An Integrated SCATSAT-1 and VIIRS Nighttime Light approach for Urban Mapping in Arid Zone

Hybrid Classification, Nighttime Light, Random Forest, SCATSAT-1, Urban Mapping.

Binay Kumar Singh1*,
Department of Computer
Science, Vivekananda Global University, Jaipur
and Department of Remote Sensing &
Geoinformatics, Birla Institute of Technology,India.
E-mail: binaykrsingh@bitmesra.ac.in
Orcid Id: 0009-0005-9474-6261

Surendra Yadav2,
Department of Computer
Science, Vivekananda Global University,
Jaipur, India.
E-mail: surendra.yadav@vgu.ac.in
Orcid Id: 0000-0002-6519-4788
Madan Mohan Agarwal3, Department of
Computer Science, Birla Institute of
Technology, India.
E-mail: mmagarwal@bitmesra.ac.in
Orcid Id: 0000-0003-4275-7485

Received:09-06-2026
Revised:15-07-2026
Accepted:23-07-2026

European Journal of Prosthodontics and Restorative Dentistry (2026) 34(5s), 114–124

An Integrated SCATSAT-1 and VIIRS Nighttime Light approach for Urban Mapping in Arid Zone

The atmospheric dust, extreme climate, and cloudy weather conditions during the monsoon season are the major challenges that are facing the urban monitoring in arid and semi-arid regions such as Rajasthan, where optical remote sensing data is often hard to obtain. In view of these limitations, this study suggests a hybrid approach to regional urban mapping in Rajasthan, India, using scatterometer data from the SCATSAT-1 satellite and VIIRS data (NTL). Areas of built-up and non-built up status were obtained using Ku-band backscatter data (σ0) with a spatial resolution of approximately 2 km. A Random Forest (RF) classifier was trained with well distributed data samples from the whole of Rajasthan collected in the month of May 2017. The classification results of SCATSAT-1 radar data were fused with NTL Radiance thresholding to overcome the inherent ambiguity in radar data between rough dry soil and urban structures. The study illustrates how SCATSAT-1 data when combined with NTL data offers a fast and weatherindependent solution for a large-scale urban monitoring and sprawl assessment in arid regions.

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