The Study on the Behavior of the Slop by VI-Ts Relationship Derived from
10 Day’s AVHRR Composite Dataset in GAME Regions
Atsushi Higuchi1, Yuichiro Fukuta1, Tetsuya Hiyama1, and Yoshihiro Fukushima2
1 Hydrospheric Atmospheric Research Center (HyARC), Nagoya University, Japan
2 Research Institute for Humanity and Nature (RIHN), Ministry of Education, Culture, Sports, Science andTechnology (MEXT), Japan.

ABSTRACTFor better understanding of the land surface processes, especially the surface moisture in
the border of wet – dry region plays an important role on the nature and/or characteristics
of atmospheric boundary layer (ABL). Satellite remote sensing technology is essential
tool to monitor or detect the surface moisture status on the regional scale. Several
method/algorithm have been developed to detect the surface/soil moisture, this study
focus on optical sensor generally boarded on operational satellites. This study was carriedout as a crosscutting investigation for GEWEX Asian Monsoon Experiment (GAME)
regions (from Tropics to Siberia). We evaluated that the “slope” estimated from the scatter
diagram of Normalize Difference Vegetation Index (NDVI) with surface temperature, as adiagnostic indicator to represent the surface status. The 10-day’s composite dataset
compiled by U.S. Geological Survey (USGS) was used. Target area expanded from N 20
to N 80 in latitude and E 60 – E 150 in longitude, can be covered mostly the GAME
regional study area. Results could be summarized as follows: 1) Basically, slope works as
an indicator of surface moisture status in whole GAME regions, 2) however in Tibet, it
was opposite sense reaction in slope from previous study, and 3) considering of scatterplot of NDVI and Ts, different phenomena detected with the threshold value of NDVI
ranged roughly from 0.25 to 0.35.
Introduction
Better understanding of the interactive process
between the land and atmosphere is one of the important
issues for both atmospheric and hydrological studies.
Modeling and/or field experimental studies help us
improvements of knowledge, however, to state and/or
monitor the “current” surface condition in regional/
global scales, satellite remote sensors is powerful tool.
Sensors boarded on the space platform can detect somecharacteristics of the target, which is largely depended on
the wave-length/frequency of sensors. Sensors withmicrowave bands can detect status of clouds, rain drop,
ice chips, snow, liquid water in land surface, by solving
radiative transfer equations (Ulaby et al., 1981). On theother hands, from visible to thermal infrared bands
sensors can measure the radiance of each measured wave
region (Asrar, 1989). Normalized difference vegetationindex (NDVI) is one of a famous product derived fromoptical remote sensor, which can represent the vegetation
activities (e.g., White et al., 1997; Hashimoto et al.,2001), vegetation related physical status (e.g., Kondoh
and Higuchi, 2001; Higuchi and Kondoh, 2000). Most ofvegetation indices were expressed as a combination ofvisible and near infrared bands, and use characteristics ofspectral reflectance of plants. Generally, visible region’sradiance of the short wave radiation is absorbed byleaves for the activities of photosynthesis, on the other
hand, leaves reflect well in near infrared ration’s radiancefor protecting the leaf temperature increase. Lots ofstudies have been published using vegetation indicesderived from satellite, for example, White et al. (1997)
detect the phenology (seasonality of vegetation) in
United States of America from time series of NDVI by
Advanced High Resolution Radiometer (AVHRR) on
boarded NOAA series polar orbit satellites. Needless tosay, surface temperature derived from satellite is one ofdominant parameters to determine the sensible heat
fluxes (e.g., Seguin et al., 1999; Matsushima and Kondo,
2000). From the end of 1980’s, researchers focused on
information by combination of vegetation index withsurface temperature. Figure 1 shows an example ofscatter diagram of NDVI with surface temperature (Ts).
As a frontier research, Nemani and Running (1989)

Dry Condition

Wet Condition
Figure 1 Scatter plot of NDVI with surface temperature(Ts) (after Nemani and Running, 1989, butslightly modified).

suggested that the slope determined from the scatter plotof NDVI and surface temperature could be expressed asa function of surface resistance in Penman-Montiethequation with comparison of biogeological model output.
Figure 1 also represents difference state of slope due tothe surface condition difference (July 14; dry, August 6;wet). Based on the Nemani and Running’s (1989)
suggestion, several progresses using the combination of
NDVI and Ts have been held by investigators (e.g.,
Nemani et al., 1993; Kondoh et al., 1998; Matsuda et al.,
2000; Hashimoto et al., 2001). Figure 2 shows anexample of the application the slope to monitor thesurface moisture status in Huaihe River Basin, China(Kondoh, et al., 1998). This figure represents good
agreement with time series of antecedent precipitationindex (API) and of slope at four reference sites. Recently,
Hashimoto et al. (2001) indicated that slope could use as
an indicator to determine the onset date from dry to rainyseason (onset of Monsoon) by applying multi-temporal
AVHRR data in Thailand. They identified the thresholdvalue of the slope detecting the date from dry to wet(rainy) season as –40 with comparison of rainfall data.

