The major wheat producing countries in the world are India, China, USA, France, Russia, Canada and Australia. Global demand for wheat is growing @ 1% per year. Crop growth and productivity are determined by a large number of factors such as genetic potential of crop cultivar, soil, weather and management variables, which vary significantly across time and space. Early prediction of crop yield is important for planning and taking various policy decisions. Many countries use the conventional techniques of data collection for crop monitoring and yield estimation based on ground – based visits and reports. These methods are subjective, costly and time consuming. Empirical models have been developed using weather data which is also associated with a number of problems. With the launching of satellites, satellite data are being used for crop monitoring and yield prediction. Most studies have revealed a strong correlation between remotely sensed NDVI and crop yield. GPS/sensor based on-the-go yield monitors are being used in the developed world for yield mapping.

The diverse techniques and their utilities are presented in this paper. Liu Honghui et.al. (1999) estimated production of winter wheat in North China plain with 96% accuracy by using Landsat Thematic Mapper(TM) data and Advanced Very High Resolution Radiometer (AVHRR) time series data. Sehgal et.al. (2001) reported the development of a prototype Crop Growth Monitoring System (CGMS) for wheat using WTGROWS simulation model in GIS environment for generating daily crop growth maps and predicting district-wise grain yield. GIS techniques and Clustering analysis were used to carry out the crop division for yield estimation by remote sensing and a two-level division was obtained by Zhou et.al.(2003). Remote Sensing (RS) – Crop Simulation Model (CSM) approaches have been demonstrated through case studies on wheat in India at different spatial scales. CSM-RS linkage has a number of applications in regional crop forecasting, agro-ecological zonation, crop suitability and yield gap analysis (Dadwal, 2004).  Two AVHRR-based Vegetation Health (VH) based indices such as Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) characterizing moisture and thermal conditions respectively were tested as predictors of winter wheat in Kansas, USA by Salazar et.al.(2006).Winter wheat was more sensitive to moisture conditions and wheat yield could be estimated from the VCI index approximately four weeks prior to harvest time.Patil (2009) reported that a multiple regression model based on NDVI and LAI was better than simple regression models based on NDVI and LAI alone. The estimated acreage, productivity and production through RS were found to be deviating by +3.19 per cent, +10.76 per cent and +13.61 per cent respectively, with that of State Department of Agriculture.