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
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
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
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
Snow cover is an important water source for vegetation growth in arid and semi-arid areas,and grassland phenology provides valuable information on the response of terrestrial ecosystems to climate change.The Mongolian...Snow cover is an important water source for vegetation growth in arid and semi-arid areas,and grassland phenology provides valuable information on the response of terrestrial ecosystems to climate change.The Mongolian Plateau features both abundant snow cover resources and typical grassland ecosystems.In recent years,with the intensification of global climate change,the snow cover on the Mongolian Plateau has changed correspondingly,with resulting effects on vegetation growth.In this study,using MOD10A1 snow cover data and MOD13A1 Normalized Difference Vegetation Index(NDVI)data combined with remote sensing(RS)and geographic information system(GIS)techniques,we analyzed the spatiotemporal changes in snow cover and grassland phenology on the Mongolian Plateau from 2001 to 2018.The correlation analysis and grey relation analysis were used to determine the influence of snow cover parameters(snow cover fraction(SCF),snow cover duration(SCD),snow cover onset date(SCOD),and snow cover end date(SCED))on different types of grassland vegetation.The results showed wide snow cover areas,an early start time,a late end time,and a long duration of snow cover over the northern Mongolian Plateau.Additionally,a late start,an early end,and a short duration were observed for grassland phenology,but the southern area showed the opposite trend.The SCF decreased at an annual rate of 0.33%.The SCD was shortened at an annual rate of 0.57 d.The SCOD and SCED in more than half of the study area advanced at annual rates of 5.33 and 5.74 DOY(day of year),respectively.For grassland phenology,the start of the growing season(SOS)advanced at an annual rate of 0.03 DOY,the end of the growing season(EOS)was delayed at an annual rate of 0.14 DOY,and the length of the growing season(LOS)was prolonged at an annual rate of 0.17 d.The SCF,SCD,and SCED in the snow season were significantly positively correlated with the SOS and negatively correlated with the EOS and LOS.The SCOD was significantly negatively correlated with the SOS and positively correlated with the EOS and LOS.The SCD and SCF can directly affect the SOS of grassland vegetation,while the EOS and LOS were obviously influenced by the SCOD and SCED.This study provides a scientific basis for exploring the response trends of alpine vegetation to global climate change.展开更多
Investigating the interrelation between snow and vegetation is essential to explain the response of alpine ecosystems to climate change.Based on the MOD10 A1 daily cloud-free snow product and MOD13 A1 NDVI(normalized ...Investigating the interrelation between snow and vegetation is essential to explain the response of alpine ecosystems to climate change.Based on the MOD10 A1 daily cloud-free snow product and MOD13 A1 NDVI(normalized difference vegetation index)data,this study analysed the spatial and temporal patterns of snow phenology including snow onset date,snow end date,snow cover days,and vegetation phenology including the start of growing season,the end of growing season and the length of growing season in the Chinese Tianshan Mountainous Region(CTMR)from 2002 to 2018,and then investigated the snow phenological effects on the vegetation phenology among different ecogeographic zones and diverse vegetation types.The results indicated that snow onset date was earlier at higher elevations and later at lower elevations,while snow end date showed opposite spatial distribution characteristics.The end of growing season occurred later on the northwest slope of the CTMR and the Yili Valley.The earliest end of growing season was in the middle part of the CTMR.A long growing season was mainly distributed along the northern slope and the Yili Valley,while a short growing season was concentrated in the middle part of the CTMR.The response of vegetation phenology to changes in snow phenology varied among vegetation types and ecogeographic zones.The effect of snow phenology on vegetation phenology was more significant in IID5(Yili Valley)than in the other ecogeographic zones.A negative correlation was observed between the start of growing season and snow end date in nearly 54.78%of the study area,while a positive correlation was observed between the start of growing season and the snow end date in 66.85%of the study area.The sensitivity of vegetation phenology to changes in snow cover varied among different vegetation types.Snow onset date had the greatest effect on the start of growing season in the four vegetation cover types(alpine meadows,alpine steppes,shrubs,and alpine sparse vegetation),whereas the snow cover days had the least impact.Snow end date had the greatest impact on the end of growing season in the alpine steppes and shrub areas.The study results are helpful for understanding the vegetation dynamics under ongoing climate change,and can benefit vegetation management and pasture development in the CTMR.展开更多
Spatio-temporal changes in the differentiation characteristics of eight consecutive phenological periods and their corresponding lengths were quantitatively analyzed based on long-term phenological observation data fr...Spatio-temporal changes in the differentiation characteristics of eight consecutive phenological periods and their corresponding lengths were quantitatively analyzed based on long-term phenological observation data from 114 agro-meteorological stations in four maize growing zones in China. Results showed that average air temperature and growing degree-days (GDD) during maize growing seasons showed an increasing trend from 1981 to 2010, while precipitation and sunshine duration showed a decreasing trend. Maize phenology has significantly changed under climate change: spring maize phenology was mainly advanced, especially in northwest and southwest maize zones, while summer and spring-summer maize phenology was delayed. The delay trend observed for summer maize in the northwest maize zone was more pronounced than in the Huang-Huai spring-summer maize zone. Variations in maize phenology changed the corresponding growth stages length: the vegetative growth period (days from sowing date to tasseling date) was generally shortened in spring, summer, and spring-summer maize, although to different degrees, while the reproductive growth period (days from tasseling date to mature date) showed an extension trend. The entire growth period(days from sowing date to mature date) of spring maize was extended, but the entire growth periods of summer and spring-summer maize were shortened.展开更多
