基于AVHRR和MODIS数据的全球植被物候比较分析

AVHRR和MODIS卫星数据在全球和区域尺度植物物候对气候变化响应研究中起着重要的作用,然而两种传感器在全球尺度物候监测的一致性有待验证。首先利用时间序列谐波分析法(HANTS)对2005年全球GIMMS AVHRR NDVI和MODIS 13A2 数据进行滤波处理;然后基于改进的动态阈值方法,提取全球植被的返青期(SOS)、枯黄期(EOS)和生长季长度(DOS);最后分区域比较和评估两种传感器提取物候参数的潜力。研究结果表明：2005年全球大部分地区植被在第100～140 d开始生长,到第260～300 d逐渐停止生长,生长季长度集中在130～180 d,并且和区域研究结果具有一致性;两种传感器提取的植被关键物候期的空间变化趋势是一致的,随着纬度升高,返青期呈现推迟趋势,枯黄期呈现提早趋势,生长季长度呈现缩短趋势;AVHRR和MODIS提取落叶林和草地的SOS、EOS和DOS在欧亚大陆和北美洲区域的相关系数大部分达到0.9以上。     

Monitoring Spatial-temporal Change of Cotton Phenology in Xinjiang and Its Response to Climate Change

Observations of vegetation phenology provide valuable information regarding ecosystem response to environmental conditions,especially to climate change.Cotton is one of the most important economic crops in Xinjiang,and its phenological change can directly reflect the change of climate in Xinjiang.This research was an attempt to extract cotton phenological parameters in Xinjiang by using 16 years’(2001 to 2016) time series MODIS Normalized Difference Vegetation Index(NDVI)：firstly,filtering noise from the time-series data using Savitzky-Golay filtered method;then detecting cotton phenology parameters (Start of Growth Season(SOS),End of Growth Season(EOS),Long of Growth Season(LOS)) using Dynamic Threshold method;finally,the spatial patterns and temporal trends of observed cotton phenological characteristics were analyzed over the past 16 years and the relationship between cotton phenology and temperature changes was also discussed.The result of this study showed that the spatial patterns of cotton phenology were significantly different in study region：SOS delayed gradually from Nanjiang to Beijiang,and mainly occurred before 151st and after 151st days respectively;EOS gradually advanced,most areas of northern Xinjiang ended up 292nd days ago,while the southern Xinjiang happened 298th days later;LOS shortened,Nanjiang is generally longer than 150 days while Beijiang is usually shorter than 150 days.The trend of cotton phenology(2001~2016) under climate change in northern and southern Xinjiang were not completely similar：SOS and EOS in southern Xinjiang showed a delay-advancing-delay-advancing trend,and LOS was unsignificantly delayed;While SOS in northern Xinjiang were slightly advanced and EOS exhibited a delay trend followed by an advancing,LOS showed a shorten-lengthen-shorten trend.In addition,cotton phenology showed a strong correlation with the temperature：SOS and EOS were positively correlated with the beginning date of 15℃ and the end date of 10℃ respectively;SOS was negatively correlated with the spring temperature,while EOS had a positive correlation with autumn temperature.     

基于MODIS数据的河北省草地和林地的物候期及其与NPP相关分析

通过2006～2016年中等分辨率成像光谱仪MODIS(Moderate Resolution Imaging Spectroradiometer)的 MCD12Q2数据集和 NPP(Net Primary Productivity,净初级生产力)数据MOD17A3HGF为数据源,研究河北省的草地和林地的物候期:生长季...     