Figure 2 Time series of the daily precipitation,
antecedent precipitation index (API) and theslope of VI/Ts relationship at Huaihe River
Basin , China (after Kondoh et al., 1998).
Several researchers have been carried out theapplication of slope to determine the surface moisturestatus in the regional scale, however, in continental scale
has not yet. Fortunately, GEWEX Asian Monsoon
Experiment (GAME: GAME, 1998) was carried out theintensive field campaign in 1998 (so-called Intensive
Observation Period; IOP) for better understanding themechanism of Asian Monsoon. Currently, sub-region’s
experimental studies produced several papers, but crosscutting studies cross the sub-regions has not been done.
Thus, Our target to apply the slope in continental scale tois GAME region (from Tropics to Siberia in EurasianContinent) as a cross cutting study of GAME.
Dataset and Methodology- 10 Day’s Composite AVHRR Dataset

We used the 10 day’s composite global AVHRR
dataset provided by United States Geological Survey(USGS: USGS, 1999). This dataset consists of ten
channels and already converted from digital numbers tophysical values, AVHRR albedo, brightness temperature(see more detail its dataset; Eidenshik and Faundeen,
1994; USGS, 1999). From ten channels, we selected
eight channels (channel 1 to 5 from original AVHRR[visible, near-IR, and three IR channels], solar zenith
angle, satellite zenith angle, and adopt date). It should be
noted that we do not adapt NDVI data (channel 6 in theoriginal 10 day’s composite data) for determination ofslope. Because, visible and near-IR channels of AVHRR
had been affected the sensor degradation (Rao and Chen,
1996), thus we apply calibrations for the sensor
degradation of channel 1 and 2 using equations by Rao
and Chen (1996). NDVI was estimated as follows:
NDVI=(channel1–channel2)/(channel1+channel2) (1)On the other hand, to estimate the surface temperature,
we adopted the split window technique, widely appliedin the field of determination of skin surface temperature.
From the numbers of previous studies, we adopted aalgorithm by McMillin and Crossby (1984), for thedetermination of Sea Surface Temperature (SST) as
follows:
Ts=a0+a1channel3+a2channel4+a3channel5(2)Here, a denotes the coefficient, a0 =3.175 [K], a1 =0.429[dimensionless], a2 =2.698 [dimensionless], and a2 =-2.139 [dimensionless]. The other unit is Kelvin.
After the computations of NDVI and surface
temperature, we removed noise data by cloud, where hadless than 273 K in Ts pixel data. In addition, we also
screened the data with sensor zenith angle was more than
40 degree, because of wide swath of AVHRR, generally
more that 40 degree’s scanned data overestimated inreflectance than the normal. Composite data could be
downloaded via Internet, and could cut with latitude andlongitude information in the interactive html protocol
interface. Thus, we downloaded as cut data withdimension of 20N – 80N in lat., and of 60E – 150E in
long., where could cover the GAME sub-regions mostly(Fig. 3). One image file size reached around 920 MB.
After the computation of NDVI and Ts for each
image, we applied the automatic estimation algorithm forthe determination slope by Nemani et al. (1993) andKondoh et al. (1998). However, we modified thisalgorithm more simply as follows:

Figure 3 Study Area dimension (sample NDVI image).

