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.     

Real-time monitoring and short-term forecasting of land surface phenology

Land surface phenology is an important process for real-time monitoring and short-term forecasting in diverse land management, health, and hydrologic modeling applications. Yet current efforts to characterize phenological processes are limited by remote sensing challenges and lack of uncertainty estimates. Here, for a global distribution of phenologically and climatically similar phenoregions, we used the Advanced Very High Resolution Radiometer to develop a conceptually and computationally simple technique for real-time and forecast applications. Our overall approach was to analyze the phenological behavior of groups of pixels without recourse to smoothing or fitting. We used a 3-step initial process: (1) define a phenoregion specific normalized difference vegetation index threshold; (2) for all days from 1982-2003, calculate the percent of pixels above the threshold (PAT); (3) calculate daily 1982-2003 empirical distributions of PAT. For real-time monitoring, the current PAT may then be compared to the historical range of variability and visualized in relation to user-defined levels. Using similar concepts, we projected daily PAT up to one month in the future and compared predicted and actual dates at which a hypothetical PAT was reached. We found that the maximum lead-time of phenological forecasts could be analytically defined for user-specified uncertainty levels. The approach is adaptable to different remote sensing technologies and provides a foundation for ascribing a sequence of ground conditions (e.g. snowmelt, vegetative growth, pollen production, insect phenology) to remotely sensed land surface phenology observations.     

Interannual vegetation phenology estimates from global AVHRR measurements: Comparison with in situ data and applications

In a previous paper, we described a procedure to correct the directional effects in AVHRR reflectance time series. The corrected measurements show much less high frequency variability than their original counterparts, which makes them suitable to study vegetation dynamics. The time series are used here to estimate the start and ending dates of the growing season for 18 years from 1982 to 1999. We focus on the interannual variations of these phenological parameters.A database of in situ phenology observations is used to quantify the accuracy of the satellite-based estimates. Although based on a limited sampling of the Northern mid and high latitudes, the comparison indicates that i) the satellite phenological product contains meaningful information on interannual onset anomalies; ii) there is a higher degree of consistency over regions covered by Broadleaf Forests, Grasses and cereal Crops than over those covered by Needleleaf Forests or Savannas; and iii) the satellite phenological product is of lower quality in regions with mountainous terrain. In favorable conditions, interannual variations of the onset are captured with an accuracy of a few days.As this satellite-derived product captures the interannual onset variability at ground-truth sites, we confidently use it to larger scales studies. Mapped at a continental scale, the onset anomalies show coherent patterns at the regional (≈ 1000 km) scale for the mid and high latitudes of the Northern hemisphere, which is consistent with a meteorological forcing. In the tropics, there is larger spatial heterogeneity, which suggests more complex controls of the phenology. The relation between vegetation phenology and climate is further investigated over Europe by comparing the variability of the satellite-derived vegetation onset and that of the winter North Atlantic Oscillation index, at a fine spatial scale. The strong correlations observed confirm that this climate forcing parameter explains most of the onset variability over a large fraction of Northern Europe (earlier onsets for positive winter NAO), with lower impact towards the south and opposite effects around the Mediterranean basin. The NAO has a predictive character as the January-February NAO index is strongly correlated with the vegetation onset that occurs around April in Northern Europe.     

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.     

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.     

Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data

Vegetation phenology is the chronology of periodic phases of development. It constitutes an efficient bio-indicator of impacts of climate changes and a key parameter for understanding and modelling vegetation-climate interactions and their implications on carbon cycling. Numerous studies were devoted to the remote sensing of vegetation phenology. Most of these were carried out using data acquired by AVHRR instrument onboard NOAA meteorological satellites. Since 1999, multispectral images were acquired over the whole earth surface every one to two days by MODIS instrument onboard Terra and Aqua platforms. In comparison with AVHRR, MODIS constitutes a significant technical improvement in terms of spatial resolution, spectral resolution, geolocation accuracy, atmospheric corrections scheme and cloud screening and sensor calibration. In this study, 250 m daily MODIS data were used to derive precise vegetation phenological dates over deciduous forest stands. Phenological markers derived from MODIS time-series and provided by MODIS Global Land Cover Dynamics product (MOD12Q2) were compared to field measurements carried out over the main deciduous forest stands across France and over five years. We show that the inflexion point of the asymmetric double-sigmoid function fitted to NDVI temporal profile is a good marker of the onset of green-up in deciduous stands. At plot level, the prediction uncertainty is 8.5 days and the bias is 3.5 days. MODIS Global Land Cover Dynamics MOD12Q2 provides estimates of onset of green-up dates which deviate substantially from in situ observations and do not perform better than the null model. RMSE values are 20.5 days (bias -17 days) using the onset of greenness increase and 36.5 days (bias 34.5 days) using the onset of greenness maximum. An improvement of prediction quality is obtained if we consider the average of MOD12Q2 onset of greenness increase and maximum as marker of onset of green-up date. RMSE decreases to 16.5 days and bias to 7.5 days.     

