Objective Absorbing aerosols, particularly black carbon (BC), exerts significant influence on the Earth's radiation budget by modifying both the amount and vertical distribution of solar radiation. Their climatic effects are especially pronounced in regions characterized by concentrated fossil fuel activities, such as large-scale coal mining areas. However, the spatial and temporal variability of their microphysical and optical properties introduces considerable uncertainty into regional radiative forcing assessments. The Zhundong Coalfield, located in eastern Xinjiang, China, is one such region where BC emissions from coal extraction and associated industrial activity are persistent yet under-characterized from a radiative perspective. This study aims to construct a rapid estimation framework for aerosol radiative forcing (ARF) over this region by integrating multi-band satellite observations with physically based scattering and radiative transfer models. The primary goal is to evaluate how aerosol optical depth (AOD), single scattering albedo (SSA), and particle size influence shortwave ARF at the top of the atmosphere (TOA), bottom of the atmosphere (BOA), and within the atmospheric column (ATM), and how ultraviolet-band data enhances the reliability of this estimation. Methods The research adopts a modular approach comprising aerosol property inversion and radiative transfer modeling. The aerosol inversion is based on a Mie scattering model incorporating a core-shell structure assumption, where BC forms the absorbing core and is coated by non-absorbing substances such as sulfate and nitrate. Satellite-derived aerosol products are used to constrain the model: MODIS provides AOD and SSA at visible wavelengths, while OMI contributes ultraviolet (UV) -band SSA and AOD information. Two experimental configurations are established-one based solely on MODIS data, and another integrating both MODIS and OMI-to assess the role of UV spectral information in constraining aerosol characteristics. Following inversion, the retrieved aerosol size and optical parameters are used as input to the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer) model to simulate instantaneous ARF at TOA, BOA, and ATM under clear-sky conditions. Radiative forcing is calculated as the difference in net shortwave flux with and without aerosols. Multiple linear regression models are then constructed using different combinations of AOD, SSA, and core radius to quantify the relationship between these parameters and simulated ARF. Regression performance is evaluated using R (2) and RMSE statistics across both single-source and combined-source scenarios. Results and Discussions First, the inclusion of OMI UV-band data significantly improves the inversion accuracy of aerosol particle size characteristics. When only MODIS data are used, the retrieved BC core sizes are relatively narrow, mostly centered around 120 nm, and the shell diameters exhibit limited variation. However, when OMI UV observations are incorporated, the core size distribution broadens, capturing particles ranging from 90 to 160 nm, while the shell diameter spans a wider interval of 300?700 nm. This improved resolution stems from the stronger sensitivity of UVs to absorption by fine-mode particles, which enhances the model's ability to distinguish subtle differences in particle morphology. The resulting total particle size distributions-core plus shell-are more consistent with reported field measurements in coal-intensive regions. These results confirm that UV data not only improve inversion detail but also reduce the uncertainty in the wavelength in the representation of aerosol mixing states. Second, the quantitative relationship between optical parameters and ARF demonstrates clear physical consistency across TOA, BOA, and ATM layers. In both MODIS-only and MODIS-OMI configurations, AOD exhibits a strong negative correlation with TOA and BOA radiative forcing (R=-0.77 and -0.78, respectively), indicating a cooling effect due to enhanced scattering and absorption of incoming solar radiation. SSA also shows a strong negative correlation with TOA and BOA forcing (R=-0.78 and -0.62, respectively), suggesting that as the aerosol becomes more scattering-dominant, its net radiative cooling effect intensifies. Conversely, AOD shows weaker but positive correlations with ATM forcing (R=0.43), suggesting an increase in atmospheric heating when aerosol loading or absorption increases. This pattern aligns with physical expectations: absorbing aerosols like BC trap energy in the atmosphere, contributing to vertical energy redistribution. The analysis confirms that SSA has a stronger explanatory power than AOD, emphasizing its role as a key driver of radiative uncertainty forcing. Third, regression model performance improves markedly with the inclusion of SSA and core size as input parameters. Under the MODIS-only scenario, models using AOD alone yield limited explanatory power, withR (2) values of 0.59 (TOA), 0.61 (BOA), and 0.18 (ATM). Adding SSA improves the fits substantially, increasingR (2) to 0.78 (TOA) and 0.67 (BOA), and to 0.21 in the ATM. Incorporating core radius into the model yields additional gains, raisingR (2) in the ATM layer to 0.23 and lowering RMSE values across all layers. In the MODIS-OMI fusion scenario, even though the number of valid observation days decreases significantly (eg, from 2589 to 954 days at the Wucaiwan site), model performance continues to improve. For example,R (2) for ATM forcing increases from 0.18 to 0.29, and RMSE decreases from 2.04 to 1.85. These results suggest that high-spectral-resolution UV data provide greater constraint on aerosol absorption properties, thereby enabling more physically consistent radiative forcing estimates, even with reduced samples. This finding supports the robustness of UV-enhanced satellite inversion strategies in regional ARF modeling. Conclusions This study presents a data-model integration framework for estimating ARF over coal mining regions using multi-source satellite observations and physically based scattering and radiative transfer models. The combination of MODIS visible and OMI ultraviolet aerosol products improves the inversion of absorbing aerosol particle size distributions and enhances the retrieval of SSA, especially under complex mixing conditions. The constructed regression models reveal that SSA exerts a greater influence on radiative forcing than AOD, and that including particle size parameters further strengthens model reliability. Despite a reduction in observational frequency due to OMI's narrower sampling, the incorporation of UV-band information leads to consistently improved model performance across all atmospheric layers, particularly in the atmospheric column. These results highlight the critical role of spectral diversity in satellite remote sensing for accurately characterizing the radiative impacts of absorbing aerosols, and demonstrate the feasibility of applying such approaches to high-emission, data-scarce environments like the Zhundong Coalfield.