low bias in height(m)and basal area(m^(2)·ha^(-1))predictions.While detailed edaphoclimatic data produce highly precise predictions and a good mechanistic understanding,the study suggested that local climatic data could also be employed.The study showed that PULSE modelling in combination with coarse level of edaphic and local climate data resulted in reasonably precise tree growth prediction and minimal bias.展开更多
Near real-time maize phenology monitoring is crucial for field management,cropping system adjustments,and yield estimation.Most phenological monitoring methods are post-seasonal and heavily rely on high-frequency time...Near real-time maize phenology monitoring is crucial for field management,cropping system adjustments,and yield estimation.Most phenological monitoring methods are post-seasonal and heavily rely on high-frequency time-series data.These methods are not applicable on the unmanned aerial vehicle(UAV)platform due to the high cost of acquiring time-series UAV images and the shortage of UAV-based phenological monitoring methods.To address these challenges,we employed the Synthetic Minority Oversampling Technique(SMOTE)for sample augmentation,aiming to resolve the small sample modelling problem.Moreover,we utilized enhanced"separation"and"compactness"feature selection methods to identify input features from multiple data sources.In this process,we incorporated dynamic multi-source data fusion strategies,involving Vegetation index(VI),Color index(CI),and Texture features(TF).A two-stage neural network that combines Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)is proposed to identify maize phenological stages(including sowing,seedling,jointing,trumpet,tasseling,maturity,and harvesting)on UAV platforms.The results indicate that the dataset generated by SMOTE closely resembles the measured dataset.Among dynamic data fusion strategies,the VI-TF combination proves to be most effective,with CI-TF and VI-CI combinations following behind.Notably,as more data sources are integrated,the model's demand for input features experiences a significant decline.In particular,the CNN-LSTM model,based on the fusion of three data sources,exhibited remarkable reliability when validating the three datasets.For Dataset 1(Beijing Xiaotangshan,2023:Data from 12 UAV Flight Missions),the model achieved an overall accuracy(OA)of 86.53%.Additionally,its precision(Pre),recall(Rec),F1 score(F1),false acceptance rate(FAR),and false rejection rate(FRR)were 0.89,0.89,0.87,0.11,and 0.11,respectively.The model also showed strong generalizability in Dataset 2(Beijing Xiaotangshan,2023:Data from 6 UAV Flight Missions)and Dataset 3(Beijing Xiaotangshan,2022:Data from 4 UAV Flight Missions),with OAs of 89.4%and 85%,respectively.Meanwhile,the model has a low demand for input featu res,requiring only 54.55%(99 of all featu res).The findings of this study not only offer novel insights into near real-time crop phenology monitoring,but also provide technical support for agricultural field management and cropping system adaptation.展开更多
Vegetation annual gross primary production(AGPP),the total yearly carbon assimilation via photosynthesis,serves as a key indicator of ecosystem carbon uptake.While AGPP variations are jointly influenced by both vegeta...Vegetation annual gross primary production(AGPP),the total yearly carbon assimilation via photosynthesis,serves as a key indicator of ecosystem carbon uptake.While AGPP variations are jointly influenced by both vegetation phenology and physiology,the effectiveness of satellite-derived indicators in capturing these variations has not been fully evaluated.This study develops and evaluates the satellite-derived phenology and physiology indicators for modeling AGPP variability.We assessed the performance of satellite-derived metrics,including solar-induced chlorophyll fluorescence(SIF),leaf area index(LAI),and enhanced vegetation index(EVI),in capturing AGPP variations.Among these,SIF-based indicators exhibited the highest accuracy(Pearson's r=0.79;root mean square error=414.7 gC·m^(-2)·year^(-1)),outperforming LAI-and EVI-based indicators.To further investigate the mechanisms driving AGPP variability,we used a structural equation model based on in situ observations to quantify the direct and indirect effects of climate on AGPP through phenology and physiology.Our results reveal that vegetation physiology,particularly the seasonal maximum gross primary production,plays a more dominant role in regulating AGPP than phenology.Furthermore,we found that globally,SIF-derived phenology indicators tend to be lower than those from LAI and EVI,whereas SIF-derived physiology indicators are elevated in tropical regions and the Southern Hemisphere.These findings highlight the potential of satellite-derived indicators in advancing AGPP modeling and emphasize the predominant role of vegetation physiology in regulating ecosystem carbon uptake.This study contributes to a refined understanding of global carbon cycle dynamics and provides insights for improving large-scale carbon assessments in the context of climate change.展开更多
The Tarim River Basin(TRB)is a vast area with plenty of light and heat and is an important base for grain and cotton production in Northwest China.In the context of climate change,however,the increased frequency of ex...The Tarim River Basin(TRB)is a vast area with plenty of light and heat and is an important base for grain and cotton production in Northwest China.In the context of climate change,however,the increased frequency of extreme weather and climate events is having numerous negative impacts on the region's agricultural production.To better understand how unfavorable climatic conditions affect crop production,we explored the relationship of extreme weather and climate events with crop yields and phenology.In this research,ten indicators of extreme weather and climate events(consecutive dry days(CDD),min Tmax(TXn),max Tmin(TNx),tropical nights(TR),warm days(Tx90p),warm nights(Tn90p),summer days(SU),frost days(FD),very wet days(R95p),and windy days(WD))were selected to analyze the impact of spatial and temporal variations on the yields of major crops(wheat,maize,and cotton)in the TRB from 1990 to 2020.The three key findings of this research were as follows:extreme temperatures in southwestern TRB showed an increasing trend,with higher extreme temperatures at night,while the occurrence of extreme weather and climate events in northeastern TRB was relatively low.The number of FD was on the rise,while WD also increased in recent years.Crop yields were higher in the northeast compared with the southwest,and wheat,maize,and cotton yields generally showed an increasing trend despite an earlier decline.The correlation of extreme weather and climate events on crop yields can be categorized as extreme nighttime temperature indices(TNx,Tn90p,TR,and FD),extreme daytime temperature indices(TXn,Tx90p,and SU),extreme precipitation indices(CDD and R95p),and extreme wind(WD).By using Random Forest(RF)approach to determine the effects of different extreme weather and climate events on the yields of different crops,we found that the importance of extreme precipitation indices(CDD and R95p)to crop yield decreased significantly over time.As well,we found that the importance of the extreme nighttime temperature(TR and TNx)for the yields of the three crops increased during 2005-2020 compared with 1990-2005.The impact of extreme temperature events on wheat,maize,and cotton yields in the TRB is becoming increasingly significant,and this finding can inform policy decisions and agronomic innovations to better cope with current and future climate warming.展开更多
Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on t...Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season.A recent development in maize phenology detection research is to use canopy height(CH)data instead of spectral indices,but its robustness in multiple treatments and stages has not been confirmed.Meanwhile,because data of a complete growth season are needed,the need for timely in-season TD identification remains unmet.This study proposed an approach to timely identify and forecast the maize TD.We obtained RGB and light detection and ranging(Li DAR)data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments.After CH estimation,the feature points(inflection point)from the Logistic curve of the CH time series were extracted as TD.We examined the impact of various independent variables(day of year vs.accumulated growing degree days(AGDD)),sensors(RGB and Li DAR),time series denoise methods,different feature points,and temporal resolution on TD identification.Lastly,we used early CH time series data to predict height growth and further forecast TD.The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from Li DAR to estimate maize CH was the most stable across treatments and stages(R~2:0.928 to0.943).For TD identification,the best performance was achieved by using Li DAR data with AGDD as the independent variable,combined with the knee point method,resulting in RMSE of 2.95 d.The high accuracy was maintained at temporal resolutions as coarse as 14 d.TD forecast got more accurate as the CH time series extended.The optimal timing for forecasting TD was when the CH exceeded half of its maximum.Using only Li DAR CH data below 1.6 m and empirical growth rate estimates,the forecasted TD showed an RMSE of 3.90 d.In conclusion,this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.展开更多
Phenology shifts influence regional climate by altering energy,and water fluxes through biophysical processes.However,a quantitative understanding of the phenological control on vegetation’s biophysical feedbacks to ...Phenology shifts influence regional climate by altering energy,and water fluxes through biophysical processes.However,a quantitative understanding of the phenological control on vegetation’s biophysical feedbacks to re gional climate remains elusive.Using long-term remote sensing observations and Weather Research and Fore casting(WRF)model simulations,we investigated vegetation phenology changes from 2003 to 2020 and quan tified their biophysical controls on the regional climate in Northeast China.Our findings elucidated that earlier green-up contributed to a prolonged growing season in forests,while advanced green-up and delayed dormancy extended the growing season in croplands.This prolonged presence and increased maximum green cover in tensified climate-vegetation interactions,resulting in more significant surface cooling in croplands compared to forests.Surface cooling from forest phenology changes was prominent during May’s green-up(-0.53±0.07°C),while crop phenology changes induced cooling throughout the growing season,particularly in June(-0.47±0.15°C),July(-0.48±0.11°C),and September(-0.28±0.09°C).Furthermore,we unraveled the contributions of different biophysical pathways to temperature feedback using a two-resistance attribution model,with aero dynamic resistance emerging as the dominant factor.Crucially,our findings underscored that the land surface temperature(LST)sensitivity,exhibited substantially higher values in croplands rather than temperate forests.These strong sensitivities,coupled with the projected continuation of phenology shifts,portend further growing season cooling in croplands.These findings contribute to a more comprehensive understanding of the intricate feedback mechanisms between vegetation phenology and surface temperature,emphasizing the significance of vegetation phenology dynamics in shaping regional climate pattern and seasonality.展开更多
Shaanxi province in China is rich in vegetation resources,making the study of vegetation phenology crucial for understanding climate change and ecological dynamics.Previous studies on Shaanxi have primarily focused on...Shaanxi province in China is rich in vegetation resources,making the study of vegetation phenology crucial for understanding climate change and ecological dynamics.Previous studies on Shaanxi have primarily focused on coarse-scale phenology,which results in the loss of essential spatial details due to the lack of finer-scale analyses.Moreover,the impact of rapid urbanisation on the phenology of various vegetation types in this region remains unclear.To address these gaps,we integrated Landsat and MODIS data to generate a daily 30 m NDVI sequence.Vegetation phenology across Shaanxi from 2000 to 2020 was then extracted and analysed,and the responses of different vegetation types to urbanisation were explored.Results indicated that accurate vegetation phenology can be obtained at a 30 m resolution,which is more precise than conventional coarse-scale investigations.The Start of Season(SOS)and End of Season(EOS)in the province advanced by-0.56 and-0.08 day/year,respectively,from 2000 to 2020.In general,the SOS gradually delays,and the EOS progressively advances with increasing latitude.However,anomalies were identified in regions south of 32.64°N and across the zone from 34.04°N to 37.56°N,which are primarily due to the effects of high elevation and rapid urbanisation,respectively.Further investigation into phenological differences among various vegetation types revealed that forests exhibit the earliest SOS,while grasslands present the latest.In addition,a significant negative correlation was found between the SOS difference and air temperature difference along the urban-rural gradient,with urbanisation having the most significant impact on cropland phenology.This study provides valuable insights into vegetation phenology and its response to urbanisation.These insights offer crucial information for ecological conservation and management strategies.展开更多
