Int J Biometeorol (2003) 47:126–138
DOI 10.1007/s00484-003-0164-4

ORIGINAL ARTICLE

Rikie Suzuki · Tomoyuki Nomaki · Tetsuzo Yasunari

West–east contrast of phenology and climate
in northern Asia revealed using a remotely sensed vegetation index
Received: 14 May 2002 / Revised: 6 January 2003 / Accepted: 6 January 2003 / Published online: 2 April 2003
 ISB 2003

Abstract The phenology of the vegetation covering northAsia (mainly Siberia) and its spatial characterstics were
investigated using remotely sensed normalized difference
vegetation index (NDVI) data. The analysis used theweekly averaged NDVI over 5 years (1987–1991) using
the second-generation weekly global vegetation index
dataset (0.144 0.144 spatial resolution). In the seasonal
NDVI cycle, three phenological events were defined foreach pixel: green-up week (NDVI exceeds 0.2), maximum week, and senescence week (NDVI drops below0.2). Generally there was a west-early/east-late gradient in
the three events in north Asia. In the zonal transect
between 45 and 50N, the timing of green-up, maximum,and senescence near 60E (Kazakh) was about 3.4, 8.7,
and 13.4 weeks earlier than near 110E (Mongolia)
respectively. It has been suggested that vegetation near
Kazakh only flourishes during a short period when waterfrom snow melt is available from late spring to earlysummer. In Mongolia, abundant water is available for thevegetation, even in midsummer, because of precipitation.
In the 50–60N zonal transect, the green-up and maximum near 40E were about 3.8 and 3.9 weeks earlier thannear 115E, respectively. As for the week of senescence,
there was no clear west–east trend. This west-to-east
phenological gradient was related to the weekly cumulative temperature (over 0 C). Weeks in which theR. Suzuki ())
IGCR, Frontier Research System for Global Change,
3173-25 Showamachi, Kanazawa-ku, Yokohama,
Kanagawa 236-0001, Japan,
e-mail: rikie@jamstec.go.jp
Tel.: +81-45-778-5541
Fax: +81-45-778-5706
T. Nomaki
Data Analysis and Research Division,
Remote Sensing Technology Center of Japan (RESTEC),Tokyo, JapanT. Yasunari
Hydrospheric Atmospheric Research Center (HyARC),
Nagoya University, Nagoya, Japan IGCR,
Frontier Research System for Global Change, Yokohama, Japan

cumalative temperature exceeded 40 C and 140 C had
a similar west-east distribution to green-up and maximum
NDVI.
Keywords Taiga · Siberia · Phenology · Green-wave ·NDVI · Soil moisture

Introduction
Vegetation cover is strongly characterized by the climateof a region, as noted in many reports (Walter 1973;
Woodward 1987). For example, larch forests in Siberiantaiga are dormant during the winter, and then green with
foliation as temperatures warm in spring and summer,
reaching the mature stage in summer. In autumn,
senescence follows a drop in temperature. This phenology–climate relationship is an important theme that has
been broadly investigated from the perspective of plant
geography.
Since the 1980s, global vegetation data have been
available from satellite measurements. Currently, massive
amounts of information on vegetation are available forclimatological and plant geographical studies over extensive regions of the globe.
Normalized difference vegetation index (NDVI)
Differences in the spectral reflectance of chlorophyll inthe visible and near-infrared parts of the spectrum provide
a way to monitor the density and vigor of green
vegetation (Tarpley et al. 1984). The National Oceanicand Atmospheric Administration (NOAA) satellite has afive-channel radiometer, the advanced very high resolution radiometer (AVHRR), with channels 1 and 2 in thevisible (0.58–0.68 mm) and near-infrared (0.725–1.10 mm)
spectral bands respectively. The normalized difference
vegetation index (NDVI), which is the best-known
vegetation index, is computed using the equation NDVI =(Ch2 – Ch1)/(Ch2 + Ch1), where Ch1 and Ch2 are the127reflectance measurements of AVHRR channels 1 and 2
respectively (Tarpley et al. 1984). The remotely sensed
NDVI allows analysis of the “greenness” of vegetation on
regional to global scales. The NDVI data, which provide
spatially continuous, long-term information on vegetation, are useful for investigating the seasonal vegetation
cycle (Gutman 1999).
Many studies have used the remotely sensed NDVI.
For example, global vegetation cover has been classified
according to the NDVI-derived phenological characteristics of vegetation (Norwine and Greegor 1983; Defries
and Townshend 1994). The global land cover characteristics database of Loveland et al. (2000), a detailed global
land cover map, used the NDVI to identify land cover.
White et al. (1997) developed a continental phenology
model of the vegetation of the United States, andcompared the result with NDVI data. In addition, attempts
have been made to relate the NDVI to meteorologicalelements, sucn as temperature (Gutman 1991), precipitation (Nicholson et al. 1990; Di et al. 1994; Shinoda 1995),
and CO2 concentration (Tucker et al. 1986).
Background and purpose of the study
This study focused on the phenological characteristics ofthe vegetation in northern Asia (mainly Siberia) as
revealed by the seasonal NDVI cycle, from the perspective of plant geography. Siberia is a key region inclimatology and related sciences. A remarkable long-term
temperature rise that may be related to global warming
has been reported, especially in winter (IPCC 1996,
2001). Ye et al. (1998) addressed long-term changes insnow depth. Myneni et al. (1997, 1998) and Kawabata etal. (2001) pointed out the significant increase in vegetation at high latitudes in the Northern Hemisphere,
scrutinizing NDVI data from the 1980s. Tucker et al.
(2001) found an increase in the NDVI from 1982 to 1991
and from 1992 to 1999 in higher northern latitudes.
Furthermore, the thick taiga forest over the extensive
region provides water vapour to the atmosphere via
transpiration (Suzuki et al. 1998). Vegetation data should
give proxy information on transpiration from the ground,so this study will improve understanding of the water
cycle over an extensive area.
As part of the GEWEX Asian Monsoon Experiment(GAME), an international project that is part of the Global
Energy and Water Cycle Experiment (GEWEX), GAMESiberia was implemented in the Lena River basin, andfocused on energy and water cycle processes (GAMEInternational Science Panel 1998). The results of thisstudy are expected to produce meaningful knowledge ofhydrometeorological processes over Siberia and in the
global climate system. Moreover, the knowledge provided
by this study will play an important role in attempts tounderstand hydrological cycles over Siberia, extending
the horizontal scale from the Lena River basin to theentire Siberia region.

