In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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composites for visual analysis (flood and standing water 
absorbs infrared wavelengths of energy and appears as 
blue/black in the RGB composite imagery), water body 
identification in AVHRR imagery evolved from qualitative 
visual interpretation to automatic quantitative extraction. The 
reflectance of AVHRR channel 2 (0.73-1.1 pm, similar to MSS 
band 7), the reflectance difference (CH2-CH1) and ratio 
(CH2/CH1) between channel 2 and 1 (0.58-0.68 pm, similar to 
MSS band 5) are used to discriminate water from land if these 
parameters are less than the threshold values. 
Domenikiotis et al. (2003) tried to use surface temperature to 
discriminate water from land surfaces. However, the 
temperature model may not work well with the flood caused by 
heavy rainfall during rainy seasons in the summer when there is 
relatively low or no temperature difference between land and 
water. Domenikiotis et al. (2003) also used Normalized 
Difference Vegetation Index (NDVI) to identify water from 
land considering that water covered surfaces usually have very 
small or even negative NDVI values. It can be seen from its 
mathematical definition that the NDVI of an area containing a 
dense vegetation canopy will tend to have positive values (say 
0.3 to 0.8), while standing water (e.g., oceans, seas, lakes and 
rivers), which have a rather low reflectance in both visible 
(VIS: from 0.4 to 0.7 pm) and near-infrared (NIR: from 0.7 to 
1.1 pm) spectral bands, result in very low positive or even 
slightly negative NDVI values. 
Regression trees have been used with remote sensing 
observations (DeFries et al., 1997; Mchaelson, Schimel, Friedl, 
Davis and Dubayah, 1994; Prince and Steninger, 1999; Hansen 
et al., 2002, Solomatine and Xue, 2004). They provide a robust 
tool to handle nonlinear relationship within large data sets. 
As described above, in previous studies, several 
parameters, including the reflectance of near infrared (NIR) 
channel, the reflectance ratio and difference between NIR and 
visible (VIS) channels, NDVI, brightness temperature at 11 or 
12 pm, and surface temperature, might be used to identify water 
from land. Linear mixture model has been used by Sheng et al. 
(2001) to derive water fraction. However, it has not yet been 
shown which parameter or combination of several parameters is 
the most effective? 
This paper explores how to derive water fraction and flood 
map from the MODIS data using regression tree (RT) method. 
Section 2 introduces the dataset used. The physics of the 
problem and decision algorithms are described in Section 3. 
Section 4 presents the results and Section 5 gives a summary 
and discussion. 
2. DATA USED 
• Surface water percentage data derived from derived 
from the 1km land/water map supplied by the USGS 
Global Land Cover Characterization Project. The 
percentage water was created by simply determining 
the percentage of 1km pixels designated as water in 
each 10' region. This data can be obtained from the 
Surface and Atmospheric Radiation Budget (SARB) 
working group, part of NASA Langley Research 
Center's Clouds and the Earth's Radiant Energy 
System (CERES) mission 
• MODIS L3 8-day composite surface reflectance 
product (MYD09A1) that is computed from the 
MODIS Level IB land bands 1, 2, 3, 4, 5, 6, 7, which 
are centered at 0.648 pm, 0.858 pm, 0.470 pm, 0.555 
pm, 1.24 pm, 1.64 pm, and 2.13 pm, respectively. 
The product is an estimate of the surface reflectance 
for each band as it would have been measured at 
ground level after removing the atmospheric 
scattering and absorption. 
• MODIS LIB calibrated reflectance at the Top of 
Atmosphere (TOA) with 1 km resolution 
(MOD021KM). 
• MODIS geolocation fields (MOD03). 
• MODIS cloud mask (MOD35) data. 
• TM (Thematic Mapper) data from the Landsat 
observations at 30-meter spatial resolution is 
used to evaluate water fraction derived from 
MODIS. 
3. METHODOLOGY 
The RT, such as the M5P, is a powerful tool for generating rule- 
based models that balance the need for accurate prediction 
against the requirements of intelligibility. RT models generally 
give better results than those produced by simple techniques 
such as multivariate linear regression, while also being easier to 
understand than neural networks. Unlike neural networks, the 
RT program generates a model with rules that describe the 
relationships between the independent and dependent 
parameters in the data set. Instead of simple regression analysis 
techniques, RT uses a piecewise regression technique. The 
piecewise regression analysis (classifying the data into different 
subsets) will yield different regression fits for different 
meteorological conditions, unlike a simple regression analysis. 
The RT program constructs an unconventional type of tree 
structure, with the leaves containing linear models instead of 
discrete classes by DT. A decision tree would categorize the 
predictions into discrete classes, but the regression tree predicts 
actual continuous values. 
Since RT integrates DT with traditional regression 
analysis. Like DT algorithm, RT algorithm can integrate all the 
possible candidate predictors, such as the MODIS channel 2 
reflectance (CH2) and channel 1 reflectance CHI, the 
reflectance ratio (CH2/CH1) and difference (CH2-CH1) 
between MODIS channel 2 and channel 1, NDVI, Normalized 
Water Difference Index (NDWI), etc., meanwhile it can 
determine continuous values, in this case water fraction, and 
giving accuracy estimates. The NDWI [45], a satellite-derived 
index from the Near-Infrared (NIR) and Short Wave Infrared 
(SWIR) channels, is also included as one input attribute. 
According to Gao [45], NDWI is a good indicator for 
vegetation liquid water content and is less sensitive to 
atmospheric scattering effects than NDVI. The MODIS 8-day 
composite data at 500-m resolution is aggregated to the same 
1/6 degree resolution of the surface water percentage map. 
In this study, the M5P (Wang and Witten, 1997), a 
reconstruction of Quinlan's M5 algorithm (Quinlan, 1992) for 
inducing trees of regression models, is used to derive water 
fraction from MODIS observations. The M5P combines a 
conventional decision tree with the possibility of linear 
regression functions at the nodes. Techniques devised by 
Breiman et al. (1984) for their CART (Classification and 
Regression Trees) system are adapted in order to deal with 
enumerated attributes and missing values. Uses features from 
the well-known CART system and reimplements Quinlan"s 
well-known M5 algorithm with modifications and seems to 
outperform it. M5P can deal effectively with enumerated 
attributes and missing values.