With the global warming, crop phenological shifts in responses to climate change have become a hot research topic. Based on the long-term observed agro-meteorological phenological data （1981-2009） and meteorological...With the global warming, crop phenological shifts in responses to climate change have become a hot research topic. Based on the long-term observed agro-meteorological phenological data （1981-2009） and meteorological data, we quantitatively analyzed temporal and spatial shifts in maize phenology and their sensitivities to key climate factors change using climate tendency rate and sensitivity analysis methods. Results indicated that the sowing date was significantly delayed and the delay tendency rate was 9.0 d·10a^-1. But the stages from emergence to maturity occurred earlier （0.1 d·10a^-1〈θ〈1.7 d·10a^-1, θ is the change slope of maize phenology）. The length of vegetative period （VPL） （from emergence to tasseling） was shortened by 0.9 d·10a^-1, while the length of generative period （GPL） （from tasseling to maturity） was lengthened by 1.7 d·10a^-1. The growing season length （GSL） （from emergence to maturity） was lengthened by 0.4 d·10a^-1. Correlation analysis indicated that maize phenology was significantly correlated with average temperature, precipitation, sunshine duration and growing degree days （GDD） （p〈0.01）. Average temperature had significant negative correlation relationship, while precipitation, sunshine duration and growing degree days had significant positive correlations with maize phenology. Sensitivity analysis indicated that maize phenology showed different responses to variations in key climate factors, especially at different sites. The conclusions of this research could provide scientific supports for agricultural adaptation to climate change to address the global food security issue.展开更多
Lake ice phenology is considered a sensitive indicator of regional climate change. We utilized time series information of this kind extracted from a series of multi-source remote sensing(RS) datasets including the MOD...Lake ice phenology is considered a sensitive indicator of regional climate change. We utilized time series information of this kind extracted from a series of multi-source remote sensing(RS) datasets including the MOD09 GQ surface reflectance product, Landsat TM/ETM_+ images, and meteorological records to analyze spatiotemporal variations of ice phenology of Qinghai Lake between 2000 and 2016 applying both RS and GIS technology. We also identified the climatic factors that have influenced lake ice phenology over time and draw a number of conclusions. First, data show that freeze-up start(FUS), freeze-up end(FUE), break-up start(BUS), and break-up end(BUE) on Qinghai Lake usually occurred in mid-December, early January, mid-to-late March, and early April, respectively. The average freezing duration(FD, between FUE and BUE), complete freezing duration(CFD, between FUE and BUS), ice coverage duration(ICD, between FUS and BUE), and ablation duration(AD, between BUS and BUE) were 88 days, 77 days, 108 days and 10 days, respectively. Second, while the results of this analysis reveal considerable differences in ice phenology on Qinghai Lake between 2000 and 2016, there has been relatively little variation in FUS times. Data show that FUE dates had also tended to fluctuate over time, initially advancing and then being delayed, while the opposite was the case for BUS dates as these advanced between 2012 and 2016. Overall, there was a shortening trend of Qinghai Lake's FD in two periods, 2000–2005 and 2010–2016, which was shorter than those seen on other lakes within the hinterland of the Tibetan Plateau. Third, Qinghai Lake can be characterized by similar spatial patterns in both freeze-up(FU) and break-up(BU) processes, as parts of the surface which freeze earlier also start to melt first, distinctly different from some other lakes on the Tibetan Plateau. A further feature of Qinghai Lake ice phenology is that FU duration(between 18 days and 31 days) is about 10 days longer than BU duration(between 7 days and 20 days). Fourth, data show that negative temperature accumulated during the winter half year(between October and the following April) also plays a dominant role in ice phenology variations of Qinghai Lake. Precipitation and wind speed both also exert direct influences on the formation and melting of lake ice cover and also cannot be neglected.展开更多
Changes in vegetation phenology are key indicators of the response of ecosystems to climate change.Therefore,knowledge of growing seasons is essential to predict ecosystem changes,especially for regions with a fragile...Changes in vegetation phenology are key indicators of the response of ecosystems to climate change.Therefore,knowledge of growing seasons is essential to predict ecosystem changes,especially for regions with a fragile ecosystem such as the Loess Plateau.In this study,based on the normalized difference vegetation index(NDVI) data,we estimated and analyzed the vegetation phenology in the Loess Plateau from 2000 to 2010 for the beginning,length,and end of the growing season,measuring changes in trends and their relationship to climatic factors.The results show that for 54.84% of the vegetation,the trend was an advancement of the beginning of the growing season(BGS),while for 67.64% the trend was a delay in the end of the growing season(EGS).The length of the growing season(LGS) was extended for 66.28% of the vegetation in the plateau.While the temperature is important for the vegetation to begin the growing season in this region,warmer climate may lead to drought and can become a limiting factor for vegetation growth.We found that increasedprecipitation benefits the advancement of the BGS in this area.Areas with a delayed EGS indicated that the appropriate temperature and rainfall in autumn or winter enhanced photosynthesis and extended the growth process.A positive correlation with precipitation was found for 76.53% of the areas with an extended LGS,indicating that precipitation is one of the key factors in changes in the vegetation phenology in this water-limited region.Precipitation plays an important role in determining the phenological activities of the vegetation in arid and semiarid areas,such as the Loess Plateau.The extended growing season will significantly influence both the vegetation productivity and the carbon fixation capacity in this region.展开更多