Some studies targeting the phenological characteristics
of vegetation, as revealed by the seasonal NDVI cycle,
have been published. Reed et al. (1994) developed amethod to monitor phenological variablility using theseasonal NDVI cycle, and applied it to land cover in the
United States. Schwarts (1994) discussed the importanceof the NDVI in studying the phenology of the vegetationof North America. However, neither study mentioned
phenology in northern Asia. Moulin et al. (1997) visulalized the phenological evolution (dormancy, growth, andsenescence) of global vegetation by analyzing theseasonal variation in the NDVI. However, such a
global-scale study provides only crude information on
localities in northern Asia.
This work was carried as part of two studies: (1)Suzuki et al. (2000), which revealed a close relationshipbetween the NDVI and the geographical distribution oftemperature and precipitation along north–south transects
in Siberia; and (2) Suzuki et al. (2001a), which examined
the phenological regionality of the NDVI and the climate
over all of Siberia. The latter study, using monthly NDVI
data, reported a west–east difference in the timing of thevegetation cycle in Siberia. The present study establisheda west–east transect, and compared the west–east vegetation phenology examined using the weekly NDVI on anannual basis in Siberia and some surrounding areas. For
investigating the phenological features of the vegetationin detail, weekly temporal resolution has a great advantage over the monthly NDVI data used in Suzuki et al.
(2001a). The west–east NDVI contrast will be discussed
in relation to seasonality in the surface air temperature,
precipitation, snow depth, and soil moisture. The result of
this study will lead to a better understanding of theessential climatological implications of seasonal vegetation change.

Materials and methods
The region targeted by the analysis is bounded by 30E and 145E
longitude, and 40N and 75N latitude, as indicated in Fig. 1. Thisregion covers Siberia, and parts of European Russia, Mongolia, andChina. This study analyzed the weekly NDVI and hydrometeorological measurements made at surface stations, averaged for 1987–
1991 (5 years). These years were chosen after considering theavailability of temperature, precipitation, and NDVI data. We
regard the 5-year mean values for these 5 years as representative of
the normal values of those parameters. For some datasets, however,time spans other than 1987–1991 were selected because of theunavailability of data for 1987–1991.
Weekly NDVI data
The second-generation weekly global vegetation index (GVI) datafrom 1987 to 1991 were used in this study. They were constructedfollowing three sets of procedures (Kidwell 1990; Tarpley 1991):
1. The original AVHRR data with a 1-km resolution obtained oneach orbital pass are sampled (every third scan line and thenfour pixels out of five in that scan line) and averaged, to derive3  5-km resolution data, called the GAC (global area coverage)
data.

128
Fig. 1 The study area, the distribution of Global Daily Summary (GDS), Historical Soviet
Daily Snow Depth (HSDSD),
Federal Climate Complex
Global Surface Summary of
Day Data Version 6 (GSSD),
gravimetric soil moisture covering the USSR (SWOD) stations, and the soil moisture in
Mongolia

2. The global daily composite is generated from the GAC for
daylight passes of the afternoon polar orbiter. This is called the“daily master array”, and has a resolution of 0.144  0.144.
3. From the daily master array, the weekly GVI data, which havethe same spatial resolution as the daily master array, areproduced. To construct the weekly (7-day) NDVI, only the
NDVI for the day with the largest (channel 2 – channel 1) (bothdigital counts) difference in the AVHRR is retained at eacharray location for the 7 days (i.e., no averaging process is used).
This eliminates clouds from the weekly composite, except forareas that were cloudy on all 7 days.

