A variety of methods have been developed to estimate crop yields using remotely sensed information including biophysical crop-simulation models that retrieve crop growth parameters from remotely sensed data and which are used as inputs to calibrate and drive the models. The main drawback of such models is that they typically require numerous crop
specific inputs such as soil characteristics, management practices, agro-meteorological data and planting dates, in order to simulate crop growth and development through the crop cycle