Temporal and spatial dynamics of phenology along the North–South Transect of Northeast Asia

Vegetation phenology is sensitive to climate change and, as such, is often regarded as an indicator of climate change. It is a common practice to extract vegetation phenological indicators based on satellite remote sensing data. In this study, we used the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Study (GIMMS) Third-Generation normalized difference vegetation index (NDVI_3G) to investigate temporal and spatial changes in phenology in Northeast Asia. Based on the maximum rate of change in the NDVI and dynamic threshold, we used the Asymmetric Gaussian model, Double Logistic method, and Savitzky-Golay filter to extract the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS), respectively, along the North–South Transect of Northeast Asia (NSTNEA) from 1982 to 2014. We then compared the differences in SOS, EOS, and LOS and considered their spatio-temporal dynamics and relationship with temperature. The results show that the Asymmetric Gaussian model has the highest stability among the three methods. Dynamic thresholds corresponding to the maximum change rate of NDVI were mainly between 0.5 and 0.6. From 1982 to 2014, the SOS in the NSTNEA region occurred approximately 0.19 days earlier each year; the trends in EOS and LOS were not significant. In general, temperature and latitude have a strong linear relationship, both of which significantly impact vegetation phenology in the NSTNEA region. In addition, elevation also significantly impacts on vegetation phenology in the NSTNEA region.     

The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests

Satellite remote sensing has the potential to contribute to plant phenology monitoring at spatial and temporal scales relevant for regional and global scale studies. Historically, temporal composites of satellite data, ranging from 8 days to 16 days, have been used as a starting point for satellite-derived phenology data sets. In this study we assess how the temporal resolution of such composites affects the estimation of the start of season (SOS) by: 1) calibrating a relationship between satellite derived SOS with in situ leaf unfolding (LU) of trembling aspen (Populus tremuloides) across Canada and 2) quantifying the sensitivity of calibrated satellite SOS estimates and trends, over Canadian broadleaf forests, to the temporal resolution of NDVI data. SOS estimates and trends derived from daily NDVI data were compared to SOS estimates and trends derived from multiday NDVI composites that retain the exact date of the maximum NDVI value or that assume the midpoint of the multiday interval as the observation date. In situ observations of LU dates were acquired from the PlantWatch Canada network. A new Canadian database of cloud and snow screened daily 1-km resolution National Oceanic and Atmospheric Administration advanced very high resolution radiometer surface reflectance images was used as input satellite data. The mean absolute errors of SOS dates with respect to in situ LU dates ranged between 13 and 40 days. SOS estimates from NDVI composites that retain the exact date of the maximum NDVI value had smaller errors (~ 13 to 20 days). The sensitivity analysis reinforced these findings: SOS estimates from NDVI composites that use the exact date had smaller absolute deviations from the LU date (0 to − 5 days) than the SOS estimates from NDVI composites that use the midpoint (− 2 to − 27 days). The SOS trends between 1985 and 2007 were not sensitive to the temporal resolution or compositing methods. However, SOS trends at individual ecozones showed significant differences with the SOS trends from daily NDVI data (Taiga plains and the Pacific maritime zones). Overall, our results suggest that satellite based estimates of vegetation green-up dates should preferably use sub-sampled NDVI composites that include the exact observation date of the maximum NDVI to minimize errors in both, SOS estimates and SOS trend analyses. For trend analyses alone, any of the compositing methods could be used, preferably with composite intervals of less than 28 days. This is an important finding, as it suggests that existing long-term 10-day or 15-day NDVI composites could be used for SOS trend analyses over broadleaf forests in Canada or similar areas. Future studies will take advantage of the growing in situ phenology networks to improve the validation of satellite derived green-up dates.     

Use of MODIS NDVI to evaluate changing latitudinal gradients of rangeland phenology in Sudano-Sahelian West Africa

This study is concerned with the implications of changing latitudinal gradients in vegetative phenology (green-up, senescence, and length of growing season) for the management of long-distance seasonal movements of livestock herds in Sudano-Sahelian West Africa. For a study area covering much of the southern half of Mali, phenological parameters were estimated using a double-logistic function fitted to seasonal NDVI trajectories for 1 km² MODIS data over the period 2000-2010. Green-up dates, senescence dates and length of growing season were all found to more strongly vary by latitude (+ 9 days/degree, − 5 days/degree and − 14 days/degree, respectively) than across years (+ 0.42 days/year, + 0.86 days/year and + 0.44 days/year respectively). Interannual and spatial variability of these parameters are highest at lower latitudes within the study area. The slopes of the relationship of phenological parameters with latitude change across the latitudinal range studied. Breakpoint analysis of annual green-up versus latitude curves identifies a mean inflection point of 13° north latitude above which the positive slope declines significantly. This previously-undescribed pattern is consistent with recent work on monsoonal dynamics showing rainfall onset being associated with an abrupt shift in the location of the ITCZ (monsoon onset) at latitudes north of 13° north latitude. The effects of the observed variation in latitudinal gradients of phenological variables on the direction and timing of regional livestock movements are discussed.     