Temperature and precipitation data

The weekly GVI still has unrealistic NDVI values, which areprobably due to the process of data construction and cloudcontamination. For this reason, this study carried out two smoothing processes: smoothing in the horizontal domain, followed bysmoothing in the time domain. For horizontal smoothing, (1) the
NDVI value at a pixel (NDVI-a) was compared with the mean
NDVI (NDVI-m) for the eight pixels adjacent to the NDVI-a pixel;(2) if the difference between NDVI-a and NDVI-m exceeded 0.1,
NDVI-a was replaced by NDVI-m; (3) this horizontal smoothing
process was executed on all GVI pixels, except for the pixels on thewest, east, south, and north boundaries of the study area.
For temporal smoothing, a 3-week moving maximum wascalculated. For week n, the highest NDVI value among weeks n – 1,n, and n + 1 was adopted as the NDVI value of week n for eachpixel. The NDVI values for the first and last weeks in the study
period were eliminated from the analysis. Although this process
reduces the temporal resolution of the NDVI, it produces a more
realistic seasonal cycle of the weekly NDVI.
As summarized by Goward et al. (1993), the GVI data productincludes problems that originate in the latitudinal difference in thegeometry of the sun, the earth’s surface, and the satellite, and in theprocess of constructing the GVI from the high-resolution dataset.
Although the GVI used in this analysis may still contain someunfavorable biases or errors in the data product, we believe that theGVI data are of sufficient accuracy for this study, which discusses
the west–east contrast of the NDVI in the same latitudinal zone; thewinter NDVI is of no concern. Although the GVI reliability forinterannual variation analysis is suspect, as with other global NDVI
datasets, the GVI has sufficient reliability for a phenology (seasonal
cycle) analysis, as noted by Gutman (1999). The weekly temporal
resolution of the GVI data product should be preferable to other,
10-day, global NDVI datasets for analysis of vegetation phenology.

Snow cover area map

Surface air temperature and precipitation data from 1987 to 1991
were obtained from the CD-ROM Global Daily Summary (GDS),
which is available from the National Climate Data Center (NCDC)
of the NOAA (National Climate Data Center 1994). The GDS
contains daily maximum/minimum temperatures, daily precipitation, and 3-h weather data at 10,277 surface WMO stations aroundthe world. There were 1,635 GDS stations in the region analyzed,
as shown in Fig. 1.

The snow cover information shown in Figs. 2 and 3 was acquiredby analyzing data (1987–1991) from the “weekly digital Northern
Hemisphere snow and ice product”, a satellite-derived snow cover
map that is compiled by the NOAA National Environmental
Satellite, Data and Information Service (NESDIS). This dataset
contains information on the presence or absence of snow cover ineach of 89  89-square gird boxes on a polar sterographic map thatcovers most of the Northern Hemisphere (Masuda et al. 1993).
Snow depth data
Snow depth is one of the climatic parameters used to discuss
vegetation phenology. Snow depths at surface stations wereobtained from the CD-ROM of the Historical Soviet Daily Snow
Depth (HSDSD) version 2.0 (Armstrong 2001), which includes
daily snow depths at 284 WMO stations, from 1881 (for the earliest
operational stations) through 1995. The stations’ distribution isshown in Fig. 1.
Snow depths in Mongolia are not covered by the HSDSD, andno suitable dataset was found for the period from 1987 to 1991 that
this study examined. As alternative snow depth information for
Mongolia, we used snow depths from 1994 to 1996 provided in theFederal Climate Complex Global Surface Summary of Day Data
Version 6 (referred to as GSSD in this paper), which was provided
by the National Climatic Data Center (National Climatic Data
Center 2001). This dataset archives daily surface meteorologicalproperties at over 8,000 WMO stations worldwide. The 51 stations
in Mongolia are shown in Fig. 1. Although the difference in periods
limits the discussion in terms of snow depth in Mongolia, the data
do provide ancillary information.

129
Soil moisture data
Gravimetric soil moisture data covering the USSR at 115 surface
stations (referred to as SWOD) were used. This dataset wasconstructed mainly from information in the Agrometeorology
Yearbook, which is archived by regional meteorological centers in
Russia. Observations were made on the 8th, 18th and 28th of eachmonth from April to October. Since data from 1988 to 1991 are notavailable in SWOD, the SWOD data from 1985 to 1987 were usedas alternative information. As with the snow depth data for
Mongolia, the different time span limits discussion in terms of soil
moisture in the USSR. However, the data do provide ancillary
information. The total soil moisture (volumetric) in the 0–10-cm
depth layer was used for the analysis.
Soil moisture data for Mongolia are available at 42 stations on
the 7th, 17th, and 27th of every month from April to October for theperiod from 1964 to 1993 (Robock et al. 2000). All the observations
were made using the gravimetric technique for each 10-cm layer
down to a depth of 1 m, with the first layer divided into two 5-cm
layers. The data are volumetric plant-available soil moisture. For
our analysis, the original plant-available soil moisture was adjusted
according to the wilting level correction table, which was preparedby the dataset producers for each station. This analysis used theplant-available soil moisture in the 5–10 cm depth layer from 1987
to 1991 in Mongolia.