The net primary productivity(NPP) is an important indicator for assessing the carbon sequestration capacities of different ecosystems and plays a crucial role in the global biosphere carbon cycle. However, in the cont...The net primary productivity(NPP) is an important indicator for assessing the carbon sequestration capacities of different ecosystems and plays a crucial role in the global biosphere carbon cycle. However, in the context of the increasing frequency, intensity, and duration of global extreme climate events, the impacts of extreme climate and vegetation phenology on NPP are still unclear, especially on the Qinghai-Xizang Plateau(QXP), China. In this study, we used a new data fusion method based on the MOD13A2 normalized difference vegetation index(NDVI) and the Global Inventory Modeling and Mapping Studies(GIMMS) NDVI_(3g) datasets to obtain a NDVI dataset(1982–2020) on the QXP. Then, we developed a NPP dataset across the QXP using the Carnegie-Ames-Stanford Approach(CASA) model and validated its applicability based on gauged NPP data. Subsequently, we calculated 18 extreme climate indices based on the CN05.1 dataset, and extracted the length of vegetation growing season using the threshold method and double logistic model based on the annual NDVI time series. Finally, we explored the spatiotemporal patterns of NPP on the QXP and the impact mechanisms of extreme climate and the length of vegetation growing season on NPP. The results indicated that the estimated NPP exhibited good applicability. Specifically, the correlation coefficient, relative bias, mean error, and root mean square error between the estimated NPP and gauged NPP were 0.76, 0.17, 52.89 g C/(m^(2)·a), and 217.52 g C/(m^(2)·a), respectively. The NPP of alpine meadow, alpine steppe, forest, and main ecosystem on the QXP mainly exhibited an increasing trend during 1982–2020, with rates of 0.35, 0.38, 1.40, and 0.48 g C/(m^(2)·a), respectively. Spatially, the NPP gradually decreased from southeast to northwest across the QXP. Extreme climate had greater impact on NPP than the length of vegetation growing season on the QXP. Specifically, the increase in extremely-wet-day precipitation(R99p), simple daily intensity index(SDII), and hottest day(TXx) increased the NPP in different ecosystems across the QXP, while the increases in the cold spell duration index(CSDI) and warm spell duration index(WSDI) decreased the NPP in these ecosystems. The results of this study provide a scientific basis for relevant departments to formulate future policies addressing the impact of extreme climate on vegetation in different ecosystems on the QXP.展开更多
It is generally accepted that climate has changed greatly on a global scale, and that the earth＇s climate has already wanned by some degrees over the past century. Ample evidence shows that there have been apparent c...It is generally accepted that climate has changed greatly on a global scale, and that the earth＇s climate has already wanned by some degrees over the past century. Ample evidence shows that there have been apparent changes in avian population dynamics, life-history traits and geographic ranges in response to global climate change. This paper briefly reviews the possible effects of climate change on avian biology and ecology all over the world, with emphasis on new findings from several long-term studies in Europe and North America, which provide unique opportunities to investigate how long-term changes in climate affect birds at both individual and population levels. The implications of such long-term studies for future bird studies in China is discussed with hope that this review can contribute to the preparation and plan for studies of climatic effects on birds in China in the future.展开更多
[Objective] This study was to explore the growth characteristics and fruit quality of a new bud mutant line, ＇Chuanzao Loquat＇. [Method] Paraffin section technique combined with field investigation method were adopt...[Objective] This study was to explore the growth characteristics and fruit quality of a new bud mutant line, ＇Chuanzao Loquat＇. [Method] Paraffin section technique combined with field investigation method were adopted to conduct com- parative analysis of shoot histomorphology and phenological phases between two Io- quat varieties, ＇Chuanzao Loquat＇ and ＇Zaozhong 6＇. [Result] ＇Chuanzao Loquat＇ branched out and unfolded leaves about half to a month earlier than ＇Zaozhong 6＇; both the flowering and fruiting phases of ＇Chuanzao Loquat＇ were three months earlier than a precocious variety, ＇Zaozhong 6＇; the proportions of epidermis, cortex parenchyma, vascular tissue and medulla were 3.7%, 14.5%, 15.9% and 65.9%, re- spectively, in spdng shoots of ＇Chuanzao Loquat＇, and 3.1%, 42.5%, 6.9% and 47.5%, respectively, in ＇Zaozhong 6＇. [Conclusion] In terms of phenological phases, ＇Chuanzao Loqua＇ is earlier than ＇Zaozhong 6＇, a currently widely planted precocious variety, and thus is an important germplasm resource of Ioquats.展开更多
基金supported by the National Natural Science Foundation of China[Grant NO.42401465 and 42401464]Yunnan Fundamental Research Projects[Grant NO.202501AT070343,202401AU070169 and 202401CF070161]+1 种基金Natural Science Fund of Kunming University of Science and Technology(KKZ3202421125)Yunnan Provincial Talent Project“High-level Talent Training Support Plan”[YNWR-QNBJ-2020-031]。
文摘Accurate phenological information is essential for measuring ecosystem dynamics and carbon uptake.Southwest China is one of the country's largest terrestrial carbon sink regions and plays a crucial role in carbon peaking and neutrality.However,its complex terrain,fragile ecosystem,and variable climate challenge carbon sink stability.Vegetation phenology significantly impacts carbon absorption and release,making accurate phenological data essential for understanding carbon sequestration dynamics.The widespread distribution of evergreen forests and their weak seasonal variation in canopy introduce significant uncertainties in extracting phenology using traditional remote sensing information in this region.These limitations can lead to inaccurate assessments of carbon sink dynamics.Therefore,precise phenology extraction and analysis are vital for improving ecosystem dynamics and the carbon cycle in Southwest China.Firstly,we employed different ways to evaluate the ability of solar-induced chlorophyll fluorescence(SIF)and traditional remote sensing information to extract phenology.Secondly,based on SIF,we analyzed the spatial and temporal changes in the start of the growing season(SOS),the end of the growing season(EOS),and the length of the growing season(LOS)from 2001 to 2020.Finally,we systematically analyzed the response of SOS and EOS to five preseason climatic factors.The results showed that(1)SIF outperformed traditional remote sensing information in extracting phenology.(2)Vegetation phenology exhibited significant spatial heterogeneity.Moreover,SOS,EOS,and LOS showed trends of advancement,delay,and extension both overall and across all vegetation types.(3)Precipitation was the main factor influencing SOS,while surface downward solar radiation and mean temperature were the main factors affecting EOS,and the phenology of different vegetation types showed a great difference in response to preseason climate factors.These findings improve our understanding of vegetation phenology and its dynamics over Southwest China.