Spatio-temporal Dynamics of Vegetation Phenology in the Mongolian Plateau during 1982~2011

Based on the third generation GIMMS NDVI time\|series datasets during 1982~2011,we extracted the start of growing season (SOS),end of growing season (EOS) and length of growing season (LOS) in the Mongolian Plateau using cumulative NDVI based logistic regression curves,change rate of curvature in NDVI logistic regression curves and change rate method of NDVI and further analyzed the spatio\|temporal changes of phenology.The results showed that the cumulative NDVI based logistic regression curves and change rate method of NDVI performed better predictions in SOS and EOS modeling,and the mean value of these two methods improved the extraction accuracy of phenology in the Mongolian Plateau.SOS in the Mongolian Plateau mostly started from the middle of April to the end of May and ended from the end of the September to the middle of the October.Most LOS ranged from 125 to 175 days.Spatially,the earlier SOS,later SOS and longer LOS occurred in the humid and sub\|humid area of the plateau,and later SOS,earlier EOS and shorter LOS occurred in arid and semi\|arid regions of the plateau.Temporally,during the 30\|year observation period,approximately,51.6% and 33.9% of the plateau experienced advanced and delayed SOS,respectively,and 21.2% and 12.4% of which are statistically significant;Approximately,35.6% and 49.8% of the study area experienced delayed EOS,respectively,and 8.2% and 12.0% of which are statistically significant;Accordingly,40.3% (17.8% are significant) and 44.8% (18.9% are significant) of the plateau showed shortening and lengthening of the LOS.     

基于NDVI数据的江苏省植被物候变化及其影响因子分析

以气候变暖为主要特征的全球气候变化与生态系统的相互作用成为影响可持续发展的重要因素。植被作为陆地生态系统的主要组成部分,在生态环境评价及碳水循环等方面具有重要作用。以江苏省为研究区,利用长时间序列的GIMMS NDVI3g数据集和气象数据,采用Logistic函数法提取该区域过去34 a(1982~2015年)植被生长期物候(Start Of Season SOS,End Of Season EOS)变化的时空分布特征,并用相关性分析法定量确定主要气象因子(温度、降水)对物候变化的贡献。结果表明：①空间上,从江苏省南部到北部,SOS呈递增趋势,EOS呈递减趋势;②时间上,大部分(83.1%)像元的SOS提前,主要分布在江苏省中部及北部地区,大多提前1~2 d/a,69.2%像元的EOS延后,大多延后0~1 d/a;③植被生长期开始SOS/EOS对温度、降水有明显响应,70.5%像元的SOS与温度呈负相关,主要位于江苏省北部及少部分南部地区,55.5%像元的SOS与降水呈负相关,55.2%像元的EOS与温度呈正相关,71.2%像元的EOS与降水呈负相关。整体上,温度的升高导致生长期提前,降水对SOS具有双向作用,秋季物候的影响因子更为复杂,温度和降水的变化并不能导致EOS的提前或者推迟。本研究加深对气候变化与植被生态系统相互作用过程的认识,为未来植被及气候变化分析提供参考。     

Detecting the effects of climate change on canopy phenology in coniferous forests in semi-arid mountain regions of China

