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Obtaining Spatial and Temporal Vegetation Data from Landsat MSS and AVHRR/NOAA Satellite

Images for a Hydrologic Model Zhangshi Yin and T.H. Lee Williams

Abstract This research describes how to obtain spatial and temporal vegetation data over a watershed from satellite images for use in a hydrologic model. Spatial vegetation data were obtained by classifying Landsat Multispectral Scanner [MSS) images into vegetation types. Temporal vegetation data were obtained by a series of Normalized Difference Vegetation In- dex [NDVI) images from Advanced Very High Resolution Ra- diometer/National Oceanic and Atmospheric Administration [AVHRR/NOAA) satellite images. An empirical vegetation model was developed to relate vegetation parameter Leaf Area Index (LAI) to the NDVI data.

The obtained spatial and temporal vegetation data were used in a hydrologic model to model hydrologic processes of the Mud Creek watershed in south-central Oklahoma. The research results show that the vegetation data obtained from the satellite imagery are more realistic than those obtained from a crop growth model. The accuracy of modeled monthly and annual runoff using vegetation data from the satellite images is improved by about 13 and 5 percent, respectively, compared with the hydrology using the crop growth model.

Introduction Vegetation is an important and dynamic element in the hydrologic cycle. It affects hydrologic processes such as inter- ception, evapotranspiration, soil water content, and runoff volume. Water resource management, flood prediction, and climate change studies all need quantitative vegetation data. Because of the spatial heterogeneity and seasonal changes in vegetation, spatial and temporal vegetation data are needed to model the hydrology in watersheds.

It is difficult to parameterize the spatial and temporal characteristics of vegetation using a ground survey. Moreo- ver, ground surveys are tedious, costly, and time consuming for large areas. With the development of satellite remote sensing, the large and frequent image coverage provides a new approach to acquisition of vegetation data. The two-di- mensional vegetation data obtained from satellite imagery are suitable for a spatially based hydrologic model. Some researchers have shown the value of obtaining vegetation data from satellite imagery (Tucker, 1980; Holben, 1980; Wardley and Curran, 1984; Justice and Townshend, 1985; Gupta, 1993). Satellite data have been used in vegetation detection, mapping, crop growth monitoring, agricultural yield estimation, ecosystem studies, and evapotranspiration. However, only a few researchers have related the classified land covers to maximum leaf area index or runoff curve numbers for hy-

Z. Yin is with the Department of Geography and T.H.L. Wil- liams is with the College of Geosciences, both at the Univer- sity of Oklahoma, Norman, OK 73019.

drologic modeling (Duchon et al., 1992; Ragan and Jackson, 1980; James and Kim, 1990).

Currently, there are several satellite systems that can be used to collect vegetation data. These can be categorized into two types of systems. The Landsat and SPOT systems provide high spatial resolution (10, 30, and 80 m) and low temporal frequency imagery. These high spatial resolution satellite sensors have long repeat cycles of 16 days for Landsat and 26 days for SPOT. In the case of cloud cover, the possible in- terval of usable coverage increases. Their time frequency of observation is not adequate to monitor phenological changes. The A V W N O A A system provides low spatial resolution (1.1 km) and high temporal frequency imagery (daily). The inex- pensive daily AVHRR images provide an opportunity for the temporal study of vegetation. AVHRR images have been widely used for the study of temporal vegetation change in various fields such as global vegetation change, environmental studies, and crop growth monitoring (Justice and Town- shend, 1985; Justice, 1986; Groten, 1993; Gupta, 1993).

The use of satellite images for vegetation study is based on different reflectances of near infrared and visible bands of vegetation. Some mathematical combinations of the near infrared and visible bands, called vegetation indices, are better indicators of vegetation. In this study, NDVI from the AVHRR images is used to parameterize vegetation. The NDm is the difference of near infrared [AVHRR band 2) and visible [AVHRR band 1) refltctance values divided by total reflectance: i.e.,

Near infrared band - Visible band NDVI =

Near infrared band + Visible band

The high reflectance of the near-infrared band and low reflectance of the red band on vegetation produce a positive NDVI. The low reflectance of the near-infrared band and high reflectance of the red band on cloud, snow, and water produce a negative NDVI. However, in hydrologic studies, the vegetation parameter generally used is LAI, which is the ratio of total leaf area to its covered ground area.

This research attempts to combine the two types of satellite imagery and uses their different advantages to obtain quantitative spatial and temporal vegetation information for hydrologic models. The first part of the research pararneter- izes spatial and temporal quantitative vegetation data from the MSS and AVHRR images. The spatial distribution of vegetation is obtained from the MSS images by unsupervised clas-

Photogrammetric Engineering & Remote Sensing, Vol. 63, No. 1, January 1997, pp. 69-77.

