Abstract
Earth observation via optical-based remote sensing is one of the effective solutions to cover the large swath and to deliver the very high-resolution dataset at the different wavelengths. But the applicability of optical imaging is limited by daytime only and adversely affected by the presence of clouds. In such scenarios, microwave data is more preferable due to the potential of penetrating through the clouds. Recently launched (26 September 2016) scatterometer satellite (SCATSAT-1) data by the Indian Space Research Organization (ISRO) has the potential of providing all-weather, day-night monitoring and daily data-delivery services at the global level. Along with the numerous advantages, the Ku-band (13.535 GHz) based SCATSAT-1 cannot provide sufficient information as provided by the multispectral optical sensors. Therefore, in the present work, the microwave-based SCATSAT-1 and optical-based MODIS (moderate resolution imaging spectroradiometer) have been fused using the nearest-neighbour approach to examine its effects in cloud removal and its applications in classification. The study has been performed over Himachal Pradesh, India. This study has also discussed the impact of different classifiers such as artificial neural network (ANN), spectral angle mapper (SAM), support vector machine (SVM), and random forest (RF), on the fusion of SCATSAT-1 (including backscattered coefficients, i.e. sigma-nought and gamma-nought at HH and VV polarizations) and MODIS dataset. Experimental results have confirmed that the accuracy of implemented classified maps significantly increases with the fusion of both datasets as compared to the individual implementation of SCATSAT-1- and MODIS-classified maps. From quantitative analysis, the RF classifier performs better as compared to other classifiers, i.e. ANN, SAM, and SVM on the fused dataset. This study has many applications in the near real-time monitoring of snow/ice, agriculture activities, and hydrological studies.
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Acknowledgements
The authors would like to express their gratitude to the anonymous referees and the editor for their constructive comments and valuable suggestions. Dr. Sartajvir Singh wishes to thank the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), India, for research fellowship under the Teachers Associateship for Research Excellence (TARE) programme. The authors would like to thank the Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC), Indian Space Research Organisation (ISRO) and Land Processes Distributed Active Archive Center (LP-DAAC)/National Snow and Ice Data Center (NSIDC), and National Aeronautics and Space Administration (NASA) for providing the scatterometer satellite (SCATSAT-1) and moderate resolution imaging spectroradiometer (MODIS) data respectively for research purposes.
Funding
This research work is financially supported by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), India, under Teachers Associateship for Research Excellence (TARE) (Grant No. TAR/2019/000354).
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Responsible editor: Biswajeet Pradhan
This paper was selected from the 3rd Conference of the Arabian Journal of Geosciences (CAJG), Tunisia 2020
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Singh, S., Tiwari, R.K., Sood, V. et al. Fusion of SCATSAT-1 and optical data for cloud-free imaging and its applications in classification. Arab J Geosci 14, 1978 (2021). https://doi.org/10.1007/s12517-021-08359-7
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DOI: https://doi.org/10.1007/s12517-021-08359-7