Modeling Seasonal Food Grain Production using GIMMS NDVI at State and National Level over India.

  • A. T. Jeyaseelan National Remote Sensing Agency, Hyderabad
  • P. S. Roy National Remote Sensing Agency, Hyderabad


Though many forecast models for predicting food grain productions are available, but most of them are based on inadequate data on rainfall, random sampling on crop cutting experiments and market inflow statistics. Now that the satellite observations are available over decadal time scales, it is being used in the model studies. This study focuses on the development of linear regression models for seasonal food grain production for each state and at national level using the GIMMS NDVI data from 1981 to 2003 over India. The models developed using 1982-2000 data has predicted seasonal food grain production during 2001-2002 and 2002-2003 within 10% at national level and within 20% at state level over food grain dominated states. Further the inclusion of area weighted rainfall along with area Integrated NDVI improves only the Kharif production relations with higher R2 and did not significantly improve the relations for Rabi and the annual production that suggests that the use of area integrated NDVI and area weighted rainfall for modeling seasonal food grain production in the country.