## 4 Conclusions  
An efficient approach has been proposed in this study to enhance the spatial resolution of MODIS LST products. The object-based downscaling (OBD) method was introduced in the LST downscaling process for MODIS LST, using high-resolution multispectral images (e.g., Landsat TM, ETM+, ASTER) as ancillary data. A unique feature of this approach is the preservation of the thermal radiance of the \"object\", composed of several MODIS LST pixels, even after disaggregation into subpixels in the resultant image. The decomposition process primarily involves two critical components: the estimation of thermal radiance (TR) from MODIS LST products and the calculation of weights for sub-objects or subpixels. These objects are generated using VNIR data and various remote sensing indices (e.g., the NDVI, the NDBI, and fractions of different endmembers) through a multiscale segmentation method. The radiance of subpixels or sub-objects is calculated based on their weights within the parent objects, which are estimated using the relationships between remote sensing indices and the LST. A series of procedures were developed for this pixel decomposition approach, including multi-resolution segmentation (MRS), which was implemented in the eCognition 10.1 software to create image objects by grouping pixels, establishing relationships between the LST and the RSI surface patterns, computing the weights for each subpixel, and determining the final temperature for the subpixels. The comparison results demonstrate that the OBD approach outperforms the PBA method, as indicated by lower ME and RMSE values across natural terrain, urban surfaces, and water bodies. The analysis reveals that two main factors--the radiance of the object and the weights of the subpixels or sub-objects--are  
Figure 11: Applicability in other regions: (**a**) The study area of the region. (**b**) A color-composite image of the region (RGB: 432). (**c**) MODIS LST product. (**d**) OBD LST decomposed result. crucial in the OBD method. Radiance errors often arise from cases where objects do not fully cover the MODIS pixel, and these errors increase with higher LST values under consistent radiance errors. The simulation of weight estimation errors for subpixels or sub-objects further confirms that the OBD method can improve the ME and the RMSE compared to the PBA method. The weight of the subpixels within the same object derived from the initial LST calculations is the most important factor for LST decomposition accuracy by the object's segmentation, classification, and LST between different RSIs. Additionally, by incorporating the relationship of adjacent pixels in the downscaling process, the OBD method ensures that the decomposed LSTs of subpixels are much closer to their true LST values. The OBD approach was applied to MODIS LST products in the eastern USA for LST downscaling, using ASTER and ETM+ VNIR data as ancillary inputs. The downscaled LST results not only increased the spatial resolution of the MODIS LST by over 30 times but also accurately reflected the distribution of the LST values observed by the ASTER LST product. More detailed spatial distributions of LST can be clearly identified in the downscaled LST images compared to the original MODIS LST. Thus, it can be concluded that the OBD approach offers a viable alternative for enhancing the spatial resolution of MODIS LST products, thereby expanding their applicability in real-world scenarios. [PERSON] and [PERSON] were principal to all phases of the investigation, including the idea for the project, modeling, analysis, and the simulation of the algorithm. [PERSON] contributed to the adaptation of the algorithm and the interpretation of modeling results. All authors have read and agreed to the published version of the manuscript. This study is supported by the Central Public-interest Scientific Institution Basal Research Fund, CAFS (NO. 2022 ZD0401) and the Postgraduate Research and Practice Innovation Program of Jiangsu Ocean University (KYCX2023-57). Not applicable.