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An attempt has been made to assimilate remotely sensed input data in mechanistic crop simulation model World Food Studies (WOFOST) for in-season wheat yield forecasting in Punjab state of India. Spatial weather data at '5 km × 5 km' grid were generated through interpolation of daily available weather data. Grid-wise sowing date was estimated from time-series normalized difference vegetation index (NDVI) data product from vegetation sensor of SPOT satellite (SPOT-VGT). The leaf area index (LAI) derived from remotely sensed data was used in the simulation model WOFOST for predicting spatial yield. The simulated wheat grain yield for each grid was aggregated to district level using the actual wheat fraction for each grid derived from remote sensing-based wheat crop map. A comparison was made between the estimated yield and that reported by Department of Agriculture. The procedure was repeated for three crop seasons to check the reliability. The results indicated that this technique could be used for spatial yield prediction at regional level with a root mean square error (RMSE) of <0.4 tonnes ha −1 at state level.

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An attempt to derive a relation between spectral data of rice canopies and their grain yield has been made in the Cuttack and Puri districts of Orissa for Kharifrice using Landsat MSS digital data of three years (1984)(1985)(1986). Necessary sensor-to-sensor transformations of Landsat MSS to IRS LISS-I and acquisition date normal::zation with respect to a typical rice growth profile were also carried out. Using srch an empirical relation, rice yield was estimated for Kharif in 1988 using IRS-IA LISS-I data at district level. Deviation of the estimated yields from the reported figures varied from 2 per cent to 14 per cent at the district-aggregated level.