0099-1112/97/6301-069$3.00/0 O 1997 American Society for Photogrammetry

and Remote Sensing

sification. A series of NDVI derived from the AVHRR satellite images is used to parameterize vegetation temporal changes. An empirical LAI model is developed to obtain LA1 from the NDVI. The second part uses the obtained vegetation data in the hydrologic model Simulator for Water Resources in Rural Basins (SWRRB] in the Mud Creek watershed in Oklahoma. The accuracy of vegetation data obtained from satellite images and their impact on hydrologic processes are analyzed and evaluated. This study will provide a new way of parameterizing vegetation data for hydrologic modeling to improve the accuracy of hydrologic models in hydrologic prediction and water resource management.

Research Methods

Study Area The study area is the Mud Creek watershed in south central Oklahoma, comprising about 1482 square kilometres of Ste- phen, Jefferson, Carter, and Love Counties (Figure 1). The watershed is slightly rolling with slopes ranging from nearly level to 8 percent. The channel network in the watershed is dendritic and the main river channel is about 40 kilometres long with six major tributaries. The water is drained from the northwest to southeast, and there are no large reservoirs and lakes to affect surface runoff routing. There are four weather stations to measure solar radiation, temperature, and precipitation. The discharge of this watershed has been mon- itored daily at an outlet since 1960. The dominant hydrologic processes are rainfall and surface flow. The stream flows are mainly distributed in late spring and early fall. The average annual water yield of the last ten years is 384 mm.

The watershed climate is temperate and continental with temperatures ranging from -13" to 43°C. Annual precipitation ranges from 432 to 1372 mm, with an average of 838 mm. Generally, spring has high rain intensity. The summer is long, hot, and occasionally dry. Most evapotranspiration occurs during the growing season from May to October, and soil moisture content is depleted during that time. The smaller amounts of moisture received in summer are often removed by high temperature and dry wind. Rainfall increases in early fall and provides timely moisture for fall grains and pastures. The winter is short and mild.

The watershed lies in the central red bed plain charac- terized by red shale and sandstone. The soils formed on the red bed materials were influenced by prairie or savanna vegetation in a warm and humid climate. Most soils in this watershed have high available water capacity, moderate permeability, and good drainage.

The watershed is mainly used for cattle ranching, winter wheat, and summer crops. Natural vegetation and planted crops cover almost the entire watershed. Native woodland is mainly along tributary streams of Mud Creek. In the north- east, woodland trees grow on loamy to sandy uplands. The species of trees include blackjack oak (quercus marilandica), post oak (quercus obtusiloba), cottonwood (populus species), willow (salix species), hackberry (celtis species), elm (elmus species), pecan (carya illinoinensis), and black walnut (jug- lans regia). Rangeland, the prevailing land use, covers ap- proximately 60 percent of the watershed. Vegetation on rangeland consists principally of native grasses and shrubs valuable for grazing. The principal crops in the watershed include winter wheat, cotton, corn, peanuts, milo, alfalfa, and grain sorghum. Winter wheat, sorghum, oat, rye, and barley are sometimes used as pasture and hay production by cattle ranchers.

The earliest grasses emerge at the beginning of March. Deciduous forests leaf out between mid-March and mid- April. The vegetation on the woodland and rangeland begins to senesce in late fall. Summer crops such as milo and cot-

o p y r a n o m t t r r - + t h e r m o m e t e r k i l o m a f e r 0 r a i n g a g e A s t r e a m g a g e

0 1 0 - Figure 1. The location of the Mud Creek Water- shed, Oklahoma.

ton are planted from the beginning of April to May, depend- ing on the actual weather. They are harvested in early fall. Winter wheat, oats, and barley normally are planted between late September and early October. Winter wheat is harvested during late May and early June. Before harvest, most of the winter wheat is already senescent.

Hydrologic Model SWRRB The hydrologic model used in this study is the Simulator for Water Resources in Rural Basins (SWRRB). The SWRRB model was developed by the USDA ARS Water Quality Laboratory in Durant, Oklahoma (Arnold et al., 1990). It is a daily water balance model that needs input variables of daily precipitation, temperature, solar radiation, soil properties, and vegetation. The model can simulate hydrologic components of evapotranspiration, percolation, surface runoff, subsurface flow, and soil moisture (Figure 2).

In the SWRRB model, the vegetation parameter LA1 is needed to simulate hydrology, which is modeled by a crop growth model. The crop phenological development is based on growing days, daily accumulated heat units, potential biomass, water, and temperature stress. Annual crops grow from planting date to harvesting date or until the accumulated heat units are equal to the potential heat units for the crops. Perennial plants maintain their root systems throughout the year even though they may become dormant after frost.

Because the purpose of this study is to parameterize the vegetation variable LA1 from satellite images, the vegetation part of the SWRRB model was modified to use the LAI obtained from Landsat and AVHRR images. The LA1 is used to model evapotranspiration that affects soil moisture, infiltra- tion rate, and runoff (Arnold et al., 1990). Precipitation, temperature, solar radiation, discharge, soil, and terrain data in the Mud Creek watershed were collected from the Oklahoma Climatological Survey, United States Geological Survey

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-

uoo*orplrn-

f P I m t h p k d I -

t ' l F~gure 2. The flow chart of the SWRRB model simulation.

