Retrieving land surface temperature from broadband longwave radiometric data is challenging because surface emissivity and temperature are nonlinearly linked, making inversion difficult under low irradiance contrast. A newly designed retrieval method estimates both broadband surface emissivity and land surface temperature directly from paired high-resolution ground measurements of upwelling and downwelling longwave irradiance. The technique pairs measurements adaptively based on a quasi-steady surface temperature criterion and applies a fixed-iteration Newton inversion, while actively monitoring numerical stability. An uncertainty propagation framework accounts for both correlated and independent measurement errors. Tested across 39 datasets from four Surface Radiation Budget Network sites, the inversion remained stable across diverse conditions. Retrieved temperatures matched independent field measurements with a root mean square error of 0.54 Kelvin, showing that accurate temperatures can be derived without external emissivity inputs.
Accurate land surface temperature data is essential for monitoring climate, weather, and environmental systems. Traditional methods often rely on external emissivity estimates, which can introduce significant errors. By determining surface emissivity and temperature directly from ground radiation sensors with rigorous uncertainty tracking, this approach improves the precision and reliability of surface temperature observations across varied atmospheric conditions.
This methodology could enable environmental monitoring organisations, meteorological agencies, and radiometric instrument makers to enhance ground-based radiation stations without purchasing external emissivity datasets. The technology appears to be applied and tested, having been validated against 39 real-world datasets across four observational sites. However, the abstract does not indicate dedicated software packages, commercial partnerships, or a timeline for integration into operational sensor hardware.
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Abstract. Accurate retrieval of land surface temperature (LST) from broadband longwave radiometric measurements is fundamentally limited by the nonlinear coupling between surface emissivity and temperature, which can render the inverse problem weakly observable under low irradiance contrast. We present a conditioning-controlled retrieval methodology that estimates broadband surface emissivity and LST directly from paired ground-based upwelling and downwelling longwave irradiance measurements acquired at high temporal resolution. The approach combines adaptive temporal pairing constrained by a quasi-steady apparent surface temperature criterion with a fixed-iteration Newton inversion, and explicitly diagnoses inversion stability through Jacobian strength, residual magnitude, and observed convergence order. A formal uncertainty propagation framework is developed for both independent and correlated irradiance error structures, enabling decomposition of irradiance-driven and emissivity-driven temperature uncertainty. The method is evaluated using 39 datasets from four Surface Radiation Budget (SURFRAD) Network sites spanning diverse atmospheric conditions. The Newton inversion exhibited stable and well-conditioned behaviour across all cases, and retrieved surface temperatures agreed with independent in-situ measurements with a root mean square error of 0.54 K and a mean absolute error of 0.47 K, consistent with propagated uncertainty estimates. Results demonstrate that reliable broadband LST retrieval can be achieved without externally prescribed emissivity products when inversion conditioning and measurement uncertainty are explicitly incorporated into the retrieval design.
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DOI: 10.5194/egusphere-2026-858-ac2
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