Remote Sensing-Based Yield Forecasting for Sugarcane (Saccharum officinarum L.) Crop in India

Early yield assessment at local, regional and national scales is a major requirement for various users such as agriculture planners, policy makers, crop insurance companies and researchers. This current study explored a remote sensing-based approach of predicting sugarcane yield, at district level, using Vegetation Condition Index (VCI), under the FASAL programme of the Ministry of Agriculture & Farmers' Welfare. 13-years' historical database (2003-2015) of NDVI was used to derive the VCI. NDVI products (MOD-13A2) of MODIS instrument on board Terra satellite at 16-day interval from first fortnight of June to second fortnight of October (peak growing period) were used to calculate the VCI. Stepwise regression technique was used to develop empirical models between VCI and historical yield of sugarcane over 52 major sugarcane-growing districts in five states of India. For all the districts, the empirical models were found to be statistically significant. A large number of statistical parameters were computed to evaluate the performance of VCI-based models in predicting district-level sugarcane yield. Though there was variation in model performance in different states, overall, the study showed the usefulness of VCI, which can be used as an input for operational sugarcane yield forecasting.