An increased understanding of the responses of forest phenology to climate on regional scales is critical to the evaluation of biochemical cycles (i.e. carbon, water, heat, and nutrient) under environmental changes. In this study, we aimed to identify climatic constraints on phenological events in an evergreen coniferous forest in semi-arid mountain regions of northern China. We quantified the start of season (SOS), end of season (EOS), and growing season length (GSL) based on satellite-derived data sets (normalized difference vegetation index (NDVI)) and investigated the relationships between these phenological events and climate factors. The results revealed discontinuous trends in phenological events throughout the study period, with neither an obvious extension nor decrement in GSL. We demonstrated that minimum temperatures controlled the dynamics of SOS and EOS, thus providing strong evidence for the need to include minimum temperature as a control on phenology in simulation models. Additionally, precipitation was coupled to the shift in maximum NDVI, as rainfall is a major climatic limitation to vegetation growth in semi-arid regions. It appears that selecting appropriate timescales to analyse the relationships between phenology and climate is critical. We illustrated that NDVI was an effective tool in an effort to gain greater understanding of the effects of environmental change on ecosystem functioning in this forest. Our results may be used as reference to track local changes in the evergreen coniferous forest dynamics under different climate change scenarios for semi-arid mountain regions.     

HJ-1A NDVI与MODIS NDVI时间序列提取植被物候特征对比研究

以HJ-1A和MODIS为数据源,通过动态阈值法提取物候特征参数,对HJ-1A NDVI和MODIS NDVI时间序列进行植被物候特征提取进行定性和定量比较,通过比较结果,提出HJ-1A NDVI数据在该应用中存在的问题,促进国产中空间高时间分辨率影像数据在植被物候信息提取研究中的应用,提高其在生态系统研究中的应用价值。结果表明:在SOS、EOS和LOS以及TOMS几个主要的物候时间点上,MODIS NDVI时间序列的标准差较小,所得物候数据更为集中,偏离度较小,所得物候数据较稳定;而HJ-1A NDVI时间序列所得物候数据的标准差较大,数据偏离程度较大,而在POS、BOS和AOS等表征植被生命周期中生长幅度数据上,其标准差较小,离散程度小。     

Spatio-temporal Analysis of Vegetation Phenology with Multiple Methods over the Tibetan Plateau based on MODIS NDVI Data

In the context of global climate change,vegetation phenology analysis based on remote sensing has become an critical method for studying the characteristics of physical and physiological changes of vegetation.This paper uses the MODIS NDVI time\|series data of 96 meteorological stations over the Tibetan Plateau during 2000\|2014 to explore the development trend of vegetation phenological and geographical environment factors of each meteorological station,typical vegetation coverage and the whole plateau region.Firstly,using three cubic spline function method (Spline),double logistic function method(D\|L)and singular spectrum analysis (SSA),NDVI time\|series data is reconstructed,then using the derivative method (Der)and threshold method (Trs),the key parameters of phenological information is extracted,after that differences and application conditions between the six methods are analyzed and compared.Secondly,using M\|K test trend analysis method,the phenological development trend of each site and area were calculated,the relationship between phenological development trend and altitude,precipitation,temperature is studied.Finally,by the Growing season length(GSL)obtained by temperature threshold method,LOS is compared and verified.in grassland and forest land cover types,SSA,Spline,D\|L combined with threshold method to get the Start of Season(SOS),end of season(EOS),Length of season (SOS)respectively is a good combination strategy.(2)The spatial differences of various phenological parameters extracted by different methods are large,and the trend is relatively consistent at small scales.Southeast humid and semi\|humid shrub steppe region and northwestern desert steppe in the Tibetan Plateau,SOS and EOS delayed,but LOS prolonged;southwestern humid region,SOS and EOS delayed,LOS shortened;widely distributed grassland,the phenological parameters did not show significant tendency.(3)Temperature is related to the development trend of phenological parameters.With temperature increasing,the phenomena of SOS advance,EOS lag are presented.Because of the complexity of the plateau landform and climate,there was no significant relationship between phenological development trend for most of the site with the altitude and precipitation,only a few sites have strong correlation,the correlation between GSL and LOS also showed similar characteristics.For remote sensing based phonological analyses,our study identify there is no method existing here that is a adaptive across all the Tibetan Plateau.in addition,at point scale the phenological parameters do not represent a significant earlier or later trend.     

