Estimation of land use and water usage in the huaihe river basinfrom field survey and satellite analysis∗O. KOZAN, K. YAMADA, K. TANAKA, S. IKEBUCHI(Water Resources Research Center, DPRI, Kyoto University,)* Water Resources Research Center, DPRI, Kyoto University Gokanosyo, Uji 611-0011, Japan.
e-mail: osamu@wrcs.dpri.kyoto-u.ac.jp

Abstract
Keyword: landuse/landcover; crop type; NDVI; NDWI; VSW index
1.

Introduction

GAME-Huaihe river Basin EXperiment (HUBEX) is
one of four intensive ﬁeld-based regional experimentsof the GEWEX Asian Monsoon Experiment (GAME).
Huaihe river basin has been suﬀering from ﬂood disaster caused by the Meiyu front for a long time. Therefore
land-atmosphere interaction and its role in the formationof meso-scale precipitation systems are some of the mostimportant research targets.
The energy and water budget of Huaihe River Basinis calculated using land-surface scheme for understanding these targets. Landuse dataset which is based on theUSGS global landuse/landcover classiﬁcation is used previous study, but the accuracy of all global classiﬁcation
are not enough in this area. For example, according tothis dataset, more than 60% of the study area is occupied
by paddy ﬁeld, but this value is obviously higher thanactual condition.
Besides, not only the crop type but also the cropping
cycle must be speciﬁed in good accuracy for the detailed
wateruse and hydrological analysis. In this sense, global
landcover datasets have not been validated well enough,and there is some problem in its direct use. In this study,
new landuse classiﬁcation, especially detailed crop pattern, in Huaihe River Basin is produced from satellite derived vegetation indices (30sec resolution) and surface meteorological data. Agricultural statistic data of 187 prefectures in Henan Province are used for validation.

2.

Satellite derived vegetation indices

SPOT-VEGETATION 10 day composite data, whichhas 30 minutes (about 1 km) resolution, is used for 1year (from Apr. 1998 to Mar. 1999 ; 36 periods). Twovegetation indices are made from three sensor data (RED,NIR, SWIR).
1) NDVI(Normalized Diﬀerence vegetation index)
2) NDWI(Normalized Diﬀerence water index)
NDVI has been widely used for remote sensing ofvegetation for many years.
This index uses radiances or reﬂectance from red channel around 0.66µm
and near-IR channel around 0.86µm. NDWI is proposed for remote sensing of vegetation liquid water(Gao [2]). That is deﬁned as (ρ(0.86µm)-ρ(1.24µm))/(ρ(0.86µm)+ρ(1.24µm)), where ρ represents the radiance in reﬂectance units. In this study, SWIR (1.581.75µm) sensor data is used, instead of 1.24µm sensor.
The features of spectral refrectance of them are dominatedby water absorption.
Then the BISE (The best index slope extraction)
method (Viovy and Arino [3]) is applied for reducing

cloudy noise in NDVI and NDWI time-series (see Fig.1).
This method is based on that plant growth is graduallyfor a certain period. In this study, 30 days is adopted asthat period. On the other hand, crop rotation (harvestand planting) causes drastic change on the land surface
for less than 30 days. For the above reason, that period
is changed to 20 days for adjusting to the sudden surfacechange during crop rotation period.

growing period

Fig. 1 : Time series of NDVI data at E116 deg, N33 deg(Dotted line；raw data，Full line；applied BISE method)

3. LANDUSE CLASSIFICATION
3.1. NDVI time seriesFirstly, all mesh (1320 × 600, 30 seconds resolution) is
categorized into three group (see Fig.2) using the accumulated NDVI value and pattern of NDVI time series.
1) nonvegetation area (city, soil, water)
2) natural vegetation area (evergreen forest, deciduousforest)
3) agricultural area (paddy ﬁeld, farmland etc.)
If one year accumulated NDVI value of each mesh isless than 10.5, that mesh is determined to nonvegetation
area. Then these meshes are classiﬁed into three categories. One is water area, which is easy to be separated
from other two categories. If the minimum NDVI valueof each mesh for one year is lower than 0, that mesh isclassiﬁed into water area. On the other hand, the small
water area and other two landuse (city, soil) are diﬃcult
to be classiﬁed. In that case, not only satellite data butalso other landuse data (for example USGS global landuse/landcover) is used for detecting water area.
Natural vegetation area includes two categories. If one
year accumulated NDVI value of each mesh is more than19, that mesh is classiﬁed into evergreen forest. If one
year accumlated NDVI value is less than 19 and vegetative
period (whose NDVI value more than 0.4) are more than
21 periods, that mesh is classiﬁed into deciduous forest.

