Although Normalized Difference Vegetation Index (NDVI) has ever been one of the widely used indices for remote sensing based agricultural analysis, its non-periodic and asynchronous availability in terms of phenological phase of the vegetation restricts applicability in precision farming. In this study we proposed a new model that generates spatiotemporal synthetic NDVI data that can be used on parcel level analysis. Continuous time fractional vegetation cover (FVC) measurement from spatially distributed agricultural observation network and asynchronous multi-temporal NDVI data from high resolution remote sensing satellite images are used in an adaptive manner. High resolution satellite images are needed to create NDVI time series and to detect changes at parcel resolution. The disadvantages of having too few images for a given season could be overcome by carrying out additional ground measurements. Spectral measurements are laborious and prone to errors and thus automatic measurements have to be performed. In studies like Kastens et all or Calera et all, it has been demonstrated the linear relation between NDVI and Leaf Area Index (LAI) and also between NDVI and plant cover.