Conference Agenda

Shortwave infrared (SWIR) imaging from uncrewed aerial vehicles (UAVs) remains rare despite strong sensitivity to canopy water and protein. We present the first field validation of a six-band VNIR/SWIR multi-camera system designed for plot-scale monitoring of winter wheat using mid-sized UAVs. The payload utilized narrow bandpass filters (910, 980, 1100, 1200, 1510, and 1650 nm; FWHM 10–12 nm) and was operated at an altitude of approximately 30 meters above ground level, achieving a ground sampling distance of approximately 4 cm. Empirical line calibration, employing in-scene gray panels, was validated against material-distinct panels and spectroradiometer measurements. The spectral response functions were approximated using Gaussian convolution due to the narrow passbands. Five bands (980–1650 nm) exhibited excellent performance: empirical line model fits achieved R² values approaching 1.000 (RMSE = 0.003–0.009), independent panel validation demonstrated near-unity slopes (R² = 0.998–0.999; RMSE = 0.005–0.013), and plot-level canopy measurements (n=36) maintained strong agreement between camera and spectroradiometer (slopes = 0.943–1.079; R² = 0.58–0.85; RMSE = 0.010–0.023). Two SWIR normalized ratio indices exhibited robust cross-sensor agreement: NRI[1100,1200] (R² ≈ 0.93) and NRI[1650,1510] (R² ≈ 0.90). The 910 nm channel displayed systematic errors (slope = 0.442±0.040 for plots; MAPE ≈ 33%) due to identified out-of-band leakage from incomplete long-wave blocking, leading to its exclusion from accuracy claims. Mitigation strategies include higher optical density short-pass blocking and system-level spectral response function verification. The filter-reconfigurable payload provides quantitative reflectance and robust SWIR indices at the plot scale by integrating panel-anchored empirical line modeling with bandpass-aware harmonization, thereby advancing operational SWIR monitoring capabilities for precision agriculture applications.

This contribution presents PanX.4, a gyrocopter-borne six-band VNIR multicamera system developed within the KIBI project on AI-based identification and classification of protected plant communities (mFUND, FKZ 19F2276) to support cross-scale monitoring at Natura 2000 sites. The system is designed for spectral alignment with Sentinel-2 MSI bands B02–B06 and B08 and is integrated into a tri-sensor airborne suite on the FlugKit carrier platform together with a high-resolution RGB camera and a complementary six-band VNIR–SWIR imaging system. Using system-level spectral response characterization and spectral band adjustment factor (SBAF) analysis based on 1,057 ECOSTRESS spectra, the study quantifies the harmonization quality between PanX.4 and Sentinel-2A, S2B, and S2C. All bands achieved R² > 0.99, while comparative screening of alternative spectral configurations showed that careful band design is critical, particularly in the red-edge region. An additional inter-satellite sensitivity analysis further indicates that harmonization should account for band-dependent differences between Sentinel-2 units when multitemporal airborne and satellite observations are combined. To support multitemporal habitat monitoring, the paper also analyzes 86,947 first-mowing observations from 2017 to 2024 and derives a three-window acquisition concept synchronized with pre-mowing, post-regrowth, and senescence phases. This creates an operationally relevant framework for planning repeated airborne campaigns that can support validation, boundary refinement, and future machine-learning workflows for habitat classification. The contribution therefore establishes the sensor-design, spectral-harmonization, and temporal-planning basis for Sentinel-2-consistent airborne monitoring at sub-meter resolution. Operational airborne image products and in-flight validation are beyond the present contribution and form the next step for future deployment.

Atmospheric correction of large-scale aerial imagery remains a major challenging, mainly due to the difficulty of accurately estimating atmospheric parameters within the images. This study proposes a novel atmospheric correction method based on satellite-derived Surface Reflectance (SR). The method is a semi-empirical linear correction approach that leverages Pseudo-Invariant Features (PIFs) as reference points. Experimental results show that, the proposed method achieves performance comparable to radiative transfer models approach when accurate atmospheric parameters are available, and provides more reliable corrections when such parameters are uncertain or unavailable.

Spectral unmixing estimates the fractional abundances of materials, having associated spectra called endmembers, in pixels acquired by imaging spectrometers. Validation of abundance estimation methods typically relies on synthetic data or comparisons to results obtained by other algorithms. This study considers results of typical abundance estimation algorithms on the DLR HySU (HyperSpectral Unmixing) benchmark dataset, which contains actual imaging spectrometer data acquired over several arrangements of known-size material patches for physically traceable validation. Abundance estimates are compared against measured target areas in pixels with different degrees of mixtures. We evaluate least squares and sparse unmixing methods across different noise scenarios on real data, and by contaminating the library through addition of non-relevant endmembers. Additionally, as a way to approximate hard sparsity constraints, we enforce cardinality constraints on endmember subsets, identifying those minimizing abundance errors relative to the full library. Results suggest that fully constrained least squares yields usually the best results, but struggles in cases of highly mixed pixels. Finally, we test quantization of abundance values as a way to enforce sparsity in non-negative least squares with limited but encouraging results. Overall, the increase in accuracy of results enforcing sparse solutions support the use of computationally efficient sparse unmixing methods in practical scenarios, part of which may become feasible if quantum computing capabilities improve in the future.

The Pelican satellite constellation, first launched by Planet Labs in 2025, continues the high-resolution imaging capability established by the SkySat program. The change to pushbroom sensor in Pelican presents new geometric challenges: satellite attitude variations and platform instabilities during acquisitions can produce band misregistration and geolocation errors that degrade downstream products. This paper presents an operational workflow developed for Pelican imagery, validated on Pelican-2, a technology demonstration satellite. The approach exploits the dual-panchromatic focal plane configuration to independently measure satellite wobble to greater accuracy than on onboard attitude sensors, combined with selective bundle adjustment and B-spline spatial correction to achieve sub-pixel band alignment without dense ground control points. Validation on 963 Pelican-2 scenes demonstrates sub-pixel band-to-band registration accuracy (RMSE < 0.12 px) and 4 m CE90 geolocation accuracy. Applications illustrate the potential for operational geoscience workflows: earthquake surface displacement mapping of the March 2025 Myanmar M7.7 rupture detects 4.0 m co-seismic offsets on the Sagaing Fault with minimal post-processing, and digital surface model generation from an opportunistic multi view acquisition yields preliminary elevation products free of jitter artifacts, demonstrating operational feasibility for constellation-scale processing.

Initial applications showcase operational potential: earthquake surface displacement mapping detects 4.0 m co-seismic offsets from the March 2025 Myanmar M7.7 rupture with minimal post-processing; digital surface model generation yields elevation products free of jitter artifacts. Results establish feasibility for constellation-scale processing and inform next-generation Pelican development.