SNIC: A spectral normalization resistant to illumination conditions for robust estimates of GAI from UAV multispectral measurements

Unmanned aerial vehicle (UAV) remote sensing plays an increasingly important role in crop phenotyping and precision agriculture. As GAI (Green Area Index) is one of the main crop characteristics desired for crop management or plant selection, several retrieval algorithms have been proposed from multispectral observations. The inputs of these retrieval algorithms could be the spectral radiance, or the spectral reflectance that is based either on the calibration over a reference panel (PanelCal) or on the use of a Downwelling Light Sensor (DLS) aboard the UAV. However, variability in illumination conditions during UAV flights introduces pronounced artifacts, leading to unreliable inputs of the retrieval algorithms that degrade the accuracy of GAI estimates.

In this study, we propose a Spectral Normalization for Illumination Invariant Calibration (SNIC) method that aims at eliminating the artefacts introduced in the retrieval algorithms when the illumination conditions are changing during the flight of a multispectral camera aboard a UAV.

A Digital Plant Phenotyping Platform (D3P) coupled with a three-dimensional radiative transfer model was employed to simulate wheat canopy reflectance and GAI across a wide range of illumination scenarios. The simulated datasets provide a physically consistent benchmark for evaluating the robustness of different radiometric calibration strategies under varying illumination conditions during the UAV flight. Our model driven GAI retrieval approach is based on XGBoost (eXtreme Gradient Boosting) regression. Four calibration strategies—Radiance, PanelCal, DLS, and SNIC—were then systematically assessed in terms of GAI retrieval performance.

This in-silico experiment demonstrates that SNIC substantially minimizes the sensitivity of GAI retrieval to illumination variability, whereas PanelCal exhibits pronounced degradation under fluctuating illumination conditions. Validation against 4,000 in situ measurements collected under diverse weather conditions further confirms that SNIC is resistant to changes in illumination conditions. The radiance-based method performs also nicely. Conversely, the reflectance-based methods suffer from severe limitations under such conditions (PanelCal) or from the artefacts introduced by the DLS sensor.