文摘Origanum elongatum(OE)is an aromatic,medicinal plant endemic to Morocco that is widely used in traditional medicine due to its biological properties.This study aimed to elucidate the chemical composition of the essential oil(EO)obtained from O.elongatum(OEEO)at three stages of its life cycle,including vegetative stage(OEEO-VS),flowering stage(OEEO-FS),and post-flowering(OEEO-PFS),as well as to evaluate its biological and antiradical characteristics.The chemical analysis of the essential oil was conducted using gas chromatography-mass spectrometry(GC-MS).The antibacterial activity was evaluated in vitro through distinct methodologies,namely,disc diffusion and microatmosphere assay;subsequently,the minimum inhibitory concentration(MIC)was then determined.The antioxidant potential was also measured by using the DPPH and FRAP assays.The GC-MS revealed the predominant of p-cymene(26.83%_31.45%),γ-terpinene(8.46%_26.95%),thymol(13%_29.54%),and carvacrol(20.25%_37.26%),in all three samples,with notable variations according to the phenological stage of the samples.The EOs extracted at three phenological stages demonstrated notable antibacterial efficacy against all the phytopathogen tested.The MICs for Erwinia amylovora exhibited a range of 6.25 and 250μg/mL.However,for Agrobacterium tumefaciens C58 and Allorhizobium vitis S4,the MICs spanned 125 and 250μg/mL.In the DPPH test,the IC50 values were 168.25±1.14,147.01±0.78,and 132.01±2.06μg/mL for EOs derived from the vegetative,flowering,and post-flowering period,respectively.In the FRAP test,the EC50 values were 164.22±1.04,215.73±1.48,and 184.06±0.95μg/mL for the same stages.The findings offer promising prospects for the phytochemical development,demonstrating how the phenological stage significantly influences the therapeutic and biotechnological potential of O.elongatum.This has the potential to open up new avenues of research in the pharmaceutical,agronomic,and environmental fields.
基金This work was supported by the National Aeronautics and Space Administration(NASA)Biodiversity and Ecological Forecasting Programs[grant number NNX11AR65G].
文摘Vegetation phenology is commonly studied using time series of multispectral vegetation indices derived from satellite imagery.Differences in reflectance among land-cover and/or plant functional types are obscured by sub-pixel mixing,and so phenological analyses have typically sought to maximize the compositional purity of input satellite data by increasing spatial resolution.We present an alternative method to mitigate this‘mixed-pixel problem’and extract the phenological behavior of individual land-cover types inferentially,by inverting the linear mixture model traditionally used for sub-pixel land-cover mapping.Parameterized using genetic algorithms,the method takes advantage of the discriminating capacity of calibrated surface reflectance measurements in red,near infrared,and short-wave infrared wavelengths,as well as the Normalized Difference Vegetation Index(NDVI)and the Normalized Difference Water Index.In simulation,the unmixing procedure reproduced the reflectances and phenological signals of grass,crop,and deciduous forests with high fidelity(RMSE<0.007 NDVI);and in empirical tests,the algorithm extracted the phenological characteristics of evergreen trees and seasonal grasses in a semi-arid savannah.The approach shows potential for a wide range of ecological applications,including detection of differential responses to climate,soil,or other factors among vegetation types.
基金National Key Research and Development Program of China,No.2023YFE0208100,No.2021YFC3000201Natural Science Foundation of Henan Province,No.232300420165。
文摘The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We retrieved the start of spring phenology(SOS)of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology.Trend analysis,partial correlation analysis,and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS.The results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of−0.473 d yr^(−1).The SOS of most forests correlated negatively with air temperature(TEMP)and positively with precipitation(PRE),suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,respectively.The dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively minor.The response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.
基金Under the auspices of National Natural Science Foundation of China(No.42201374,42071359)。
文摘The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ.Although previous studies have established a stronger correlation between leaf onset and diurnal temperature than between leaf onset and average temperature,current research on modeling spring phenology based on diurnal temperature indicators remains limited.In this study,we confirmed the start of the growing season(SOS)sensitivity to diurnal temperature and average temperature in boreal forest.The estimation of SOS was carried out by employing K-Nearest Neighbor Regression(KNR-TDN)model,Random Forest Regres-sion(RFR-TDN)model,eXtreme Gradient Boosting(XGB-TDN)model and Light Gradient Boosting Machine model(LightGBM-TDN)driven by diurnal temperature indicators during 1982-2015,and the SOS was projected from 2015 to 2100 based on the Coupled Model Intercomparison Project Phase 6(CMIP6)climate scenario datasets.The sensitivity of boreal forest SOS to daytime temperature is greater than that to average temperature and nighttime temperature.The LightGBM-TDN model perform best across all vegetation types,exhibiting the lowest RMSE and bias compared to the KNR-TDN model,RFR-TDN model and XGB-TDN model.By incorporating diurn-al temperature indicators instead of relying only on average temperature indicators to simulate spring phenology,an improvement in the accuracy of the model is achieved.Furthermore,the preseason accumulated daytime temperature,daytime temperature and snow cover end date emerged as significant drivers of the SOS simulation in the study area.The simulation results based on LightGBM-TDN model exhibit a trend of advancing SOS followed by stabilization under future climate scenarios.This study underscores the potential of diurn-al temperature indicators as a viable alternative to average temperature indicators in driving spring phenology models,offering a prom-ising new method for simulating spring phenology.