Variations in temperature and precipitation affect local ecosystems. Considerable spatial and temporal heterogeneity exists in arid ecosystems such as desert steppes. In this study, we analyzed the spatiotemporal dy- ...Variations in temperature and precipitation affect local ecosystems. Considerable spatial and temporal heterogeneity exists in arid ecosystems such as desert steppes. In this study, we analyzed the spatiotemporal dy- namics of climate and vegetation phenology in the desert steppe of Inner Mongolia, China using meteorological data （1961-2010） from 11 stations and phenology data （2004-2012） from 6 ecological observation stations. We also estimated the gross primary production for the period of 1982-2009 and found that the annual mean tem- perature increased at a rate of 0.47~C/decade during 1961-2010, with the last 10 years being consistently warmer than the 50 years as an average. The most significant warming occurred in winters. Annual precipitation slightly decreased during the 50-year period, with summer precipitation experiencing the highest drop in the last 10 years, and spring precipitation, a rise. Spatially, annual precipitation increased significantly in the northeastern and eastern central areas next to the typical steppe. From 2004 to 2012, vegetation green-up and senescence date advanced in the study area, shortening the growing season. Consequently, the primary productivity of the desert steppe de- creased along the precipitation gradient from southeast to northwest. Temporally, productivity increased during the period of 1982-1999 and significantly decreased after 2000. Overall, the Last decade witnessed the most dramatic climatic changes that were likely to negatively affect the desert steppe ecosystem. The decreased primary produc- tivity, in particular, decreases ecosystem resilience and impairs the livelihood of local farmers and herdsmen.展开更多
Early crop yield forecasting is important for food safety as well as large-scale food related planning. The phenology-adjusted spectral indices derived from Moderate Resolution Imaging Spectroradiometer （MODIS） data...Early crop yield forecasting is important for food safety as well as large-scale food related planning. The phenology-adjusted spectral indices derived from Moderate Resolution Imaging Spectroradiometer （MODIS） data were used to develop liner regression models with the county-level corn yield data in Northeast China. We also compared the different spectral indices in predicting yield. The results showed that, using Enhanced Vegetation Index （EVI）, Normalized Difference Water Index （NDWI） and Land Surface Water Index （LSWI）, the best time to predict corn yields was 55-60 days after green-up date. LSWI showed the strongest correlation （R2=0.568）, followed by EVI （R2=0.497） and NDWI （R2=0.495）. The peak correlation between Wide Dynamic Range Vegetation Index （WDRVI） and yield was detected 85 days after green-up date （RZ=0.506）. The correlation was generally low for Normalized Difference Vegetation Index （NDVI） （R2=0.385） and no obvious peak correlation existed for NDVI. The coefficients of determination of the different spectral indices varied from year to year, which were greater in 2001 and 2004 than in other years. Leave-one-year-out approach was used to test the performance of the model. Normalized root mean square error （NRMSE） ranged from 7.3 to 16.9% for different spectral indices. Overall, our results showed that crop phenology-tuned spectral indices were feasible and helpful for regional corn yield forecasting.展开更多
Background:Vegetation phenology research has largely focused on temperate deciduous forests,thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions...Background:Vegetation phenology research has largely focused on temperate deciduous forests,thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions.Results:Using satellite solar-induced chlorophyll fluorescence(SIF)and MODIS enhanced vegetation index(EVI)data,we applied two methods to evaluate temporal and spatial patterns of the end of the growing season(EGS)in subtropical vegetation in China,and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation.Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods(dynamic threshold method and derivative method)was later than that derived from gross primary productivity(GPP)based on the eddy covariance technique,and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks,respectively.We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation(accounting for more than 73%and 62%of the study areas,respectively),but negatively correlated with preseason maximum temperature(accounting for more than 59%of the study areas).In addition,EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors,and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests,shrub and grassland.Conclusions:Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China.We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region.These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China,and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.展开更多
Researchers continually demonstrated through published literature how LiDAR could create unparalleled measurements of ecosystem structure and forest height.There are a number of studies conducted utilizing waveform Li...Researchers continually demonstrated through published literature how LiDAR could create unparalleled measurements of ecosystem structure and forest height.There are a number of studies conducted utilizing waveform LiDAR products for terrestrial monitoring,but those that deal specifically with the assessment of space-borne waveform LiDAR for monitoring and modeling of phenology is very limited.This review highlights the waveform LiDAR system and looks into satellite sensors that could link waveform LiDAR and vegetation phenology,such as the proposed NASA’s Global Ecosystem Dynamics Investigation(GEDI)and the Japanese Experimental Module(JEM)-borne LiDAR sensor named MOLI(Multi-footprint Observation LIDAR and Imager).Further,this work examines the richness and utility of the waveform returns and proposes a spline-function-derived model that could be exploited for estimating the leaf-shooting date.The new approach may be utilized for ecosystem-level phenological studies.展开更多