The small shift in the dates of the weeks in each year was
ignored in the analysis, and it was assumed that the first week ineach of the 5 years started on the 1st of January (see Table 2). The 5
year mean of the weekly NDVI was calculated by averaging theweekly NDVIs values for the corresponding weeks from 1987 to
1991. As for the 5-year mean weekly snow cover, a pixel with asnow cover frequency exceeding 50% in a week was regarded as a“snow-cover existing pixel” on a weekly basis.
The first and last weeks of the year were not calculated becauseof the 3-week moving maximization process that was used for the
NDVI time series. The 5-year mean NDVI values and snow coverfrom the 2nd (8–14 January) to 51st (17–23 December) weeks were
derived.
The weekly mean temperature and weekly precipitation for the
5 years were calculated by slotting GDS daily data into NDVI
weeks, as shown in Table 2. The daily temperature was calculatedusing (Td max+Td min)/2, where Td max and Td min are the daily
maximum and minimum temperatures, respectively (the daily mean
temperature is not available in the GDS).
The daily HSDSD and GSSD snow depth data were also slotted
into the weeks (Table 2), and the weekly means calculated. The soil
moisture values used were the original three time month–1 values,i.e., they were not converted into weekly values.

Results
Construction of 5-year mean weekly data
The NDVI data for the leap year (1988) have an extra overlapping
week; that is, the 15th week covers 8–14 April, while the 16th week
covers 11–18 April, and the 17th week covers 18–24 April. In orderto match the weeks with those in other years, the 16th week in 1988
was omitted (to give 52 weeks in each year). Moreover, there aresmall differences in the calendar dates that the weeks span (i.e., thedates of the beginning and end of each week) in the weekly NDVI
and weekly snow cover data, as shown in Table 1.

Table 1 The first date of the first week of the weekly normalized
difference vegetation index (NDVI) and snow cover data in each
year.
Year

NDVI

Snow cover

1987
1988
1989
1990
1991

1 Jan 1987
1 Jan 1988
2 Jan 1989
1 Jan 1990
31 Dec 1990

29 Dec 1986
4 Jan 1988
2 Jan 1989
1 Jan 1990
31 Dec 1990

Table 2 The weeks used in theanalysis according to calendar
date

The NDVI seasonal cycle and its spatial distribution
General features of NDVI distribution
Six examples of the 5-year mean weekly NDVI distribution are shown in Fig. 2. As ancillary information, the5-year mean weekly snow cover is shown.
In the 9th week (26 February to 4 March), mostregions are snow-covered, and the NDVI is very small.
The NDVI is slightly higher in the central area (80–110E, 50–60N), probably because of the exposed green
canopy of non-deciduous forest (Suzuki et al. 2001a). On
the basis of information for one site in a taiga forest near
Yakutsk, Suzuki et al. 2001b showed that the forest
canopy is exposed (no rime or snow accreted) from late
winter to spring, when the ground is snow covered.
In spring (30 April–6 May; 18th week), vegetation
activity is clearly detected by the NDVI distribution. The

Week

Date

Week

Date

Week

Date

2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

8–14 Jan
15–21 Jan
22–28 Jan
29 Jan–4 Feb
5–11 Feb
12–18 Feb
19–25 Feb
26 Feb–4 Mar
5–11 Mar
12–18 Mar
19–25 Mar
26 Mar – 1 Apr
2–8 Apr9–15 Apr
16–22 Apr
23–29 Apr

18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34

30 Apr–6 May
7–13 May
14–20 May
21–27 May
28 May–3 Jun
4–10 Jun
11–17 Jun
18–24 Jun
25 Jun – 1 Jul
2 – 8 Jul
9–15 Jul
16–22 Jul
23–29 Jul
30 Jul – 5 Aug
6–12 Aug
13–19 Aug
20–26 Aug

35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51

27 Aug–2 Sep
3–9 Sep
10–16 Sep
17–23 Sep
24–30 Sep
1–7 Oct
8–14 Oct
15–21 Oct
22–28 Oct
29 Oct–4 Nov
5–11 Nov
12–18 Nov
19–25 Nov
26 Nov–2 Dec
3–9 Dec
10–16 Dec
17–23 Dec

130
Fig. 2 Six examples of theweekly normalized difference
vegetation index (NDVI) distribution. The date to the left ofeach map is the first date of therespective week.
Snow coverFig. 3 Weekly change in the NDVI averaged in the region bounded
by 50E, 90E, 55N, and 60N. Hatched area The period of snow
cover calculated from the National Oceanic and Atmospheric

Administration (NOAA) / National Environmental Satellite Dataand Information Service (NESDIS) snow cover data

first area showing green-up of the NDVI is located in thewestern part of the region (30–40E, 40–60N). Moreover, with the northward recession of the snow cover, the
NDVI increases slightly in the snow-cleared area. In mid
May (14–20 May; 20th week), the green-up extends
eastward, and forms a west–east green zone, while the
NDVI also starts to increase in the southeastern area
(around 130E, 45N). This green-up is attributed tofoliation of the forest ecosystem.
In July, the foliage in most of Siberia is mature. The
snow cover has melted completely, and an extensive high-