基金National Natural Science Foundation of China,No.52379053,No.52022108The Key Research Project of Science and Technology in Inner Mongolia Autonomous Region of China,No.NMKJXM202208,No.NMKJXM202301The Project Funded by the Water Resources Department of Inner Mongolia Autonomous Region of China,No.NSK202103。
文摘Accurate spatio-temporal land cover information in agricultural irrigation districts is crucial for effective agricultural management and crop production.Therefore,a spectralphenological-based land cover classification(SPLC)method combined with a fusion model(flexible spatiotemporal data fusion,FSDAF)(abbreviated as SPLC-F)was proposed to map multi-year land cover and crop type(LC-CT)distribution in agricultural irrigated areas with complex landscapes and cropping system,using time series optical images(Landsat and MODIS).The SPLC-F method was well validated and applied in a super-large irrigated area(Hetao)of the upper Yellow River Basin(YRB).Results showed that the SPLC-F method had a satisfactory performance in producing long-term LC-CT maps in Hetao,without the requirement of field sampling.Then,the spatio-temporal variation and the driving factors of the cropping systems were further analyzed with the aid of detailed household surveys and statistics.We clarified that irrigation and salinity conditions were the main factors that had impacts on crop spatial distribution in the upper YRB.Investment costs,market demand,and crop price are the main driving factors in determining the temporal variations in cropping distribution.Overall,this study provided essential multi-year LC-CT maps for sustainable management of agriculture,eco-environments,and food security in the upper YRB.
基金supported by the National Natural Science Foundation of China(32171777)the Natural Science Foundation of Heilongjiang for Distinguished Young Scientists(JQ2023F002)the Fundamental Research Funds for Central Universities(2572023CT16).
文摘Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms driving climate change,phenological monitoring is essential.Traditional methods of defining phenological phases often rely on fixed thresholds.However,with the development of technology,deep learning-based classification models are now able to more accurately delineate phenological phases from images,enabling phenological monitoring.Despite the significant advancements these models have made in phenological monitoring,they still face challenges in fully capturing the complexity of biotic-environmental interactions,which can limit the fine-grained accuracy of phenological phase identification.To address this,we propose a novel deep learning model,RESformer,designed to monitor tree phenology at a fine-grained level using PhenoCam images.RESformer features a lightweight structure,making it suitable for deployment in resource-constrained environments.It incorporates a dual-branch routing mechanism that considers both global and local information,thereby improving the accuracy of phenological monitoring.To validate the effectiveness of RESformer,we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin,focusing on the phenology of Acer.The images were classified into seven distinct phenological stages,with RESformer achieving an overall monitoring accuracy of 96.02%.Furthermore,we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate(GCC)index and ten popular classification models.The results showed that RESformer excelled in fine-grained monitoring,effectively capturing and identifying changes in phenological stages.This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.
基金supported by the Ministry of Science and Technology(No:2019FY101602).
文摘Climate warming is reshaping the phenology of plants in recent decades,with potential implications for forest productivity,carbon sequestration,and ecosystem functioning.While the effects of warming on secondary growth phenology is becoming increasingly clear,the influenceof environmental factors on different developmental phases of xylem remains to be quantified.In this study,we investigated the temporal dynamics of xylem cell enlargement,wall-thickening,and the interval between these events in twelve temperate tree species from Northeast China over the period 2019–2024.We found that both cell enlargement and wall-thickening advanced significantlyin response to climate warming,with species-specific variations in the rate of advancement.Importantly,the advancing rate of wallthickening was greater than that of cell enlargement,leading to a shortening of the interval between these two events.Linear mixed-effects models revealed that photoperiod,forcing temperature,and precipitation were the primary environmental drivers influencingthe timing of both cell enlargement and wall-thickening,with photoperiod emerging as the most important factor.These results suggest that climate warming accelerates the heat accumulation required for the transition from xylem cell enlargement to wall-thickening,thereby shortening the time interval between these two developmental stages.Beyond contributing valuable multi-year xylem phenological data,our results provide mechanistic insights that enhance predictions of wood formation dynamics under future climate scenarios and improve the accuracy of forest carbon models.
基金supported by the University of Ottawa and a NSERC Discovery grant(DGECR-2019-00289,RGPIN-2019-05000)the National Geographic Society,the University of California Los Angeles(Faculty Senate and Division of Life Sciences),an RMBL research fellowship,and the U.S.National Science Foundation(NSF IDBR-0754247 and DEB-1119660 and 1557130 to D.T.B.,as well as DBI 0242960,07211346,1226713,and 1755522 to RMBL).
文摘Climate change and its resulting effects on seasonality are known to alter a variety of animal behaviors including those related to foraging,phenology,and migration.Although many studies focus on the impacts of phenological changes on physiology or ftness enhancing behaviors,fewer have investigated the relationship between variation in weather and phenology on risk assessment.Fleeing from predators is an economic decision that incurs costs and benefts.As environmental conditions change,animals may face additional stressors that affect their decision to fee and infuence their ability to effectively assess risk.Flight initiation distance(FID)—the distance at which animals move away from threats—is often used to study risk assessment.FID varies due to both internal and external biotic and physical factors as well as anthropogenic activities.We asked whether variation in weather and phenology is associated with risk-taking in a population of yellow-bellied marmots(Marmota faviventer).As the air temperature increased marmots tolerated closer approaches,suggesting that they either perceived less risk or that their response to a threat was thermally compromised.The effect of temperature was relatively small and was largely dependent upon having a larger range in the full data set that permitted us to detect it.We found no effects of either the date that snow disappeared or July precipitation on marmot FID.As global temperatures continue to rise,rainfall varies more and drought becomes more common,understanding climate-related changes in how animals assess risk should be used to inform population viability models.