Changes in vegetation spring dates in the second half of the twentieth century

This study aims at estimating trends in spring phenology from vegetation index and air temperature at 2?m height. To this end, we have developed a methodology to infer spring phenological dates from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) time-series, which are then extrapolated to the period 1948–2006 with the help of Reanalysis data, using its 2?m height air temperature parameter. First, yearly NDVI is fitted to a double-logistic function for the whole extent of the GIMMS database (1981–2003). This fitting procedure allows us to describe, on a yearly basis, the NDVI evolution for each pixel through the estimation of six parameters which include the spring date. Retrieved spring date time-series are then upscaled to Reanalysis database resolution and compared to degree-day amounts. Those degree-day amounts are estimated for various thresholds in order to determine the best thresholds for their calculations on a pixel-by-pixel basis. Once the correct thresholds are identified by correlation with corresponding GIMMS spring date time-series, spring dates are estimated for the whole extent of the Reanalysis database (1948–2006). Finally, Mann–Kendall trend tests are conducted on degree-day-retrieved spring date time-series and trends are estimated only for those pixels that show statistically significant trends. These trends in spring occurrence have an average value of –0.03 days per year, but range between –0.9 and?+0.9 days per year, depending on the considered areas. Since the approach is based only on air temperature, retrieved spring dates for vegetation whose growth is limited by water are unreliable, as correlation analysis confirms. The obtained spring date trends show good coherence with previous studies and could be used for climate change impact studies, especially in polar and temperate areas, where the model is more reliable.     

Depicting the Asymmetries of Vegetation Phenology over Northeast China Using Remote Sensing NDVI Dataset

Vegetation phenology is an important ecological indicator for global climate change.Plant greenup phenology in the spring time has been well studied,whereas autumn phenology and its asymmetry with spring phenology still remain unclear.Here,the GIMMS NDVI3g dataset for Northeast China was applied to extract the key phenological parameters during plant growth process,then three phenological asymmetry indices were defined according to the difference between greenup rate and senescence rate(AsyR),growth length in spring and autumn(AsyL),mean vegetation greenness index in spring and autumn(AsyV).First,plant growing curve was fitted with double logistic function and the phenological parameters was calculated.Second,the spatiotemporal pattern of asymmetry indices was explored.The results indicate that the three phenological asymmetry indices show a significant interannual variability and a time cycle of around ten years.The direction of amplitude for AsyV and AsyL was opposite with that of AsyR.Three indices could depict the phenological asymmetries from various perspectives and have a degree of uncertainty.The landscape pattern for AsyV and Asy R is similar.AsyV and AsyR show a capability of distinguishing cropland and natural vegetation cover.AsyL reflects a complex spatial distribution.Phenological asymmetries reveal that coniferous forest and broad-leaved forest present a dominant control of senescence vegetation activities.These natural vegetation commonly show a growth feature of rapid growth in spring and slow decrease in autumn.Cropland exhibits a slowly growing rate in spring and a rapid decrease in autumn.Phenological asymmetry is not significant in grassland area.Phenological asymmetry could enhance our knowledge on ecosystem carbon sink.In a practical way,phenological asymmetry could serve as a useful tools in vegetation type classification,agricultural investigation and plant ecosystem management.     