(USGS) Water Resources Division and United States Depart- ment of Agriculture (USDA) Soil Conservation Service.

The SWRRB model is a spatially distributed hydrologic model. The watershed is divided into 80- by 80-m grids to reflect the different characteristics of terrain, geology, soil, vegetation, and precipitation between grids. In the grids, all parameters are assumed spatially uniform. The hydrologic processes are simulated grid by grid over the whole watershed.

Spatial and Temporal Vegetation The vegetation data for the Mud Creek watershed were obtained from Landsat MSS and AVHRRINOAA satellite images. Two Landsat ~ s s images from path 28 and row 36 were used: 9 May 1990 and 2 1 July 1990, respectively. The spatial resolution of the MSS images is 80 m by 80 m. A total of 40 biweekly AVHRR images of the conterminous United States - 19 for 1990 from 2 March to 20 December and 2 1 for 1991 from 4 January to 26 December - were used. The spatial resolution of the AVHRR images is 1.1 km by 1.1 km.

Multi-date images were used to distinguish vegetation types because spectral signatures changed during the growing seasons. The two MSS images were registered to Universe Transverse Mercator (UTM) coordinates by a first-order linear matrix transformation. The grid cell size of the rectified images is 80 m by 80 m. The study watershed was then cut out using the Mud Creek watershed boundary (Plate 1).

The two MSS images were classified by unsupervised classification. In order to achieve the best land-cover result, a multi-ste~ classification was conducted. In the multi-ste~

implemented by overlay analysis. For example, if the land were vegetated in May but bare in July, it was inferred to be winter wheat. Similarly, if the land were bare in May but vegetated in July, it was treated as a summer crop.

The final classification result is illustrated in Plate 2. The spatial vegetation data from the MSS images (Table 1) are very close to that from the ground survey by the USDA Soil conservation Service (1982). According to the scs, the land use did not change much during those years. The vegetation classes from the Landsat MSS images were combined with temporal data from the AVHRR images to provide detailed spatial and temporal vegetation data for the SWRRB model.

The temporal vegetation data were obtained from the AVHRR images. The NDVI data from the AVHRR images were registered to UTM coordinates by linear rectification. The grid cell size of the rectified NDVI image is 80 by 80 m. The study area was cut from the registered image using the Mud Creek watershed boundary (Plate 3). All 40 NJJVI images for the two-year period 1990-1991 were processed in the same way.

A temporal series of NDVI values shows the temporal profiles of vegetation growth. The 40 NDVI images for 1990 and 1991 were used for parameterizing the vegetation temporal change. The area of one vegetation type from the classification of the MsS images was used to mask the same area of NDVI images. The average NDVI value of the vegetation type was recorded, and all 40 NDvI images were processed in the same way. The process was repeated for each vegetation type. A series of the mean values of consecutive NDVI images was plotted on a graph to show the temporal vegetation changes.

However, the temporal NDVI curves of the different vegetation types are not distinct because of mixed pixels. The mixed pixels are mixtures of different vegetation types because of the coarse spatial resolution. These mixed pixels cannot represent the temporal characteristics of individual vegetation types. In order to represent the actual temporal vegetation change, pure or homogeneous pixels of vegetation types must be selected. Homogeneous areas of woodland, rangeland, winter wheat, and summer crops larger than the 1-km resolution of the A= were selected by comparing the MSS images and their classification with a series of AVHRR NLIVI images. After pure pixels were selected, the NDW values of these homogeneous pixels were recorded from all consecutive NDVI images and used as representative curves of temporal change for the four major vegetation types (Figure 3). The pure NDVI temporal curves of the four vegetation types have distinct temporal characteristics that represent their phenologic changes.

LAI-NDVI Model The temporal NDVI values represent relative changes of seasonal vegetation rather than the amount of vegetation. In this study, the SWRRB model needs the quantitative vegetation parameter LAI instead of the relative vegetation data NDW. Therefore, an important problem is to establish the relationship between the NDVI and LAI. This research developed a

TABLE 1. THE VEGETATION COVER OF THE MUD CREEK WATERSHED FROM THE

LANDSAT MSS IMAGES AND FROM THE GROUND SURVEY classificaiion, the good classes were extracted and the ri- Land Cover Landsat MSS Ground Survey maining uncertain parts were classified again until the whole image was accurately classified. The different classes were Woodland 15.44% 14.68% assigned to different vegetation categories according to Range 52.74% 56.27% ground truth from a field trip to the watershed. Winter Wheat 16.39% 14.06%

The classification results for the two MSS images were Summer Crops 14.81% 14.15% Bare Land 0.57% 0.71% compared using the knowledge of vegetation phenology to Wat, 0.06% 0.13%

distinguish different vegetation types. The comparison was

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Plate 2. The spatial vegetation of the Mud Creek Water- shed from the Landsat Mss images.

Woodland Ranee linter Wbeat

Cro ,d

U N D S A T M S S IMAGE (July 21, 1990)

b ..-- cated "the NDVI are sensitive to relatively small changes in biomass and that changes in green biomass of less than 250 kg/ha dry matter can be detected by the satellite."

