Passive Microwave Remote Sensing of Soil Moisture and Regional Drought Monitoring - Monitoring and Predicting Agricultural Drought: A Global Study - page 102

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relationships between T B and API that included a quantitative vegetation
correction. This was accomplished using NDVI with a variety of satellites.
One example is the study by Ahmed (1995). In that study, a long record
of SMMR C-band data in the SGP was analyzed. Individual vegetation
regions (as defined by NDVI) were identified, and a regression function
was established for each. The performance of the regression varied with
the NDVI level. Sensitivity depended on the slope, and the slope depended
on NDVI. Equations were developed to predict the regression slope based
on NDVI. Teng et al. (1993) performed a similar analysis using SSM/I data.
Wang (1985) examined SMMR C and X bands and Skylab S-194 L-
band observations over the SGP. Two different regions were defined by veg-
etation level, and a regression was developed for each. The results showed
that the vegetation level affected the sensitivity and that the sensitivity for
a given region decreased as frequency increased. It was concluded that at
10.7 GHz it would be difficult to monitor soil moisture under even light
vegetation. A wide variety of studies have been conducted that established
regional and seasonal relationships between T B and API. None of these
efforts produced a robust and transferable approach or product.

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C onclusions
Soil moisture maps, an improved index of drought and inputs to GCMs,
are possible through the use of passive microwave remote sensing of soil
moisture. These products could provide information useful for drought
monitoring and prediction. Because drought indices and GCMs do not
consider actual soil moisture conditions, this approach could have great
value for agriculture. Results could improve the accuracy, timing, and
re liability of the indices and predictions. Global soil moisture data will
be a reality as new and improved satellite sensor systems are implemented.
It is also possible that these products could be integrated with vegetation
in dex data and existing drought indices to develop improved methods for
m onitoring and predicting drought.

[102

Re ferences
Ah med, N.U. 1995. Estimating soil moisture from 6.6-GHz dual polarization,
and/or satellite derived vegetation index. Intl. J. Remote Sens. 16:687-708.
Ar ya, L.M., J.C. Richter, and J.F. Paris. 1983. Estimating profile water storage from
surface zone soil moisture measurements under bare field conditions. Water
Resources Res. 19:403-412.
Bl anchard, B.J., M.J. McFarland, T.J. Schmugge, and E. Rhodes. 1981. Estima-
tion of soil moisture with API algorithms and microwave emission. Water Re-
sources Bull. 17:767-774.
Choudhury, B.J., and R.E. Golus. 1988. Estimating soil wetness using satellite data.
Intl. J. Remote Sens. 9:1251-1257.
Hallikainen, M.T., F.T. Ulaby, M.C. Dobson, M.A. El-Rayes, and L. Wu. 1985.