Wet and dry boarder of the land surface
revealed by several satellites dataset
Higuchi, A.1,∗ , M.K. Yamamoto2 , A. Kondoh1 and K. Nakamura3
1

Center for Environmental Remote Sensing (CEReS), Chiba University, Yayoi-cho 1-22,
Inage-ku, Chiba 263-8522, Japan.
2
Research Fellow of a 21st Century COE (Center of Excellence) Program ”Dynamic of
the Sun-Earth-Life interactive system”, Graduate School of Environmental Studies, Nagoya
University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.
3
Hydrospheric Atmospheric Research Center (HyARC), Nagoya University, Furo-cho,
Chikusa-ku, Nagoya 464-8601, Japan.
∗
tel:+81-43-290-3858, fax:+81-43-290-3857, e-mail:higu@faculty.chiba-u.jp
ABSTRACT
Land-atmosphere’s interactions and dynamics of those influence on the precipitation process itself.
Longer time data storage (years from several to a decade) of satellite images makes it possible togenerate the proxy dataset of climatology (rainfall, vegetation activity, land surface thermal status).
We try to interpret “Where is the boarder or edge of wet and dry?” by the several satellites dataset
only with annual bases. TRMM (PR and TMI), PAL, and in-situ estimated water deficit dataset weused. Results are summarized as follow: 1) Intercomparison of annual rainfall between PR and TMI
indicates the similarty over globe, but TMI is not suitable in LAPS region. 2) An overlay the water
deficit on an annal rainfall map makes us to interpret that not only land cover difference (forest or
crop field) but also the manner of precipitation and convective activities (from diurnal to seasonalin time series) influenced on the value of water deficit. 3) Stress index over vegetated surface isseems as inverse map of precipitation, but over monsoon Asia, land cover difference was dominant,and 4) RGB composite image (rainfall, vegetation, stress index) is one of diagnostic methodologies
to interpret where is the boarder of wet and dry, but such boarders are complex functions among
meteorology, hydrology and social sciences (politics and management of land).

1. Introduction
Land-atmosphere’s interactions and dynamics of
those influence on the precipitation process itself. Integrated field observations or system make it possible
to improve of our understandings. Numerical modeling is based on lots of results of our understandings (both theoretical and empirical). Satellite observations have been contributed to see and detect thestate of the Earth, even though by the selected wavelength. Advantages of satellite observations are as
that: Spatially uniform data acquisition and repeatability. Longer time data storage (years from several
to a decade) of satellite images makes it possible togenerate the proxy dataset of climatology (rainfall,
vegetation activity, land surface thermal status, andso on).
Fortunately, TRMM (Tropical Rainfall Measurement Mission) works beyond its initial life-time andprovide more than seven year’s data. Thus maximum utilize of such dataset with other long-term
measured dataset such as AVHRR (Advanced VeryHigh Radiometer) provide and indicate global climatology based on satellite observations only.
One of the targets in LAPS project is: ‘Is wet anddry boarders influenced on the precipitation process
themselves?’. Thus, even though an annual bases,

the intercomparison of precipitation, vegetation, andthermal condition of the land surface is valid for better understandings of land-atmosphere’s interaction.
We addressed the objective of this study as that simply: Where is the boarder or edge of wet and dry?
This study also has an aspect of retrospective for theclassics of climatology (e.g., Holdledge’s or Budyko’s
climate classifications) with new technologies.

2. Dataset and Methodology
TRMM: We used the level2 products (corrected signals into physical variables and estimated or retrieved
as rainrate by the algorithms) for PR (Precipitation
Radar; 2A25) and TMI (TRMM Microwave Imager;
2A12). Even though the monthly products (level3)
also available, we averaged and aggregated from allorbits data into monthly surface-rainrate grided products over the globe. Used versions of both are version
5 (currently version 6 released). Averaging period isfrom 1998 to 2003. Spatial resolution is 0.2 degree inlatitude and longitude.
PAL (pathfinder AVHRR land): PAL was provided from GSFC (Smith et al., 1997). We used 8-km
10 days composite dataset. Data themselves available
from 1981 to 2001, but to account for systematic bias
and/or trend due to sensor changes, we used only
from 1995 to 1999 (NOAA-14). Used channels are:

Annual Rainfall (98-03) observed by PR
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Original spatial resolution is 8 km, but aggregatedinto 0.1 or 0.2 degree.

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• After that, we estimate the index as a ratio.

