The exponential rise in 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 (R2≈0.46 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” (σ≈0.020), whereas asphalt exhibited unexpected high spatial heterogeneity (σ> 0.09) due to surface aging and challenging conventional assumptions. This framework establishes a rigorous, scalable standard for validating “New Space” data products in complex urban domains.
Go et al. (2026) studied this question.