ACMapping: Agricultural Crop Mapping/Retrieval Using Synthetic Aperture Radar (SAR) Data

Agriculture appears to play a significant role in both a nation’s food security and economic development. Image processing techniques can be applied to Synthetic Aperture Radar (SAR) images with high spatial resolution. These images are backscattered to overcome the noises like clouds, smoke, rain, trees, etc. Traditionally Optical images are used for crop monitoring which has less brightness of pixel as it depends only on one variable, the amount of light reflected by the earth’s surface. Whereas in SAR images the brightness of each pixel primarily depends on at least 8 variables which give high resolution than optical images. The main aim of our project is to develop a model in Agriculture Field for Crop Growth Monitoring using SAR data. We used Sentinel 2 imagery data and processed them with Advanced Remote Sensing techniques. For crop monitoring analytics, there are different types of Vegetation Indices available. These indices help in remote sensing by providing useful insights of crops. In general, the Normalized Difference Vegetation Index (NDVI) is the most widely Vegetation Index to report biomass, density due to its versatility and credibility and it gives better accuracy during the whole crop production time. Using this parameter, we develop a methodology for Agricultural Crop Mapping/Retrieval using SAR images.