2000

0

• As a numerator, an additional threshold of morethan 35 o C in Ts, based on the threshold of water stressed surface over vegetated surface in Nemani and Running (1997; Notes that in the original meaning is the threshold of energy limited
or water limited for the growth of vegetation),

120˚W

1000

00
20
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00

0
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180˚

2000

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25

25
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40˚N

500

• As a denominator, number of count for the timeseries of PAL at the same geographycal location
were checked with thresholds of more than 0.1
in NDVI (here is the identification of vegetatedsurface or not) and of more than 5 o C in Ts,
based on the Kira’s warm index.

several methods including Morton’s method. Here weused calculated results of Kondoh (1984) with Mortons method as water deficit. Should be noted thatoriginal in-situ data was old that compare with theother satellites dataset (TRMM, PAL).

2000

NDVI, channels 4 and 5 (thermal-IR). Surface temperature (Ts) was estimated by the technique of split
windows of Price’s classical formula.
Surface thermal stress index over the vegetated surface, defined here, is based on the thresholds as follows:

00

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[mm/year]
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Figure 2: Global distribution of annual rainfall estimated from TRMM-PR (upper). Averaging period
is from 1998 to 2003. spatial resolution is 0.2 degree both in latitude and longitude. Lower represents
same one but zoom up over LAPS target area.
Annual Rainfall (98-03) observed by TMI
60˚E

120˚E

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2000

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[mm/year]
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40˚S

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Figure 1: Schematic diagram for the concept of complementary relationship between potential and actual evapotranspiration (After Kondoh et al., on website1 )

30˚N

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Water deficit: Water deficit is the term based on therelationship between potential evaporation and actual
evapotranspiration (Figure 1). In the annual bases,
if the surface condition becomes drier, potential evaporation increased due to the atmospheric request, but
actual evapotranspiration reduces by the lack of evaporative water (soil or water in vegetation, and so on).
Such relationship was defined as “complementary relationship” at the field of hydrology. Applied thisrelationship, Morton (1978; 1983) developed formulae for the estimation of actual (areal) evapotranspiration from potential evaporation. Kondoh (1984)
demonstrated the geographical characteristics of actual evapotranspiration over monsoon Asia by the1 http://dbx.cr.chiba-u.jp/gdes/Globaldatasets/HydroChara/

0

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[mm/year]
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Figure 3: Same as Figure 2, but for TRMM-TMI.

3. Results and Discussion
3.1. Intercomparison of annual rainfall between PR and TMI
Figures 2 and 3 show the annual rainfall distributions over a globe and LAPS target region. BothPR and TMI seems similar over the globe (uppersof both), however zoom up LAPS region (lower), PRs
precipitation map seems naturally, especially wet anddry boarders (Huaihe river itself is the boarder ofthem) based on our understandings (e.g., Kondoh etal., 1998). Focus on that of TMI (Figure 3), retrieved algorithm differs by surface condition which
was switched by open sea, coastal and land.

75˚E

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[Unit: mm/year]

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Epotential- Eact

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[mm/year]
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Figure 4: Annual rainfall distribution over monsoon Asia (gray image with contour). Circles represents thewater deficit estimated in Kondoh (1994). The relative size of circles indicates amount of water deficit.
30˚W

45˚N

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15˚S
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[Freq.]
1.0

Figure 5: Geographical map of surface temperature stress over the “vegetated surface” derived from PAL.
Thresholds are: more than 0.1 in NDVI and more than 5 o C in surface temperature as denominators, inaddition, with conditions of more than 35 o C for numerators. Should be noted that non-vegetated surface (lessthan 0.1 in NDVI) also colored as white (zero).
Overestimation is found over the coastal region
and underestimation is confirmed on the Yanzi River(maybe Yanzi River was categorized as coastal). Furuzawa et al. (2005) was conducted intercomparisonof the product of TMI with that by PR, in addition,version 6 was greatly improved its quality in TMI
rainrate. However, direct measurements (PR) is suitable for this study. We used PR dataset only for thefurther intercomparisons.
3.2. Overlay the water deficit on the annal
rainfall map
Figure 4 shows the overlay of water deficit on
the map of annal rainfall derived from TRMM-PR.
Asanuma et al. (2004) showed long-term trend of
pan-evaporation (as a proxy of potential evaporation), over Japan. Japan is wet land compare to othercountries over monsoon Asia. As an evidence, water
deficit is (size of circles) small on Japan. To checkbriefly, large water deficits are confirmed over India
and Thailand. Both two countries deforestation already finished due to artificial land cover changes.

