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
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
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
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
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
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
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
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
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
Phenological changes play a central role in regulating seasonal variation in the ecological processes,exerting significant impacts on hydrologic and nutrient cycles,and ultimately influencing ecosystem functioning suc...Phenological changes play a central role in regulating seasonal variation in the ecological processes,exerting significant impacts on hydrologic and nutrient cycles,and ultimately influencing ecosystem functioning such as carbon uptake.However,the potential impact mechanisms of phenological events on seasonal carbon dynamics in subtropical regions are under-investigated.These knowledge gaps hinder from accurately linking photosynthetic phenology and carbon sequestration capacity.Using chlorophyll fluorescence remote sensing and productivity data from 2000 to 2019,we found that an advancement in spring phenology increased spring gross(GPP)and net primary productivity(NPP)in subtropical vegetation of China by 2.1 gC m^(-2)yr^(-1)and 1.4 gC m^(-2)yr^(-1),respectively.A delay in autumn phenology increased the autumnal GPP and NPP by 0.4 gC m^(-2)yr^(-1)and 0.2 gC m^(-2)yr^(-1),respectively.Temporally,the contribution of the spring phenology to spring carbon uptake increased significantly during the study period,while this positive contribution showed a nonsignificant trend in summer.In comparison,the later autumn phenology could significantly contribute to the increase in autumnal carbon uptake;however,this contributing effect was weakened.Path analysis indicated that these phenomena have been caused by the increased leaf area and enhanced photosynthesis due to earlier spring and later autumn phenology,respectively.Our results demonstrate the diverse impacts of vegetation phenology on the seasonal carbon sequestration ability and it is imperative to consider such asymmetric effects when modeling ecosystem processes parameterized under future climate change.展开更多
Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimat...Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle(UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading(IH) and full heading(FH), and panicle initiation(PI), and growth period after transplanting(GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model(DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest(RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th(R^(2) = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features(CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI(R^(2) = 0.834, RMSE = 4.344 d), IH(R^(2) = 0.877, RMSE = 2.721 d), and FH(R^(2) = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.展开更多
Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while r...Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.展开更多
Significant changes to the world’s climate over the past few decades have had an impact on the development of plants.Vegetation in high latitude regions,where the ecosystems are fragile,is susceptible to climate chan...Significant changes to the world’s climate over the past few decades have had an impact on the development of plants.Vegetation in high latitude regions,where the ecosystems are fragile,is susceptible to climate change.It is possible to better understand vegetation’s phenological response to climate change by examining these areas.Traditional studies have mainly investigated how a single meteorological factor affects changes in vegetation phenology through linear correlation analysis,which is insufficient for quantitatively revealing the effects of various climate factor interactions on changes in vegetation phenology.We used the asymmetric Gaussian method to fit the normalized difference vegetation index(NDVI)curve and then used the dynamic threshold method to extract the phenological parameters,including the start of the season(SOS),end of the season(EOS),and length of the season(LOS),of the vegetation in this study area in the Tundra-Tagar transitional zone in eastern and western Siberia from 2000 to 2017.The monthly temperature and precipitation data used in this study were obtained from the climate research unit(CRU)meteorological dataset.The degrees to which the changes in temperature and precipitation in the various months and their interactions affected the changes in the three phenological parameters were determined using the GeoDetector,and the results were explicable.The findings demonstrate that the EOS was more susceptible to climate change than the SOS.The vegetation phenology shift was best explained by the climate in March,April,and September,and the combined effect of the temperature and precipitation had a greater impact on the change in the vegetation phenology compared with the effects of the individual climate conditions.The results quantitatively show the degree of interaction between the variations in temperature and precipitation and their effects on the changes in the different phenological parameters in the various months.Understanding how various climatic variations effect phenology changes in plants at different times may be more intuitive.This research provides as a foundation for research on how global climate change affects ecosystems and the global carbon cycle.展开更多
Flowering phenology of plants,which is important for reproductive growth,has been shown to be influenced by climate change.Understanding how flowering phenology responds to climate change and exploring the variation o...Flowering phenology of plants,which is important for reproductive growth,has been shown to be influenced by climate change.Understanding how flowering phenology responds to climate change and exploring the variation of this response across plant groups can help predict structural and functional changes in plant communities in response to ongoing climate change.Here,we used long-term collections of 33 flowering plant species from the Gongga Mountains(Mt.Gongga hereafter),a biodiversity hotspot,to investigate how plant flowering phenology changed over the past 70 years in response to climate change.We found that mean flowering times in Mt.Gongga were delayed in all vegetation types and elevations over the last 70 years.Furthermore,flowering time was delayed more in lowlands than at high elevations.Interestingly,we observed that spring-flowering plants show earlier flowering times whereas summer/autumn plants show delayed flowering times.Non-synchronous flowering phenology across species was mainly driven by changes in temperature and precipitation.We also found that the flowering phenology of 78.8%plant species was delayed in response to warming temperatures.Our findings also indicate that the magnitude and direction of variation in plant flowering times vary significantly among species along elevation gradients.Shifts in flowering time might cause trophic mismatches with co-occurring and related species,affecting both forest ecosystem structure and function.展开更多
Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depic...Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depiction.This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources.To address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area.It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland classification.The results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grassland types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland types.The overall accuracy reaches 82.61%,with a kappa coefficient of 0.79.Compared to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94%and 0.19,respectively.Notably,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38%.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to September.The sandy meadow exhibits a longer growing season compared with typical grassland and meadow,and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.展开更多
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.
