A phenology-based vegetation index for improving ratoon rice mapping using harmonized Landsat and Sentinel-2 data

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.

Phenology of different types of vegetation and their response to climate change in the Qilian Mountains,China

The Qilian Mountains(QM)possess a delicate vegetation ecosystem,amplifying the evident response of vegetation phenology to climate change.The relationship between changes in vegetation growth and climate remains complex.To this end,we used MODIS NDVI data to extract the phenological parameters of the vegetation including meadow(MDW),grassland(GSD),and alpine vegetation(ALV))in the QM from 2002 to 2021.Then,we employed path analysis to reveal the direct and indirect impacts of seasonal climate change on vegetation phenology.Additionally,we decomposed the vegetation phenology in a time series using the trigonometric seasonality,Box-Cox transformation,ARMA errors,and Trend Seasonal components model(TBATS).The findings showed a distinct pattern in the vegetation phenology of the QM,characterized by a progressive shift towards an earlier start of the growing season(SOS),a delayed end of the growing season(EOS),and an extended length of the growing season(LOS).The growth cycle of MDW,GSD,and ALV in the QM species is clearly defined.The SOS for MDW and GSD occurred earlier,mainly between late April and August,while the SOS for ALVs occurred between mid-May and mid-August,a one-month delay compared to the other vegetation.The EOS in MDW and GSD were concentrated between late August and April and early September and early January,respectively.Vegetation phenology exhibits distinct responses to seasonal temperature and precipitation patterns.The advancement and delay of SOS were mainly influenced by the direct effect of spring temperatures and precipitation,which affected 19.59%and 22.17%of the study area,respectively.The advancement and delay of EOS were mainly influenced by the direct effect of fall temperatures and precipitation,which affected 30.18%and 21.17%of the area,respectively.On the contrary,the direct effects of temperature and precipitation in summer and winter on vegetation phenology seem less noticeable and were mainly influenced by indirect effects.The indirect effect of winter precipitation is the main factor affecting the advance or delay of SOS,and the area proportions were 16.29%and 23.42%,respectively.The indirect effects of fall temperatures and precipitation were the main factors affecting the delay and advancement of EOS,respectively,with an area share of 15.80%and 21.60%.This study provides valuable insight into the relationship between vegetation phenology and climate change,which can be of great practical value for the ecological protection of the Qinghai-Tibetan Plateau as well as for the development of GSD ecological animal husbandry in the QM alpine pastoral area.

The legacy effects of rubber defoliation period on the refoliation phenology,leaf disease,and latex yield

The leaf phenology of trees has received particular attention for its crucial role in the global water and carbon balances,ecosystem,and species distribution.However,current studies on leaf phenology have mainly focused on temperate trees,while few studies including tropical trees.Little attention has been paid to globally extensive industrial plantations.Rubber plantations are important to both the local and global economies.In this study,we investigated the legacy effects of defoliation phenology on the following year’s leaf flushing,leaf disease,and also latex yield of rubber trees,an economically important tree to local people and the world.Results show that extended duration of defoliation increased the subsequent duration of refoliation and rates of infection by powdery mildew disease,but led to reduced latex yield in March.This legacy effect of rubber defoliation may relate to the carbohydrate reserved in the trees.A longer duration of defoliation would consume more reserved carbohydrates,reducing available reserves for disease defense and latex production.Extended duration of defoliation period was associated with either a lower temperature before the cessation of latex tapping in October-November and/or a higher temperature after the cessation of latex tapping in December-January.Leaf falling signals the end of photosynthetic activities in deciduous trees.Thus,the leaf falling phenology will impact ecological processes involving rubber trees.Our findings indicated that the inclusion of defoliation periods in future rubber trees’ research,will be crucial to furthering our understanding of leaf flushing,powdery mildew disease,and latex yield.

