Hybrid classification-clustering approach for export-non export grape area mapping and health estimation using Sentinel-2 satellite data

Early information on crops spatial extent is useful from food security standpoint. Growing number of remote sensing satellites are making it easier to map the crop areas in a rapid and cost-effective way. Grape being an commercial fruit crop of India, it is useful to map the grape areas for acreage estimation and monitor its health continuously to get optimum yield. The main contribution of this paper is mapping areas under export and non-export grapes and their health estimation based on hybrid classification-clustering approach using Sentinel-2 satellite data. Nashik district of Maharashtra, India was chosen as study area which is known to be a famous grape belt. We have used Sentinel-2 image of 31st December 2016 to carry out the classification. Two step classification approach have been followed wherein first step involves classification of grape and non-grape areas using broadband spectral features. Normalized Difference Vegetation Index (NDVI), Land Surface Water Index (LSWI) and Tasseled Crop Transformation (TCT) components viz. Brightness (BR), Greenness (GR) and Wetness (WT) estimated using Sentinel-2 data and slope estimated from ASTER DEM were used during the first step. We evaluate the performance of classifiers such as Artificial Neural Networks (ANN), Random Forest (RF) and Support Vector Machine (SVM). Result shows RF performs best among all classifiers with overall classification accuracy of 93.57 % on validation dataset. Further, comparison of grape areas estimated using our proposed approach with statistics obtained from district agriculture department shows deviation of only 1.84 % in the total grape area. Second step involves classification of export and non-export grapes. Texture features such as entropy, homogeneity and dissimilarity estimated from red-edge bands (Bands 5,6,7) of sentinel-2 have been fed as an input to the same set of classifiers. Results depict the maximum classification accuracy of 81.82 % using RF classifier. Further, estimated export grape area was compared with district agriculture statistics which shows the deviation of 7.98 %. It is also important to monitor the crop health continuously to get the better yield and returns. We have further carried out the health estimation of non-export grape areas. The objective was to get the clusters of healthy, low stressed and high stressed grape areas. ISODATA clustering has been carried out using the indices such as Difference Vegetation Index (DVI), Inverted Red-Edge Chlorophyll Index (IRECI) and NDVI45, Red-Edge Position (REP) and NDVI. Comparison of clusters with ground truth yield data of 31 fields shows that the fields with high stress have obtained lesser yields than others. We envision to provide health related information to the farmers through our existing mKRISHI(îRplatform for entire crop season using proposed approach.