985 
Table 2. Summary of data acquisition and on-site field measurements during the WG’92 Experiment, 
where DOY=day of year 1992, TM=Landsat Thematic Mapper, ERS-1=ERS-1 Synthetic Aperture 
Radar, OD=atmospheric optical depth, RP=radiosonde atmospheric profile, VS=vegetation 
measurements at 2-6 sites, SM=gravimetric soil moisture at 4 sites, STR=ground- (G) and aircraft- 
based (A) measurements of surface temperature and reflectance. Taken from Moran et aL (1993). 
DOY 
Date 
Weather 
TM 
ERS-l 
VS 
OD 
RP 
SM 
STR 
114 
23 Apr 
Marginal 
yes 
25 Apr 
21-23 Apr 
yes 
yes 
yes 
G 
130 
9 May 
Clear 
14 May 
yes 
yes 
yes 
G 
146 
25 May 
Cloudy 
27-28 May 
yes 
yes 
G 
162 
10 Jun 
Clear 
yes 
18 Jun 
yes 
yes 
yes 
G 
178 
26 Jun 
Clear 
yes 
22-23 Jun 
yes 
yes 
G 
194 
12 Jul 
Clear 
yes 
14-15 Jul 
yes 
G 
210 
28 Jul 
Cloudy 
28 Jul 
yes 
yes 
G&A 
224 
11 Aug 
Clear 
11-12 Aug 
yes 
G 
226 
13 Aug 
Marginal 
yes 
13 Aug 
yes 
yes 
yes 
G 
242 
29 Aug 
Cloudy 
27 Aug 
yes 
250 
6 Sep 
Clear 
yes 
G&A 
251 
7 Sep 
Clear 
yes 
G&A 
258 
14 Sep 
Cloudy 
15-16 Sep 
yes 
A 
274 
30 Sep 
Clear 
yes 
1 Oct 
30 Sep 
yes 
yes 
yes 
G&A 
290 
16 Oct 
Clear 
16 Oct 
16-17 Oct 
yes 
yes 
yes 
G&A 
306 
1 Nov 
Clear 
yes 
yes 
yes 
yes 
G&A 
322 
17 Nov 
Clear 
yes 
18-20 Nov 
yes 
yes 
yes 
G&A 
In the following discussion, research results from Monsoon’90 and WG’92 have been grouped into the five 
components illustrated in Figure 1. Most of the citations in the next sections are from an upcoming special issue 
of Water Resources Research, dedicated to the research results of the Monsoon’90 Experiment (Kustas and 
Goodrich, 1994). 
3.1. Storm Model and Storm Inputs (boxes 1 and 5 of Figure 1). 
The analysis of Monsoon’90 data for storm modeling has focused on implementation of a distributed, physically- 
based rainfall-runoff model (KJNEROS, Woolhiser et al., 1990) at the small catchment (4.4 ha) and medium 
catchment (631 ha) scales. Goodrich et al. (1994) utilized soil moisture determined by airborne passive 
microwave instruments, ground-based observations and from a simple water balance model to define prestorm 
initial soil water content for KJNEROS. For a small and medium-sized catchment, it appeared that a basin wide 
average initial soil water content was sufficient for runoff simulations. This result suggests that satellite-based 
microwave systems which suffer from low resolution may still provide acceptable pre-storm soil moisture data 
for computing runoff in this environment. On the other hand, this study also showed that detailed information 
of the rainfall distribution was critical for accurate runoff simulation. 
3.2. Interstorm Model and Interstorm Inputs (boxes 2 and 4 of Figure 1). 
3.2.1. Basic Correction and Pre-processing of Remotely Sensed Data. The Monsoon’90 and WG’92 
experiments provided the data sets necessary to document the potential errors in atmospheric correction of optical