The second assumption is that the maximum NDVI values observed in the images correspond to the maximum LAI of the vegetation cover. This is also indicated by Justice (1986); i.e., "the maximum NDVI should correspond to the maximum amount of green vegetation present." The maxi-

Plate 3. The NDVI from NOAA AVHRR images of the Mud Creek Watershed in 1991.

(b) Plate 1. The Landsat MSS images (bands 4, 3, and 2 for the red, green, and blue channels) of the Mud Creek Wa- tershed on 9 May 1990 and 21 July 1990.

general model to relate the NDvI to LAI based on two assumptions.

The first assumption is that the relationship between LA1 and NDVI is linear. Many researchers have indicated that LA1 and NDVI have a linear relationship (Wiegand,l979; Tucker, 1980; Ajai, 1983; Wardley and Curran, 1984). They also have verified that NDVI is sensitive to biomass. Justice (1986) indi-

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J 1 82 163 244 325 406 487 568 649 130

days

Figure 3. The NDVI of vegetation types of the Mud Creek Watershed obtained from NOAA AVHRR images. The vegetation types are woodland (WL), rangeland (RL), winter wheat (WW), and summer crop (WC).

mum and minimum NDW were selected from the maximum and minimum values of the NDVI in 1 9 9 0 and 1 9 9 1 . The values of Maximum LA1 are obtained from the SWRRB model manual (Arnold et al., 1 9 9 0 ) . The maximum NDW values are assigned to maximum LA1 and the minimum NDVI are assigned to minimum LA1 (0).

Based on the above two assumptions, a general empiric LAI-NDVI model was developed to obtain LAI from NDW. The LAI-NDW model is expressed in the following equation:

LAI, = LAI,, * (NDW, - NDW,,) / ( N D h , - NDQ,) (2)

where max, min, and i are maximum, minimum, and biweekly NDVI image number, respectively.

The SWRRB model is run on daily time steps, but the NDW data are currently biweekly data. Based on an assumption of linear change in LA1 between AVHRR observation dates, daily LAI values for the entire year are obtained by linear interpolation of the biweekly LA1 (Figure 4).

Hydrology Simulation The SWRRB model simulates hydrologic processes based on the meteorology, vegetation, soil, and terrain variables for the watershed. After all these data were processed, they were input to the SWWRB model. Before the hydrologic simulation, a major procedure in the hydrologic simulation is calibrating the model. In this study, the parameters soil permeability and evapotranspiration rate were calibrated because they di- rectly affect runoff volume. The observed rainfall and runoff data in 1988 were used for calibration of the model. The procedure of calibration is to properly adjust soil permeability and evapotranspiration rate and make simulated runoff most close to the observed runoff. The calibrated model was vali- dated by the existing rainfall and runoff data in 1 9 8 9 . After the verification, the vegetation data from the crop growth model and satellite images and other data in 1 9 9 0 and 1 9 9 1 were input to the model to simulate the hydrology. At the end of simulation, the modeled hydrology and statistical data of the simulation were output for analysis and evaluation.

Results and Discussion

Vegetation Analysis The characteristics of temporal NDvI of woodland, rangeland, winter wheat, and summer crops are shown in Figure 3. The

woodland and rangeland are perennial vegetation and their growth depends on seasonal change, so they have similar curve trends. But the NDW magnitudes of woodland and rangeland are different because woodland generally has high biomass and rangeland has low biomass. The NDm curve of summer crops is a little later than those for woodland and rangeland because the planting dates are after April. At the beginning, the NDW curve of young summer crops is smaller than those for woodland and rangeland. After July, the NDVI curve of summer crops is in the middle and is distinct from woodland and rangeland because the biomass of mature summer crops is larger than rangeland and less than woodland. The winter wheat curve rises in early November and decreases after May in accordance with winter growth dates.

The temporal NDVI curves obtained from the AVHRR satellite images show a single large general curve in accordance with expectations but also have some small fluctuations. Fluctuaiions can be actual vegetation changes such as grazing, harvesting, and forest cutting. For example, the winter wheat on this watershed is grazed by cattle and the pasture land and hay land are often cut for forage, which would give short-term fluctuation in the ~ V I . So it is possible these factors cause the variation of the peaks and valleys of the LAI. But some other possible reasons for these fluctuations of the NDVI values also need to be considered.

First, the NDVI can be affected by vegetation photosynthetic activities. According to some researchers, the N n W are affected by vegetation stress, and the near infrared band is sensitive to photosynthetic activity (Sellers, 1 9 8 5 ; Harris, 1 9 8 7 ) . Even though vegetation has the same biomass or LAI, photosynthetic activity and plant stress affect the NDW values. Strong photosynthetic activities increase N D ~ I values and also lead to more water consumption by evapotranspiration. From this viewpoint, photosynthetic influence on the NDVI will help accurately estimate evapotranspiration and hydrologic processes because it reflects real evapotranspiration. In other words, perhaps the N D ~ are better related to evapotranspiration than is the commonly used LA1 parameter. It is known that photosynthesis is affected by vegetation stress. Vegetation stress occurs under extraordinary situations of radiation, temperature, precipitation, and soil moisture. Comparing the NDVI with radiation, temperature, and precipitation, the NDW have some correlations with these parameters. The peaks of the NDVI always occur after rainfall, high

7

6

5

a 4

3

2

1

0

days

1 - W L 2 - R L 3 - W W 4 - S C

Figure 4. The temporal vegetation LAI of the Mud Creek Watershed obtained from NOAA AVHRR images. The vegetation types are woodland (WL), rangeland (RL), winter wheat (WW), and summer crop (WC).

