Authors : R. R. Nagpure
Page Nos : 414-418
Description :
Abstract: Crop identification with remote sensing data facilitates only when crop has sufficiently grown-up. However, forecasting of crop at sowing stage would require use of weather data and information on economic factors controlling the farmer\'s response. Time series remote sensing data is being used to monitor the crop through its growing period. Vegetation indices and weather parameter derived from surface and satellite observations are used to develop the crop growth monitoring system. Present paper deals with the methodology adopted for discrimination of special crop using NDVI time series derived from multi-date Satellite data. Study area covers the dominant Rabi crop growing districts of Maharashtra state. Multi-date satellite data for rabi season of 2013-14 have been used. Two-stage classification of this dataset by unsupervised Iterative Self Organizing Data Analysis Technique, labeling of classes based on temporal spectral profiles of Rabi Sorghum and other competing crops and decision rule based integration were followed to generate final classified image. This hybrid classification technique takes advantage of inherent clustering tendency of vegetation and non-vegetation classes in feature space with temporal dimension added to it in terms of Normalized Difference Vegetation Index (NDVI) time series data. It also makes use of signatures of known crop classes for labeling the clusters. Satellite data, being coarse, intricacies involved in its spatial accuracy are not covered in this study as it aims at acreage estimation only. Special Rabi crop acreage estimated by this approach for the state is 3721.12 (\'000\') ha. The only purpose of multi-forecasting at state level stage serves well by adopting this hybrid approach.
Keywords: Satellite data, Temporal profiles, Crop Acreage Forecasting.
