Integration of remote sensing data and simulation model to estimate rice yield

Remote sensing techniques have the potential to provide information on agricultural crops quantitatively, instantaneously and above all nondestructively over large areas. Crop simulation models describe the relationship between physiological processes in plants and environmental growing conditions. The integration between remote sensing data and crop growth simulation model is an important trend for yield estimation and prediction. In this study, a new model (Rice-SRS) was developed based mainly on the ORYZA1 model and modified to accept remote sensing data as input from different sources. The model can accept three kinds of NDVI data: NOAA AVHRR (LAC)-NDVI, NOAA AVHRR (GAC)-NDVI and radiometric measurements NDVI. The integration between NOAA AVHRR (LAC) data and simulation model as it applied by Rice-SRS resulted in accurate estimation for rice yield in the Shaoxing area, characterized by reducing the estimating error to 1.027%, 0.794% and (-0.787%) for early, single and late season respectively. Utilizing NDVI data derived from NOAA AVHRR (GAC) as input in Rice-SRS can offer good estimation for rice yield with average error (-7.43%). Testing the new model for radiometric measurements was successful, since the average estimation error for 10 varieties under early rice conditions was less than 1%