Effect of climate change on vegetation phenology of different land-cover types on the Tibetan Plateau

Research in vegetation phenology change has been one heated topic of current ecological and climate change study. The Tibetan Plateau, as the highest plateau of the earth, is more vulnerable and sensitive to climate change than many other regions. In this region, shifts in vegetation phenology have been intensively studied during recent decades, primarily based on satellite-retrieved data. In this study, we explored the spatiotemporal changes of vegetation phenology for different land-cover types in the Tibetan Plateau and characterized their relationship with temperature and precipitation by using long-term time-series datasets of normalized difference vegetation index (NDVI) from 1982 to 2014. Diverse phenological changes were observed for different land-cover types, with an advancing start of growing season (SOS), delaying end of growing season (EOS) and increasing length of growing season (LOS) in the eastern Tibetan Plateau where meadow was the dominant vegetation type, but with the opposite changes in the steppe and sparse herbaceous or sparse shrub regions which are mostly located in the northwestern and western edges of the Tibetan Plateau. Correlation analysis indicated that sufficient preseason precipitation may delay the SOS of evergreen forests in the southeastern Plateau and advance the SOS of steppe and sparse herbaceous or sparse shrub in relatively arid areas, while the advance of SOS in meadow areas could be related to higher preseason temperature. For EOS, because it is less sensitive to climate change than SOS, the response of EOS for different land-cover types to precipitation and temperature were more complicated across the Tibetan Plateau.     

京津唐地区城市化对植被物候的影响研究

城市化过程使得土地利用、地表植被覆盖发生着显著的变化,如何定量化描述城市化对植被物候的影响越来越受到各方的关注。基于京津唐地区2001~2006年NDVI时间序列影像,得出了京津唐地区植被物候空间分布格局,计算出北京、天津、唐山3个核心城市城区的距离变量与平均植被物候,并分析了城市化对植被物候指标的影响趋势。结果表明:①2001~2006年,北京城市化使得城区及离城区较近的地方植被生长开始时间提前、结束时间推后、生长季周期变长、NDVI振幅减小;②天津和唐山的城市化使得城区及离城区较近的地方植被生长开始时间延后、结束时间提前、生长季周期变短\,NDVI振幅减小;③城市化对植被物候的影响与该地区城市扩张类型存在相关性关系。     

Land surface phenology of North American mountain environments using moderate resolution imaging spectroradiometer data

Monitoring and understanding plant phenology is becoming an increasingly important way to identify and model global changes in vegetation life cycle events. High elevation biomes cover twenty percent of the Earth's land surface and provide essential natural resources. These areas experience limited resource availability for plant growth, development, and reproduction, and are one of the first ecosystems to reflect the harmful impact of climate change. Despite this, the phenology of mountain ecosystems has historically been understudied due to the rough and variable terrain and inaccessibility of the area. In addition, although numerous studies have used synoptically sensed data to study phenological patterns at the continental and global scales, relatively few have focused on characterizing the land surface phenology in mountainous areas. Here we use the MODIS/Terra + Aqua satellite 8-day 500 m Nadir BRDF Adjusted Reflectance product to quantify the land surface phenology. We relate independent data for elevation, slope, aspect, solar radiation, and temperature as well as longitude and latitude with the derived phenology estimates. We present that satellite derived SOS can be predicted based on topographic and weather variables with a significant R²adj between 0.56 and 0.62 for the entire western mountain range. Elevation and latitude exhibit the most significant influences on the timing of SOS throughout our study area. When examined at both the local and regional scales, as well as when accounting for aspect and temperature, SOS follows closely with Hopkins' Bioclimatic Law with respect to elevation and latitude.     