Temporal and spatial variability of the beginning and end of daily spring freeze/thaw cycles derived from scatterometer data

The variability of snowmelt dates is important for the terrestrial carbon balance in boreal and subarctic environments. Scatterometers such as the K_u-Band QuikScat have been proven applicable for the detection of surface thaw. We present an improved method for the capture of thaw events based on significance of diurnal differences with respect to long term noise. For each 10 km × 10 km grid point two products are derived for the major thaw period: 1) the onset of thaw and 2) the end of daily freeze/thaw cycles at the surface. Both dates may be related to biogeochemical processes, especially carbon fluxes in boreal forests. The onset of the spring thaw period coincides with the first days of increased CO₂ fluxes above the late winter baseline. The end of daily freeze/thaw cycles corresponds to the switch from source to sink in evergreen boreal forest environments as illustrated by comparison with eddy-flux tower data and xylem sap flow records from other investigators.The approach is suitable for detecting freeze/thaw cycle periods in boreal forest and tundra biomes. The mean absolute difference in end of freeze/thaw cycling date within the central Siberian study area (3 Mio km² comprising tundra, boreal forest and steppe grassland) was 9 days for 2000 to 2004. Largest mean differences occurred in the southern taiga and all tundra regions, which were highest for spring 2000.The improved extraction method delivers more precise products from the viewpoint of carbon accounting in evergreen boreal forest environments. This widens the application potential of scatterometer data beyond the current status.     

Monitoring elevation variations in leaf phenology of deciduous broadleaf forests from SPOT/VEGETATION time-series

In mountain forest ecosystems where elevation gradients are prominent, temperature gradient-based phenological variability can be high. However, there are few studies that assess the capability of remote sensing observations to monitor ecosystem phenology along elevation gradients, despite their relevance under climate change. We investigated the potential of medium resolution remotely sensed data to monitor the elevation variations in the seasonal dynamics of a temperate deciduous broadleaf forested ecosystem. Further, we explored the impact of elevation on the onset of spring leafing. This study was based on the analysis of multi-annual time-series of VEGETATION data acquired over the French Pyrenees Mountain Region (FPMR), in conjunction with simultaneous ground-based observations of leaf phenology made for two dominant tree species in the region (oak and beech). The seasonal variations in the perpendicular vegetation index (PVI) were analyzed during a five-year period (2002 to 2006). The five years of data were averaged into a one sole year in order to fill the numerous large spatio-temporal gaps due to cloud and snow presence - frequent in mountains - without altering the temporal resolution. Since a VEGETATION pixel (1 km²) includes several types of land cover, the broadleaf forest-specific seasonal dynamics of PVI was reconstructed pixel-by-pixel using a temporal unmixing method based on a non-parametric statistical approach. The spatial pattern of the seasonal response of PVI was clearly consistent with the relief. Nevertheless the elevational or geographic range of tree species, which differ in their phenology sensitivity to temperature, also has a significant impact on this pattern. The reduction in the growing season length with elevation was clearly observable from the delay in the increase of PVI in spring and from the advance of its decrease in the fall. The elevation variations in leaf flushing timing were estimated from the temporal change in PVI in spring over the study area. They were found to be consistent with those measured in situ (R2 > 0.95). It was deduced that, over FPMR, the mean delay of leaf flushing timing for every 100 m increase in elevation was estimated be approximately 2.3 days. The expected estimation error of satellite-based leaf unfolding date for a given elevation was approximately 2 days. This accuracy can be considered as satisfactory since it would allow us to detect changes in leafing timing of deciduous broadleaf forests with a magnitude equivalent to that due to an elevation variation of 100 m (2.3 days on average), or in other words, to that caused by a variation in the mean annual air temperature of 0.5 °C. Although averaging the VEGETATION data over five years led to a loss of interannual information, it was found to be a robust approach to characterise the elevation variations in spring leafing and its long-term trends.     

