moisture easily. But in remote sensing society, vegetation 
canopy has been the important reasons affecting the sensitivity 
of radar backscattering to soil moisture for long time, there is 
no empirical model gives good accuracy, especially when 
vegetation is dense. 
1.3 SMEX02 Experiment 
SMEX02 involved microwave remote sensing and in situ 
sampling of soil moisture with the major objectives of: 
development of models of microwave emission and backscatter 
from soil and vegetation; development of soil moisture and 
vegetation retrieval algorithms; and validation of airborne and 
spacebome remotely sensed soil moisture and vegetation 
measurements over a range of spatial scales. In order to satisfy 
these objectives, an ideal test site with significant biomass 
levels as well as varying soil moisture conditions was needed. 
The study site chosen was Walnut Creek, a small watershed in 
Iowa. This watershed has been studied extensively by the 
USDA and hence was well instrumented for in situ sampling of 
hydrologic parameters. The terrain is undulating and the land 
cover type for the watershed region is primarily agricultural 
with com and soybeans being the major crops. The experiments 
were conducted from 25th June through 12th July 2002 during 
which the soybean fields grew from essentially bare soils to 
vegetation water content of 1-1.5 kg/m 2 , while the cornfields 
grew from 2-3 to 4-5 kg/m 2 . 
1.4 The PALS Instrument 
The passive and active L/S band sensor (PALS) is a 
nonscanning real aperture microwave radiometer and radar 
operating at 1.41 and 2.69 GHz (radiometer) and 1.26 and 3.15 
GHz (radar) with multiple polarizations. The radiometer 
operates at V and H polarizations, while the radar operates at 
VV, HH, and HV polarizations. 
The PALS instrument was flown over the SMEX02 region on 
June 25th, 27th, and July 1st, 2nd, 5th, 6th, 7th, and 8th, 2002 
(corresponding to day of year, DOY, 176, 178, 182, 183, 186, 
187, 188, and 189, respectively). Initial soil conditions were dry, 
but scattered thunderstorms occurred on the evenings of July 
4th, 5th, and 6th enabling a wetting and subsequent dry down to 
be observed. The PALS radiometer and radar provided 
simultaneous observations of horizontally and vertically 
polarized L- and S-band brightness temperatures, radar 
backscatter measured in VV, HH, and VH configurations, and 
nadir- and boresight-looking thermal infrared surface 
temperature. The instrument was flown on a C-130 aircraft 
(velocity f70 m/s) at a nominal altitude of ~3500 ft with the 
angle of incidence on the surface being ~ 45°. In this 
configuration, the instantaneous 3 dB footprint on the surface 
was 330-470 m. The instrument thus sampled a single-line 
footprint track along the flight path. Aircraft location and 
navigation data were also recorded. 
1.5 Landsat TM and In-Situ Database 
As part of SMEX02, two Landsat Thematic Mapper (TM) 
scenes from Landsat 5 and four Landsat Enhanced Thematic 
Mapper plus (ETM+) from Landsat 7 were acquired during the 
primary study period. These data were used to produce high 
resolution (30 m) NDVI and NDWI data sets. The Landsat TM 
images list is shown in Table 1. 
An extensive in situ dataset consisting of gravimetric soil 
moisture, surface and subsurface temperatures, bulk density, 
surface roughness, vegetation water content, crop type, and 
radiometer and radar observations were collected over 31 field 
sites out of which 21 were cornfields and 10 were soy. 
Date 
Landsat No 
Path 
Row 
June 6 
7 
27 
31 
June 23 
5 
26 
31 
July 1 
7 
26 
31 
July 8 
7 
27 
31 
July 16 
5 
27 
31 
July 17 
7 
26 
31 
Table. 1 Landsat TM Coverage for SMEX02 
2. VWC ESTIMATED 
For long time, NDVI has been used to estimated VWC with 
limited success. The limitation is related to its saturation when 
vegetation coverage is dense. NDVI is based on the red and 
near-infrared bands, which are located in the strong chlorophyll 
absorption region and high reflectance plateau of vegetation 
canopies respectively. Therefore, NDVI represents chlorophyll 
rather than water content. For Landsat TM/ETM+, /? NIR and 
/?re D correspond to bands 4 and 3. 
NDVI=(R nir -/? red )/( J?nir + ^red) 
Gao 131 developed the Normalized Difference Water 
Index(NDWI) for determination of VWC based on physical 
principles: 
NDWI=(/? nir -/? SW i R )/( /'’nir+^swir) 
Where R swir is the reflectance or radiance in a short wave 
infrared wavelength channel (1.2-2.5 um). For Landsat 
TM/ETM+, J? NIR and R S wir correspond to bands 4 and 5. Several 
papers [4|t5] has proved that NDWI is superior to NDVI in the 
estimating of VWC, because its saturation was observed one- 
week lag than NDVI in experiment period. In this part, we 
compared VWC derived from NDVI and NDWI, the results 
were compared to in-situ measured at SMEX02. Overall, there 
are totally 27 samples of VWC for soybean and 48 for com in 
SMEX02, and six Landsat images were acquired, as shown in 
table 1, Piecewise Cubic Hermite Interpolation was used to 
derived daily NDVI and NDWI for the experiment period, the 
correlation between NDVI/NDWI and VWC set up by 
T.J.Jackson [41 is used: when VWC is predicted by NDWI, the 
functions are as follows: 
Com: VWC = 9.82 * NDWI + 0.05 
Soybean: VWC= 1.44 * NDWI 2 + 1.36 * NDWI + 0.34 
When NDVI are used , the functions are: 
Com: VWC = 192.64 * NDWI 5 -417.46* NDWI 4 +347.96* 
NDWI 3 -138.93* NDWI 2 +30.699* NDWI- 
2.822 
Soybean: VWC= 7.63* NDWI 4 -11.41* NDWI 3 +6.87* NDWI 2 - 
1.24* NDWI+0.13 
As the results shown in Fig.l, results of estimating VWC from 
NDWI has higher correlation and lower RMSE errors in both 
soybean and com fields (especially in com fields). A more