Annual changes in MODIS vegetation indices of Swedish coniferous forests in relation to snow dynamics and tree phenology

Remote sensing provides spatially and temporally continuous measures of forest reflectance, and vegetation indices calculated from satellite data can be useful for monitoring climate change impacts on forest tree phenology. Monitoring of evergreen coniferous forest is more difficult than monitoring of deciduous forest, as the new buds only account for a small proportion of the green biomass, and the shoot elongation process is relatively slow. In this study, we have analyzed data from 186 coniferous monitoring sites in Sweden covering boreal, southern-boreal, and boreo-nemoral conditions. Our objective was to examine the possibility to track seasonal changes in coniferous forests by time-series of MODIS eight-day vegetation indices, testing the coherence between satellite monitored vegetation indices (VI) and temperature dependent phenology. The relationships between two vegetation indices (NDVI and WDRVI) and four phenological indicators (length of snow season, modeled onset of vegetation period, tree cold hardiness level and timing of budburst) were analyzed.The annual curves produced by two curve fitting methods for smoothening of seasonal changes in NDVI and WDRVI were to a large extent characterized by the occurrence of snow, producing stable seasonal oscillations in the northern part and irregular curves with less pronounced annual amplitude in the southern part of the country. Measures based on threshold values of the VI-curves, commonly used for determining the timing of different phenological phases, were not applicable for Swedish coniferous forests. Evergreen vegetation does not have a sharp increase in greenness during spring, and the melting of snow can influence the vegetation indices at the timing of budburst in boreal forests. However, the interannual variation in VI-values for specific eight-day periods was correlated with the phenological indicators. This relation can be used for satellite monitoring of potential climate change impacts on northern coniferous spring phenology.     

Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.     

利用遥感监测长江三角洲森林植被物候对气候变化的响应

植物的物候与气候等环境因素息息相关,是指示气候与自然环境变化对生态影响的重要指标。目前,气候变暖日益为人所关注,使用遥感技术研究植物物候与气候变化之间的关系具有重要的意义。监测人口密度高和城市经济发达地区的植物物候对气候变暖的响应,可以揭示区域热环境变化及其产生的生态效应。本研究选取长江三角洲地区为研究区域,使用SPOT卫星VGT传感器的长时间NDVI数据序列,对经济发达区域森林植被的NDVI序列进行非对称性高斯函数拟合法平滑处理,并提取与研究其物候特征,发现①NDVI与气温具有较强相关性,随气候变暖,森林植被NDVI年均值有增加趋势;②森林植被生长活跃期起始日期提前,终止日期延后,时长有明显的延长趋势,生长活跃期内NDVI有所增加;③森林植被NDVI极大值与极小值出现日期均明显提前,NDVI极大值有增大趋势,而极小值呈下降趋势,年内极差增加,NDVI增长期缩短,衰落期延长;④森林植被在春、夏两季NDVI均值有所增长,秋季无明显变化,冬季略有降低。     

Assessment of the SeaWinds scatterometer for vegetation phenology monitoring across China

Vegetation phenology tracks plants' lifecycle events, revealing the response of vegetation to global climate changes. Changes in vegetation phenology also influence fluxes of carbon, water, and energy at local and global scales. In this study, we analysed a time series of Ku-band radar backscatter measurements from the SeaWinds scatterometer on board the Quick Scatterometer (QuickSCAT) to examine canopy phenology from 2003 to 2005 across China. The thaw season SeaWinds backscatter and Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) time series were significantly correlated in 20 of the 22 sites (p < 0.05). A weighted curve-fitting method was applied to detect the start of season and end of season from both data sets. The SeaWinds scatterometer generally detected earlier timing of spring leaf-out and later fall senescence than the MODIS LAI data sets. The SeaWinds backscatter detected phenological metrics in 75.85% of mainland China. Similar spatial patterns were observed from the SeaWinds backscatter and MODIS LAI time series; however, the average standard deviation of the scatterometer-detected metrics was lower than that of MODIS LAI products. Overall, the phenological information from the SeaWinds scatterometer could provide an alternative view on the growth dynamics of land-surface vegetation.     

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.     