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.
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 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.
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 Natural Science Foundation of China,No.42371121Joint Fund for Regional Innovation and Development of the National Natural Science Foundation of China,No.U22A20570Science and Technology Innovation Program of Hunan Province,China,No.2022RC4027。
文摘Phenological changes play a central role in regulating seasonal variation in the ecological processes,exerting significant impacts on hydrologic and nutrient cycles,and ultimately influencing ecosystem functioning such as carbon uptake.However,the potential impact mechanisms of phenological events on seasonal carbon dynamics in subtropical regions are under-investigated.These knowledge gaps hinder from accurately linking photosynthetic phenology and carbon sequestration capacity.Using chlorophyll fluorescence remote sensing and productivity data from 2000 to 2019,we found that an advancement in spring phenology increased spring gross(GPP)and net primary productivity(NPP)in subtropical vegetation of China by 2.1 gC m^(-2)yr^(-1)and 1.4 gC m^(-2)yr^(-1),respectively.A delay in autumn phenology increased the autumnal GPP and NPP by 0.4 gC m^(-2)yr^(-1)and 0.2 gC m^(-2)yr^(-1),respectively.Temporally,the contribution of the spring phenology to spring carbon uptake increased significantly during the study period,while this positive contribution showed a nonsignificant trend in summer.In comparison,the later autumn phenology could significantly contribute to the increase in autumnal carbon uptake;however,this contributing effect was weakened.Path analysis indicated that these phenomena have been caused by the increased leaf area and enhanced photosynthesis due to earlier spring and later autumn phenology,respectively.Our results demonstrate the diverse impacts of vegetation phenology on the seasonal carbon sequestration ability and it is imperative to consider such asymmetric effects when modeling ecosystem processes parameterized under future climate change.
基金supported by the National Key Research and Development Program of China (Grant No.2022YFD2300700)the Open Project Program of the State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute (Grant No.2023ZZKT20402)+1 种基金the Agricultural Science and Technology Innovation Program, the Central Public-Interest Scientific Institution Basal Research Fund, China (Grant No.CPSIBRF-CNRRI-202119)the Zhejiang ‘Ten Thousand Talents’ Plan Science and Technology Innovation Leading Talent Project, China (Grant No.2020R52035)。
文摘Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle(UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading(IH) and full heading(FH), and panicle initiation(PI), and growth period after transplanting(GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model(DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest(RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th(R^(2) = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features(CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI(R^(2) = 0.834, RMSE = 4.344 d), IH(R^(2) = 0.877, RMSE = 2.721 d), and FH(R^(2) = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.
基金supported by the National Natural Science Foundation of China(42271360 and 42271399)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(CAST)(2020QNRC001)the Fundamental Research Funds for the Central Universities,China(2662021JC013,CCNU22QN018)。
文摘Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.
