Next frontier of Earth observation: Quantum-enhanced multispectral remote sensing for ultra-low signal environments

Optical Earth observation is approaching a fundamental physical limit imposed by photon scarcity, rendering many critical environments, such as polar night regions, dense forest canopies, and optically deep ocean layers, effectively unobservable by classical radiometric sensors. This limitation is not primarily technological but statistical, arising from the assumptions underlying classical photon detection, which fail in ultra-low-light regimes. This study introduces quantum-enhanced multispectral remote sensing (QEMRS) as a fundamentally new observational framework that exploits non-classical photon correlations to extract information below the classical noise floor. Primarily focusing on advances in quantum photonics, we synthesize experimental evidence demonstrating sub-shot-noise detection, coincidence-based measurements, and quantum illumination that remains robust under high loss. The analysis shows a specific radiance regime, approximately 10 −3 to 10 −4 W m −2 sr −1 and below, where classical optical sensing is intrinsically ineffective, yet quantum advantage becomes both meaningful and measurable. To ensure operational relevance, the study demonstrates a two-layer calibration and validation strategy that links SI-traceable radiometry with quantum observables to maintain consistency with existing Earth observation datasets. The study further shows that correlation-based quantum sensing can remain viable despite atmospheric attenuation and decoherence. While near-term deployment is constrained by engineering challenges such as cryogenic operation, power budgets, and duty cycles, these barriers are not fundamental. Overall, QEMRS is positioned as a complementary sensing modality that enables spectral and biogeochemical observations under significantly low-photon environments, opening a new observational domain for Earth and planetary science. However, it does not aim to replace classical optical passive remote sensing and/or other active imaging like synthetic aperture radar (SAR) or light detection and ranging (LiDAR) techniques.