Land surface phenology dynamics and climate variations in the North East China Transect (NECT), 1982–2000

Land surface phenology dynamics reflect the response of the Earth's biosphere to inter‐ and intra‐annual dynamics of the Earth's climate and hydrologic regimes. Investigations of land surface phenology dynamics and its relation to long‐term climate variation could help us to detect the response of regional vegetation to climate variation. The present study developed a new algorithm for detecting regional land surface phenology dynamics (ARLSPD) and demonstrated it in detecting the vegetation response to inter‐annual climate variability in the North East China Transect (NECT), a mid‐latitude semi‐arid terrestrial transect with strong gradients in environmental conditions and vegetation formations. The spatial–temporal patterns of greenup‐onset date, maturity date, and senescence date during the period of 1982–2000 are presented. The resultant spatial–temporal patterns of land surface phenology were quite consistent with the land‐cover characteristics, moisture, and temperature gradients. The relations between inter‐annual variations in phenology and seasonal climate were investigated. It was found that besides human disturbance, land surface phenology depended primarily on the combined effects of preseason temperature and precipitation. The relative influence of preseason temperature and precipitation on land surface phenology was changing, which led to the different responses of land surface dynamics to climate variation along the moisture gradient in the NECT. In the arid and semi‐arid region of NECT, the dates of onset for phonological events in temperate typical grassland were most significantly related to the precipitation during the preceding 2–4 months. Temperature‐induced drought stress during the preceding months could delay greenup onset in cropland/grassland mosaic, and advance senescence in temporal typical grassland, and in cropland/grassland mosaic. The regional phenology algorithm, theoretically also applicable for complex ecosystems characterized by annual multiple growth cycles, is expected to couple with large‐scale biogeochemical models to regulate dynamically land surface phenology.     

东北植被生长峰值特征的变化及对气候和物候的响应

全球气候变化导致植被生长的季节性节律事件(如返青期、衰落期和生长峰值期等)发生显著变化.植被返青期、衰落期和生长季长度的变化已经得到广泛报道,植被生长峰值代表植被光合作用能力和对气候变化的响应,目前关于植被生长峰值特征(时间点和最大生长幅度)的时空变化和控制机理的研究相对较少,仍需在不同区域深入探讨.以植被覆盖度较好的...     

Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest

Phenology is a key component of monitoring terrestrial ecosystem variations in response to global climate change. Satellite-measured land surface phenology (LSP) has been widely used to assess large scale phenological patterns and processes. However, the accuracy of LSP is rarely validated with spatially compatible field data due to the significant spatiotemporal scale mismatch. In this study, we employ intensive field observations specifically designed to address this deficiency. High density/frequency spring phenological observations were collected in a mixed seasonal forest during 2008 and 2009. A landscape up-scaling approach was used to derive landscape phenology (LP) indices from plot-level observations in order to validate Moderate-resolution Imaging Spectroradiometer (MODIS) based LSP. Results show that the MODIS Enhanced Vegetation Index (EVI) derived start of spring season (SOS) measure was able to predict LP full bud burst date with absolute errors less than two days. In addition, LSP derived SOS captured inter-annual variations and spatial differences that agreed with ground observations. Comparison of complete time series of LP and LSP revealed that fundamental differences exist between the two observation means, e.g., LP development had increased influence on LSP during the course of spring onset. Therefore, inferring continuous LP processes directly from LSP measures could be problematic. However, using LSP derived from techniques such as logistic curve modeling for extracting seasonal markers appears more promising. This study contributes to a more explicit understanding of the linkages between remotely sensed phenology and traditionally observed (ground-based) phenology.     

Comparison of vegetation phenological metrics extracted from GIMMS NDVIg and MERIS MTCI data sets over China

Evaluating vegetation phenology is crucial for a better understanding of the effects of climate change on the terrestrial ecosystem. The scientific community has used various vegetation index data sets from different sensors to quantify vegetation phenology from regional to global scales. The normalized difference vegetation index (NDVI) related to photosynthetic activities is the most widely used index. Recently, a number of published articles have used the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) to measure vegetation phenology. MTCI can closely represent the red-edge position (REP). Unlike NDVI, MTCI is more sensitive to high values of chlorophyll content. However, the consistency of vegetation phenological metrics derived from MTCI and NDVI needs to be further explored. This study compared two phenological metrics, i.e. onset of greenness (OG) and end of senescence (ES), extracted from MERIS MTCI data and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) first generation NDVI (NDVIg) data, which has the longest time records, at nine regions in China from 2003 to 2006. The results showed that the differences of OG and ES vary between different vegetation types, regions, and years, although both NDVI and MTCI time series capture the growth patterns well for most vegetation types. Compared to ES, the OG estimates are more consistent. NDVI yields in general later ES estimates than MTCI.     

Determination of phenological dates in boreal regions using normalized difference water index

Monitoring and understanding plant phenology are important in the context of studies of terrestrial productivity and global change. Vegetation phenology, such as dates of onsets of greening up and leaf senescence, has been determined by remote sensing using mainly the normalized difference vegetation index (NDVI). In boreal regions, the results suffer from significant uncertainties because of the effect of snow on NDVI. In this paper, SPOT VEGETATION S10 data over Siberia have been analysed to define a more appropriate method. The analysis of time series of NDVI, normalized difference snow index (NDSI), and normalized difference water index (NDWI), together with an analysis of in situ phenological records in Siberia, shows that the vegetation phenology can be detected using NDWI, with small effect of snow. In spring, the date of onset of greening up is taken as the date at which NDWI starts increasing, since NDWI decreases with snowmelt and increases with greening up. In the fall, the date of onset of leaf coloring is taken as the date at which NDWI starts decreasing, since NDWI decreases with senescence and increases with snow accumulation. The results are compared to the results obtained using NDVI-based methods, taking in situ phenological records as the reference. NDWI gives better estimations of the start of greening up than NDVI (reduced RMSE, bias and dispersions, and higher correlation), whereas it does not improve the determination of the start of leaf coloring. A map of greening up dates in central Siberia obtained from NDWI is shown for year 2002 and the reliability of the method is discussed.     