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x

7

6

5

3 4

3

2

1

0

days

1 - W L 2 - R L 3 - - 4 - S C

Figure 5. The temporal vegetation MI of the Mud Creek Watershed obtained from the crop growth model. The vegetation types are woodland (WL), rangeland (RL), winter wheat (WW), and summer crop (WC).

radiation, and high temperatures that affect photosynthetic activities. Conversely, the valleys of the NDVI often occur at the time of low radiation, low temperatures, and no rainfall.

Another factor that can affect the fluctuations of the NDVI is the environmental factor. Physical environment factors such as sun angle, view angle, atmosphere, haze, dust, terrain, and soil background can affect N D ~ I . Lo (1986) has indicated that sun angle affects red and near infrared reflectance and finally affects mVI. Groten (1993) found that NDm are affected by cloud and atmospheric effects. Some researchers have reported that soil background wetness decreases soil reflectance and increases magnitudes of NDW (Justice, 1986; Rao, 1993). Therefore, the relationships between environmental factors and NDVI need to be quantified in the future to allow for more precise vegetation measurement from satellite imagery.

Because no ground vegetation survey has been conducted before and a survey for such a large watershed is not feasible, the modeled LA1 (Figure 4) are evaluated by comparing them with the LA1 of the crop growth model (Figure 5). The modeled LAI from the satellite images have longer growing seasons and the magnitudes are also larger. The growing slopes of the modeled LA1 from satellite images are not as steep as the LAI from the crop growth model. The LA1 from the crop growth model are almost at zero for the first one and a half months since planting. Then they grow very rap- idly and quickly reach the maximum LAI. The modeled LA1 from the satellite images grows and senesces gradually.

Winter wheat was planted in early October and emerged by the end of October. From the satellite images, the winter wheat appeared in November and December. In mid-Decem- ber, the LA1 of winter wheat decreased to almost zero because the temperature was under crop minimum growing temperature and the only winter pasture at this time was in- tensively grazed by cattle. But the crop growth model cannot model this special phenomenon. According to the crop growth model, the LAI of winter wheat was still growing during the winter. It is also found that, after harvesting of winter wheat in late May and early June, the LAI of winter wheat were zero in the crop growth model until October. But the LA1 of winter wheat from the satellite images gradually rose again after harvesting and suddenly dipped in September. The reason is that weeds grew in the winter wheat field after the harvesting and before the planting of winter wheat in

September. The LAI of weeds in winter wheat fields disap- peared when winter wheat was planted.

The summer crops appeared on the MI from the satellite images earlier than the LAI from the crop growth model. The summer crops in this watershed are various and their planting dates are different. Because the crop growth model used only one date, it could not include some crops that were planted before that date. Also some weeds already grew in the crop fields before the planting of summer crops. This increases the LA1 of summer crops before the planting dates and makes it similar to woodland and rangeland. This case shows that the satellite images are responding to the real vegetation situation in the watershed and will help estimate evapotranspiration more accurately. Another possible reason is that the reflectance of summer crops may be affected by surrounding woodland and rangeland because of the coarse spatial resolution of the AVHRRINOAA images and the small summer crop plots. This problem will be avoided when higher spatial resolution images become available.

According to the LA1 from satellite images, the grasses and forests emerged in March and have an LAI of about 2 in April. But the LA1 from the crop growth model was almost zero until May and then reached a maximum very quickly. Also, the woodland, rangeland, and summer crops from the crop growth model almost senesced to an LA1 of zero at the end of September. According to the field trip and observa- tions in the watershed, the woodland and rangeland actually can last until November and during that time still consume water by transpiration. This situation is seen in the M I curve from the satellite images which began to senesce in Septem- ber and reach the LAI of zero in late November. The growing seasons of vegetation, especially woodland and rangeland, are much longer than those seen in the crop growth model.