文摘Individual phenological life-history variations in the context of seasonal conditions are well documented in fshes and birds.However,amphibians,a group heavily affected by habitat loss and fragmentation,have received relatively little attention regarding research on life-history adaptations.Here we present 3 years of data on the timing of reproductive activity in a suburban European green toad(Bufotes viridis)population.We found annually consistent patterns of reproductive activity and investigated whether these were caused by allochrony or individual attributes.Body size(a proxy for age),body condition,and sex signifcantly affected the timing of reproductive activity.However,most individuals showed considerable overlap in their reproductive timeframe,refuting the existence of allochronic subpopulations.Our fndings may indicate life-history adaptations in the direction of a faster lifestyle in response to hazardous environments.We propose to focus further research efforts on phenological variations in the context of environmental conditions,and that phenological variations should be considered more strongly in amphibian conservation efforts.
基金The National Key Research and Development Program of China(Grant No.2021YFD2201203)the 5·5 Engineering Research&Innovation Team Project of Beijing Forestry University(No.BLRC2023C05)the Key Research and Development Program of Shandong Province(No.2021SFGC02050102)。
文摘Short rotation plantation forestry(SRF)is being widely adopted to increase wood production,in order to meet global demand for wood products.However,to ensure maximum gains from SRF,optimised management regimes need to be established by integrating robust predictions and an understanding of mechanisms underlying tree growth.Hybrid ecophysiological models,such as potentially useable light sum equation(PULSE)models,are useful tools requiring minimal input data that meet the requirements of SRF.PULSE models have been tested and calibrated for different evergreen conifers and broadleaves at both juvenile and mature stages of tree growth with coarse soil and climate data.Therefore,it is prudent to question:can adding detailed soil and climatic data reduce errors in this type of model?In addition,PULSE techniques have not been used to model deciduous species,which are a challenge for ecophysiological models due to their phenology.This study developed a PULSE model for a clonal Populus tomentosa plantation in northern China using detailed edaphic and climatic data.The results showed high precision and low bias in height(m)and basal area(m^(2)·ha^(-1))predictions.While detailed edaphoclimatic data produce highly precise predictions and a good mechanistic understanding,the study suggested that local climatic data could also be employed.The study showed that PULSE modelling in combination with coarse level of edaphic and local climate data resulted in reasonably precise tree growth prediction and minimal bias.
基金supported by grants from the National Key Research and Development Program of China(2022YFD2001103)the National Natural Science Foundation of China(42371373)。
文摘Near real-time maize phenology monitoring is crucial for field management,cropping system adjustments,and yield estimation.Most phenological monitoring methods are post-seasonal and heavily rely on high-frequency time-series data.These methods are not applicable on the unmanned aerial vehicle(UAV)platform due to the high cost of acquiring time-series UAV images and the shortage of UAV-based phenological monitoring methods.To address these challenges,we employed the Synthetic Minority Oversampling Technique(SMOTE)for sample augmentation,aiming to resolve the small sample modelling problem.Moreover,we utilized enhanced"separation"and"compactness"feature selection methods to identify input features from multiple data sources.In this process,we incorporated dynamic multi-source data fusion strategies,involving Vegetation index(VI),Color index(CI),and Texture features(TF).A two-stage neural network that combines Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)is proposed to identify maize phenological stages(including sowing,seedling,jointing,trumpet,tasseling,maturity,and harvesting)on UAV platforms.The results indicate that the dataset generated by SMOTE closely resembles the measured dataset.Among dynamic data fusion strategies,the VI-TF combination proves to be most effective,with CI-TF and VI-CI combinations following behind.Notably,as more data sources are integrated,the model's demand for input features experiences a significant decline.In particular,the CNN-LSTM model,based on the fusion of three data sources,exhibited remarkable reliability when validating the three datasets.For Dataset 1(Beijing Xiaotangshan,2023:Data from 12 UAV Flight Missions),the model achieved an overall accuracy(OA)of 86.53%.Additionally,its precision(Pre),recall(Rec),F1 score(F1),false acceptance rate(FAR),and false rejection rate(FRR)were 0.89,0.89,0.87,0.11,and 0.11,respectively.The model also showed strong generalizability in Dataset 2(Beijing Xiaotangshan,2023:Data from 6 UAV Flight Missions)and Dataset 3(Beijing Xiaotangshan,2022:Data from 4 UAV Flight Missions),with OAs of 89.4%and 85%,respectively.Meanwhile,the model has a low demand for input featu res,requiring only 54.55%(99 of all featu res).The findings of this study not only offer novel insights into near real-time crop phenology monitoring,but also provide technical support for agricultural field management and cropping system adaptation.
基金support from the National Natural Science Foundation of China(Nos.42450226,42371483,and 42171308)the Natural Science Foundation of Guangdong Province,China(No.2025A1515010770)the Postdoctoral Fellowship Program of CPSF(No.GZB20240880).
文摘Vegetation annual gross primary production(AGPP),the total yearly carbon assimilation via photosynthesis,serves as a key indicator of ecosystem carbon uptake.While AGPP variations are jointly influenced by both vegetation phenology and physiology,the effectiveness of satellite-derived indicators in capturing these variations has not been fully evaluated.This study develops and evaluates the satellite-derived phenology and physiology indicators for modeling AGPP variability.We assessed the performance of satellite-derived metrics,including solar-induced chlorophyll fluorescence(SIF),leaf area index(LAI),and enhanced vegetation index(EVI),in capturing AGPP variations.Among these,SIF-based indicators exhibited the highest accuracy(Pearson's r=0.79;root mean square error=414.7 gC·m^(-2)·year^(-1)),outperforming LAI-and EVI-based indicators.To further investigate the mechanisms driving AGPP variability,we used a structural equation model based on in situ observations to quantify the direct and indirect effects of climate on AGPP through phenology and physiology.Our results reveal that vegetation physiology,particularly the seasonal maximum gross primary production,plays a more dominant role in regulating AGPP than phenology.Furthermore,we found that globally,SIF-derived phenology indicators tend to be lower than those from LAI and EVI,whereas SIF-derived physiology indicators are elevated in tropical regions and the Southern Hemisphere.These findings highlight the potential of satellite-derived indicators in advancing AGPP modeling and emphasize the predominant role of vegetation physiology in regulating ecosystem carbon uptake.This study contributes to a refined understanding of global carbon cycle dynamics and provides insights for improving large-scale carbon assessments in the context of climate change.