The relationship between ERS-2 SAR backscatter and soil moisture: Generalization from a humid to semi-arid transect

The sources of variation (environment, genotype and date of measurement) of spectral reflectance indices describing biomass and its physiological status, and their potential use for providing accurate and non‐destructive estimates of crop phenology and yield, were studied on canopies of several collections of durum wheat genotypes showing adaptation to different Mediterranean environments. Spectral reflectance was measured during grain filling. All spectral indices and grain yields showed significant differences between contrasting environments in terms of water availability. Photosynthetic area indices and senescence indices were good indicators, for all genotype collections, of biomass and phenology, respectively, when comparing a wetter site with a drier site. When crop development was accelerated by growing plants under high temperature, provided by a spring‐sown trial under Mediterranean conditions, all spectral indices showed significant variation within a period of one week through grain filling, reflecting the changes in crop phenology and the onset of senescence. The reported changes in the values, and even the signs, of the correlation coefficients across genotypes between grain yield and some reflectance indices might reflect genotypic differences in response (by avoidance) to high temperature and drought during late grain filling. Spectral reflectance data may help to understand phenological characteristics of durum wheat canopies, such as crop duration, provided the date of measurement is well chosen.     

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.     

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.     

Assessing spatio‐temporal variations in plant phenology using Fourier analysis on NDVI time series: results from a dry savannah environment in Namibia

Time series of Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) were used to capture plant phenology in Etosha National Park, a dry savannah environment in Namibia. Data from two consecutive growing periods with different precipitation conditions were included to study impacts of inter‐seasonal rainfall variations on a highly water‐limited ecosystem. Additionally, a contemporary reference map with four major vegetation units was used to compare phenology between plant formations. Phenological attributes were acquired for both seasons using Fourier analysis. Parameters were calculated for the entire study area and further stratified with respect to the mapping units of the reference. Vegetation growth was found to vary significantly between the two periods in accordance with available rainfall data. Additionally, separability of vegetation entities based on Fourier parameters was weak due to within‐class scattering and was commonly outranged by inter‐seasonal variations. Finally, discrimination of cover types was tested by combining selected Fourier parameters in a clustering procedure. Spatial class distribution was compared to the reference statistically and only a moderate correspondence was discovered. We conclude that Fourier‐based NDVI attributes are limited for cover‐type discrimination across space and time, as they only quantify certain aspects of plant phenology and seem to be largely altered by the actual rainfall situation.     

The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India

India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. The phenology of these natural vegetation types is often controlled by climatic condition. Estimating phenological variables will help in understanding the response of tropical and subtropical vegetation to climate change. The study investigated the annual and inter-annual variation in vegetation phenology in India using satellite remote sensing. The study used time-series data of the only available satellite measured index of terrestrial chlorophyll content (MERIS Terrestrial Chlorophyll Index) from 2003 to 2007 at 4.6 km spatial resolution. A strong coincidence was observed with expected phenological pattern, in particular, in inter-annual and latitudinal variability of key phenological variables. For major natural vegetation type the onset of greenness had greater latitudinal variation compared to the end of senescence and there was a small or no leafless period. In the 2003-04 growing season a late start for the onset of greenness was detected at low-to-mid latitudes and it was attributed to the extreme cold weather during the early part of 2003. The length of growing season varied from east to west for the major cropping areas in the Indo-Gangetic plain, for both the first and second crops. For the first time, this study attempted to establish a broad regional phenological pattern for India using remotely sensed estimation of canopy chlorophyll content using five years of data. The overall patterns of phenological variables detected from this study broadly coincide with the pattern of natural vegetation phenology revealed in earlier community level studies. The results of this study suggest the need for an organised network combining ground and space observation which is at presently missing in India.     

Global land surface phenology trends from GIMMS database

A double logistic function has been used to describe global inventory mapping and monitoring studies (GIMMS) normalized difference vegetation index (NDVI) yearly evolution for the 1981 to 2003 period, in order to estimate land surface phenology parameter. A principal component analysis on the resulting time series indicates that the first components explain 36, 53 and 37% of the variance for the start, end and length of growing season, respectively, and shows generally good spatial homogeneity. Mann–Kendall trend tests have been carried out, and trends were estimated by linear regression. Maps of these trends show a global advance in spring dates of 0.38 days per year, a global delay in autumn dates of 0.45 days per year and a global increase of 0.8 days per year in the growing seasons validated by comparison with previous works. Correlations between retrieved phenological parameters and climate indices generally showed a good spatial coherence.     