The area-weighted LAI of the entire watershed from the two kinds of vegetation data are shown in Table 2. The temporal distribution and vegetation amount of the two kinds of data can be more easily compared than can individual vegetation types. From the comparison of the two kinds of LAI,

TABLE 2. THE COMPARISON OF MONTHLY AND ANNUAL LA1 FROM THE SATELLITE IMAGES AND THE CROP GROWTH MODEL

Month LA1 from NDVI LA1 from CGM

1 0.18 0.35 2 0.34 0.69 3 0.86 0.79 4 1.80 0.93 5 2.75 1.31 6 3.14 3.08 7 2.02 3.67 8 2.19 3.39 9 2.14 0.93

10 2.34 0.05 11 1.25 0.09 12 0.46 0.38

1990 19.46 15.63

1 0.11 0.63 2 0.40 0.82 3 1.12 0.89 4 2.04 0.97 5 3.11 1.37 6 2.82 2.72 7 2.48 3.63 8 2.29 3.24 9 2.13 0.89

10 2.02 0.06 11 0.95 0.08 12 0.49 0.20

1991 20.06 15.50

CGM: Crop growth model

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the total biomass of the LA1 from satellite images is larger than the LAI from the crop growth model. According to Table 2, the w from the satellite images is about 30 percent larger than the crop growth model. The LA1 from the satellite images is also intuitively and qualitatively better than the LA1 from the crop growth model. Satellite remote sensing can de- tect real vegetation activities and evapotranspiration that cannot be modeled by the crop growth model.

Modeled Hydrology The modeled hydrology using the vegetation data from the satellite images is shown in Table 3 and Figure 6. The modeled hydrologic results are evaluated by comparing the hydrologic results with the observed data and the hydrology modeled using the LAI from the crop growth model. Com- pared with the observed runoff, the average monthly absolute difference of modeled hydrology using the NDVI model is 1.50 mm and the average monthly percent difference is 17.33 percent. Therefore, the modeled monthly runoff and the observed monthly runoff are in good agreement and the modeled monthly hydrology is satisfactory for hydrologic study. It is also noted that the high percent differences generally occur during the months with small runoff volumes because a small change of modeled runoff will yield a large percent er- ror. In contrast, the months with large modeled runoff have small percent differences. During the summer, hot weather and high evapotranspiration produce small runoff volumes, so the percent differences in summer are generally higher. The monthly differences in spring 1990 and winter 1991 are small because several extraordinary storms produced very large runoff volume.

The average monthly absolute difference of modeled hydrology using the crop growth model is 2.97 mm and the average monthly percent difference is 30.97 percent (Table 4). Compared with the observed hydrology and using the crop growth model, the average monthly absolute difference using the satellite images is improved by 1.47 mm and the average monthly percent difference is improved by 13.64 percent.

months

-c precipitation + runoff +I+ e v a p o ~ i r e t i o n

-m- percolation -A- soil water

Figure 6. The modeled hydrology using LAI from the satellite images.

The modeled hydrology in individual months also con- firms that the vegetation data from the satellite images and their modeled hydrology are better than the vegetation data from the crop growth model and their modeled hydrology (Table 4 and Figure 7). From Table 2, the LA1 in April 1990 are 1.80 from the satellite images and 0.93 from the crop growth model. Because of evapotranspiration, the modeled runoff using the satellite images and the crop growth model are 126.82 mm and 134.77 mm, respectively (Table 4). Com- pared with the observed discharge of 125.16 mm, the differences are 1.66 mm and 9.82 mm, respectively. Hence, the vegetation data from the satellite images produce a more ac- curate hydrologic result. This means that the vegetation data from the crop growth model are underestimated and produce larger runoff volume and that the vegetation data from satellite images are more appropriate. Conversely, in July 1990,

TABLE 3. THE MODELED HYDROLOGY USING LA1 FROM THE SATELLITE IMAGES (MM)

Month P Surf Subs D Ob E T ET Perc SW

1 79.02 4.07 1.62 5.69 6.14 31.87 3.11 34.98 9.14 129.51 2 95.41 5.66 2.21 7.87 8.39 35.52 5.87 41.39 10.85 164.45 3 153.15 50.60 24.28 74.88 68.42 40.11 14.85 54.96 17.48 170.20 4 244.57 93.39 33.43 126.82 125.16 31.28 30.08 61.31 27.85 198.59 5 149.36 82.11 16.07 98.18 98.62 23.80 47.49 71.29 17.03 162.45 6 37.56 1.31 1.12 2.43 2.42 25.13 55.22 80.35 4.23 114.00 7 65.84 3.11 1.61 4.72 5.46 38.46 34.88 73.34 7.43 95.35 8 44.76 2.83 1.14 3.97 4.07 31.77 37.82 69.59 5.28 61.27 9 119.89 10.91 4.21 15.12 12.88 27.62 36.95 64.57 13.71 88.76

10 20.88 1.93 1.14 3.07 2.48 13.70 40.41 54.11 2.29 51.17 11 109.90 8.71 2.79 11.50 4.60 24.96 21.59 46.55 9.45 94.57 12 45.96 2.19 0.93 3.12 2.81 25.94 7.94 33.88 5.14 98.39

