In particular, PRISMA data have shown excellent results in spectral separability compared to multispectral data [34]. Other platforms are also expected to become available soon, such as CHIME (Copernicus Hyperspectral Imaging Mission for the Environment), developed by the European Space Agency [27]. Hyperspectral data at a spatial resolution of 30 m \(\times\) 30 m are excellent for classifying images in natural environments [34]. However, this spatial resolution is not ideal in urban environments, where images are typically composed of spectrally mixed pixels [35], canopies are fragmented, and are often made up of different species grouped close together [2]. Viable techniques that overcome these limitations and increase the potential of using HIS for urban classification include spectral unmixing and subpixel analysis [35; 36]. Recent developments in EO technology have also introduced alternatives that improve the spatial resolution of hyperspectral data using pansharpening, if the panchromatic band is available, or hypersharpening when data are fused with higher spatial resolution data, if available [37; 38]. Previous studies [37; 38; 39] have explored the potential of hypersharpening by showing that it is appropriate to use multispectral data with a medium resolution satellite, such as Sentinel 2, for data fusion with hyperspectral data. This technique requires both overlapping acquisition dates, as well as very reliable geocoding and the spatial alignment of the two datasets [39]. Pre-processed products that are easily and directly usable are provided by satellite data distribution agencies. For example, ASI distributes the L2D PRISMA data, an atmospherically corrected and orthorectified reflectance data [30]. Of course, the orthorectification process is an essential step in data fusion involving products from different sensors, especially regarding areas with complex topography. Moreover, despite the convenience of L2D level preprocessing, the PRISMA technical documentation [40] guarantees a planimetric accuracy of no more than 200 m for standard preprocessing without ground control points (GCPs). Thus, fusing data from different satellites in urban areas poses significant challenges related to such factors as the presence of buildings and complex topography. Unless spatial misalignments are carefully addressed to avoid unreliable approximations, these procedures produce outcomes that are not always predictable and may be more complex compared to pansharpening. In hypersharpening, algorithms must work with a sharpening image that was acquired by a different satellite and consequently shifted in time and taken from a different angle [38; 41]. The use of coregistered panchromatic images would avoid these problems. It should be noted that pansharpening only improves the spatial resolution since it only uses the panchromatic band [38]. It is therefore of interest to understand whether the pansharpening techniques often used to improve image interpretation can also be used to improve data classification. One of the objectives of the PRISMA satellite, which is a precursor to the application mission, is to test the feasibility of pansharpening hyperspectral data using a coregistered pan image [30; 42], which can provide greater reliability and simplicity of use. This paper aims to respond to this challenge and show a case study example of a practical application. Specifically, this study's main objectives are:  
1. To evaluate the contribution of pansharpening to improving image classification accuracy using hyperspectral data so that it is also useful in an urban setting;
2. To apply the method to a real-world problem, specifically urban tree monitoring, through a simple pansharpening application that only uses the more easily accessible panchromatic band. To achieve the first objective, results derived using an unrefined PRISMA image with a 30 m ground sampling distance (GSD) are compared to results obtained from classifying an enhanced PRISMA image that has a 5 m spatial resolution achieved through pansharpening.