Sensitivity of forest phenology in China varies with proximity to forest edges

Background:Shifts in forest phenological events serve as strong indicators of climate change.However,the sensitivity of phenology events to climate change in relation to forest origins has received limited attention.Moreover,it is unknown whether forest phenology changes with the proximity to forest edge.Methods:This study examined the green-up dates,dormancy dates,time-integrated NDVI(LiNDVI,a measure of vegetation productivity in growing season),and their sensitivities to climatic factors along the gradients of distance(i.e.proximity)to forest edge(0–2 km)in China's natural forests(NF)and planted forests(PF).For the analysis,field-surveyed data were integrated with Moderate Resolution Imaging Spectroradiometer(MODIS)NDVI from 2000 to 2022.Results:Our results reveal that PF had earlier green-up dates,later dormancy dates,and higher LiNDVI than NF.However,green-up sensitivities to temperature were higher at the edges of NF,whereas no such pattern was observed in PF.Conversely,the sensitivity of dormancy dates remains relatively stable from the inner to the edge of both NF and PF,except for a quadratic change in dormancy date sensitivity to precipitation found in NF.Additionally,we found that the green-up sensitivity to temperature increased with decreasing proximity to edge in NF evergreen forests,while it showed the opposite trend in PF evergreen forests.Furthermore,we observed that the precipitation impact on green-up dates shifts from postponing to advancing from the inner to the edge of NF,whereas precipitation dominantly postpones PF's green-up dates regardless of the proximity to edge.The LiNDVI exhibits higher sensitivity to precipitation at the edge areas,a phenomenon observed in NF but not in PF.Conclusions:These results suggest that the responses of forests to climate change vary with the distance to the edge.With increasing edge forests,which results from fragmentation caused by global changes,we anticipate that desynchronized phenological events along the distance to the edge could alter biogeochemical cycles and reshape ecosystem services such as energy flows,pollination duration,and the tourism industry.Therefore,we advocate for further investigations of edge effects to improve ecosystem modelling,enhance forest stability,and promote sustainable tourism.

Spatiotemporal variation in snow cover and its effects on grassland phenology on the Mongolian Plateau

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.

Alpine vegetation responses to snow phenology in the Chinese Tianshan mountainous region

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.

Driving force and changing trends of vegetation phenology in the Loess Plateau of China from 2000 to 2010

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.

Effects of climate change on phenology and primary productivity in the desert steppe of Inner Mongolia

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.

Corn Yield Forecasting in Northeast China Using Remotely Sensed Spectral Indices and Crop Phenology Metrics

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.

Characterizing Spatial Patterns of Phenology in Cropland of China Based on Remotely Sensed Data

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.

Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China

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.

Simulating Phenology,Growth and Yield of Transplanted Rice at Different Seedling Ages in Northern Iran Using ORYZA2000

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.

Mapping rapeseed planting areas using an automatic phenology-and pixel-based algorithm(APPA) in Google Earth Engine

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.

Extracting Vegetation Phenology Metrics in Changbai Mountains Using an Improved Logistic Model

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.

Response of phenology- and yield-related traits of maize to elevated temperature in a temperate region

Extreme high temperatures detrimental to maize production are projected to occur more frequently with future climate change.Phenology and yield-related traits were investigated under several levels of elevated temperature in two early-maturing hybrid cultivars:Junda 6(grown in northeastern China)and Chalok 1(grown in South Korea).They were cultivated in plastic houses in Suwon,Korea(37.27°N,126.99°E)held at target temperatures of ambient(AT),AT+1.5°C,AT+3°C,and AT+5°C at one sowing date in 2013 and three different sowing dates in 2014.Vegetative and reproductive growth durations showed variation depending on sowing date,experimental year,and cultivar.Growth duration tended to decrease,but not necessarily,with temperature elevation,but somewhat increased again above a certain temperature.High temperature-dependent variation was greater during grain filling than in the vegetative period before anthesis.Elevated temperature showed no significant effects on duration or peak dates of silking and anthesis,and thus on anthesis–silking interval.Grain yield tended to decrease with temperature elevation above ambient,showing a sharper linear decrease with mean growing season temperature increase in Junda 6 than in Chalok 1.The decrease in kernel number accounted for a much greater contribution to the yield reductions due to temperature elevation than did the decrease in individual kernel weight in both cultivars.Individual harvestable kernel weight was not significantly affected by temperature elevation treatments.Kernel number showed a linear decrease with mean growth temperature from early ear formation to early grain-filling stage,with Junda 6 showing a much severer decrease than Chalok 1.Kernel number reduction due to temperature elevation was attributable more to the decrease in differentiated ovule number than to the decrease in kernel set in Chalok 1,but largely to the decrease of kernel set in Junda 6.