a) Scanning the pixel data within a window domain(defined as 50 pixels and 50 lines, approximately
equivalent in scale of 50 by 50 km) with 0.001 interval in
NDVI step.
b) In the algorithm of Kondoh et al. (1998), pixel data
adopt the maximal value in Ts with ranging 2 K at each
NDVI interval for determination of slope. In this study,
we also adopt the same as Kondoh et al.’s (1998)
algorithm.
c) To calculate the slope (regression line of NDVI and Ts
with selected pixels in b), both Nemani et al. (1993) andKondoh et al. (1998) screened the pixels to determine theminus value of the slope. In detail, if TsMAX(i) >TsMAX(i+1), where TsMAX(i) is maximal value at i’s value
in NDVI, TsMAX(i+1) would adapt for determination, but
in the case of opposite sign, TsMAX(i) would not add. This
study did not apply this screening algorithm, thus we
could gain both plus and minus values in the slope.
Analysis period of dataset was from 1-10 Feb.,
1995 to 20-30 Jan., 1996 (i.e., 36 images). The whole ofdataset is available in 1992 and 1993, but the whole of
one year. We focus on the seasonality and variability ofthe slope within GAME sub-region, thus analysis period
was limited only 1995 – 1996.
-Global station dataset (Global Daily Summary; GDS)
As reference data, we also use the meteorologicalstation’s dataset, so-called global daily summary (GDS),
that have been compiled and published by National
Climate Data Center: NCDC, 2001). Our focus of thisstudy is better understanding of behaves in slope at thedifferent climate/biome zones within GAME sub-regions,thus we selected fifteen station sites from GDS shown as
Fig. 4. We used the daily precipitation and minimal/
maximal air temperature from the element of
measurements for comparisons of time series of slope.

April (Fig.5) and middle of July (Fig. 6) as examples ofslope images. Dark colors in each figure represent seep
slope (corresponding to “dry” condition), and counters
indicate the border when slope value exceed zero (i.e.,
slope has positive value). In the middle of April (Fig. 5),in the most regions of Tropics (e.g., India, Indochina
Peninsra) was just before the monsoon phase (premonsoon phase; Hashimoto et al., 2001), slope in Tropic
region represented the steep value more than –40, suchvalues are reasonable to consider the climate phase of
those regions. Generally, steep values (dry) in slope
concentrated in West side of study area both Fig. 5 and 6.
To image the annual precipitation distribution in this area,
slope distribution could express the status of surface
moisture generally, however, to see in the regional scale,several feature could be pointed out. Firstly, in Fig. 5,
Tibetan Plateau has a positive value in slope (to see
clearly the border of Tibetan Plateau). In Fig. 6, border is
not clear as Fig. 5, but has positive value. Secondly,
definite graduation from south to north could be
identified in China.

Figure 5 Spatial distributions of slope derived from
20-30 Apr., 1995’s image, by VI/Ts relationship.
Dark color indicates steep values in slope, andcounters represent the border of slope is zero.

Figure 6 Same as Fig. 5, but for 10-20 Jul., 1995.

Figure 4 Selected station sites for validation of slope’s
behavior within the study area.
Results and Discussion-Spatial and time variability in slope
Figure 5 and Figure 6 show the spatial distributionsof slope estimated by this study’s algorithm for middle of- Comparisons of slope with precipitation and airtemperature at selected stations
To specify the detail of slope at different
climate/biome regions, we picked up the time series ofslope at the selected station in Fig. 4, and compared withmeasurements elements at meteorological stations.
Figure 7 shows the time series of slope, ten days
precipitation, and air temperature at selected stations.
As an example of Tropics, Calcutta (22.7N, 88.6E),
located in India, has a definite phase difference of dry(north-east monsoon; winter) and of wet (south-west
monsoon).

< West ̲Sibelia > T omsk N56.5 E83.5

-25
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DOY

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m ea n ̲t em p erat ur e [ ℃]0150

P r eci pita tio n [ mm / 10 da ys] S LOP E

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0< Mongolia > Dunhua ng N40.90 E94.41

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mea n̲t empe rat u re [℃]

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180 210 240 270 300< Tibet > Tout ouhe N34.13 E 92.26

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SLOPE

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DO Y< Hubex > Shou-Xia n N32.58 E 116.77

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]

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0330 360 390

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Figure 7 Time series of slope, 10 days precipitation, and air temperature at selected stations
Time series of slope picked up from multi-temporal slope
images represented that steep values in dry season (until
110 day of year [DOY]), since monsoon break (onset of
monsoon) in 120 DOY, slope increased rapidly withpositive and kept positive. It should be noted that thisalgorithm could not applied to snow-covered regions(e.g., high latitude zone in winter) because of data lackfor the estimation of regression line NDVI and surface
temperature. However, even in high latitude region(Tomsk, located in west Siberia, 56.5N, 83.5E), during
summer, could estimate the slope. According to theprevious studies for the application of slope into Tropics(Matsuda et al., 2000; Hashimoto et al., 2001), and into
Temperate zone (Nemani et al., 1993; Kondoh et al.,1998), slope performed well for the inferring of surface

moisture status, and if land surface became drier, slope
would represent steeper. This study could agree withthose. However, Tibet (Toutohe, 34.13N, E92.26) has an
opposite sense in the time series of slope against these of
precipitation. During winter, slope has high “positive”
steep value, but during summer (in monsoon), slope
concentrated around zero, and sometimes has a negativevalue. To consider the simple principle of the slope withsurface condition, these responses cannot explain fromthe previous studies. Tibetan Plateau locates more thanfour thousand meter in elevation, so Tibet is uniqueregion to compare others.
- Scatter diagram of NDVI and surface temperature atselected stations.