Green leaf phenology at Landsat resolution: Scaling from the field to the satellite

Despite the large number of in situ, plot-level phenological measurements and satellite-derived phenological studies, there has been little success to date in merging these records temporally or spatially. In this research, we bridge this scale gap through higher resolution satellite records (Landsat) and quantify the accuracy of satellite-derived metrics with direct field measurements. We compiled fifty-seven Landsat scenes from southern New England (P12 R51) from 1984 to 2002. Green vegetation areal abundance for each scene was derived from spectral mixture analysis and a single set of endmembers. The leaf area signal was fit with a logistic-growth simulating sigmoid curve to derive phenological markers (half-maximum leaf-onset and offset). Spring leaf-onset dates in homogenous stands of deciduous forests displayed significant and persistent local variability. The local variability was validated with multiple springtime ground observations (r² = 0.91). The highest degree of verified small-scale variation occurred where contiguous forests displayed leaf-onset gradients of 10-14 days over short distances (< 500 m). These dramatic gradients occur in of low-relief (< 40 m) upland regions. The patterns suggest that microclimates resulting from springtime cold-air drainage may be influential in governing the start of leaf growth; every 4.16 m loss in elevation delayed spring leaf onset by 1 day. These microclimates may be of crucial importance in interpreting in situ records and interpolating phenology from satellite data. Regional patterns from the Landsat analyses suggest topographic, coastal, and land-use controls on phenology. Our results indicate that deciduous forests in the Providence, RI metropolitan area leaf out 5-7 days earlier than comparable rural areas. The platform-independent curve-fit methodology may be extended across platforms and field data. The methodologically consistent approach, in tandem with Landsat data, allows an effective scaling from plot to satellite phenological observations.     

Analysis of climate change impacts on lake ice phenology in Canada using the historical satellite data record

Variability and trends in lake ice dynamics (i.e. lake ice phenology) are related to climate conditions. Climate influences the timing of lake ice melt and freeze onset, ice duration, and lake thermal dynamics that feedback to the climate system initiating further change. Phenology records acquired in a consistent manner and over long time periods are required to better understand variability and change in climate conditions and how changes impact lake processes. In this study, we present a new technique for extracting lake ice phenology events from historical satellite records acquired by the series of Advanced Very High Resolution Radiometer (AVHRR) sensors. The technique was used to extend existing in-situ measurements for 36 Canadian lakes and to develop records for 6 lakes in Canada's far north. Comparison of phenology events obtained from the AVHRR record and in-situ measurements show strong agreement (20 lakes, 180 cases) suggesting, with high confidence especially in the case of break-up dates, the use of these data as a complement to ground observations. Trend analysis performed using the combined in-situ and AVHRR record ∼ 1950-2004 shows earlier break-up (average — 0.18 days/year) and later freeze-up (average 0.12 days/year) for the majority of lakes analyzed. Less confidence is given to freeze-up date results due to lower sun elevation during this period making extraction more difficult. Trends for the 20 year record in the far north showed earlier break-up (average 0.99 days/year) and later freeze-up (average 0.76 days/year). The established lake ice phenology database from the historical AVHRR image archive for the period from 1985 to 2004 will to a certain degree fill data gaps in the Canadian in-situ observation network. Furthermore, the presented extraction procedure is not sensor specific and will enable continual data update using all available satellite data provided from sensors such as NOAA/AVHRR, MetOp/AVHRR, MODIS, MERIS and SPOT/VGT.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

基于多源数据的禹城农田生态系统冬小麦生育期识别方法比较研究

多源数据与其技术方法逐渐被应用于植被物候的研究当中,但基于多源数据物候识别方法间的差异性比较及定量化评估工作还有待加强。以山东禹城农田生态系统为例,探讨了基于多源数据,NDVI、EVI、数字相机图片、碳通量数据(NEE)以及人工实测数据获取的冬小麦主要生育日期的结果进行差异比较及定量化评估。结果表明:①通过碳通量数据获取的主要生育日期的计算结果与人工实测结果最接近,各阶段差异均<3 d;通过数字相机图片获取的结果仅次于通过碳通量数据获取的结果,而通过遥感数据NDVI、EVI获取的结果与人工实测结果差距最大;②通过NDVI、EVI两种数据获取的冬小麦主要生育期结果具有极显著的相关性,最高达到R²=0.857(P<0.001);③基于多源数据获取的冬小麦主要生育期的计算结果,均显示出禹城站冬小麦返青期提前,蜡熟期推迟,生长季长度变长的年际变化特征。