基金funded by the Tianshan Yingcai Program of the Xinjiang Uygur Autonomous Region(2022TSYCCX0038)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Y2022108)the Postdoctoral Fellowship Program of Chinese Postdoctoral Science Foundation(CPSF)(GZC20232962).
文摘The Tarim River Basin(TRB)is a vast area with plenty of light and heat and is an important base for grain and cotton production in Northwest China.In the context of climate change,however,the increased frequency of extreme weather and climate events is having numerous negative impacts on the region's agricultural production.To better understand how unfavorable climatic conditions affect crop production,we explored the relationship of extreme weather and climate events with crop yields and phenology.In this research,ten indicators of extreme weather and climate events(consecutive dry days(CDD),min Tmax(TXn),max Tmin(TNx),tropical nights(TR),warm days(Tx90p),warm nights(Tn90p),summer days(SU),frost days(FD),very wet days(R95p),and windy days(WD))were selected to analyze the impact of spatial and temporal variations on the yields of major crops(wheat,maize,and cotton)in the TRB from 1990 to 2020.The three key findings of this research were as follows:extreme temperatures in southwestern TRB showed an increasing trend,with higher extreme temperatures at night,while the occurrence of extreme weather and climate events in northeastern TRB was relatively low.The number of FD was on the rise,while WD also increased in recent years.Crop yields were higher in the northeast compared with the southwest,and wheat,maize,and cotton yields generally showed an increasing trend despite an earlier decline.The correlation of extreme weather and climate events on crop yields can be categorized as extreme nighttime temperature indices(TNx,Tn90p,TR,and FD),extreme daytime temperature indices(TXn,Tx90p,and SU),extreme precipitation indices(CDD and R95p),and extreme wind(WD).By using Random Forest(RF)approach to determine the effects of different extreme weather and climate events on the yields of different crops,we found that the importance of extreme precipitation indices(CDD and R95p)to crop yield decreased significantly over time.As well,we found that the importance of the extreme nighttime temperature(TR and TNx)for the yields of the three crops increased during 2005-2020 compared with 1990-2005.The impact of extreme temperature events on wheat,maize,and cotton yields in the TRB is becoming increasingly significant,and this finding can inform policy decisions and agronomic innovations to better cope with current and future climate warming.
基金supported by National Science and Technology Major Project(2022ZD0115701)Nanfan Special Project,CAAS(YBXM2305,YBXM2401,YBXM2402,PTXM2402)+1 种基金National Natural Science Foundation of China(42071426,42301427)the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences。
文摘Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season.A recent development in maize phenology detection research is to use canopy height(CH)data instead of spectral indices,but its robustness in multiple treatments and stages has not been confirmed.Meanwhile,because data of a complete growth season are needed,the need for timely in-season TD identification remains unmet.This study proposed an approach to timely identify and forecast the maize TD.We obtained RGB and light detection and ranging(Li DAR)data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments.After CH estimation,the feature points(inflection point)from the Logistic curve of the CH time series were extracted as TD.We examined the impact of various independent variables(day of year vs.accumulated growing degree days(AGDD)),sensors(RGB and Li DAR),time series denoise methods,different feature points,and temporal resolution on TD identification.Lastly,we used early CH time series data to predict height growth and further forecast TD.The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from Li DAR to estimate maize CH was the most stable across treatments and stages(R~2:0.928 to0.943).For TD identification,the best performance was achieved by using Li DAR data with AGDD as the independent variable,combined with the knee point method,resulting in RMSE of 2.95 d.The high accuracy was maintained at temporal resolutions as coarse as 14 d.TD forecast got more accurate as the CH time series extended.The optimal timing for forecasting TD was when the CH exceeded half of its maximum.Using only Li DAR CH data below 1.6 m and empirical growth rate estimates,the forecasted TD showed an RMSE of 3.90 d.In conclusion,this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.
基金supported by the Strategic Pri-ority Research Program(A)of the Chinese Academy of Sciences(Grant No.XDA28080503)the National Natural Science Foundation of China(Grant No.42071025)+1 种基金the Youth Innovation Promotion Associa-tion of Chinese Academy of Sciences(Grant No.2023240)the Pacific Northwest National Laboratory which is operated for DOE by Battelle Memorial Institute under Contract DE-A06-76RLO 1830.
文摘Phenology shifts influence regional climate by altering energy,and water fluxes through biophysical processes.However,a quantitative understanding of the phenological control on vegetation’s biophysical feedbacks to re gional climate remains elusive.Using long-term remote sensing observations and Weather Research and Fore casting(WRF)model simulations,we investigated vegetation phenology changes from 2003 to 2020 and quan tified their biophysical controls on the regional climate in Northeast China.Our findings elucidated that earlier green-up contributed to a prolonged growing season in forests,while advanced green-up and delayed dormancy extended the growing season in croplands.This prolonged presence and increased maximum green cover in tensified climate-vegetation interactions,resulting in more significant surface cooling in croplands compared to forests.Surface cooling from forest phenology changes was prominent during May’s green-up(-0.53±0.07°C),while crop phenology changes induced cooling throughout the growing season,particularly in June(-0.47±0.15°C),July(-0.48±0.11°C),and September(-0.28±0.09°C).Furthermore,we unraveled the contributions of different biophysical pathways to temperature feedback using a two-resistance attribution model,with aero dynamic resistance emerging as the dominant factor.Crucially,our findings underscored that the land surface temperature(LST)sensitivity,exhibited substantially higher values in croplands rather than temperate forests.These strong sensitivities,coupled with the projected continuation of phenology shifts,portend further growing season cooling in croplands.These findings contribute to a more comprehensive understanding of the intricate feedback mechanisms between vegetation phenology and surface temperature,emphasizing the significance of vegetation phenology dynamics in shaping regional climate pattern and seasonality.