Thus, as a first approximation, land cover changes
from forest to crop field influence on the value of water
deficit. However, compare to the value of water deficit
at the approximately same amount of precipitation,over Tropic (e.g., on the Marine Continent) and subTropics (e.g., Taiwan) have different features. Such
differences implicitly represent that the manner orcharacteristics of rainfall (e.g., Hirose and Nakamura,
2004; 2005) and convective activities (e.g., Ohsawa etal., 2001; from diurnal to seasonal in time, from small
isolated convective or associated frontal/could clusters in space) would be also influenced on the valueof water deficit.
3.3. Temperature stress index over vegetatedsurface
Figure 5 shows a global map of temperature stressindex (frequency of more than 35 o C over vegetatedsurface), Figure 6 displays zoom up image over
LAPS region. Compare to upper of Figure 1, Figure 5 seems to the inverse map. Especially Sahelregion over Africa Continent is water is control fac-

tor for the vegetation growth. To say other words, if
vegetation activities increased over Sahel, it is always
under water stress. To see the other regions, over India, North Australia, and Thailand are close to 1 (always more than 35 o C). Over temperate regions, frequencies are reduced maybe due to large amplitude in
air temperature (low temperature in winter). Focus
on the LAPS target region, such simple index represented well where is stressed or not. But comparewith rainfall map, here is more complex functions of
topography, land use/cover and human activities.
NDVI > 0.1, Ts_min > 5, Ts_max > 35
105˚E

5

0.3

4

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[Freq.]
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0.1

This study tackled to interpret the boarder or edgeof wet and dry by, the several satellites dataset onlywith annual bases. Results are summarized as follow:• Intercomparison of annual rainfall between PR
and TMI seems similar over the globe, but TMI
is not suitable in LAPS region.
• An overlay the water deficit on annal rainfall
makes us to interpret that both land cover difference (forest or crop field) and the manner of precipitation and convective activities process (fromdiurnal to seasonal in time series) influenced on
the value of water deficit.

0.1

0.
1

35˚N

130˚E
40˚N

1

5
0.
0.2

0.

110˚E

4. Concluding remarks

3

0.5
0.4

0.

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100˚E
40˚N

sistances as a diagnostic methodology.

0.2

0.3

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0.5

Figure 6: Same as Figure 5, but zoom up LAPS
target area.
3.4. Rainfall - Vegetation Index (NDVI) - Surface Stress Index’s interaction (RGB composite image)
Figures 7 and 8 show the composite images assigned for rainfall map derived from TRMM-PR as
blue, annal NDVI (normalized difference vegetationindex) as green and surface stress index defined before as red. Such composition allow us integrated interruptions of the three components, i.e. input water- vegetation - surface thermal condition (wet or dry).
Colors change, for example, merged colors of green
and red without blue appears as a kind of yellow. In
the case of merging blue and red, purple would be
generated. To see the Figure 7, Sahel region coloredas red with changes red - green - blue, from edgesof Sahara to Equator. Such colors changes represent
rainfall gradation (i.e., water) is dominant factor todecide the presence of vegetation and boarder of wet
and dry.
However, most of regions are not to easy to explain such as Sahel region. Especially over monsoon
Asia (Figure 8). Such difficulties for the interpretations with three components, largely caused by the topography (both the nature of rainfall and landscape)and presence of the human activities (e.g., land coverchanges), we believe. Satellite observations allows us
to recognize that nature is complex, but can display
with spatially uniform dataset. We think that thecomposite image of several components is one of as-• Surface temperature stress index, defined in thisstudy, over vegetated surface is seems as inverse
map of precipitation, but over monsoon Asia,
land cover difference was dominant.
• RGB composite image (rainfall, vegetation, land
surface thermal condition) is one of diagnostic
methodologies to interpret where is the boarderof wet and dry, but such boarders are complex
functions among meteorology, hydrology and social sciences (politics and management of land).
Now is on the way to interpret and re-producingof the control factors for the vegetation, i.e., radiation - water - temperature (energy) (Nemani et al.,2003). Maximum utilization of satellite observations
data has a possibility to explain above, we do believe.
Acknowledgements: This research was not only supported by CREST/LAPS, but also by a grant from the
Ministry of Education, Culture, Sport, Science and Technology (MEXT), Japan, Grant-in-Aid for Young Scientists (A), 15680021, 2003. All figures were constructedwith the help of GMT System (Wessel and Smith, 1991).
In addition, we also appreciated activities of other GNU
open sources and distributions of Linux operating system(Vine Linux, CentOS and FedoraT M Core projects).

