Introduction
1.4. Yield Estimation in India
India underwent a series of successful agricultural revolutions, starting with the "green" revolution in wheat and rice in the 1970s, the "white" revolution in milk and, in the 1980s, the "yellow" revolution in oil seeds. Despite these major transformations, the agricultural sector continues to be dominated by a large number of small landholders (70 % of rural people and 8 % of urban household depend on agriculture). The country is also marked by large fluctuations in agricultural output, though to a declining extent with the development of irrigation facilities, adoption of new technologies and changes in cropping patterns (FAO 2000a). The traditional approach of crop estimation in India involves complete enumeration (except in a few states where sample surveys are employed) for estimating crop acreage and sample surveys based on crop cutting experiments (CCE) for estimating crop yield. The crop acreage and corresponding yield estimate data are used to obtain production estimates. These yield surveys are extensive; plot yield data being collected using stratified multistage random sampling techniques (Government of India 2002, Singh et al. 1992, Singh et al. 2002. Although the approach is fairly comprehensive and reliable, the cost is more and the accuracy and timeliness of crop production statistics needs to be improved. Yield estimates predicted before actual production are required for taking various policy decisions. Hence, early assessment of crop yield is necessary, particularly in countries that depend on agriculture as their main source of economy. With the successful launching of satellites, remote sensing can play a vital role in the yield estimation process. To achieve timely and accurate information on the status of crops and crop yield, there is need to have an up-to-date crop monitoring system that provides accurate information on yield estimates way before the harvesting period. The earlier and more reliable information the greater the value (Hamar et al.1996, Reynolds et al. 2000). Remote sensing data has the potential and the capacity to achieve this. Prediction and estimation of yield is closely related to the capability of identifying crop species and certain agronomic variables such as maturity, density, vigour, and disease which can be used as yield indicators (Nualchawee, 1984). The use of remotely sensed data in crop acreage estimation has been demonstrated by various researchers in different parts of the world (Saha and Jonna, 1994). Satellite data are complementary to data from GIS, Global Positioning System (GPS), yields monitor, and pencilled notes on the back of envelopes. Data from all sources should be brought together to give the grower the best opportunity to maximize yield and quality (Arvik, 1997). Tea yield is influenced by a large number of factors such as crop genotype, soil characteristics, cultural practices, weather conditions and biotic influences, such as weeds, diseases and pests. Two approaches adopted for yield modelling are remotely sensed data or derived parameters that are directly related to yield, and the others is remotely sensed data that are used to estimate some of the biometrics parameters, which in turn are input parameters to a yield model. Geographic information system can handle, manipulate and analyze data from different sources and coordinate systems, scales and formats (Navalgund, 1994). Remote sensing and crop growth simulation models have become increasingly recognized as potential tools for growth monitoring and yield estimation (Bauman, 1992). In the last few years, attention has been paid towards using satellite remote sensing data in tea crop estimation surveys, in view of its advantages over traditional procedures in terms of cost effectiveness and timeliness in the availability of information over larger areas. Murthy et al. (1995) reported the validity of crop yield models with satellite derived normalized difference vegetation index (NDVI) determined by the strength of association between the two variables including the model.