This study used time-series of global inventory modeling and mapping studies （GIMMS） normalized difference vegetation index （NDVI） datasets at a spatial resolution of 8 km and 15-d interval to investigate the spat...This study used time-series of global inventory modeling and mapping studies （GIMMS） normalized difference vegetation index （NDVI） datasets at a spatial resolution of 8 km and 15-d interval to investigate the spatial patterns of cropland phenology in China. A smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent processing for identifying cropping systems and extracting phenological parameters, the starting date of growing season （SGS） and the ending date of growing season （EGS） was based on the smoothed NVDI time-series data. The results showed that the cropping systems in China became complex as moving from north to south of China. Under these cropping systems, the SGS and EGS for the first growing season varied largely over space, and those regions with multiple cropping systems generally presented a significant advanced SGS and EGS than the regions with single cropping patterns. On the contrary, the phenological events of the second growing season including both the SGS and EGS showed little difference between regions. The spatial patterns of cropping systems and phenology in Chinese cropland were highly related to the geophysical environmental factors. Several anthropogenic factors, such as crop variety, cultivation levels, irrigation, and fertilizers, could profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.展开更多
Remotely sensing images are now available for monitoring vegetation dynamics over large areas.In this paper,an improved logistic model that combines double logistic model and global function was developed.Using this m...Remotely sensing images are now available for monitoring vegetation dynamics over large areas.In this paper,an improved logistic model that combines double logistic model and global function was developed.Using this model with SPOT/NDVI data,three key vegetation phenology metrics,the start of growing season (SOS),the end of growing season (EOS) and the length of growing season (LOS),were extracted and mapped in the Changbai Mountains,and the relationship between the key phenology metrics and elevation were established.Results show that average SOS of forest,cropland and grassland in the Changbai Mountains are on the 119th,145th,and 133rd day of year,respectively.The EOS of forest and grassland are similar,with the average on the 280th and 278th,respectively.In comparison,average EOS of the cropland is relatively earlier.The LOS of forest is mainly from the 160th to 180th,that of the grassland extends from the 140th to the 160th,and that of cropland stretches from the 110th to the 130th.As the latitude increases for the same land cover in the study area,the SOS significantly delays and the EOS becomes earlier.The SOS delays approximately three days as the elevation increases 100 m in the areas with elevation higher than 900 m above sea level (a.s.l.).The EOS is slightly earlier as the elevation increases especially in the areas with elevation below 1200 m a.s.l.The LOS shortens approximately four days as the elevation increases 100 m in the areas with elevation higher than 900 m a.s.l.The relationships between vegetation phenology metrics and elevation may be greatly influenced by the land covers.Validation by comparing with the field data and previous research results indicates that the improved logistic model is reliable and effective for extracting vegetation phenology metrics.展开更多
Rice crop growth and yield in the north Iran are affected by crop duration and phenology.The purpose of this study was to calibrate and validate the ORYZA2000 model under potential production based on experimental dat...Rice crop growth and yield in the north Iran are affected by crop duration and phenology.The purpose of this study was to calibrate and validate the ORYZA2000 model under potential production based on experimental data for simulating and quantifying the phenological development,crop duration and yield prediction of rice crop influenced by different seedling ages.In order to calibrate and validate the crop parameters of ORYZA2000 model,a two-year field experiment was conducted under potential growth condition for transplanted lowland rice during the 2008-2009 rice growing seasons,using three rice varieties with three seedling ages(17,24 and 33 days old).The results showed that the seedling age changed crop duration from 7 to 10 d.The ORYZA2000 model could predict well,but consistently underestimated the length of growing period.The range in normalized root mean square error(RMSEn) values for each phenological stage was between 4% and 6%.From our evaluation,we concluded that ORYZA2000 was sufficiently accurate in simulation of yield,leaf area index(LAI) and biomass of crop organs over time.On average,RMSEn values were 13%-15% for total biomass,18%-21% for green leaf biomass,17%-20% for stem biomass,16%-23% for panicle biomass and 24%-26% for LAI.The RMSEn values for final yield and biomass were 12%-16% and 6%-9%,respectively.Generally,the model simulated LAI,an exceeded measured value for younger seedlings,and best-fit was observed for older seedlings of short-duration varieties.The results revealed that the ORYZA2000 model can be applied as a supportive research tool for selecting the most appropriate strategies for rice yield improvement across the north Iran.展开更多
The timely and rapid mapping of rapeseed planting areas is desirable for national food security. Most current rapeseed mapping methods depend strongly on images with good observations obtained during the flowering sta...The timely and rapid mapping of rapeseed planting areas is desirable for national food security. Most current rapeseed mapping methods depend strongly on images with good observations obtained during the flowering stages. Although vegetation indices have been proposed to identify the rapeseed flowering stage in some areas, automatically mapping rapeseed planting areas in large regions is still challenging.We developed an automatic phenology-and pixel-based algorithm(APPA) by integrating Landsat 8 and Sentinel-1 satellite data. We found that the Normalized Rapeseed Flowering Index shows unique spectral characteristics during the flowering and post-flowering periods, which distinguish rapeseed parcels from other land-use types(urban, water, forest, grass, maize, wheat, barley, and soybean). To verify the robustness of APPA, we applied APPA to seven areas in five rapeseed-producing countries with flowering images unavailable. The rapeseed maps by APPA showed consistently high accuracies with producer accuracies of 0.87–0.93 and F-scores of 0.92–0.95 based on 4503 verification samples. They showed high spatial consistency at the pixel level with the land cover Scientific Expertise Centres(SEC) map in France,Crop Map of England in United Kingdom, national-scale crop-and land-cover map of Germany, and Annual Crop Inventory in Canada at the pixel level. We propose APPA as a highly promising method for automatically and efficiently mapping rapeseed areas.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 National Natural Science Foundation of China(41861014)the Natural Science Foundation of Inner Mongolia Autonomous Region,China(2020BS03042,2020BS04009)the Scientific Research Start-up Fund Projects of Introduced Talents(5909001803,1004031904).
