Analysis of multi-frequency and multi-polarization SAR data for wetland mapping in Hamoun-e-Hirmand wetland

The complex, dynamic and narrow boundaries between vegetation types make wetland mapping challenging. Hereafter the case study of the Hamoun-e-Hirmand wetland is considered by analysing eight Synthetic Aperture Radar (SAR) Images acquired in dry and wet periods with three wavelengths (X-band similar to 3 cm, C-band similar to 6 cm, and L-band similar to 25 cm), three polarizations (HH, VV and VH), and four incidence angles (22 degrees, 30 degrees, 34 degrees and 53 degrees). Then, the Support Vector Machine (SVM) classification method was applied to classify TerraSAR-X, Sentinel-1, and ALOS-PALSAR images. The final wetland land cover map was created by combining the classification results obtained from each sensor. In the case in question, results show that TerraSAR-X (X-band, HH-53 degrees) and Sentinel-1 data (C-band, W-34 degrees) were useful for determining the flooded vegetation area in the wet period. This is crucial for the conservation of water bird habitats since flooded vegetation is an ideal environment for the nesting and feeding of water birds. PALSAR data (L-band in both HH and VH polarizations, 30 degrees) were capable of separating the classes of vegetation density in the wetland. In the dry period, Sentinel-1 (VV and VH, 34 degrees) and TerraSAR-X (HH, 22 degrees and 53 degrees) had higher potential in land cover mapping than PALSAR (HH and VH, 30 degrees). Based on these results, Sentinel-1 in VV and VH provides the highest ability to discriminate between dry and green plants. TerraSAR-X is better for separating meadow and bare land. The results obtained in this paper can reduce the ambiguity in selecting satellite data for wetland studies. The results can also be used to produce more accurate data from satellite images and to facilitate wetland investigation, conservation and restoration.