1990 1166.29 266.83 90.55 357.38 341.46 335.38 336.05 671.43 134.14 118.59

1 60.94 4.76 3.69 8.45 9.74 29.47 1.90 31.37 10.20 109.31 2 18.58 1.65 0.87 2.52 2.61 35.94 6.91 42.85 3.06 79.46 3 44.86 5.01 3.30 8.31 7.50 32.82 19.34 52.16 6.85 57.10 4 57.94 5.61 3.29 8.90 8.25 30.31 35.22 65.53 7.69 32.48 5 118.19 9.93 3.79 13.72 11.73 29.49 53.71 83.20 15.06 38.69 6 94.01 10.23 3.36 13.59 11.70 32.90 48.69 81.59 11.92 25.60 7 158.15 13.21 5.63 18.84 18.27 37.74 42.83 80.57 19.86 64.48 8 86.32 3.12 1.29 4.41 4.36 35.40 39.54 74.94 7.62 63.83 9 182.43 15.41 5.55 20.96 18.27 36.07 36.78 72.85 22.94 129.51

10 83.12 4.25 2.36 6.61 4.74 27.25 34.88 62.13 8.11 135.78 11 50.65 5.35 2.84 8.19 5.76 26.70 16.40 43.10 7.50 127.64 12 143.76 64.34 18.52 62.86 64.15 28.22 8.46 36.68 22.31 149.55

1991 1098.95 142.87 54.49 177.36 167.15 368.16 346.41 714.57 144.02 63.37

P: Precipitation, Surf: Surface runoff, Subs: Subsurface flow, D: Direct runoff, Ob: Observation of runoff, E: Evaporation, T: Transpiration, ET: Evapotranspiration, Perc: Percolation, SW: Soil water.

PE&RS January 1997 75

vegetation data from the satellite images are better than the crop growth model because the vegetation data from the lat- ter are overestimated in July. The LA1 from the crop growth model is 3.67 and from the satellite images is 2.02 (Table 2). The hydrologic results using the satellite images, the crop growth model, and observed data are 4.72 mm, 4.10 mm, and 5.46 mm, respectively. The same situation as in April occurred in October 1990. According to the vegetation data from the satellite images, vegetation is still growing and the LA1 is 2.34 but, according to the vegetation data from the crop growth model, vegetation is senesced and the LAI is only 0.05. Apparently, the vegetation from the crop growth model is underestimated; hence, it yields higher runoff. So the vegetation impact on hydrology is shown on the monthly hydrology.

The modeled annual hydrology (Tables 3 and 4) indi- cates the same result as the monthly hydrology. For example, the differences of the modeled annual runoff using vegetation data from satellite images and the observed annual runoff in 1990 and 1991 are 4.66 and 6.11 percent, respectively. The modeled annual runoff differences using the crop growth model in 1990 and 1991 are 9.73 and 11.22 percent, respectively. The accuracy of the modeled hydrology using the vegetation data from satellite imagery is improved by 4 to 5 percent on an annual basis, compared to the crop growth model. The modeled hydrologic accuracy using the vegetation data from satellite images is better than using the crop growth model and is close to the observed runoff.

Summary and Conclusion This study shows how to obtain the spatial and temporal vegetation data in the Mud Creek watershed by combining Landsat MSS and AVHRRINOAA satellite images. The spatial vegetation data were obtained from Landsat MSS images by image classification. The temporal change of vegetation was parameterized by a series of NDVI from AVHRRINOAA satellite

months

+ NnVl mdel + CGM mndel -A- nhewed

Figure 7. The evaluation of modeled runoff using LA1 from satellite images.

images. The temporal NDVI of vegetation types were repre- sented by the reflectance values of pure vegetation types. The NDVI curves of vegetation types reflect actual vegetation status over time. Based on previous research and statistical analysis, a general empiric LAI-NDVI model was developed to relate the NDVI to LAI. The daily LA1 were obtained from the biweekly LAI by linear interpolation. This research success- fully obtained spatial and temporal vegetation data for hydrologic study by combining two different resolution satellite images.

The obtained vegetation data LA1 were applied in the hydrologic model SWRRB to model hydrologic processes. From the modeled hydrology, the hydrologic elements have good

TABLE 4. THE EVALUATION OF MODELED AND OBSERVED RUNOFFS (MM)

NDVI CGM Observed NDVI Percent CGM Percent Month runoff runoff runoff diff. diff. diff. diff.

1 5.69 5.25 6.14 -0.45 -7.33 -0.89 -14.50 2 7.87 7.07 8.39 -0.52 -6.19 -1.32 -15.73 3 74.88 75.14 68.42 6.46 9.44 6.72 9.82 4 126.82 134.77 125.16 1.66 1.33 9.61 7.68 5 98.18 105.34 98.62 -0.44 -0.45 6.72 6.81 6 2.43 2.93 2.42 0.01 0.41 0.51 21.07 7 4.72 4.10 5.46 -0.74 -13.55 -1.36 -24.91 8 3.97 3.23 4.07 -0.10 -0.25 -0.84 -20.64 9 15.12 16.89 12.89 2.23 17.30 4.00 31.03

10 3.07 3.87 2.48 0.59 23.79 1.39 56.05 11 11.50 12.68 4.60 6.90 150.00 8.08 175.65 12 3.12 3.40 2.81 0.31 11.03 0.59 21.00 i