NDVI (over 0.4) zone is seen between 50N and 60N
latitude (2–8 July; 27th week). This high-NDVI zone
roughly corresponds to the forest area (Suzuki et al.
2000). In the area south of 50N, except east of 115E, the
NDVI is small, due to arid land cover types, such as the
Kazakh steppe, Kyzylkum desert, Takla Makan desert,
and Gobi desert. The NDVI over the tundra vegetation in
the Arctic is also relatively small.
August is generally the beginning of the senescenceseason for vegetation in Siberia. In the week from 27
August to 2 September (35th week), an extensive green

131area can be seen between 50 and 60N, but the NDVI islower than in July. The NDVI in the southeastern area isstill large, while the NDVI in the western area issignificantly lower than in July. In autumn, the vegetation
changes from the senescence to the dormancy phase. The
NDVI is small, as in winter, and snow cover is seen in thenorthern part of the region (1–7 October; 40th week).
Spatial features of phenological events
The results in the previous section tell us that thephenological characteristics differ with area. In thissection, 3 specific weeks are picked as parametersreflecting the phenological events of the vegetation: (1)green-up, defined as the week when the NDVI firstexceeds 0.2 in the year (W-a), (2) maximum, defined as
the week when the annual maximum NDVI occurs (W-b),and (3) senescence, defined as the first week after the
maximum that the NDVI drops below 0.2 (W-c).
An NDVI threshold of 0.2 was selected for W-a andW-c. This value is slightly higher than in previous
research; Malingreau (1986), for example, defined thegrowing season as the period with an NDVI higher than0.1, while Lloyd (1990) regarded the growing season as
the period when the NDVI exceeded 0.099. We chose arather higher threshold because we had to consider theeffect of snow, which covers most of the region fromautumn to spring, in the seasonal NDVI cycle.
Figure 3 shows the snow-covered period and theweekly NDVI averaged for the region 50–90E, 55–60N,
as an example. This figure demonstrates that the NDVI
increases abruptly and exceeds 0.1 as the snow melts.
Since the NDVI of a snow-covered surface is smaller thanthat of other land surfaces because of its spectral
characteristic, this abrupt increase might include a
component that is due to the thaw itself. Therefore, toexamine changes in vegetation, we set the NDVI threshold at 0.2.
The distribution of these three specific parameters is
examined in Fig. 4. GVI pixels in which the NDVI did notexceed 0.2 years-round were excluded from this analysis(indicated in gray in Fig. 4).
The area with the earliest W-a, before the 18th week
(30 April–6 May), was located mainly around the Blackand Caspian Seas. W-a was relatively early (18th–22ndweek) in a zone (west–east) roughly between 50 and60N (referred to as the 50-60N zone). The W-a north of60N gradually became later farther northward, and the
W-a in tundra areas was after the 29th week (16–22 July).
The W-a south of 50N was also late, especially in thearid area of Mongolia. Within the 50–60N zone, the W-a
gradually became later eastward around Lake Baikal(22nd week; 28 May–3 June) from the western area, while
W-a was early (19th week; 7–13 May) near the Sea ofJapan coast.
W-b contains more essential information on thevegetation phenology in Siberia than W-a or W-c,because W-a and W-c can change with the definition ofFig. 4a–c The distribution of the weeks when the NDVI exceeded
0.2 (W-a) (a), NDVI reached the annual maximum (W-b) (b), anddropped below 0.2 (W-c) (c). Gray Pixels in which the NDVI did
not exceed 0.2, excluded from the analysis. Black A pixel with anearlier week than the earliest week on the color scale. White A pixel
with a later week than the latest week on the color scale

the thresshold value (i.e., 0.2) in the seasonal NDVI cycle,
while the maximum NDVI is not altered by the definition.
The overall distribution of W-b was similar to that of
W-a. W-b occurred earliest in the western region (30–80E) between 45 and 50N, which includes the Kazakh
steppe, in the 20th week (14–20 May). In contrast, theW-b near the Mongolian steppe was the latest, after the
31st week (30 July–5 August). There was a marked westearly/east-late constrast in the W-b in Kazakh andMongolia (11 weeks) in the 45–50N zone. In the 50–
60N zone, there was also a west–east gradient; that is,

132
Fig. 5 The west–east profile of
the weeks in the 45–50N zone
when the NDVI exceeded 0.2(a), the NDVI annual maximum
occured (b), and the NDVI
dropped below 0.2 (c). Solidline The smoothed line produced using a low-pass filter

the western area generally had an earlier W-b than theeastern area (especially around Lake Baikal). To the northof the 50–60N zone, the week gradually became later,
and the latest week in the tundra area was the 31st week
(30 July–5 August), which was 7 weeks later than in theearliest area.
In the map of W-c, areas with extremely early values(before the 27th week, 2–8 July) are seen in the Kazakh
steppe area, similar to the map of W-b, while W-c in thesoutheast area, including Mongolia, occurred after the 39th
week (24–30 September). In the 50–60N zone, thegradient of W-c is less apparent than those of W-a andW-b.
Using the NDVI, the regional phenology of green-up,
maximum, and senescence can clearly be seen using W-a,
W-b, and W-c respectively. This study discusses thewest–east contrast in the phenological characteristics of
W-a, W-b, and W-c in the 45–60N zone. Although asouth-north phenological contrast was also apparent, thiswest-to-east phenology of NDVI, the so-called gree-wave,
is more interesting.