Sensitivity of vegetation phenology detection to the temporal resolution of satellite data

Vegetation phenology derived from satellite data has increasingly received attention for applications in environmental monitoring and modelling. The accuracy of phenological estimates, however, is unknown at the regional and global level because field validation data are insufficient. To assess the accuracy of satellite‐derived phenology, this study investigates the sensitivity of phenology detection to both the temporal resolution of sampling and the number of consecutive missing values (usually representing cloud cover) in the time series of satellite data. To do this, time series of daily vegetation index data for various ecosystems are modelled and simulated using data from Moderate‐Resolution Imaging Spectroradiometer (MODIS) data. The annual temporal data are then fitted using piecewise logistic functions, which are employed to calculate curvature change rate for detecting phenological transition dates. The results show that vegetation phenology can be estimated with a high precision from time series with temporal resolutions of 6–16 days even if daily data contains some uncertainties. If the temporal resolution is no coarser than 16 days for time series sampled using an average composite, the absolute errors are less than 3 days. On the other hand, the phase shift of temporal sampling is shown to have limited impacts on phenology detection. However, the accuracy of phenology detection may be reduced greatly if missing values in the time series of 16‐day MODIS data occur around the onsets of phenological transition dates. Even so, the probability that the absolute error in phenological estimates is greater than 5 days is less than 4% when only one period is missing in the time series of 16‐day data during vegetation growing seasons; this probability increases to 20% if there are two consecutive missing values.     

Satellite monitoring of boreal forest phenology and its climatic responses in Eurasia

The fragile ecosystems in boreal Eurasia are sensitive to global climate change. Land surface phenology provides an important tool for us to better understand the current status of boreal forest and its climatic responses in this remote zone. This study utilizes the new-generation AVHRR GIMMS NDVI3g products in 1982–2011 to extract four phenological metrics in the study region, including start of season (SOS), end of season (EOS), season length (LOS), and middle of season (MOS). Linear and Mann–Kendall trend analyses are performed to examine their spatiotemporal patterns and relationships with climatic variables assisted with the Climate Research Unit re-analysis climatic data sets. While advanced spring greening is observed in agreement with past studies, our results reveal that the SOS advance mostly occurs in mixed forests in southern Eurasia. More importantly, this study extracts the opposite trends for the end of season – advanced EOS in coniferous forests above 60°N and delayed EOS in mixed forests below. Overall, temperature in May–October has consecutively increased in the past 30 years. Precipitation has also increased but with a fragmented pattern. The advanced SOS across Eurasia is highly correlated with a warmer spring (April and May) in Eurasia. The EOS has a strong, negative relationship with fall precipitation (September). Further investigation is suggested to examine the opposite EOS trends and their environmental/ecological consequences in different forest zones of boreal Eurasia.     

Phenology of vegetation in Southern England from Envisat MERIS terrestrial chlorophyll index (MTCI) data

Given the close association between climate change and vegetation response, there is a pressing requirement to monitor the phenology of vegetation and understand further how its metrics vary over space and time. This article explores the use of the Envisat MERIS terrestrial chlorophyll index (MTCI) data set for monitoring vegetation phenology, via its estimates of chlorophyll content. The MTCI was used to construct the phenological profile of and extract key phenological event dates from woodland and grass/heath land in Southern England as these represented a range of chlorophyll contents and different phenological cycles. The period 2003–2008 was selected as this was known to be a period with temperature and phenological anomalies. Comparisons of the MTCI-derived phenology data were made with ground indicators and climatic proxy of phenology and with other vegetation indices: MERIS global vegetation index (MGVI), MODIS normalized difference vegetation index (NDVI) and MODIS enhanced vegetation index (EVI). Close correspondence between MTCI and canopy phenology as indicated by ground observations and climatic proxy was evident. Also observed was a difference between MTCI-derived phenological profile curves and key event dates (e.g. green-up, season length) and those derived from MERIS MGVI, MODIS NDVI and MODIS EVI. The research presented in this article supports the use of the Envisat MTCI for monitoring vegetation phenology, principally due to its sensitivity to canopy chlorophyll content, a vegetation property that is a useful proxy for the canopy physical and chemical alterations associated with phenological change.     