文摘Snow cover is an important water source for vegetation growth in arid and semi-arid areas,and grassland phenology provides valuable information on the response of terrestrial ecosystems to climate change.The Mongolian Plateau features both abundant snow cover resources and typical grassland ecosystems.In recent years,with the intensification of global climate change,the snow cover on the Mongolian Plateau has changed correspondingly,with resulting effects on vegetation growth.In this study,using MOD10A1 snow cover data and MOD13A1 Normalized Difference Vegetation Index(NDVI)data combined with remote sensing(RS)and geographic information system(GIS)techniques,we analyzed the spatiotemporal changes in snow cover and grassland phenology on the Mongolian Plateau from 2001 to 2018.The correlation analysis and grey relation analysis were used to determine the influence of snow cover parameters(snow cover fraction(SCF),snow cover duration(SCD),snow cover onset date(SCOD),and snow cover end date(SCED))on different types of grassland vegetation.The results showed wide snow cover areas,an early start time,a late end time,and a long duration of snow cover over the northern Mongolian Plateau.Additionally,a late start,an early end,and a short duration were observed for grassland phenology,but the southern area showed the opposite trend.The SCF decreased at an annual rate of 0.33%.The SCD was shortened at an annual rate of 0.57 d.The SCOD and SCED in more than half of the study area advanced at annual rates of 5.33 and 5.74 DOY(day of year),respectively.For grassland phenology,the start of the growing season(SOS)advanced at an annual rate of 0.03 DOY,the end of the growing season(EOS)was delayed at an annual rate of 0.14 DOY,and the length of the growing season(LOS)was prolonged at an annual rate of 0.17 d.The SCF,SCD,and SCED in the snow season were significantly positively correlated with the SOS and negatively correlated with the EOS and LOS.The SCOD was significantly negatively correlated with the SOS and positively correlated with the EOS and LOS.The SCD and SCF can directly affect the SOS of grassland vegetation,while the EOS and LOS were obviously influenced by the SCOD and SCED.This study provides a scientific basis for exploring the response trends of alpine vegetation to global climate change.
基金supported by the National Natural Science Foundation of China(41761014)the“One Hundred Outstanding Young Talents Training Program”of Lanzhou Jiaotong University,the National Natural Science Foundation of China(41971094)the Youth Innovation Promotion Association CAS(2019414)。
文摘Investigating the interrelation between snow and vegetation is essential to explain the response of alpine ecosystems to climate change.Based on the MOD10 A1 daily cloud-free snow product and MOD13 A1 NDVI(normalized difference vegetation index)data,this study analysed the spatial and temporal patterns of snow phenology including snow onset date,snow end date,snow cover days,and vegetation phenology including the start of growing season,the end of growing season and the length of growing season in the Chinese Tianshan Mountainous Region(CTMR)from 2002 to 2018,and then investigated the snow phenological effects on the vegetation phenology among different ecogeographic zones and diverse vegetation types.The results indicated that snow onset date was earlier at higher elevations and later at lower elevations,while snow end date showed opposite spatial distribution characteristics.The end of growing season occurred later on the northwest slope of the CTMR and the Yili Valley.The earliest end of growing season was in the middle part of the CTMR.A long growing season was mainly distributed along the northern slope and the Yili Valley,while a short growing season was concentrated in the middle part of the CTMR.The response of vegetation phenology to changes in snow phenology varied among vegetation types and ecogeographic zones.The effect of snow phenology on vegetation phenology was more significant in IID5(Yili Valley)than in the other ecogeographic zones.A negative correlation was observed between the start of growing season and snow end date in nearly 54.78%of the study area,while a positive correlation was observed between the start of growing season and the snow end date in 66.85%of the study area.The sensitivity of vegetation phenology to changes in snow cover varied among different vegetation types.Snow onset date had the greatest effect on the start of growing season in the four vegetation cover types(alpine meadows,alpine steppes,shrubs,and alpine sparse vegetation),whereas the snow cover days had the least impact.Snow end date had the greatest impact on the end of growing season in the alpine steppes and shrub areas.The study results are helpful for understanding the vegetation dynamics under ongoing climate change,and can benefit vegetation management and pasture development in the CTMR.
基金National Natural Science Foundation of China,No.41671037Youth Innovation Promotion Association of CAS,No.2016049+1 种基金Key Research Program of Frontier Sciences,CAS,No.QYZDB-SSW-DQC005Program for "Kezhen" Excellent Talents in IGSNRR,CAS,No.2017RC101
文摘Spatio-temporal changes in the differentiation characteristics of eight consecutive phenological periods and their corresponding lengths were quantitatively analyzed based on long-term phenological observation data from 114 agro-meteorological stations in four maize growing zones in China. Results showed that average air temperature and growing degree-days (GDD) during maize growing seasons showed an increasing trend from 1981 to 2010, while precipitation and sunshine duration showed a decreasing trend. Maize phenology has significantly changed under climate change: spring maize phenology was mainly advanced, especially in northwest and southwest maize zones, while summer and spring-summer maize phenology was delayed. The delay trend observed for summer maize in the northwest maize zone was more pronounced than in the Huang-Huai spring-summer maize zone. Variations in maize phenology changed the corresponding growth stages length: the vegetative growth period (days from sowing date to tasseling date) was generally shortened in spring, summer, and spring-summer maize, although to different degrees, while the reproductive growth period (days from tasseling date to mature date) showed an extension trend. The entire growth period(days from sowing date to mature date) of spring maize was extended, but the entire growth periods of summer and spring-summer maize were shortened.
