LAUSR

Image-based fusion is a state-of-the-art process to extract vital information by combining the two or more images acquired from different satellite sensors. Recently launched (September 26, 2016) Indian Space Research Organization’s (ISRO) Ku-band (13.5 GHz)-based Scatterometer Satellite (SCATSAT-1) as an active microwave sensor can offer the day–night, all-weather monitoring services, which are not possible with the optical-based visible and infrared remote sensing satellites. Therefore, the fusion of optical and microwave data offers the cloud-free detection of Earth surface transitions and helps in emergency response to natural hazards, security, and defense. The objectives of the proposed framework are: 1) nearest neighbor-based fusion (NNF) of ISRO’s SCATSAT-1 and National Aeronautics and Space Administration’s (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) optical data; 2) generation of thematic maps using artificial neural network (ANN)-based classification of the fused data; 3) detection of spatiotemporal variations via postclassification comparison (PCC)-based change detection; 4) cross-referencing with well-defined fusion methods, i.e., Gram–Schmidt (GS), Brovey transformation (BT), and Ehlers; and 5) impact analysis of clouds on the input dataset and fusion methods. This study has been conducted over the Western Himalayas to estimate the snow cover changes under cloudy conditions with two datasets, i.e., winter and monsoon. The experimental outcomes confirm the efficacy of the proposed framework in the effective removal of clouds, generation of classified maps, and change maps. The present study includes an exhaustive list of applicative situations for cloud-free monitoring using freely and daily-based SCATSAT-1 and MODIS datasets.