Single date in-season digital satellite data coinciding with peak vegetative stage of the crop is analyzed for acreage estimation using an hybrid approach of supervised and supervised classification. Stratified random sample segment approach was adopted for analysis in the beginning. Districtwise area and production estimates are provided by extracting and analyzing satellite data for each of the 20 districts of Haryana state, one of the major agricultural state of India. During the last 15 years of estimation the spatial resolution of the satellite data has gradually improved from 72.5 m of LISS-I to 36.25 m of LISS-II and finally to 23.5 m of LISS-III from a series of Indian Remote Sensing Satellites. Besides the spatial resolution, the spectral and temporal resolution of the satellite data has also improved which drastically improved the crop discrimination. Both accuracy as well as precision of the estimates improved over the years, as reflected by the relative deviation and CV values respectively, for various districts. However, the northern districts having large contiguous areas of the crop showed better accuracy and precision as compared to southern districts having a mix of various crops. Yield of wheat was estimated based upon multiple regression models developed using satellite based spectral vegetation indices along with meteorological and historical yield data. Zonal Spectro-trend, Trend-agromet and Spectro-trend-agromet models for wheat production forecasting were developed for different regions of the state of which the last one gave the best results. The performance of wheat acreage and yield estimates was evaluated by computing Relative Deviations (RD%) with Department of Agriculture (DOA) estimates. RD of the remote sensing estimates in comparison to DOA estimates have significantly improved since the beginning of the project. Presently remote sensing technology is able to provide district level acreage and production estimates with 95 % accuracy. For the remote sensing estimates to be really useful in the planning process, the timeliness of the estimates is an important factor as the ground based estimates are available much after the harvest of the crop. Timeliness of the remote sensing wheat estimates have improved substantially over the years due to availability of efficient hardware/ software, better spatial, spectral and temporal resolution of satellite data. Key Words: Indian Remote Sensing Satellite, Forecasting, Wheat Production, Vegetation Index, Yield Models