基金National Natural Science Foundation of China, No.41671037, No.41301091 The National Key Research and Development Program of China, No.2016YFA0602402 The Youth Innovation Promotion Association of CAS, No.2016049
文摘With the global warming, crop phenological shifts in responses to climate change have become a hot research topic. Based on the long-term observed agro-meteorological phenological data （1981-2009） and meteorological data, we quantitatively analyzed temporal and spatial shifts in maize phenology and their sensitivities to key climate factors change using climate tendency rate and sensitivity analysis methods. Results indicated that the sowing date was significantly delayed and the delay tendency rate was 9.0 d·10a^-1. But the stages from emergence to maturity occurred earlier （0.1 d·10a^-1〈θ〈1.7 d·10a^-1, θ is the change slope of maize phenology）. The length of vegetative period （VPL） （from emergence to tasseling） was shortened by 0.9 d·10a^-1, while the length of generative period （GPL） （from tasseling to maturity） was lengthened by 1.7 d·10a^-1. The growing season length （GSL） （from emergence to maturity） was lengthened by 0.4 d·10a^-1. Correlation analysis indicated that maize phenology was significantly correlated with average temperature, precipitation, sunshine duration and growing degree days （GDD） （p〈0.01）. Average temperature had significant negative correlation relationship, while precipitation, sunshine duration and growing degree days had significant positive correlations with maize phenology. Sensitivity analysis indicated that maize phenology showed different responses to variations in key climate factors, especially at different sites. The conclusions of this research could provide scientific supports for agricultural adaptation to climate change to address the global food security issue.
基金Opening Foundation Project of the State Key Laboratory of Cryosphere Sciences,CAS,SKLCS-OP-2016-10National Natural Science Foundation of China,No.41261016,No.41561016Youth Scholar Scientific Capability Promoting Project of Northwest Normal University,No.NWNU-LKQN-14-4
文摘Lake ice phenology is considered a sensitive indicator of regional climate change. We utilized time series information of this kind extracted from a series of multi-source remote sensing(RS) datasets including the MOD09 GQ surface reflectance product, Landsat TM/ETM_+ images, and meteorological records to analyze spatiotemporal variations of ice phenology of Qinghai Lake between 2000 and 2016 applying both RS and GIS technology. We also identified the climatic factors that have influenced lake ice phenology over time and draw a number of conclusions. First, data show that freeze-up start(FUS), freeze-up end(FUE), break-up start(BUS), and break-up end(BUE) on Qinghai Lake usually occurred in mid-December, early January, mid-to-late March, and early April, respectively. The average freezing duration(FD, between FUE and BUE), complete freezing duration(CFD, between FUE and BUS), ice coverage duration(ICD, between FUS and BUE), and ablation duration(AD, between BUS and BUE) were 88 days, 77 days, 108 days and 10 days, respectively. Second, while the results of this analysis reveal considerable differences in ice phenology on Qinghai Lake between 2000 and 2016, there has been relatively little variation in FUS times. Data show that FUE dates had also tended to fluctuate over time, initially advancing and then being delayed, while the opposite was the case for BUS dates as these advanced between 2012 and 2016. Overall, there was a shortening trend of Qinghai Lake's FD in two periods, 2000–2005 and 2010–2016, which was shorter than those seen on other lakes within the hinterland of the Tibetan Plateau. Third, Qinghai Lake can be characterized by similar spatial patterns in both freeze-up(FU) and break-up(BU) processes, as parts of the surface which freeze earlier also start to melt first, distinctly different from some other lakes on the Tibetan Plateau. A further feature of Qinghai Lake ice phenology is that FU duration(between 18 days and 31 days) is about 10 days longer than BU duration(between 7 days and 20 days). Fourth, data show that negative temperature accumulated during the winter half year(between October and the following April) also plays a dominant role in ice phenology variations of Qinghai Lake. Precipitation and wind speed both also exert direct influences on the formation and melting of lake ice cover and also cannot be neglected.
基金supported by the“Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues’’of the Chinese Academy of Sciences(Grant No.XDA05060104)
文摘Changes in vegetation phenology are key indicators of the response of ecosystems to climate change.Therefore,knowledge of growing seasons is essential to predict ecosystem changes,especially for regions with a fragile ecosystem such as the Loess Plateau.In this study,based on the normalized difference vegetation index(NDVI) data,we estimated and analyzed the vegetation phenology in the Loess Plateau from 2000 to 2010 for the beginning,length,and end of the growing season,measuring changes in trends and their relationship to climatic factors.The results show that for 54.84% of the vegetation,the trend was an advancement of the beginning of the growing season(BGS),while for 67.64% the trend was a delay in the end of the growing season(EGS).The length of the growing season(LGS) was extended for 66.28% of the vegetation in the plateau.While the temperature is important for the vegetation to begin the growing season in this region,warmer climate may lead to drought and can become a limiting factor for vegetation growth.We found that increasedprecipitation benefits the advancement of the BGS in this area.Areas with a delayed EGS indicated that the appropriate temperature and rainfall in autumn or winter enhanced photosynthesis and extended the growth process.A positive correlation with precipitation was found for 76.53% of the areas with an extended LGS,indicating that precipitation is one of the key factors in changes in the vegetation phenology in this water-limited region.Precipitation plays an important role in determining the phenological activities of the vegetation in arid and semiarid areas,such as the Loess Plateau.The extended growing season will significantly influence both the vegetation productivity and the carbon fixation capacity in this region.
