Measuring land surface temperature accurately using broadband longwave radiation sensors is challenging because surface emissivity and temperature are interlinked, particularly when radiation contrast is low. A new retrieval method determines both broadband surface emissivity and land surface temperature directly from paired ground-based upwelling and downwelling radiation measurements taken at high temporal resolution. The technique uses adaptive temporal pairing alongside a Newton inversion method, actively tracking stability through mathematical indicators of convergence. An uncertainty framework also accounts for both independent and correlated measurement errors. When evaluated across 39 datasets from four Surface Radiation Budget Network sites covering diverse weather conditions, the method demonstrated stable performance. Retrieved surface temperatures matched independent on-site measurements with a root mean square error of 0.54 Kelvin and a mean absolute error of 0.47 Kelvin, showing that accurate temperatures can be derived without external emissivity data.
Accurate measurement of ground temperature is critical for climate monitoring, weather forecasting, and surface energy studies. Conventional radiometric techniques often require external estimates of surface emissivity, which can introduce significant errors. By retrieving emissivity and temperature simultaneously from the same instrument feed and quantifying operational uncertainties, this approach improves the accuracy and autonomy of ground-based environmental observation stations.
This methodology could be incorporated into ground-based radiometric station firmware, weather network analytics platforms, and environmental monitoring software. The underlying algorithm has been applied and tested on field datasets across multiple observation sites, demonstrating research validation. Practical deployment would require translation into automated processing pipelines for environmental observation agencies, agricultural services, or manufacturers of radiometric 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-ac1
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