NovaSAR-1 is a very low-cost small SAR satellite. The mission key features and instrument specialism of the NovaSAR-1 satellite have been designed to make it suitable for a wide range of applications, such as forestry, disaster monitoring (particularly flooding), agriculture, and maritime observations (including ship and oil slick detection)[1]. Suitability of various applications of diverse kinds from low-resolution environmental observations to the thorough study of definite ground target areas demonstrates the potential of the NovaSAR-1 mission. The NovaSAR-1 mission has been developed with an application-focused approach based on medium resolution applications keeping in mind the fulfillment of user’s needs in the most lucrative way [1]. This mission is a quite special and different spaceborne SAR mission from all other previous missions launched for earth’s observation because its payload consists of an S-band SAR sensor, which is less common in spaceborne SAR systems [2], [3]. This mission is operating around 3.2 GHz frequency which is near to the C-band and between the SAR frequencies of X- and L-band. Therefore, the S-band SAR system can be reasonably expected to serve a similarly wide range of applications to those demonstrated by systems currently operating in the C-band[4]. S-band SAR may be expected to serve as a trade-off between high and low-frequency SAR systems to overcome the limitations of both high and low-frequency SAR systems up to some extent[5]. For example it is found successful in detection of below-canopy inundation in floodplain forests due to the specular doublebounce scattering in the tropical forest and wetlands regions of the Amazon Basin [6], maritime Surveillance [7], [8], terrestrial and marine environments [9] and data association or fusion techniques for object identifications [10].
The radar backscattering coefficient σ° provides quantitative information about the back-scattered radiation from images surface features. It is a function of both sensor and ground parameters including radar frequency, polarisation, the incidence angle of the electromagnetic waves emitted, roughness, geometric shape, and dielectric properties of the target [11]. The scattering characteristics of different surface features vary according to their structural, and dielectric properties [12]. for example, due to the structural properties, urban and manmade features exhibit double-bounce scattering which makes the appearance of these features very bright in SAR images; forest area shows intermediate backscatter due to the mixed scattering from leaves, stems, ground, and branches; calm water (smooth surface) looks very dark (low backscatter) because of high dielectric constant and impenetrability properties; rough sea exhibits increased backscatter due to the ripples and water waves formed by wind current effects.
In all polarization channels of SAR signal, the back-scattering coefficient has a strong correlation with the geological parameters of particular land cover such as surface roughness and soil moisture content [13], which make it useful for geologists. Forest cover mapping, delineation of deforestation, and tree height estimation using SAR tomography are some of the applications related to the field of forestry. SAR helps in the strategic operational planning in emergency or disaster scenarios by observing ground movements even in bad weather and nighttime condition. SAR images are found to be very much suitable for the identification of sea objects because of the very bright appearance of sea objects in SAR images against the dark sea surface in the background[14].
Studies regarding the assessment of back-scattering characteristics of different space-borne and airborne SAR sensors operating in X, C, and L-band frequency range has already been carried out and providing useful information in several dimensions of applications namely agriculture, maritime surveillance, geological study, forestry, military, and homeland security, glaciology and many more with some advantages as well as disadvantages. Already operating SAR sensors in high and low-frequency domains are either advantageous or disadvantageous due to their high or low-frequency nature when used for certain specific applications. This study is important because it assesses the performance of S-band SAR which is expected to serve as a trade-off between high and low-frequency SAR systems to overcome the limitations of both high and low-frequency SAR systems up to some extent. In several previous studies, S-band exhibited better performance when compared with C- and X-band SAR. S-band backscatter was found to have high sensitivity to the forest canopy characteristics across all polarisations and incidence angles which establishes the usefulness of S-band SAR data in the field of forestry[15]. SAR signals at shorter wavelengths (C- and X-bands) are known to saturate rapidly with forest biomass due to lower canopy penetration. The microwave pulse primarily interacts with the foliage and smaller branches in the upper canopy layers at these wavelengths[16]. The results of a study of soil moisture retrieval for X- and S-band using the Integral Equation Model (IEM) applied through Artificial Neural Network (ANN) showed a very good performance in S-band in comparison with X-band[17].
The presented study includes a preliminary assessment of back-scattering characteristics of S-band SAR datasets. The separability analysis of the radar backscattering coefficient of NovaSAR-1 S-band (Stripmap and ScanSAR) HH polarization datasets corresponding to different LULCs of the Indian region has been carried out. The range values for minimum and maximum mean σ० for urban, bare soil, forest, water, cropland, and road features were computed for separability analysis of different LULC. Three different parametric separability analysis methods namely Euclidean Distance, Transformed Divergence and Jefferies-Matusita have been carried out for the separability analysis of different LULC classes of experimental sites.