Effects of El Niño on spring phenology of the highest mountain in north-east Asia

Normalized difference vegetation index (NDVI) data on the highest mountain in north-east Asia were analysed to understand their temporal variability and response to large-scale El Niño–Southern Oscillation (ENSO) events. We demonstrated that El Niño events played an important role in determining the phenology conditions in the Mt Baekdu area in north-east Asia. The analysis confirmed that the onset of phenological spring was earlier during ENSO years. This was evident from a negative trend of about??16 days for each increase of 1 in the ENSO index in year-to-year variations in spring timing and those in ENSO magnitudes. Over two decades, the phenological phases were negatively correlated with air temperature variations under atmospheric warming at the mountain. However, such changes in NDVI are not likely to be affected by changes in local precipitation, as inferred from the analysis of forest types in this area. On the basis of NDVI changes during ENSO years, the results of this study emphasized the importance of the elevation effect and forest types on the ecological response. Moreover, we addressed a significant remote connection between local phenology at the highest mountain in north-east Asia and large-scale atmospheric and oceanic phenomena.     

21世纪初中国农作物与自然植被物候时空差异

利用离散小波方法对2001~2012年MODIS EVI时序数据进行平滑,基于动态阈值法提取我国植被物候信息,探讨农作物和自然植被物候的时空变化特征。结果表明:(1)我国第一季农作物开始、峰值和结束日期主要以华北平原为中心随海拔的上升而推迟,而自然植被物候更早20d左右,且随海拔的上升先推迟后提前;(2)物候在时序上有显著变化的第一季区域,43.98%开始日期、52.83%峰值日期呈现提前趋势,多在开始晚、结束早的西南区及东北与内蒙古交界处,其余区域开始、峰值日期及81.80%结束日期呈推迟趋势,发生在开始早、结束晚的黄土高原及双季农作区;农作物物候推迟幅度小于自然植被。     

Cross-scalar satellite phenology from ground, Landsat, and MODIS data

Phenological records constructed from global mapping satellite platforms (e.g. AVHRR and MODIS) hold the potential to be valuable tools for monitoring vegetation response to global climate change. However, most satellite phenology products are not validated, and field checking coarse scale (≥ 500 m) data with confidence is a difficult endeavor. In this research, we compare phenology from Landsat (field scale, 30 m) to MODIS (500 m), and compare datasets derived from each instrument. Landsat and MODIS yield similar estimates of the start of greenness (r² = 0.60), although we find that a high degree of spatial phenological variability within coarser-scale MODIS pixels may be the cause of the remaining uncertainty. In addition, spatial variability is smoothed in MODIS, a potential source of error when comparing in situ or climate data to satellite phenology. We show that our method for deriving phenology from satellite data generates spatially coherent interannual phenology departures in MODIS data. We test these estimates from 2000 to 2005 against long-term records from Harvard Forest (Massachusetts) and Hubbard Brook (New Hampshire) Experimental Forests. MODIS successfully predicts 86% of the variance at Harvard forest and 70% of the variance at Hubbard Brook; the more extreme topography of the later is inferred to be a significant source of error. In both analyses, the satellite estimate is significantly dampened from the ground-based observations, suggesting systematic error (slopes of 0.56 and 0.63, respectively). The satellite data effectively estimates interannual phenology at two relatively simple deciduous forest sites and is internally consistent, even with changing spatial scale. We propose that continued analyses of interannual phenology will be an effective tool for monitoring native forest responses to global-scale climate variability.     

Fire-induced changes in green-up and leaf maturity of the Canadian boreal forest

Recent studies of vegetation phenology of northern forests using satellite data suggest that the observed earlier spring increase and peak amplitude of the normalized difference vegetation index (NDVI) are a result of climate warming. In addition to undergoing an increase in temperature, the northern forests of Canada have also seen a dramatic increase in area burned by wildfire over the same time period. Using the Canadian Large Fire Database, we analyzed the impact fire had on the phenological dates derived from fitting a logistical model to yearly data from 2004 for several different subsets of both AVHRR-NDVI and MODIS LAI in wildfire dominated terrestrial ecozones. Fire had a significant but complex effect on estimated phenology dates. The most recently burned areas (1994–2003) had later green-up dates in two ecozones for AVHRR data and all ecozones for MODIS. However, older forested (not burned during 1980–2003) had estimated green-up dates 1 to 9 days earlier than the entire forested area in the MODIS LAI data. These data corroborate studies in Canada and demonstrate that fire history is influencing boreal forest phenology and growing season LAI.