基金The National Key R&D Program of China,No.2023YFD2000105The Shaanxi Province Science and Technology Innovation Team,No.2021TD-51The Shaanxi Province Geoscience Big Data and Geohazard Prevention Innovation Team(2022)。
文摘Shaanxi province in China is rich in vegetation resources,making the study of vegetation phenology crucial for understanding climate change and ecological dynamics.Previous studies on Shaanxi have primarily focused on coarse-scale phenology,which results in the loss of essential spatial details due to the lack of finer-scale analyses.Moreover,the impact of rapid urbanisation on the phenology of various vegetation types in this region remains unclear.To address these gaps,we integrated Landsat and MODIS data to generate a daily 30 m NDVI sequence.Vegetation phenology across Shaanxi from 2000 to 2020 was then extracted and analysed,and the responses of different vegetation types to urbanisation were explored.Results indicated that accurate vegetation phenology can be obtained at a 30 m resolution,which is more precise than conventional coarse-scale investigations.The Start of Season(SOS)and End of Season(EOS)in the province advanced by-0.56 and-0.08 day/year,respectively,from 2000 to 2020.In general,the SOS gradually delays,and the EOS progressively advances with increasing latitude.However,anomalies were identified in regions south of 32.64°N and across the zone from 34.04°N to 37.56°N,which are primarily due to the effects of high elevation and rapid urbanisation,respectively.Further investigation into phenological differences among various vegetation types revealed that forests exhibit the earliest SOS,while grasslands present the latest.In addition,a significant negative correlation was found between the SOS difference and air temperature difference along the urban-rural gradient,with urbanisation having the most significant impact on cropland phenology.This study provides valuable insights into vegetation phenology and its response to urbanisation.These insights offer crucial information for ecological conservation and management strategies.
基金supported by the National Natural Science Foundation of China (U2243227)。
文摘The net primary productivity(NPP) is an important indicator for assessing the carbon sequestration capacities of different ecosystems and plays a crucial role in the global biosphere carbon cycle. However, in the context of the increasing frequency, intensity, and duration of global extreme climate events, the impacts of extreme climate and vegetation phenology on NPP are still unclear, especially on the Qinghai-Xizang Plateau(QXP), China. In this study, we used a new data fusion method based on the MOD13A2 normalized difference vegetation index(NDVI) and the Global Inventory Modeling and Mapping Studies(GIMMS) NDVI_(3g) datasets to obtain a NDVI dataset(1982–2020) on the QXP. Then, we developed a NPP dataset across the QXP using the Carnegie-Ames-Stanford Approach(CASA) model and validated its applicability based on gauged NPP data. Subsequently, we calculated 18 extreme climate indices based on the CN05.1 dataset, and extracted the length of vegetation growing season using the threshold method and double logistic model based on the annual NDVI time series. Finally, we explored the spatiotemporal patterns of NPP on the QXP and the impact mechanisms of extreme climate and the length of vegetation growing season on NPP. The results indicated that the estimated NPP exhibited good applicability. Specifically, the correlation coefficient, relative bias, mean error, and root mean square error between the estimated NPP and gauged NPP were 0.76, 0.17, 52.89 g C/(m^(2)·a), and 217.52 g C/(m^(2)·a), respectively. The NPP of alpine meadow, alpine steppe, forest, and main ecosystem on the QXP mainly exhibited an increasing trend during 1982–2020, with rates of 0.35, 0.38, 1.40, and 0.48 g C/(m^(2)·a), respectively. Spatially, the NPP gradually decreased from southeast to northwest across the QXP. Extreme climate had greater impact on NPP than the length of vegetation growing season on the QXP. Specifically, the increase in extremely-wet-day precipitation(R99p), simple daily intensity index(SDII), and hottest day(TXx) increased the NPP in different ecosystems across the QXP, while the increases in the cold spell duration index(CSDI) and warm spell duration index(WSDI) decreased the NPP in these ecosystems. The results of this study provide a scientific basis for relevant departments to formulate future policies addressing the impact of extreme climate on vegetation in different ecosystems on the QXP.
文摘It is generally accepted that climate has changed greatly on a global scale, and that the earth＇s climate has already wanned by some degrees over the past century. Ample evidence shows that there have been apparent changes in avian population dynamics, life-history traits and geographic ranges in response to global climate change. This paper briefly reviews the possible effects of climate change on avian biology and ecology all over the world, with emphasis on new findings from several long-term studies in Europe and North America, which provide unique opportunities to investigate how long-term changes in climate affect birds at both individual and population levels. The implications of such long-term studies for future bird studies in China is discussed with hope that this review can contribute to the preparation and plan for studies of climatic effects on birds in China in the future.
文摘[Objective] This study was to explore the growth characteristics and fruit quality of a new bud mutant line, ＇Chuanzao Loquat＇. [Method] Paraffin section technique combined with field investigation method were adopted to conduct com- parative analysis of shoot histomorphology and phenological phases between two Io- quat varieties, ＇Chuanzao Loquat＇ and ＇Zaozhong 6＇. [Result] ＇Chuanzao Loquat＇ branched out and unfolded leaves about half to a month earlier than ＇Zaozhong 6＇; both the flowering and fruiting phases of ＇Chuanzao Loquat＇ were three months earlier than a precocious variety, ＇Zaozhong 6＇; the proportions of epidermis, cortex parenchyma, vascular tissue and medulla were 3.7%, 14.5%, 15.9% and 65.9%, re- spectively, in spdng shoots of ＇Chuanzao Loquat＇, and 3.1%, 42.5%, 6.9% and 47.5%, respectively, in ＇Zaozhong 6＇. [Conclusion] In terms of phenological phases, ＇Chuanzao Loqua＇ is earlier than ＇Zaozhong 6＇, a currently widely planted precocious variety, and thus is an important germplasm resource of Ioquats.