Discussion
This section considers the west–east phenological contrast
that is manifested as the green-wave. Two zonal transects,
45–50N and 50–60N, were selected to discuss the west–
east contrast, and W-a, W-b, and W-c were examined inrelation to climatological parameters. The 45–50N zone
corresponds primarily to semi-arid regions that arecovered by steppe vegetation, such as the Kazakh andMongolian steppe. As Suzuki et al. (2000) discussed, thevegetation near Kazakh is strongly dominated by theavailable water. The 50–60N zone corresponds toforested regions, including taiga, and a high NDVI
west–east zone forms in this transect in summer, as
Suzuki et al. (2001a) demonstrated.

West–east phenological contrast
in semi-arid area (45–50N)
As Fig. 4 shows, W-a, W-b, and W-c occur very earlynear the Kazakh steppe, and form a remarkable westearly/east-late phenological contrast between the Kazakh
and Mongolia steppes, which are located at the same
latitude. This section focuses on this west–east contrast in
the 45–50N zone and its relation to climatologicalfactors.
Figure 5 shows the mean W-a, W-b, and W-c,
averaged over the pixels in individual south-north strips,
which were 0.144 longitude wide, in the zone between
45 and 50N. There were 800 strips from 30E to 145E,
and each strip contained 34 NDVI pixels. Pixels for which
the annual maximum did not exceed 0.2 were excluded
from the calculation of the mean values shown in Fig. 5.
The smoothed lines were drawn using a low-pass filter
that applied a moving-average method with normal curve
weights (Holloway 1958). The frequency response of thefilter was set to 0.5 at 1 cycle/degree.
For W-a and W-b, a west-early/ease-late gradient wasapparent. Near 60E, W-a and W-b were weeks 20.4 and21.7, respectively, while near 110E, they were weeks
23.8 and 30.4. There were 3.4- and 8.7-week time lags in
W-a and W-b respectively. W-c was relatively late westof 50E, whereas it was earliest between 55 and 70E
(i.e., near the Kazakh steppe). W-c was 13.4 weeks earlier
near 60E than near 110E. Moreover, the time between
W-a to W-c was noticeably short (3.8 weeks) in the
Kazakh region, illustrating the short growing period there.
As representative of the western and eastern areas in
the 45–50N transect, the Kazakh (45–75E, 45–50N)
and Mongolia (100–120E, 45–50N) areas were selected,
and Fig. 6 compares seasonal cycles in weekly temperature, precipitation, snow depth, and soil moisture for thetwo areas. The soil moisture in the Kazakh area is themean from 1985 to 1987. The snow depth in the
Mongolian area is the mean from 1994 to 1996. The

133
Fig. 6 The seasonal change inthe weekly NDVI, temperature,
precipitation, snow depth, and10-day soil moisture in two arid
regions: Kazakh (left) andMongolia (right) in the 45–
50N zone. The soil moisturefor the Kazakh area and thesnow depth for the Mongolian
area for 1985–1987 and 1994–
1996 respectively. Other statistics are the means from 1987 to
1991. The figures in the panel
are the number of stations used
to calculate the mean temperature, precipitation, snow depth,and soil moistureother statistics are the means from 1987 to 1991, theactual time span of this study.
As Fig. 6 shows, the seasonal change in the NDVI in
the Kazakh area showed early green-up and a shortgrowing period, while that in the Mongolian area
exhibited late green-up and a late maximum. Suzuki etal. (2001a) classified seasonal NDVI cycles at 611 sites inSiberia, on the basis of monthly NDVI values, andobtained ten classes for the seasonal NDVI cycle.
According to that classification, the Kazakh area ismainly class B, which is characterized by early green-up
and low NDVI, while the primary class in the Mongolian
area is class D2, which is characterized by a high NDVI
and a late maximum. These features are consistent with
our results.
Conspicuous differences between these two areas arealso seen in their precipitation, snow depth, and soil
moisture. In the Kazakh area, the snow was deep (about
26 cm) in late February, and it disappeared in late April.
Although soil moisture data before March were unavailable, and the statistical period was from 1985 to 1987, thesoil moisture in the Kazakh area was highest (10.3 mm in
the surface 10-cm layer) in early April, when the snow
had almost finished melting. Subsequently, the soil
moisture decreased to a minimum in August. The small
amount of precipitation in the Kazakh area (Fig. 6) was
insufficient to stop the decrease in soil moisture.
In the Mongolian area, the precipitation showed an
obvious unimodal seasonal cycle, with a maximum(22 mm week–1) in mid July. Although the snow was
not as deep in winter as in the Kazakh area, the soil
moisture remained relatively high (arround 10 mm in the
5–10-cm layer) from April to October.