基金supported by the State Key Basic Research Development Program of China (2012CB722201)the National Basic Research Program of China (31200414, 31060320, 30970504)+1 种基金the National Basic Research Program of Inner Mongolia (2009ms0603)the Earmarked Fund for Modern Agro-Industry Technology Research System
文摘Variations in temperature and precipitation affect local ecosystems. Considerable spatial and temporal heterogeneity exists in arid ecosystems such as desert steppes. In this study, we analyzed the spatiotemporal dy- namics of climate and vegetation phenology in the desert steppe of Inner Mongolia, China using meteorological data （1961-2010） from 11 stations and phenology data （2004-2012） from 6 ecological observation stations. We also estimated the gross primary production for the period of 1982-2009 and found that the annual mean tem- perature increased at a rate of 0.47~C/decade during 1961-2010, with the last 10 years being consistently warmer than the 50 years as an average. The most significant warming occurred in winters. Annual precipitation slightly decreased during the 50-year period, with summer precipitation experiencing the highest drop in the last 10 years, and spring precipitation, a rise. Spatially, annual precipitation increased significantly in the northeastern and eastern central areas next to the typical steppe. From 2004 to 2012, vegetation green-up and senescence date advanced in the study area, shortening the growing season. Consequently, the primary productivity of the desert steppe de- creased along the precipitation gradient from southeast to northwest. Temporally, productivity increased during the period of 1982-1999 and significantly decreased after 2000. Overall, the Last decade witnessed the most dramatic climatic changes that were likely to negatively affect the desert steppe ecosystem. The decreased primary produc- tivity, in particular, decreases ecosystem resilience and impairs the livelihood of local farmers and herdsmen.
基金supported by the National Basic Research Program of China(2010CB950902)the National Natural Science Foundation of China(41371002)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA05090310)
文摘Early crop yield forecasting is important for food safety as well as large-scale food related planning. The phenology-adjusted spectral indices derived from Moderate Resolution Imaging Spectroradiometer （MODIS） data were used to develop liner regression models with the county-level corn yield data in Northeast China. We also compared the different spectral indices in predicting yield. The results showed that, using Enhanced Vegetation Index （EVI）, Normalized Difference Water Index （NDWI） and Land Surface Water Index （LSWI）, the best time to predict corn yields was 55-60 days after green-up date. LSWI showed the strongest correlation （R2=0.568）, followed by EVI （R2=0.497） and NDWI （R2=0.495）. The peak correlation between Wide Dynamic Range Vegetation Index （WDRVI） and yield was detected 85 days after green-up date （RZ=0.506）. The correlation was generally low for Normalized Difference Vegetation Index （NDVI） （R2=0.385） and no obvious peak correlation existed for NDVI. The coefficients of determination of the different spectral indices varied from year to year, which were greater in 2001 and 2004 than in other years. Leave-one-year-out approach was used to test the performance of the model. Normalized root mean square error （NRMSE） ranged from 7.3 to 16.9% for different spectral indices. Overall, our results showed that crop phenology-tuned spectral indices were feasible and helpful for regional corn yield forecasting.
基金supported by the National Natural Science Foundation of China(Grant No.41901117)Natural Science Foundation of Hunan Province,China(Grant No.2020JJ5362)+1 种基金the Outstanding Youth Project of Hu’nan Provincial Education Department(No.18B001)the Natural Sciences and Engineering Research Council of Canada(NSERC)Discover Grant.
文摘Background:Vegetation phenology research has largely focused on temperate deciduous forests,thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions.Results:Using satellite solar-induced chlorophyll fluorescence(SIF)and MODIS enhanced vegetation index(EVI)data,we applied two methods to evaluate temporal and spatial patterns of the end of the growing season(EGS)in subtropical vegetation in China,and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation.Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods(dynamic threshold method and derivative method)was later than that derived from gross primary productivity(GPP)based on the eddy covariance technique,and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks,respectively.We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation(accounting for more than 73%and 62%of the study areas,respectively),but negatively correlated with preseason maximum temperature(accounting for more than 59%of the study areas).In addition,EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors,and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests,shrub and grassland.Conclusions:Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China.We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region.These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China,and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.
文摘Researchers continually demonstrated through published literature how LiDAR could create unparalleled measurements of ecosystem structure and forest height.There are a number of studies conducted utilizing waveform LiDAR products for terrestrial monitoring,but those that deal specifically with the assessment of space-borne waveform LiDAR for monitoring and modeling of phenology is very limited.This review highlights the waveform LiDAR system and looks into satellite sensors that could link waveform LiDAR and vegetation phenology,such as the proposed NASA’s Global Ecosystem Dynamics Investigation(GEDI)and the Japanese Experimental Module(JEM)-borne LiDAR sensor named MOLI(Multi-footprint Observation LIDAR and Imager).Further,this work examines the richness and utility of the waveform returns and proposes a spline-function-derived model that could be exploited for estimating the leaf-shooting date.The new approach may be utilized for ecosystem-level phenological studies.