Table 1: Landcover/landuse type and classiﬁcation index
landcover/use typeevergreen forest
deciduous forestCityWater areaWheat ＋ Rice
Wheat ＋ Maize
Wheat ＋ Soybean
Wheat ＋ Penuts
Wheat ＋ Cotton
Wheat ＋ Other cropclassification index
1 year accumulated NDVI value is more than 19.0
NDVI value of 21 period is more than 0.4
1year accumulated NDVI value is less than 10.5
1 year accumulated NDVI value is less than 10.5
more than 1 period NDVI is less than 0
degree-days temperature is 1900〜2400
NDWI value is 0.2 from 13 to 17 period
degree-days temperature is 1400〜1700，0 ＜ L1/L2 ＜ 1.2
degree-days temperature is 1400〜1700，0.8 ＜ L1/L2 ＜ 3.0
degree-days temperature is 1700〜1900
degree-days temperature is 1900〜2400
other condition

Above two groups are determined easily. On the contrary,other area which includes many kinds of agricultural area
is diﬃcult. In this study, the characteristics of agricultural
cycle are used for classiﬁcation.

3.2.

Cropland classification

Most of agricultural area is cropland, and many kindsof crop are grown in the Huaihe river basin. The dominant
cropland in south part of the Huaihe river basin is paddy
ﬁeld, while that in other part is farmland. All agriculturalarea is classiﬁed broadly into 3 types of cropland, ﬁrst isfarmland (winter wheat + summer crop), second is paddy
ﬁeld (1 cropping rice) and third is farm + paddy ﬁeld
(winter wheat + rice). Winter wheat is the dominantcrop in winter-spring season. For example, more than 98% of agricultural area in the Hunan province is the winterwheat farm in winter-spring season.
Paddy ﬁeld is classiﬁed using NDWI data. The reﬂectance of SWIR sensor at water is low as well as othertwo sensors (RED and NIR), but it is higher than otherat soil. Therefore, if the lowest NDWI value for crop rotation period is less than 0.2, that mesh is judged as paddyﬁeld.

3.3.

Degree-days method

The summer growing period can be estimated fromNDVI time series (see Fig.1). The deﬁnition of growing period is the diﬀerence between the planting season(when the NDVI time series begin to rise continuously atspring-summer season) and the harvesting season (whenthe NDVI time series drop more than 0.1 and the droppedvalue is less than 0.5 at autumn season).
The growing period is an useful index for crop classiﬁcation, but it is diﬀerent at each area and crop. In thisstudy, the degree-days temperature, as deﬁned Eq. (1),is calculated using daily mean air temperature. If thedaily mean air temperature is more than 10 degree, it isintegrated for the growing period (see Fig.1). The accumulated temperature of each crop is determined as Table1
from local agricultural manual (Chu. et al [4]).
The degree-days temperature : k day’s air temperature;
Tk ，Standard temperature (10 degree); Tb ，planting period; a，harvesting period; b

Tk > 10 degree,b

(Tk − Tb )

(1)

k=a

Rice and cotton, maize and soybean belong to the same
temperature range. Rice and cotton are separated using

Fig. 2 : Cropland classiﬁcation of Huaihe river basin

Fig. 3 : Percentage of cropland in Hunan province

NDWI index as above. Maize and soybean are separatedusing the curve of NDVI time series. The NDVI curve ofmaize is more rapidly than that of soybean. In this study,
L1 is deﬁned as the diﬀerence between the value of peak
period and that of 40 days before from the peak, L2 isdeﬁned as the diﬀerence between that of 20 days before
and that of 40 days before. If L1/L2 is lager than 1.2,
that mesh is soybean farm. If L1/L2 is smaller than 0.8,
that mesh is maize farm. If L1/L2 is between 0.8 and 1.2,
that mesh is mixed farm.

