ANNOUNCEMENTS
The substantial increase in agricultural food production is one of the key targets to supports the rapidly rising global population. Since the population has been growing in a rapid manner the global food security is a major challenge which can be faced in this century. India being an agrarian country, 50% of the population’s livelihood depends on the agricultural sector so, the regular monitoring and assessment of agricultural crops are essential to avoid the impacts of any un-foreseen incidents. The accurate estimation of crop specific sown area and accurate mapping of crop distribution are the key steps to monitor crops. So, this study aims to classify in-season crop accurately and simulation of crop phenology. For classifying in-season crop different sensors and approaches has been used to classify the crop types accurately over Nakashipara region. Sentinel-2A, Landsat, Hysis and NDVI time-series data, and NDVI NDWI threshold-based classification has been used to classify the crop types accurately over the time. In which, Snetinel-2A gives more accurate results than the other two sensor and NDVI time series profile-based classification reached the maximum accuracy compare to those methods. For the simulation of crop phenology two approaches has been used one is derivative approach by which we estimate the maximum greenness of mustard crop and the other one is depending on sowing date criteria. The validation has been made for the simulation process in which the results are very closely related to estimated maximum greenness. From this simulating result we can estimate crop phenology for any crop by knowing its sown date.
Keywords: Phenology, NDVI, NDWI, Hysis, Derivate approach, In-season crop
