The Extraction Method of Rape Oil Planting Distribution Using Per-Field Classification Based Optical and Sentinel-1 SAR Images Data

The aim of this research was to improve the accuracy of crops distribution classification using Sentinel-1 SAR data. The method involves extracting the crop field border using ZY-3 satellite multi-band optical data based on image segment and merge methods, then averaging the RADAR backscatter coefficient of SAR data within each object which eliminates the influence of coherent speckle noise. Through the analysis of SAR backscattering characteristics of various ground objects which created pure pixels (identified by ground investigating using GPS), we found that the backscattering coefficient values were higher than other crops in March and April. We determined that the rape oil plant areas were characterized with SAR backscattering coefficient values being more than 2.1 and less than 3.5, and the NDVI index being greater than 0.3 on 27th February (filtered for the nonplanting areas). Using these rules and SAR data that eliminated the influence of coherent speckle noise, we calculated rape oil planting spatial distribution in Jiangling County. The classification results were verified using GPS within a 5.35 km2 (length 2.57 km, width 2.08 km) area which had wheat and rape oil planted, and the KAPPA coefficient was 0.88. The accuracy of the spatial distribution of rape oil plants was improved over the use of traditional classification methods. The SAR data from Sentinel-1 is not affected by clouds, therefore data needed for this method can be obtained regularly. This method is suitable for crop cultivation spatial distribution information needed for business operations.