Following the objectives of the mission, Sentinel-2 (S2) makes a significant contribution to land monitoring, climate change, emergency management and security, and related problems. To increase the spatial resolution, researchers have been challenged to obtain a synthesized panchromatic band with a fine resolution for all S2 bands. The capacity to collect satellite imagery with a short revisit time at different spatial resolutions increases the ability for more accurate data fusion applications. Our work presents an investigation of different synthesized panchromatic bands for producing high-resolution S2 data. We produced a synthesized environment from PlanetScope (PS) data to evaluate the performance of different panchromatic bands for the enhancement of S2 10- and 20-m bands. For this, imagery over three different study areas with different characteristics was chosen: Pakistan, North Macedonia, and Turkey. After the three different synthesized panchromatic bands were produced, we fused the panchromatic bands with the 10- and 20-m S2 bands with three different state-of-the-art pansharpening techniques. Experimental comparison between the three newly produced panchromatic bands indicates that all synthesized bands can enhance the spatial resolution of the original multispectral bands. Since the difference in spatial resolution may be critical for more detailed image classification, for future studies, we recommend investigation of the fused data for different land cover applications.
Water monitoring is an important part of water resource management and has become an essential aspect of remote sensing. A number of indices have been developed for water extraction using satellite images. Even though all indices can extract the extent of a water body, none can do so without including a noise component, such as topographic shadows, cloud shadows, snow, ice, and buildup areas, all of which have spectrally similar characteristics under certain circumstances. In order to select the best index for water body extraction, several water indices have been compared. This paper proposes a method for extracting water bodies called the water extraction surface temperature index (WESTI). This method uses normalized difference water index (NDWI) and land surface temperature to eliminate the noise components, especially in mountainous and cold areas where other indices have very low accuracy. The results have shown that WESTI improves the NDWI results by removing more than 80% of topographic shadows, with an overall accuracy of 99% in all cases.
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