**5. Discussion**

As previously mentioned, the goal of this study was to propose an efficient and easily implemented method to perform radiometric corrections of UAV-borne hyperspectral imagery—without adding embedded sensors and with minimum equipment (i.e., only two Spectralon panels and a field spectrometer)—which is adequate for "hard-to-access" ecosystems, such as the huge intertidal mudbanks along the Amazon-influenced, northeast coast of South America. Smith and Milton [55] reported that the empirical line method allows the calibration of remotely sensed data to reflectance with errors of only a few percent. This efficiency was confirmed for our UAV data by the obtained SAM distances and correlation scores. Here, we calculated a higher SAM distance for the pioneer mangrove forest than for the benthic biofilm. This result is coherent with the higher diversity observed among the mangroves spectra (both with the field spectrometer and with the UAV-borne camera—Figure 7c,d). Indeed, point-wise measurement (with the field spectrometer) and very high spatial resolution hyperspectral images (with UAV-borne camera) reflect the complex morphology induced by the mangroves canopy. On the contrary, a slightly lower correlation coefficient was obtained for the benthic biofilm. This could be due to the higher disparity, both spatially and temporally, of the biofilm concentration at the sediment surface, alongside local changes in sediment elevation and bioturbation activities [37].

This method of radiometric corrections, especially the initial calibration step, can also be used to monitor the drift of the manufacturer's calibration. As previously mentioned, Hyper-DRELIO data are noisier for wavelengths higher than 800 nm. Proctor and He [8] explained that this effect is common and is due to the combination of a sharp decrease of quantum efficiency in the NIR, the lower solar output in the NIR, and reduced sensitivity of the imager resulting in lower signal and greater noise. The proposed radiometric correction process relies on simplifying assumptions, which are likely to cause some errors. For instance, remaining noise can be due to DC, which has been considered steady here. Besides, as the "sun–target–sensor" geometry varies for all the pixels recorded by the camera, bidirectional reflectance effects can also influence the signal [33]. As mentioned by Garzonio et al. [34], light intensity could be optimized by placing neutral filters to avoid saturation for very high light intensities. Lastly, the proposed approach to take into account temporal variations of irradiance suits for the variations of ambient light but fails to take into account local effects, which are not captured by the field spectrometer above the 99% Spectralon panel. Therefore, some parts of the images can be affected by spatial changes in the illumination fields, due to isolated clouds or shadows originating from the geometrical configuration of the scene (e.g., presence of trees).

Moreover, as the Spectralons panels are of great importance in this method for radiometric corrections, the results would be sensitive to defects in Spectralon surfaces. For that matter, Bachmann et al. [48] mention that white Spectralon panels are subject to repeated handling and exposed to various environmental factors. Therefore, their calibration coefficients drift over time.

The methodology proposed can be extended to other types of areas, provided that topography-induced illumination differences are taken into account [59].

For now, we have not assessed the impact of platform vibrations on the radiometric and spectral stability of the hyperspectral camera (shift, band broadening, etc.). According to Garzonio et al. [34], however, the impact of platform mechanical vibrations would be almost insignificant in terms of band centre, width, and radiometric response.

Field access in the mangroves developing under Amazonian influence is particularly difficult. Thus, in this particular rapidly evolving ecosystem, future work should aim to assess the extent to which the very high spatial and spectral resolution provided by hyperspectral UAV can capture the biological complexity of the substrate. The good correlations found in this study between drone imagery after radiometric corrections and the in situ spectrometer measurements for the benthic biofilm and pioneer mangroves should allow simultaneous mapping of mangrove forests and benthic biofilm distribution at the mudbank scale, integrating small-scale heterogeneity, caused by the combined effects of geomorphology, tides, and biology (e.g., bioturbation). This method, tested in mangroves under limited human impacts, shows new possibilities for monitoring mangrove ecosystems facing different levels of pressure and subsequent alteration along the nearby coastline (Guyana and Surinam), as well as in other biogeographic regions with other mangrove species and dynamics.

#### **6. Conclusions**

Hyper-DRELIO allowed hyperspectral data to be collected above few hectares of mangrove forests and mudbanks in French Guiana, with both high spatial resolution and high spectral resolution in the VNIR domain. One of the main advantages of drones being their flexibility, the associated imagery calibration procedures have to be as simple as possible to keep the latter. This study proposes an easy, in situ radiometric calibration method, dedicated to drone-based hyperspectral surveys, without adding embedded sensors and with minimum equipment, using only two Spectralon (white and grey) and a ground spectrometer. The proposed procedure enables to calibrate the sensor, by correcting lining effects and transforming the raw relative DN generated by the hyperspectral camera into reflectance values standardized to in situ illumination conditions.

The radiometric corrections were applied to a small subset of a dataset collected above mudbanks colonised by benthic biofilm and a pioneer mangrove forest. Besides the fact that the shapes of spectra are globally consistent between the radiometrically corrected Hyper-DRELIO spectra and the in situ typical spectra, their degree of similarity was assessed using the SAM distance and correlation coefficient. SAM distance values of 0.039 above biofilm and 0.159 above pioneer mangrove forest, together with associated correlation coefficients (of 0.96 and 0.97, respectively), are greatly satisfying for substrate classification. Future work will consist of applying this method to the entire study area, in order to spatialize the results, and comparing hyperspectral and in situ data in order to obtain the finest possible classification of the various detectable elements.

**Author Contributions:** Conceptualization, M.J. and G.S.; data curation, M.J.; formal analysis, M.J., G.B. and P.L.; funding acquisition, E.M., N.L.D. and C.D.; investigation, M.J., G.B., E.M., J.A., P.G., G.T. and J.F.; methodology, M.J. and G.S.; project administration, E.M.; resources, E.M., G.T. and C.D.; software, M.J.; supervision, E.M., N.L.D., P.L., G.T. and C.D.; validation, M.J. and G.B.; visualization, M.J.; writing—original draft preparation, M.J., G.B. and E.M.; writing—review and editing, M.J., G.S., G.B., E.M., N.L.D., G.T. and C.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the French National Agency under the programs "Investissements d'Avenir" (LabexMER: ANR-10-LABX-19; EQUIPEX CRITEX: ANR-11-EQPX-0011) and by the CNRS MITI program ("Pépinière Interdisciplinaire de Guyane" through the BIOGEOMORPHO project). This work was supported by ISblue project, Interdisciplinary graduate school for the blue planet (ANR-17-EURE-0015) and co-funded by a grant from the French government under the program "Investissements d'Avenir".

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data available on request due to privacy restrictions.

**Acknowledgments:** The authors are grateful to the Reserve Naturelle de l'Amana, Parc Naturel Régional de Guyane, DEAL Guyane for the use of their facilities and to I. Bihannic, A. Gardel, M. Jolivet, and S. Morvan for their help on the field. This paper is a GDR Liga's contribution.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