Figure 7 shows the temporal pattern of NDVI withlongitude and precipition in the 45–50N zone. There isgood agreement between the NDVI and precipitation, i.e.,areas with high precipitation generally correspond to thehigh NDVI domain. Especially in the region east of100E, there is similar temporal variation in NDVI and
precipitation. Green-up of the NDVI coincided with thestart of the rainy season in May–June. The maximum
NDVI (W-b) occurred at almost the same time as theprecipitation maximum. The NDVI decreased with precipitation in the autumn. In other regions with relatively
large amounts of precipitation, such as the regions within
30–45E and 80–87E, NDVI varied with the seasonalvariation in precipitation.
By contrast, between 50 and 80E, the NDVI was notas high as it was east of 100E. Instead, a small NDVI
maximum was found in early summer, which was seen in
the NDVI variation in the Kazakh area (Fig. 6). Although
the precipitation in this region was small all year round,deep snow cover prevailed until March. These facts
suggest that water is available for vegetation in the
Kazakh area only for a very short period after snowmelt,
and this season is the only time that vegetation flourishes.
Consequently, the vegetation matures in late May whenwater from melting snow is still abundant in the soil. In
addition, the span from W-a to W-c in the Kazakh area isconsiderably shorter than that in the Mongolian area. In
the Mongolian area, large amounts of precipitation insummer support the occurrence of the mature stage ofvegetation in midsummer, 8.7 weeks after it occurs in the
Kazakh area. It is also possible that the winter soil
temperature in the Kazakh area is kept higher by the thicksnow cover compared with the Mongolian area, possibly
inducing the earlier green-up in the Kazakh area.

134
Fig. 7 Time longitude crosssection of the NDVI (color
scale) and precipitation (black
contour: mm week–1) from the15th to 40th weeks, and thesnow depth (cm) from the 5th to14th weeks in the 45–50Nzone. The snow depth east of120E was not plotted owing toan insufficient number of stations. W-a, W-b, and W-c aresuperimposed

Fig. 8 The west–east profile of
the week in the 50–60N zone
when the NDVI exceeded 0.2(a), the NDVI annual maximum
occured (b), and the NDVI
dropped below 0.2 (c). Solidline The smoothed line produced using a low-pass filter

West–east phenological contrast in forest area (50–60N)
Figure 8 shows the mean W-a, W-b, and W-c in the zone
between 50 and 60N. There were 800 strips from 30E
to 145E, and each strip had 69 pixels. The smoothed line
was drawn as in Fig. 5. Likewise, pixels for which theannual maximum did not exceed 0.2 were excluded forthe calculations of mean value.

The west-early/east-late gradient of the W-a and W-b
zonal profiles is obvious in these graphs. Around 40E,
W-a occurred at about 18.6 weeks (beginning of May),
while W-a near 115E was at 22.4 weeks (beginning ofJune), i.e., green-up at 40E was 3.8 weeks earlier thanthat at 115E. W-b shows a similar profile. W-b near 40E
was at about 25.1 weeks (mid June), while W-b near

135
Fig. 9 The seasonal change inthe weekly NDVI, temperature,
precipitation, snow depth, and10-day soil moisture in two
forest areas: the west (left) andeast (right) areas in the 50–
60N zone. The soil moisturedata in the east area is from1985 to 1987; soil moisture forthe west area was not available.
The other statistics are themeans from 1987 to 1991. The
figures in the panel are thenumber of stations used to calculate the mean of temperature,
precipitation, snow depth, andsoil moisture

115E was at about 29.0 weeks (mid July), a 3.9-weekdifference.
W-a and W-b east of 115E were progressively earlier.
However, the west-early/east-late pattern for W-a andW-b is a general characteristic of the zonal profile in the
Siberian vegetation phenology. No zonal trend is apparent
for W-c. The earliest W-c (34.4 weeks, end of August)
was at 65.6E, and the latest W-c (37.9 weeks, mid
September) was near 87.6E.
As for the analysis for the 45–50N zone, the seasonal
cycles of the weekly temperature, precipitation, snow
depth, and 10-day soil moisture in the west (40–50E, 50–
60N) and east (120–130E, 50–60N) areas are compared in Fig. 9. Unfortunately, no soil moisture data are
available for the west area because there is no SWOD
station in this area. The soil moisture plot for the east areais the mean from 1985 to 1987. The west area ispredominantly class E2 according to the classification by
Suzuki et al. (2001a), while the primary classes in the eastarea are D1 and F. These three classes have large
amplitudes in the seasonal NDVI cycle, and class E2 ischaracterized by early occurrence of the maximum NDVI.
These features agree well with the results of this study, as
indicated in Fig. 9.
In Fig. 9, there is an obvious difference in the timing of
the phenology between the west and east areas in the 50–
65N zone. W-a in the west area occured around the
beginning of March, while it occurred around mid May inthe east area. W-b occurred in the 25th week (18–24 June)in the west area and in the 29th week (16–22 July) in theeast area, i.e., it was 4 weeks earlier in the west.
Although no obvious difference like that found in the
45–50N zone is seen, there are some differences in theseasonal temperature cycle in the 50–60N transect. The

