Objective Hyperspectral imaging technology plays a crucial role in earth observation and quantitative remote sensing, where data quality fundamentally depends on precise spectral and radiometric calibration. However, mid-wave infrared hyperspectral imaging systems often suffer from spectral response drift and radiometric characteristic changes in complex field environments. The coupling between these two factors leads to cross-propagation of spectral-radiometric errors, which severely limits the accuracy of traditional stepwise calibration methods. This study aims to address this challenge by proposing a novel joint calibration method capable of simultaneously retrieving spectral and radiometric parameters to enhance the overall accuracy and efficiency of field calibration.

Methods A spectral-radiometric joint calibration model based on atmospheric absorption feature weighting was established. Firstly, the stable carbon dioxide (CO₂) absorption feature near 4.3 μm was selected as a natural reference. An objective function was constructed to minimize the difference between simulated radiance and sensor-observed radiance. The model innovatively integrated the center wavelength shift (Δλ), the Full Width at Half Maximum (FWHM) scaling factor (s), and the radiometric calibration coefficients (gain k and offset b) into a unified iterative variable space. An adaptive weight vector was built by combining spectral gradient, curvature, absorption depth, and CO₂ prior information to strengthen constraints in key spectral regions. An improved Levenberg-Marquardt algorithm was then employed to efficiently and robustly solve this weighted nonlinear least squares problem through multi-parameter synchronous optimization.

Results and Discussions Validation using multi-scene field-measured data, including artificial targets and natural terrain, demonstrated that the joint calibration method significantly outperforms traditional stepwise methods. Specifically: 1) Joint calibration reduced the spectral center wavelength calibration error to below 10% (with an optimal shift of −0.6 nm), while accurately correcting the FWHM from an initial laboratory value of 20 nm to 21.88 nm. 2) For radiometric calibration, the joint method achieved a remarkably low root mean square error (RMSE) of 0.0792 W·m⁻²·sr⁻¹·μm⁻¹. 3) Comparative experiments indicated that the RMSE of the traditional stepwise calibration method was 0.1130 W·m⁻²·sr⁻¹·μm⁻¹. This demonstrates that the joint calibration reduced the comprehensive error by 29.9%, effectively suppressing parameter coupling effects.

Conclusions This study proposes an absorption-feature-weighted method for the spectral-radiometric joint calibration of mid-wave infrared hyperspectral imagers. The constructed joint calibration model integrates the construction of a full-channel weight vector, adjustment of spectral calibration parameters, and optimization of radiometric calibration coefficients. Validation and comparative analysis using multi-scenario field-measured data demonstrate that the joint calibration method significantly enhances overall calibration performance. Specifically, the spectral center wavelength shift was optimized to −0.6 nm, the FWHM was adjusted to 21.88 nm, and the radiometric calibration achieved the lowest reconstruction error (RMSE = 0.0792 W·m⁻²·sr⁻¹·μm⁻¹) with excellent fitting consistency. All these metrics are markedly superior to the results obtained from traditional stepwise calibration. This confirms the effectiveness of the joint calibration in suppressing inter-parameter coupling errors, providing a reliable technical pathway to address the accuracy limitations inherent in traditional stepwise calibration methods.