Introduction
1.3. Need for Crop Yield Forecasting
Forecasting crop yield well before harvest is crucial especially in regions characterized by climatic uncertainties. This enables planners and decision makers to predict how much to import in case of shortfall or to export in case of surplus. It also enables governments to put in place strategic contingency plans. Therefore, monitoring of crop development and of crop growth, and early yield prediction are generally important. Crop yield estimation in many countries is based on conventional techniques of data collection for crop and yield estimation based on ground-based field visits and reports. Such reports are often subjective, costly, time consuming and are prone to large errors, leading to poor crop yield assessment and crop area estimations (Reynolds et al. 2000). In some countries weather data are also used (de Wit & Boogaard 2001, Liu & Kogan 2002) and models based on weather parameters have been developed. This approach is associated with a number of problems including the spatial distribution of the weather station, incomplete and/or unavailable timely weather data, and weather observations that do not adequately represent the diversity of weather over the large areas where crops are grown (Dadhwall & Ray 2000, de Wit & Boogaard 2001, Liu & Kogan 2002, Rugege 2002). Objective, standardized and possibly cheaper/faster methods that can be used for crop growth monitoring and early crop yield estimation are imperative. Many empirical models have been developed to try and estimate yield before harvesting. However, most of the methods require data that are not easily available. The models complexity, their data demand, and methods of analysis, render these models unpractical, especially at field level. With the development of satellites, remote sensing images provide access to spatial information at global scale; of features and phenomena on earth on an almost real-time basis. They have the potential not only in identifying crop classes but also of estimating crop yield (Mohd et al. 1994); they can identify and provide information on spatial variability and permit more efficiency in field scouting (Schuler 2002). Remote sensing could therefore be used for crop growth monitoring and yield estimation.