## 4 Soil water and drought monitoring for early-warning applications  
With the advent of grid-based remotely-sensed rainfall data, the application of crop water balance models for crop monitoring and yield forecasting has gained increased acceptance by various international, national and local organizations around the world. Soil water is a key state variable in hydrological modeling and determines the partitioning of rainfall into runoff and deep percolation, and also controls the rate of evapotranspiration (ET). Although the estimation of actual evapotranspiration (ET\({}_{\text{a}}\)) is the ultimate goal of many researchers for hydrological and agronomical applications, it is often difficult to quantify and requires expensive instrumentation. However, hydrological modeling techniques are used to estimate ET\({}_{\text{a}}\). The two basic modeling techniques to estimate ET\({}_{\text{a}}\) are based on either energy balance (e.g., [PERSON] et al, 1998; [PERSON] et al, 2005; [PERSON] et al, 2007a) principles or water balance-based algorithms (e.g.,[PERSON] et al, 1998; [PERSON] and [PERSON], 2003). For monitoring large areas using remotely sensed data, the water balance approach provides an operational advantage in terms of data availability. While the energy balance models are mainly driven by the thermal data, the water balance models are driven by rainfall. Naturally, cloud cover is an issue to provide daily estimates of ET\({}_{\text{a}}\) on rain-fed agriculture from the energy balance models. On the other hand, availability of satellite-derived rainfall data at various temporal and spatial scales makes operational estimation of ET\({}_{\text{a}}\) using a water balance model a relatively easy task for various decision makers in agriculture and natural resources. The most widely used water balance technique for operational use is the FAO water balance algorithm that produces the crop water requirement satisfaction index (WRSI), which is also known as the crop specific drought index (CSDI). The WRSI shows the relative relationship (ratio/percent)  
Figure 9: Wetness index vs. latent heat flux for a wheat field. between the supply (from rainfall and existing soil moisture) and demand (crop demand to meet its physiological needs) using observed data from the beginning of the crop season (planting) until current date. A value of 100 indicates all the crop demand has been met while values less than 50 generally indicates a severe water shortage that could lead to complete failure of the crop ([PERSON] 1992). Values between 50 and 100 will indicate different degrees of crop stress and yield reductions from shortage of adequate supply. FAO studies ([PERSON] and [PERSON] 1977) have shown that WRSI can be related to crop production using a linear yield-reduction function specific to a crop. [PERSON] et al. (1993) enhanced the concept of crop water balance modeling using crop-stage specific drought sensitivity coefficients for corn. The Farmine Early Warning System Network (FEWS NET) demonstrated a regional implementation of the FAO WRSI in a grid-cell modeling environment ([PERSON] and [PERSON] 2002). Furthermore, [PERSON] and [PERSON] (2003) enhanced the geospatial model by introducing the concept of maximum allowable depletion (MAD) and soil water stress factor from irrigation engineering for better estimation of ETa as a function of soil water content. The current version of the USGS/FEWS NET GeoSpatial WRSI model is operational with daily and 10-day outputs (available here: [[http://earlywarning.usgs.gov/adds/](http://earlywarning.usgs.gov/adds/)]([http://earlywarning.usgs.gov/adds/](http://earlywarning.usgs.gov/adds/)))  
The seasonal crop water requirement satisfaction index for a crop is based on the water supply and demand that a crop experiences during a growing season.