References
Asanuma, J., H. Kamimera and M. Lu(2004): Pan evaporation trends in Japan and its relevance to thevariability of the hydrological cycle. Tenki, 51 (9),667-678. (in Japanese with English abstract).
Furuzawa, Akimoto F. and K. Nakamura(2005): Differences of rainfall estimates over land by TropicalRainfall Measuring Mission (TRMM) Precipitation
Radar and TRMM Microwave Imager (TMI) - Dependence on storm height.J. Appl. Meteor., 44(3),367-383.
Hirose, M. and K. Nakamura(2004): Spatiotemporal variation of the vertical gradient of rainfall rate observed by the TRMM Precipitation Radar. J. Climate, 17 (17), 3378-3397.

120 W

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60 W

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60 E

90 E

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150 E

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30 N

20 N

20 N

10 N

10 N

0

0

10 S

10 S

20 S

20 S

30 S

30 S

120 W

90 W

60 W

0

0

30 W

500

0.0

0.1

0.0

30 E

60 E

1000

0.2

0.3

0.2

120 E

0.7

P [mm/year]
2000
annual NDVI
0.8
Ts Stress Index
1.0

1500

0.4

0.4

90 E

0.5

0.6

0.6

0.8

150 E

Figure 7: Global map of Red-Green-Blue composite images. Blue is assigned for the annual precipitation
derived from TRMM-PR, green color is allocated to the annual NDVI averaged from the dataset of PAL, andred is amplitude of the surface temperature stress index over ”vegetated” surface estimated from the time seriesof PAL. See the text for the determination of stress index. All images were aggregated with 0.2o x 0.2o . Gray
areas represent the grids that any pixels appeared with more than 0.1 in 10 days NDVI (stress index overvegetated surface can not estimated.
70 E

80 E

90 E

100 E

110 E

120 E

130 E

140 E

35 N

35 N

30 N

30 N

25 N

25 N

20 N

20 N

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15 N

10 N

10 N

5N

5N

0

0

5S
70 E

80 E

90 E

0
0.0
0.0

100 E

500
0.1

0.2
0.2

110 E

1000
0.3

0.4

0.4

120 E

130 E

1500
0.5
0.6

0.6

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P [mm/year]
2000
annual NDVI
0.8
Ts Stress Index
1.0

Figure 8: Same as Figure 7, but zoom up monsoon Asia. Spatial resolution is same as above.
Hirose, M. and K. Nakamura(2005): Spatial and diurnal
variation of precipitation systems over Asia observed by TRMM Precipitation Radar. J. Geophy.
Res., 110, D05106, doi: 10.1029/2004JD004815.
Kondoh, A.(1994): Comparison of the evapotranspirations in Monsoon Asia estimated from different
methods. Hydrology (J. Japan Asso. Hydrol. Sci.),
24 (1), 11-30. (in Japanese with English abstract).
Kondoh, A., A. Higuchi, S. Kishi, T. Fukuzono and J. Li(1998): The use of multi-temporal NOAA/AVHRR
data to monitor surface moisture status in the
Huaihe River Basin, China. Adv. Space Res., 22,645-654.
Morton, F.I.(1978): Estimating evapotranspiration frompotential evaporation: Practicality of an iconoclastic approach. J. Hydrol., 38, 1-32.
Morton, F.I.(1983): Operational estimates of areal evapotranspiration and their significance to the scienceand practice of hydrology. J. Hydorol., 66, 1-76.
Nemani, R.R. and S. Running(1997): Land cover charac-terization using red, near-IR, and thermal-IR datafrom NOAA/AVHRR. Ecological Applications, 7,79-90.
Nemani, R.R., C.D. Keeling, H. Hashimoto, W.M. Jolly,
S.C. Piper, C.J. Tucker, R.B. Myneni and S.W.
Running (2003):
Climate-driven increases in
global terrestrial net primary production from1982 to 1999. Science, 300, 1560-1563.
Ohsawa, T., H. Ueda, T. Hayashi, A. WatanabeandJ.
Matsumoto (2001): Diurnal variations of convective activity and rainfall in Tropical Asia. J.
Meteor. Soc. Japan, 79 (1B), 333-352.
Smith, P.M., S.N.V. Kalluri, S.D. Price and R. DeFries(1997): The NOAA/NASA pathfinder AVHRR
8-km land data set. Photogrammetric Engineeringand Remote Sensing, 63, 12-32.
Wessel, P. and W.H.F. Smith(1991): Free software helps
map and display data. EOS Trans. American Geophy. Union, 72 (441), 445-446.