Spatio-temporal variation of spring phenology in Tibetan Plateau and its linkage to climate change from 1982 to 2012

The influence of climate change on vegetation phenology is a heated issue in current climate change study.We used GIMMS-3g NDVI data to detect the spatio-temporal dynamics of the start of the growing season(SGS) over the Tibetan Plateau(TP) from 1982 to 2012 and to analyze its relationship with temperature and precipitation.No significant trend was observed in the SGS at the regional scale during the study period(R^2 = 0.03,P = 0.352).However,there were three time periods(1982-1999,1999-2008 and 2008-2012) with identifiable,distinctly different trends.Regions with a significant advancing trend were mainly scattered throughout the humid and semi-humid areas,whereas the regions with a significant delaying trend were mostly distributed throughout the semi-arid areas.Statistical analysis showed that the response of the SGS to climate change varies spatially.The SGS was significantly correlated with the spring temperature and the start of the thermal growth season(STGS) in the relatively humid area.With increasing aridity,theimportance of the spring temperature for the SGS gradually decreased.However,the influences of precipitation and winter temperature on the SGS were complicated across the plateau.

Precipitation as a control of vegetation phenology for temperate steppes in China

Using the NDVI ratio method, the authors extracted phenological parameters from NOAA-AVHRR NDVI time-series data （1982-2008）. The start of the growing season （SOS） and the date of maximum NDVI （Peak-t） correlated significantly with the mean annual precipitation along regional gradients of the steppes. Along the south transect （located at a lower latitude with a higher annual mean temperature） there was a positive correlation between the end of the growing season （EOS） and the mean annual precipitation along precipitation gradients （R2 = 0.709, p 〈 0.0001）. However, along the north transect （located at higher latitude with lower annual mean temperature）, the EOS was slightly negatively related with the mean annual precipitation （R2 = 0.179, p 〈 0.1）. There was positive correlation between the length of the growing season and the annual precipitation along two transects （R2 = 0.876, p 〈 0.0001 for the south transect; R2 = 0.290, p 〈 0.01 for the north transect）. Thus, for the Inner Mongolian steppe, it is precipitation rather than temperature that determines the date of the SOS.

A Process-Based Model for Simulating Phasic Developmentand Phenology in Rice

A simulation model for phasic and phenological development of rice was developed using the scale of physiological development time, based on the ecophysiological development processes. The interaction of daily thermal effectiveness, photoperiod effectiveness and intrinsic earliness(before heading), and basic filling duration factor(after heading)determined the daily physiological effectiveness, which accumulated to get physiological development time. The Beta and quadratic functions were used to describe daily thermal and photoperiod effectiveness, respectively. Five specific genetic parameters were added to adjust the genotypic differences in rice development so that all different varieties could reach the same physiological development time at a given development stage. The stages of seedling emergence, panicle initiation, heading, and maturity were validated using sowing dates under different ecological environments, with the RMSE of 1. 47, 5. 10, 4.58 and 3.37 days, respectively. The results showed that the model was not only explanatory and systematic but also accurate and applicable.

Analysis of the Impact of Climate Change on the Phenology of Animals

Using linear regression and correlation analysis method,the variation trend characteristics of average temperature,sunshine,precipitation and the phenology of five kinds of animals(Barn Swallows,Frogs,Cryptotympana atra,Crickets,Indian Cuckoo) in Huimin County during 1980-2008 were analyzed.On this basis,the relationship between the phenological phases of various animals and monthly temperature,sunshine and precipitation was analyzed.And the reasons that the phenological phases of various animals adapted to the climatic factors were also discussed.

Impacts of Snow Cover on Vegetation Phenology in the Arctic from Satellite Data

The dynamics of snow cover is considered an essential factor in phenological changes in Arctic tundra and other northern biomes. The Moderate Resolution Imaging Spectroradiometer （MOD1S）/Terra satellite data were selected to monitor the spatial and temporal heterogeneity of vegetation phenology and the timing of snow cover in western Arctic Russia （the Yamal Peninsula） during the period 2000 10. The magnitude of changes in vegetation phenology and the timing of snow cover were highly heterogeneous across latitudinal gradients and vegetation types in western Arctic Russia. There were identical latitudinal gradients for ＂start of season＂ （SOS） （r2 = 0.982, p〈0.0001）, ＂end of season＂ （EOS） （r2 = 0.938, p〈0.0001）, and ＂last day of snow cover＂ （LSC） （r2 = 0.984, p〈0.0001）, while slightly weaker relationships between latitudinal gradients and ＂first day of snow cover＂ （FSC） were observed （r2 = 0.48,p〈0.0042）. Delayed SOS and FSC, and advanced EOS and LSC were found in the south of the region, while there were completely different shifts in the north. SOS for the various land cover features responded to snow cover differently, while EOS among different vegetation types responded to snowfall almost the same. The timing of snow cover is likely a key driving factor behind the dynamics of vegetation phenology over the Arctic tundra. The present study suggests that snow cover urgently needs more attention to advance understanding of vegetation phenology in the future.