## 1 Introduction
Traditional pixel-based approaches often struggle in heterogeneous landscapes, leading to the so-called \"salt and pepper effect\", where spatial relationships between pixels are ignored, resulting in fragmented and contextually poor outputs [31, 32]. This phenomenonoccurs because pixel-based methods analyze each pixel independently, based solely on its spectral properties (e.g., reflectance or radiance values), without considering the spatial relationships or contextual information between neighboring pixels. In heterogeneous landscapes, where landcover types vary significantly over short distances, this approach fails to capture the continuity and spatial patterns of land surface features. For example, in urban areas, a single pixel might cover parts of a building, a road, and vegetation, leading to mixed spectral signatures that are difficult to interpret accurately. As a result, the output often appears as a noisy, patchy image with abrupt transitions between pixel values, resembling the scattered appearance of salt and pepper--hence the term \"salt and pepper effect\". This lack of spatial coherence and contextual understanding limits the ability of pixel-based methods to accurately represent complex land surface processes, particularly in areas with high spatial variability. In contrast, OBIA not only considers the spectral properties of pixels but also integrates spatial information, such as shape, texture, and neighborhood relationships, significantly improving the ability to identify and classify complex land surface features [39]. This approach has been successfully applied in various domains, such as landcover classification [30,31], forest structure analysis [29], and damage assessment. Compared to single pixels, the \"objects\" generated by segmentation offer significant advantages in terms of spectral information (e.g., the statistical values per band: mean, minimum, maximum values, mean ratios, etc.) and spatial dimension information (e.g., distance, neighborhood, topologies, etc.) [44, 45, 46]. The OBIA method can efficiently overcome the so-called \"salt and pepper effect\" phenomenon, which is prevalent in traditional pixel-based image analysis [47]. Recent attention has been drawn to using high-resolution satellite imagery (e.g., IKONOS, QuickBird, Worldview) or relatively high-resolution multiscale satellite imagery (e.g., ASTER, Landsat TM) to evaluate forest land use structures [48,49], enhance the accuracy of landcover/land use information in heterogeneous areas [50,51], and assess damage caused by building collapses during earthquakes and conflicts. Despite these advances, little work has been carried out on LST downscaling using OBIA, as OBIA is traditionally an upscaling process that aggregates high-resolution pixels into relatively lower-resolution \"objects\". However, by reversing the conventional OBIA process, could this approach yield improved results in LST downscaling? The objective of this paper is to introduce the OBIA method for MODIS LST downscaling, using relatively high-spectral-resolution multispectral imagery (e.g., Landsat TM, ETM+, ASTER) as auxiliary data [39,52,53]. The key breakthrough in this study lies in the proper creation of objects and maintaining their thermal radiance after downscaling. The proposed approach involves three main steps: the initial radiance estimation of the parent object, the decomposed radiance of sub-objects, and the final temperature determination [12,54,55,56]. After outlining the theoretical principles and image processing procedures of the OBIA-based downscaling (OBD) approach, we validate its accuracy by comparing it with various pixel-based approaches (PBAs) through a designed experiment. Consequently, this paper is organized with a Section 2 presenting the details of the OBD approach, a Section 3 to validate and discuss the approach, and a Section 4 summarizing the key findings of this study.