annual range of temperature in the east area (about 48 C)
is much greater than in the west area (34 C). This ismainly due to the much colder winters in the east area(–28.3 C in the 3rd week) than in the west (–12.1 C in
the 5th week).
To discuss the relationship between temperature andvegetation, a cumulative measure of temperature from the
beginning of the year is often used, such as the warmthindex (Kira 1948) or modified warmth index (Suzuki etal. 2000). This analysis examined the cumulative weekly
mean temperature exceeding 0 C at each staton from the
beginning of the year (Tc), based on the threshold
temperatures established in Suzuki et al. (2000) for
Siberian vegetation.
Figure 10 shows the weeks in which Tc exceeded 40 C(W-40) and 140 C (W-140). This plot reveals a westearly/east-late trend in the profiles of both W-40 and
W-140. Near 40E, W-40 and W-140 occur in roughly the
19th (early May) and 25th (late June) weeks, respectively.
Near 112E, the respective times are the 23rd (early June)and 30th (late July) weeks. East of 112E, there is a slightincrease in both W-40 and W-140. This west–east trend isquite similar to the trends in W-a and W-b shown in Fig. 8.
Figure 11 contains scatter diagrams of W-a/W-40 andW-b/W-140. There are linear relationships with highcorrelation coefficients for both plots (0.81 for W-a andW-40, 0.84 for W-b and W-140).
These results suggest that the west-fast/east-slow
temperature accumulation in the seasonal cycle plays animportant role in the climatological environment thatcharacterizes the west-early/east-late times of the greenup and the mature state of vegetation. In this analysis, thecritical values of the cumulative temperature (i.e., 40 C
and 140 C) were determined by trial and error. In

136
Fig. 10 The west–east profileof the week in the 50–60Nzone when Tc exceeded 40 C(a; dots) and Tc exceeded 140 C(b; stars)

Fig. 11 Correlations between
W-a and W-40 (left) and W-b
and W-140 (right)

addition, the result of the W-a/W-40 relation depends onthe definition of the threshold NDVI for W-a, i.e., 0.2 inthis study. Although it is not clear why cumulative
temperatures of 40 C and 140 C specifically correspondto W-a and W-b respectively, the climatological environment may have implications for plant physiology. Thesecritical values may change with climatic and vegetation
zone, and such an analysis will be performed on a global
scale in the future.

Conclusions
The phenological regionality of the vegetation in northAsia was investigated using remotely sensed NDVI data.
The 5-year (1987–1991) mean weekly NDVI time series
was calculated from the second-generation weekly GVI
dataset, which covers the earth with a 0.144 spatial
resolution. Temperature, precipitation, snow depth, andsoil moisture seasonal cycles were examined to interpretthe seasonal NDVI cycle.
In winter, the NDVI is very low throughout the region.
Beginning at the end of April, green-up occurs in thewestern part of the region, and a high NDVI west–eastzone forms at 50–60N latitude in summer. The NDVI inthe southern arid area and northern tundra is never high,even in summer. In the autumn, the NDVI drops, and thedormancy season occurs in winter, when the NDVI isonce again small throughout the region.
An NDVI green-wave propagating from west to east
was seen in the 45–60N zone in the analysis of weekly
data that reflects a phenological time lag between thewestern and eastern parts of the study area. Threephenological events were defined and calculated for eachpixel: green-up (W-a), maximum (W-b), and senescence(W-c) weeks. Generally, there was a west-early/east-latespatial gradient in all three events. In the zone between
45 and 50N, green-up, maximum, and senescenceoccurred near 60E (Kazakh) about 3.4, 8.7, and 13.4
weeks earlier than near 110E (Mongolia) respectively.
The vegetation in Kazakh flourishes during a short period
when snowmelt is available from late spring to earlysummer, while in Mongolia, abundant water is available
for the vegetation, even in midsummer, because of thelarge amount of precipitation.
In the 50–60N zone, W-a and W-b near 40E wererespectively about 3.8 and 3.9 weeks earlier than they
were near 115E. There was no clear west–east gradient137

in W-c. This west-early/east-late phenological contrast
was related to the weekly cumulative temperature (over0 C). The weeks in which the cumulative temperature
exceeded 40 C and 140 C had a west–east trend very
similar to those of the green-up and maximum weeks of
the NDVI, respectively.
Remotely sensed NDVI data revealed aspects of thephenology of the vegetation in north Asia. The essential
relationship between the vegetation and climate in Siberia
was dominated by moisture in the 45–50N zone and bytemperature in the 50–60N zone. These results agree
quite well with the results of Suzuki et al. (2000), whoinvestigated their relationship in south-north transects.
This paper has highlighted west–east differences in theseasonal cycle and discussed their climatological background. These results improve our knowledge of theclimate and of the phenology and distribution of vegetation. Moreover, the results improve our understanding of
the impact of vegetation on climate over an extensive areaof the globe. In the future, these results will be comparedto surveys of other terrestrial areas to evaluate othervegetation-climate relationships. Topography, soil type,
and permafrost can also influence vegetation. These
relationships could be the target of a future study using
the NDVI.
Acknowledgements The authors thank Dr. Dennis Dye (Frontier
Research System for Global Change) for his suggestions andadvice, and the referees for their useful comments, which
contributed to the final version of the manuscript. All the pictures
were constructed with the help of the GMT System (Wessel andSmith 1991). This study was partly supported by a grant-in-aid ofscientific research from the Ministry of Education, Science, Sports,
and Culture, Japan (“Better Understanding of Water and Energy
Circulation on a Continental Scale based on Satellite Remote
Sensing”; 08241105).

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