Fuzzy supervised classification of JERS-1 SAR data for soil salinity studies

Remote sensing of surface features has been intensively used to identify and map salt-affected areas. Salt-tolerant vegetation is among the indicators used to separate saline-alkaline areas from non-affected ones. However, the vegetation cause spectral confusion and erroneous labelling between salinity and alkalinity classes when working with optical sensors such as Landsat TM or Spot. Thus, this paper evaluates the capabilities of the L-band of the Japanese JERS-1 SAR radar satellite to resolve spectral confusions caused by topsoil textures and vegetation, thus improving the detection and mapping of areas degraded by salinity processes. A fuzzy overlay model is used to classify the JERS-1 radar satellite image. The study shows that fuzzy classification of JES-1 SAR data provides overall reliable detection of areas degraded by salinity-alkalinity processes