Bridging the Time-Space Scale Gap: A Physics-Informed UAV Upscaling Framework for Radiometric Validation of Microsatellite Constellations in Heterogeneous Built Environments

The exponential rise of microsatellite constellations offers unprecedented temporal resolution for urban monitoring. However, ensuring the radiometric integrity of these sensors over heterogeneous built environments remains a critical challenge due to low signal-to-noise ratios and spectral uncertainties. Traditional vicarious calibration relies on homogeneous pseudo-invariant calibration sites (PICS) in deserts, which fail to represent the spectral complexity and adjacency effects of urban landscapes. This study presents a novel triple-platform validation framework integrating ground (Hyperspectral), UAV (Multispectral), and satellite (Sentinel-2) data to bridge the "Point-to-Pixel" scale gap. We introduce a physics-informed "Double Calibration" protocol—combining the empirical line method with spectral response function convolution—and a block kriging spatial upscaling technique to mathematically model intra-pixel heterogeneity. Results from a 2025 campaign in a complex urban environment (Cheongju, Republic of Korea) demonstrate that simple point-averaging introduces significant representation errors (R^2\approx0.46 with time lag). In contrast, our UAV-based block kriging approach recovered high correlations even with a 1-day time lag and dramatically improved the coefficient of determination (R2) under simultaneous acquisition conditions: from 0.68 to 0.92 in the blue band and to 0.96 in the NIR band. Furthermore, quantitative spatial analysis identified artificial grass as the most stable "Urban PICS" (\sigma\approx0.020), whereas Asphalt exhibited unexpected high spatial heterogeneity (\sigma>\ 0.09) due to surface aging, challenging conventional assumptions. This framework establishes a rigorous, scalable standard for validating "New Space" data products in complex urban domains.