1990 357.38 374.68 341.46 15.92 4.66 33.22 9.73

1 8.45 7.93 9.75 -1.30 -13.33 -1.82 -18.67 2 2.52 2.16 2.61 -0.09 - 3.45 -0.45 -17.24 3 8.31 9.43 7.51 0.80 10.65 1.92 25.57 4 8.90 10.92 8.26 0.64 7.75 2.66 32.20 5 13.72 15.12 11.74 1.98 16.87 3.38 28.79 6 13.59 14.29 11.71 1.88 16.05 2.58 22.03 7 18.84 16.24 18.27 0.57 3.12 -2.03 -11.11 8 4.41 3.93 4.36 0.05 1.15 -1.43 -32.80 9 20.96 23.06 18.29 2.67 14.60 4.77 26.07

10 6.61 7.25 4.74 1.87 39.45 2.51 52.95 11 8.19 9.25 5.76 2.43 42.19 3.49 60.59 12 62.86 66.33 64.15 -1.29 -2.01 2.18 3.39

1991 177.36 185.91 167.15 10.21 6.11 18.76 11.22 Total absolute difference 35.98 411.21 71.25 735.50 Average monthly absolute difference 1.50 17.33 2.97 30.97

CGM: Crop growth model.

January 1997 PE&RS

relationships and reflect the impact of different vegetation data on hydrologic processes. The comparison between the modeled hydrology by the satellite images and by the crop growth model shows that the vegetation data obtained from the satellite images are more realistic than those of the crop growth model. From the analysis of modeled hydrology, the vegetation data from the satellite images yield better hydrologic results than does the crop growth model.

This research provides a new approach to obtaining vegetation data from satellite images for hydrologic models and studies. It is evident that obtaining broad categories of vegetation types at watershed scale from AVHRRINOAA images is feasible. Especially, in the relatively homogeneous and large rural watersheds where ground vegetation data are not easy to obtain, this method can be used to obtain vegetation parameters. In this method, no detailed ground observation data are needed except the maximum LAI of vegetation types. The developed LAI-NDVI mode is intended for general use where extensive field work to obtain vegetation data is difficult. For water resource planning, land-cover management, and climate change study in large watershed and regions, this method can be effective and practical.

Satellite images improve the accuracy of parameterizing the vegetation element in hydrologic models. Satellite images are helpful in the development of distributed and continuous hydrologic models and will contribute to hydrologic prediction and water resource management.

Acknowledgment This research was funded by the NSF EPSCoR Program (NSF grant 0 ~ ~ 9 1 0 8 7 7 1 ) and the Oklahoma State Regents for Higher Education.

References Ajai, D.S. Kanat, 1983. Spectral Assessment of Leaf Area Index,

Chlorophyll Content, and Biomass of Chickpea, Photogrammet- ric Engineering b Remote Sensing, 49(12):1721-1750.

Arnold, J.G., J.R. Williams, A.D. Nicks, and N.B. Sammons, 1990. SWRRB: A Basin Scale Simulation Model for Soil and Water Re- sources Management, Texas A&M University Press, College Station, Texas.

Duchon, Claude E., Jayne M. Salisbury, T.H. Lee Williams, and Arlin D. Nicks, 1992. An Example of Using Landsat and GOES Data in

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Holben, B.C., 1980. Spectral Assessment of Soybean Leaf Area and Leaf Biomass, Photogrammetric Engineering b Remote Sensing, 45(5):651-656.

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Justice, C.O., 1986. Monitoring East African Vegetation Using AVHRR Data, International Journal of Remote Sensing, 7(11): 1453-1474.

Justice, C.O., and J.R.G Townshend, 1985. Analysis of the Phenology of Global Vegetation Using Meteorological Satellite Data, Inter- national Journal of Remote Sensing, 6(8):1271-1328.

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Ragan, R.M., and T.J. Jackson, 1980. Runoff Synthesis Using Landsat and SCS Model, Proceeding Journal of Hydrology Division, ASCE, 106(5):667-668.

Rao, Narasimha, 1993. Relation Between Root Zone Soil Moisture and NDVI of Vegetated Fields, International Journal of Remote Sensing, 14(3):441-451.

Sellers, P.J., 1985. Canopy Reflectance, Photosynthesis and Transpi- ration, International Journal of Remote Sensing, 6(8):1335-1372.

Tucker, Compton J., 1980. Relationship of Spectral Data to Grain Yield Variation, Photogrammetric Engineering b Remote Sens- ing, 46(5):657-666.

USDA Soil Conservation Service, 1982. Resources Inventory of Oklahoma, Stillwater, Oklahoma.

Wardley, N.W., and P.J. Curran, 1984. The Estimation of Green Leaf Area Index from Remotely Sensed Airborne Multi-spectral Scan- ner Data, International Journal of Remote Sensing, 5(4):671-679.

Wiegand, C.L., 1979. Leaf Area Index Estimates of Wheat from Land- sat and Their Implications for Evapotranspiration and Crop Modeling, Agronomy Journal, 71(2):336-342.

(Received 27 July 1994; revised and accepted 7 April 1995; revised 11 May 1995)

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