4. VALIDATION
4.1. Agricultural statistics
There are many kinds of agricultural statistics data inthis region. In particular, there is detailed data in Hunan
province that located on the west part of the Huaihe river
basin. There are 187 prefectures in Hunan province, andeach prefecture has land area data (see Fig.3), croplandarea data (e.g. rice, wheat, soybean, maze, cotton), crop
yields data, irrigated cropland data and so on. In thisstudy, statistics land area and cropland area data are usedfor validation. Firstly, the statistical land area data ofComparison CroplandComparison Landarea

Comparison Paddyfield

3000

4500of farmland is 0.766 and that of paddy ﬁeld is 1.545, that
results indicate the decreasing eﬀect of paddy ﬁeld is lager
than that of farmland.

2000

4000
2500

3000
2500
2000

Mesh data(km2)

1500
Mesh data(km2)

Mesh data(km2)

3500
2000

1500

1000

Y=1.0011x-3.93
CC=0.9873
RMSE=115.87(km2)500

Y=1.3057x+235.46
CC=0.8037
RMSE=516.77(km2)500000500 1000 1500 2000 2500 3000 3500 4000 4500
Landarea data(km2)

500

1000
1500
2000
Landarea data(km2)

Comparison Maizefarm

2500

3000

01500

1500
Mesh data(km2)

1500
Mesh data(km2)

2000

1000

Y=1.1171x+57.11
CC=0.6917
RMSE=143.93(km2)

50005001000
Landarea data(km2)

1500

2000

(2)

Ragr = (Rmax − Rmin ) × β

2000

(3)

1000

Y=1.1772x+63.10
CC=0.6150
RMSE=146.65(km2)

5000001500

Landarea data(km2)2000

Y=1.3468x-47.35
CC=0.7183
RMSE=166.12(km2)

1000

αV
αV + S + W

Comparison Nutfarm

2000

500

500

Comparison Cottonfarm

1000

R=

Y=1.2663x-4.16
CC=0.9082
RMSE=70.90(km2)

500

00

Mesh data(km2)

1000

1000

1500

0500

1000
1500
Landarea data(km2)

2000

0500

1000
1500
Landarea data(km2)

2000

Fig. 4 : Scatterplot of statistics data and estimated meshdata at each prefectures (upper low ; land area, cropland,rice), (lower low ; maize, cotton, nuts)

4.2.

Cropland occupied ratio

Vegetation-Soil-Water (VSW) index is devised for monitoring land cover conditions (Yamagata et al. [5]). The
VSW index is deﬁned as a natural extension of PV1 (seeFig.5) for monitoring not only vegetation but also soil andwater conditions as well. The deﬁnition of VSW index
is shown in Fig.7, which shows the relationship between
VSW indices and the end member triangle on a NIR-Red
Scatter Plot.
The vegetation ratio is estimated using the triangle andEq. (2), PV means vegetation index, PW means water
index and PS means soil index in Fig.7. The R of Eq.
(2) indicates the vegetation ratio, which include naturaland agricultural vegetation. Natural vegetation changes
little for summer season, on the other hand agriculturalvegetation drops rapidly at crop rotation period. The croprotation period in this area is around May or June.
Consequently, the maximum and minimum V ratio aresearched from may to august for separating agriculturalfrom natural vegetation. The maximum ratio indicatesboth of them, and the minimum ratio indicates only natural vegetation. Therefore using the diﬀerence between
Rmax and Rmin (see Eq. (2)), the agricultural V ratio(Ragr ) of each mesh is estimated. The value of ’α’ inEq. (2) is determined as 1.298 for maximizing the correlation coeﬃcient around regression line of scatter plot(see Fig.6). Then, the ’β’ in Eq. (3) value is determined
to 1.160 as equalizing the amount of estimated croplandarea to the statistical one. In case of farmland and paddy
area, ’α’ is used as the same value of cropland (1.298), and’β’ is calculated individually as equalizing the amount ofestimated area to the statistical one (see Fig.6). The ’β’Fig. 5 : Relationship in a NIR-Red scatter plot (left Fig.;
PV1 and the soil line, right Fig.; VSW index and the endmember traingle)
Comparison CroplandComparison Paddy

Comparison Farmlan
d

3000

2000

2000

1500

1500

Mesh data(km2)

2000

1500

Mesh data(km2)2500

Mesh data(km2)

prefectures with the land area estimated from 1km meshdata (which is made from 2km resolution digital map)
are compared. The accuracy of estimated land area isrelatively well, but other classiﬁed data tend to be overestimated (see Fig.4).
The most considerable reason of over estimate is that 1
km is not enough in resolution for cropland categorise andcrop pattern estimation. In other words, the whole areaof each mesh classiﬁed into cropland do not correspondto the actual one. In this study, VSW index is used for
calculation of the cropland occupied ratio of each mesh.

