A land surface wetness product is derived from the Special Sensor Microwave Imager (SSMI), which flies on a polar orbiting satellite with global coverage. The frequencies observed by the Special Sensor Microwave Imager are sensitive to liquid water near the earth's surface. The surface wetness can originate from multiple sources (i.e. precipitation, snow melt, irrigation). In conjunction with the Department of Agriculture we seek to establish the utility of the wetness product for real time monitoring of crop conditions around the world, and demonstrate the benefit of actual SSMI observations over modeled or interpolated analyses. We provide numerous examples showing how SSMI observations provide a more realistic spatial structure in areas where surface observations are limited. Moreover, due to the sparcity of in situ observations in many rural and poor areas of developing counties extreme events are frequently undetected. Consequently, by using the SSMI to observe the true spatial distribution of water near the surface in near real time, there can be timely action to mitigate the spread of water borne diseases and famine. In addition, we demonstrate that the surface wetness product has a strong correspondence with the upper level soil moisture at many locations. By analyzing the wetness values over an extended period, one can usually determine its association with deeper soil moisture (e.g., it was excessively wet two weeks ago; therefore, deep soil moisture is probably abundant, although it appears that the surface has dried out). This product is unique and the talk demonstrates the value of the SSMI observations to enhance monitoring activity, to validate global circulation model, and/or be assimilated into energy or water budget balances.

The relationship between brightness temperatures at different frequencies is used to dynamically derive the amount of liquid water in each SSM/I observation; i.e., there are no static apriori assumptions in the computation of the wetness values. They are derived at 1/3 degree resolution, and are calibrated and validated using independent high resolution in situ observations. A 15 year climatology (1988 to 2002) serves as the base period for monthly and weekly anomalies. The wetness product is based on a standardized cumulative probability gamma function with values from zero to one hundred percent. The standardization procedure accounts for variation in surface features around a region (i.e., forest, lakes, farm land, mountains), time of year (i.e., wet versus dry season), and soil type (i.e., clay versus sandy soil).