Estimation of winter wheat phenology under the influence of cumulative temperature and soil salinity in the Yellow River Delta,China, using MODIS time-series data

Understanding the impact of environmental factors on crop phenology is significant in predicting crop growth stages, agricultural decision-making, and yield estimation. Here, using Moderate Resolution Imaging Spectroradiometer time-series data, we present phenological detection mechanisms and an explanation for the phenological variability linked to environmental drivers, such as cumulative temperature and soil salinity, for winter wheat (Triticum aestivum L.) in the Yellow River Delta in 2013. The 8-day normalized difference vegetation index was fitted to a double Gaussian function. Phenological phases, such as the green-up and heading phases, were extracted using maximum curvature approaches. The spatial characteristics of the phenological patterns were investigated. The relationships between the phenological phases and cumulative temperature were explored. Then, the relationships between the phenological phases and soil salinity were evaluated by selecting sites with similar soil fertility and temperature forcing. This study concluded that the regional average green-up date occurred on 5 March, and the regional average heading date occurred on 9 May. The spatial distributions of the green-up and heading phases showed a gradual delay from the southwest to the northeast and from the south to the north. The green-up phase lagged 4–5 days for every 10 degree days that the cumulative temperature decreased. The heading phase lagged 1–2 days for every 10 degree days that the cumulative temperature decreased. The green-up phase in a non-salinization region might be approximately 5–9 days earlier than that in a severe or moderate salinization region. The heading phase in a severe region might occur approximately 1–8 days earlier than that in a non-salinization or moderate salinization region. The method proposed in this article may be useful for understanding the impact of temperature and soil salinity on phenology and could be used to better manage winter wheat in coastal salinization areas.     

Can EVI-derived land-surface phenology be used as a surrogate for phenology of canopy photosynthesis?

Canopy phenology plays a prominent role in determining the timing and magnitude of carbon uptake by many ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) Global Land Cover Dynamics product developed from the enhanced vegetation index (EVI) provides broad spatial and temporal coverage of land-surface phenology (LSP), and may serve as a useful proxy for the phenology of canopy photosynthesis. Here, we compare the MODIS growing season start and end dates (SOS and EOS) with the seasonal phenology of canopy photosynthesis estimated using the eddy covariance approach. Using 153 site-years obtained from the Ameriflux database, we calculated the SOS and EOS of gross primary production (GPP) and canopy photosynthesis capacity (CPC) for seven different boreal and temperate vegetation types. CPC is GPP at maximum radiation, estimated by fitting half-hourly GPP and radiation to a rectangular hyperbolic function. We found large mean absolute differences of up to 53 days, depending on vegetation type, between the phenology of canopy development and photosynthesis, indicating that remotely sensed LSP is not a robust surrogate of seasonal changes in GPP, particularly for evergreen needleleaf forests. This limited correspondence of ecosystem carbon uptake with the MODIS LSP product points to the need for improved remotely sensed proxies of GPP phenology.     

A biophysically based and objective satellite seasonality observation method for applications over the Arctic

Despite wide applications of remote-sensing data with high temporal resolution for monitoring phenology, two persistent problems have prevented the realization of their full potential. The first is the subjectivity in defining thresholds for a phenological event (e.g. the start or end of growing season ? SOS or EOS). The second is the use of various arbitrarily selected filtering and smoothing algorithms for constructing vegetation index seasonal profiles in order to reduce the noise caused by residue cloud contamination and aerosol variations. In this study, we addressed both problems by developing a biophysically based and objective satellite seasonality observation method (BLOSSOM) for application over Canada’s Arctic. Application of the BLOSSOM method to three northern Canadian national parks (Ivvavik, Wapusk, and Sirmilik) proved that the method is operational. Using the uncertainties in the vegetation index and its threshold, we estimated the overall mean uncertainties as being ?5.3 to 3.4 days, ?4.2 to 5.2 days, and ?6.2 to 8.4 days, respectively, for SOS, EOS, and growing season length (GSL). Further independent tests against SOS, determined using records of snow cover at nearby climate stations (as ‘truth’), indicate that the mean absolute error is less than 3.6 ± 0.2 days.