50

Tomsk, Russia (West Siberia), 56.5 N, 83.5

50

Dunhuang, China (Inner Mongolia), 40.9 N,

Toutouhe, China (Tibet), 34.13 N, 92.26 E

50Jun., 1-10, 1995, sample = 856/

Jun., 1-10, 1995, sample = 2475

Jun., 1-10, 1995, sample = 2307

40

30

Surface Tem perature

Surface Tem perature

Surface Tem perature

40

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10

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NDVI
Shou-Xian, China (HUBEX), 32.58 N, 116.77
50

Calcutta, India, 22.7 N, 88.6 E

Jul., 1-10, 1995, sample = 2174

Apr., 10-20, 1995, sample = 1893/2

40

Surface Tem perature

Surface Tem perature

40

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10
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NDVI

NDVI

Figure 8 Examples of scatter diagram of NDVI with surface temperature at selected stations windows area.
threshold value of NDVI.
50

Surface Tem perature

To validate unique characteristic of slope in Tibetan
Plateau, scatter diagrams of NDVI with surface
temperature at selected stations were shown in Fig. 8.
Both Tropics and Temperate zone, slope worked well assame as previous studies, show the typical scattering that
express as decreasing surface temperature with theincrease in NDVI. However, in Tibetan Plateau, surface
temperature increased with NDVI increase. Generally, in
low NDVI value scattering area (from 0 to 0.3 in NDVI),
scatterings ware similar with that in Tibetan plateau. We
identified the hypothesis to explain such scattering
difference in low NDVI area and high NDVI area, ananalysis carried out illustrated in Fig. 9. Determination ofthreshold value in NDVI from scatter diagram is asfollows:
1 Search surface temperature’s peak in each NDVI
interval (interval is 0.01)
2 Comparison of peak Ts value at each NDVI interval
3 Adapt the highest Ts and determine as the threshold
NDVI
Figure 10 shows the distribution of threshold NDVI
during analysis period. During images, we adopted
minimal threshold NDVI value in comparison ofmulti-temporal images. Threshold values wereconcentrated in the range of 0.25 – 0.35. This result
suggested us that this threshold value has meaning asuniversal, and we have to consider the different behavior
of VI-Ts relationship bellow and above the thresholdvalue in NDVI. Figure 11 suggests the schematic
diagram of VI-Ts relationship in below and above the40

30

Determination of NDVI threshold
1 Search Ts peak in each NDVI
insterval (0.01)
2. Compare each Ts peaks
3. Adopt highest Ts and determin
as threshold NDVI

20
0.0

Threshold NDVI 0.5

1.0

NDVI

Figure 9 A schematic diagram for determination of thethreshold NDVI.

Figure 10 Distribution of threshold in NDVI derived
from Fig. 9’s algorithm.

Phenomena influence on the differnt respons
VI-Ts relationship, represented by the "slo
50

Tibet

Tropics, Temerate
Siberia (summer)

Surface Tem perature

40

30

20

Ts decreased with transpiration
10

0
0.0

Big difference in specific heatand vegetation (Cw) make increa
leaf temperature ?

0.5

1.0

NDVI

Figure 11 schematic diagram of considerable phenomena
influence on the different response in VI-Ts
relationship.
Below the threshold value in NDVI (see the area lessthan 0.25 in NDVI in Fig. 10), difference of the specificheat of soil (represented as Cs) and vegetation (Cw)maybe lead the vegetated sub-area’s surface temperature
increased. On the other hands, above the threshold, canexplain a simple principle already discussed in previous
studies as follows: high NDVI values area (dense
vegetated area) has a high transpiration rate because of
the availabilities of leaves, thus according to energy
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already launched (e.g., Terra) and ready to launched (e.g.,aqua, ADEOS-II), on boarded such new generation
satellites have high spectral sensors (e.g., Moderate
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Acknowledgements
The authors thank to Dr. Rama Nemani, Numerical
Terradynamic Simulation Group (NTSG), University ofMontana, USA, and Dr. Kenlo Nishida, Institute ofAgricultural and Forest Engineering, University of Tsukuba,
Japan, to give us the useful comments and suggestions.
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Japan, for fruitful comments of our results, especially uniquebehavior of the slope in Tibetan Plateau.
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