Using the Special Sensor Microwave Imager to Monitor Liquid Water Near the Surface

The frequencies flown on the Special Sensor Microwave Imager are sensitive to liquid water near the earth's surface. These frequencies are window channels, which receive the majority of the radiation from the surface, although a couple of the channels are also sensitive to water vapor. Liquid water near the surface depresses the emissivity, however the magnitude of this depression changes with wavelength. We use the relationship between the brightness temperatures at the various frequencies to identify the magnitude of liquid water in each SSMI observations, and these measurements are archived at a 1/3o resolution for morning and afternoon overpasses. The data are averaged at 1 degree resolution throughout the globe for each month during the period 1992-1997. Climatology and anomalies are computed from this base period. We compare these anomalies to precipitation anomalies derived from the Global Precipitation Climatological Network, to determine the relationship between precipitation and surface wetness. The analysis was performed for 6 agricultural regions across 6 continents. There is generally an excellent correspondence between two variables, and the correlation increases as the wetness index is compared to precipitation anomalies accumulated over prior months. This indicates that in many areas of the world the wetness index has a strong correspondence to the upper layer of the soil moisture, and that it retains memory over a 2 to 3 month period. The best relationship was over the western Sahel, where precipitation and wetness share 78% of their variance with a two month accumulative lag. Other areas with strong lagged relationships were Central France, and northern Argentina. The memory is shorter for southeastern Australia, and Central China. The weakest correlations occur over the southeastern U.S. The mechanism behind each of these relationships is discussed. This study also addresses the ability of the wetness index to observe the rate of snow melt, and the consequent runoff into the river basin. The flood over the Red River valley in the spring of 1997 served as a case study. Satellite derived snow cover and in situ based temperature, precipitation, snow depth, and river gauges serve as validation of the wetness index, and its correspondence surface runoff. Results indicate that the wetness index and river discharge have a similar response to snow melt. Therefore the wetness index could provide valuable information on snow melt, particularly in areas where in situ data is sparse.