基金International Cooperation and Exchange of the National Natural Science Foundation of China,No.42061134019Major Special Project-The China High-Resolution Earth Observation System,No.30-Y30F06-9003-20/22。
文摘Significant changes to the world’s climate over the past few decades have had an impact on the development of plants.Vegetation in high latitude regions,where the ecosystems are fragile,is susceptible to climate change.It is possible to better understand vegetation’s phenological response to climate change by examining these areas.Traditional studies have mainly investigated how a single meteorological factor affects changes in vegetation phenology through linear correlation analysis,which is insufficient for quantitatively revealing the effects of various climate factor interactions on changes in vegetation phenology.We used the asymmetric Gaussian method to fit the normalized difference vegetation index(NDVI)curve and then used the dynamic threshold method to extract the phenological parameters,including the start of the season(SOS),end of the season(EOS),and length of the season(LOS),of the vegetation in this study area in the Tundra-Tagar transitional zone in eastern and western Siberia from 2000 to 2017.The monthly temperature and precipitation data used in this study were obtained from the climate research unit(CRU)meteorological dataset.The degrees to which the changes in temperature and precipitation in the various months and their interactions affected the changes in the three phenological parameters were determined using the GeoDetector,and the results were explicable.The findings demonstrate that the EOS was more susceptible to climate change than the SOS.The vegetation phenology shift was best explained by the climate in March,April,and September,and the combined effect of the temperature and precipitation had a greater impact on the change in the vegetation phenology compared with the effects of the individual climate conditions.The results quantitatively show the degree of interaction between the variations in temperature and precipitation and their effects on the changes in the different phenological parameters in the various months.Understanding how various climatic variations effect phenology changes in plants at different times may be more intuitive.This research provides as a foundation for research on how global climate change affects ecosystems and the global carbon cycle.
基金supported by Jiangxi Provincial Department of Education Science and Technology Research Project(GJJ2200433)the Natural Science Foundation of Jiangxi,China(#20224BAB213033)+2 种基金the National Key Research and Development Program of China(#2018YFA0606104)National Natural Science Foundation of China(#32125026,#31988102)the Strategic Priority Research Program of Chinese Academy of Sciences(#XDB31000000).
文摘Flowering phenology of plants,which is important for reproductive growth,has been shown to be influenced by climate change.Understanding how flowering phenology responds to climate change and exploring the variation of this response across plant groups can help predict structural and functional changes in plant communities in response to ongoing climate change.Here,we used long-term collections of 33 flowering plant species from the Gongga Mountains(Mt.Gongga hereafter),a biodiversity hotspot,to investigate how plant flowering phenology changed over the past 70 years in response to climate change.We found that mean flowering times in Mt.Gongga were delayed in all vegetation types and elevations over the last 70 years.Furthermore,flowering time was delayed more in lowlands than at high elevations.Interestingly,we observed that spring-flowering plants show earlier flowering times whereas summer/autumn plants show delayed flowering times.Non-synchronous flowering phenology across species was mainly driven by changes in temperature and precipitation.We also found that the flowering phenology of 78.8%plant species was delayed in response to warming temperatures.Our findings also indicate that the magnitude and direction of variation in plant flowering times vary significantly among species along elevation gradients.Shifts in flowering time might cause trophic mismatches with co-occurring and related species,affecting both forest ecosystem structure and function.
基金supported by the National Natural Science Foundation of China[grant number 42001386,42271407]within the ESA-MOST China Dragon 5 Cooperation(ID:59313).
文摘Due to the small size,variety,and high degree of mixing of herbaceous vegetation,remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories,lacking detailed depiction.This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources.To address this issue,this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area.It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification,thereby enhancing the accuracy and refinement of grassland classification.The results demonstrate the following:(1)To meet the supervision requirements of grassland resources,we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method,specifically applicable to natural grasslands in northern China.(2)By utilizing the high-spatial-resolution Normalized Difference Vegetation Index(NDVI)synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model(STNLFFM),we are able to capture the NDVI time profiles of grassland types,accurately extract vegetation phenological information within the year,and further enhance the temporal resolution.(3)The integration of multi-seasonal spectral,polarization,and phenological characteristics significantly improves the classification accuracy of grassland types.The overall accuracy reaches 82.61%,with a kappa coefficient of 0.79.Compared to using only multi-seasonal spectral features,the accuracy and kappa coefficient have improved by 15.94%and 0.19,respectively.Notably,the accuracy improvement of the gently sloping steppe is the highest,exceeding 38%.(4)Sandy grassland is the most widespread in the study area,and the growth season of grassland vegetation mainly occurs from May to September.The sandy meadow exhibits a longer growing season compared with typical grassland and meadow,and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.
