The arXiv preprint (v1, May 2026) demonstrates a physics-guided neural network that ingests GOES-East ABI's 16-band 10-minute imagery plus ERA5 meteorology and MODIS reflectance to retrieve XCO2 at ~2 km resolution across the western hemisphere. Training relies on collocated OCO-2/OCO-3 soundings without an independent validation hold-out set disclosed, yielding urban plumes and agricultural drawdowns visible at sub-hourly scales. This approach directly addresses the sparse sampling that has constrained dedicated LEO sensors since OCO-2's 2014 launch (Eldering et al., Atmos. Meas. Tech. 2017). Yet the work understates how 10-minute revisit enables diurnal cycle capture missed by both OCO and GOSAT, a capability that could tighten inverse-model flux estimates over megacities when fused with ground networks such as those in the NIST Urban Testbed program. Limitations remain stark: single-pixel retrievals cannot match the 0.5 ppm precision of OCO-2, and aerosol or cloud contamination likely inflates errors during peak convection seasons. Because this is an unreviewed preprint, claims of operational verification support require peer scrutiny. The method nevertheless signals a shift from episodic snapshots toward continuous geostationary greenhouse-gas layers, an advance mainstream climate reporting has overlooked in favor of aggregate emission inventories.