1000

1000

1000

Y=1.0344X-20.20
CC=0.8234
RMSE=216.76(km2)
beta=1.160

500

00500

1000
1500
2000
Landarea data(km2)

2500

3000

Y=0.8347X+61.76
CC=0.8228
RMSE=144.69(km2)
beta=0.766500

Y=1.1817X-7.45
CC=0.9102
RMSE=62.47(km2)
beta=1.545

5000005001000
1500
Landarea data(km2)

2000

0500

1000

1500

2000

Landarea data(km2)

Fig. 6 : Scatterplot of statistics and degital-mesh data(cropland, farmland, paddy ﬁeld)

5. Water budget analysis
5.1. Shiguan river basin
Shiguan river basin is composed of 3 areas. 2 areas arelocated on the upstream of 2 water reservoirs (Meishan,Nianyushan), and another area is located on the downstream. Since all irrigation data exists in the downstream
region, it is deﬁned as the target area for this analysis.
Landuse data was validated from agricultural statistics data, then estimated water usage are validated fromthe water budget analysis in Shiguan river basin that located on the south part of HUBEX (shaded area of Fig.7).
There are many kinds of hydrological data (e.g. discharge,irrigation and rain) in this basin and its catchment area
is 5930km2 . Using landuse and cropland occupied ratio
data (see Fig.8, Fig.9), land surface scheme is applied tothis basin.

Fig. 7 : Location of Shiguan river basin in HUBEX

84.7mm (in average unit) irrigation water is estimatedin this simulation, and this value equivalent to 0.2139 billion t water. Judging from irrigation data, estimated irrigation water is considered reasonable and proper.

Fig. 8 : Landuse of Shiguan river basin

Fig. 11 : Estimated irrigation water in the north Shiguan

6.

Fig. 9 : Cropland occupied ratio in the north Shiguan

5.2.

Water budget calculation

In this study, hourly meteorological mesh data (sphere:E115.0-116.2，N31.0-32.4) is created in 30 seconds (about1.1km) resolution using the same method of previous
study (Kozan. et al [1]). Using irrigation rule (see [1]),hourly water and energy ﬂux are calculated by land surface scheme (SiBUC) for 123 days (1/5/1998-31/8/1998).
Fig.10 shows an accumulated value of water budget
components (rainfall, evapotranspiration, and runoﬀ) inthe downstream region (catchment area is 2.525 thousandkm2 ). Accumlated rainfall of this region for 123 days is738mm (in average unit), evapotranspiration is 408.9mm,runoﬀ is 238.2mm and irrigation water is 84.7mm.
Total Water Budget for upstream of Bengbu222 (121330km^2)
800
738.4
700
600

rain(obs)

(mm)

500
408.9

400
evap(SiBUC)
300

238.2
200

runoff(SiBUC)
84.7

100
irrigation in0-100
0500

1000

1500

2000

2500

1hour (5/1 => 8/31)

Fig. 10 : Time series of water budget in the downstream
area of Shiguan river basin (accumulated rainfall, evapotranspiration, runoﬀ)

5.3.

Irrigation water validation

There are 9 irrigation canals in the Shiguan river basin.
All of 2 canals’ water (0.01828 billion t) is used in thedownstream region and most of other 2 canals’ water(0.32899 billion t) is also used.

CONCLUSION

In this study, new landuse data is produced from satellite and surface meteorological data, and it is validated
using agricultural statistic data. Then VSW method isapplied for improvement of over estimation. It is conﬁrmed that this classiﬁcation and VSW method are available. Furthermore, this landuse data is applied to theland surface scheme, and it is performed for water budget
calculation in the Shiguan river basin. Availability of thisdata for the hydrological study is also conﬁrmed.

ACKNOWLEDGEMENTS
The authors would like to thank the following organizations. SPOT satellite data used in this study wasobtained by the SPOT VEGETATION Programme. And
the surface meteorogical data was obtained from NCDC.
The local ﬁeld survey and observation were supported byJST/CREST and RIHN.

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