基金supported by the National Natural Science Foundation of China (40930101,40971218)the 948 Program,Ministry of Agriculture of China (2009-Z31)the Foundation for National Non-Profit Scientific Institution,Ministry of Finance of China (IARRP-2010-2)
文摘This study used time-series of global inventory modeling and mapping studies （GIMMS） normalized difference vegetation index （NDVI） datasets at a spatial resolution of 8 km and 15-d interval to investigate the spatial patterns of cropland phenology in China. A smoothing algorithm based on an asymmetric Gaussian function was first performed on NDVI dataset to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination. Subsequent processing for identifying cropping systems and extracting phenological parameters, the starting date of growing season （SGS） and the ending date of growing season （EGS） was based on the smoothed NVDI time-series data. The results showed that the cropping systems in China became complex as moving from north to south of China. Under these cropping systems, the SGS and EGS for the first growing season varied largely over space, and those regions with multiple cropping systems generally presented a significant advanced SGS and EGS than the regions with single cropping patterns. On the contrary, the phenological events of the second growing season including both the SGS and EGS showed little difference between regions. The spatial patterns of cropping systems and phenology in Chinese cropland were highly related to the geophysical environmental factors. Several anthropogenic factors, such as crop variety, cultivation levels, irrigation, and fertilizers, could profoundly influence crop phenological status. How to discriminate the impacts of biophysical forces and anthropogenic drivers on phenological events of cultivation remains a great challenge for further studies.
基金Under the auspices of Major State Basic Research Development Program of China (No.2009CB426305)Cultivation Foundation of Science and Technology Innovation Platform of Northeast Normal University (No.106111065202)
文摘Remotely sensing images are now available for monitoring vegetation dynamics over large areas.In this paper,an improved logistic model that combines double logistic model and global function was developed.Using this model with SPOT/NDVI data,three key vegetation phenology metrics,the start of growing season (SOS),the end of growing season (EOS) and the length of growing season (LOS),were extracted and mapped in the Changbai Mountains,and the relationship between the key phenology metrics and elevation were established.Results show that average SOS of forest,cropland and grassland in the Changbai Mountains are on the 119th,145th,and 133rd day of year,respectively.The EOS of forest and grassland are similar,with the average on the 280th and 278th,respectively.In comparison,average EOS of the cropland is relatively earlier.The LOS of forest is mainly from the 160th to 180th,that of the grassland extends from the 140th to the 160th,and that of cropland stretches from the 110th to the 130th.As the latitude increases for the same land cover in the study area,the SOS significantly delays and the EOS becomes earlier.The SOS delays approximately three days as the elevation increases 100 m in the areas with elevation higher than 900 m above sea level (a.s.l.).The EOS is slightly earlier as the elevation increases especially in the areas with elevation below 1200 m a.s.l.The LOS shortens approximately four days as the elevation increases 100 m in the areas with elevation higher than 900 m a.s.l.The relationships between vegetation phenology metrics and elevation may be greatly influenced by the land covers.Validation by comparing with the field data and previous research results indicates that the improved logistic model is reliable and effective for extracting vegetation phenology metrics.
基金supported by HARAZ-Extension and Technology Development Center (HETDC) in Amol City,Iran
文摘Rice crop growth and yield in the north Iran are affected by crop duration and phenology.The purpose of this study was to calibrate and validate the ORYZA2000 model under potential production based on experimental data for simulating and quantifying the phenological development,crop duration and yield prediction of rice crop influenced by different seedling ages.In order to calibrate and validate the crop parameters of ORYZA2000 model,a two-year field experiment was conducted under potential growth condition for transplanted lowland rice during the 2008-2009 rice growing seasons,using three rice varieties with three seedling ages(17,24 and 33 days old).The results showed that the seedling age changed crop duration from 7 to 10 d.The ORYZA2000 model could predict well,but consistently underestimated the length of growing period.The range in normalized root mean square error(RMSEn) values for each phenological stage was between 4% and 6%.From our evaluation,we concluded that ORYZA2000 was sufficiently accurate in simulation of yield,leaf area index(LAI) and biomass of crop organs over time.On average,RMSEn values were 13%-15% for total biomass,18%-21% for green leaf biomass,17%-20% for stem biomass,16%-23% for panicle biomass and 24%-26% for LAI.The RMSEn values for final yield and biomass were 12%-16% and 6%-9%,respectively.Generally,the model simulated LAI,an exceeded measured value for younger seedlings,and best-fit was observed for older seedlings of short-duration varieties.The results revealed that the ORYZA2000 model can be applied as a supportive research tool for selecting the most appropriate strategies for rice yield improvement across the north Iran.
基金funded by the National Natural Science Foundation of China (42061144003)。
文摘The timely and rapid mapping of rapeseed planting areas is desirable for national food security. Most current rapeseed mapping methods depend strongly on images with good observations obtained during the flowering stages. Although vegetation indices have been proposed to identify the rapeseed flowering stage in some areas, automatically mapping rapeseed planting areas in large regions is still challenging.We developed an automatic phenology-and pixel-based algorithm(APPA) by integrating Landsat 8 and Sentinel-1 satellite data. We found that the Normalized Rapeseed Flowering Index shows unique spectral characteristics during the flowering and post-flowering periods, which distinguish rapeseed parcels from other land-use types(urban, water, forest, grass, maize, wheat, barley, and soybean). To verify the robustness of APPA, we applied APPA to seven areas in five rapeseed-producing countries with flowering images unavailable. The rapeseed maps by APPA showed consistently high accuracies with producer accuracies of 0.87–0.93 and F-scores of 0.92–0.95 based on 4503 verification samples. They showed high spatial consistency at the pixel level with the land cover Scientific Expertise Centres(SEC) map in France,Crop Map of England in United Kingdom, national-scale crop-and land-cover map of Germany, and Annual Crop Inventory in Canada at the pixel level. We propose APPA as a highly promising method for automatically and efficiently mapping rapeseed areas.
