Investigating impacts of calibration methodology and irradiance variations on lightweight drone-based sensor derived surface reflectance products

Abstract

The miniaturisation of multispectral sensors in recent years have resulted in a proliferation of applications particularly in vegetation-focused studies using lightweight drones. Multi-camera arrays (MCAs), capable of capturing information over different wavelength intervals using separate cameras with specific band-pass filters, are now commonplace in this field. However, data from MCAs require a considerable amount of geometric and radiometric corrections if high quality reflectance products are to be delivered. Some aspects of this workflow can be handled by commercial software packages (e.g. Pix4D and Agisoft Metashape), using black box algorithms, however radiometric uncertainties within products are not reported to the end-user by the software. We present the results of two experiments using a low-cost MCA complete with irradiance sensor (Parrot Sequoia), which set out to assess the accuracy and consistency of hemispherical-conical surface reflectance factors from MCA data. Using reference panels in the field, we found that the empirical line method (ELM) generated the smallest RMSEs (0.0037) when compared to simplified single-panel based workflows; while for the latter there was little difference between using a calibrated Spectralon® panel or grey card imaged prior to the flight (0.0215 vs 0.0154 average over the four bands). Errors for a vegetated target within the survey flight were larger and comparable for all cases. Furthermore, a study on median vegetation index values for single vegetation canopies showed that illumination correction using irradiance data still yields significant differences in resulting values between two acquisitions during changing direct and diffuse irradiance conditions. We therefore highlight the importance of critical assessment prior to integrating drone derived MCA-measured reflectance factors into further geospatial workflows.

Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions

“…The reference panel used in this study was a Kodak grey-card, an inexpensive reference panel common in photography, with an approximate 20% reflectance across bands. In a previous study, the difference between the use of a grey card reference and an assumed Lambertian Spectralon ® panel of higher (40%) reflectance was shown to be minimal within this calibration procedure [34].…”

Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites

“…The collection of multispectral drone time-series in Arctic ecosystems has limitations and challenges. Recent studies have discussed challenges with radiometric consistency and repeatability when using compact multispectral drone sensors (81,117,118) . Due to logistical constraints, we were not able to always conduct surveys under optimal conditions due to sun angle or cloud cover, nor as frequently as planned due to wind or precipitation (Table S2), which likely introduced bias and/or noise into our drone data (e.g., Figure 4b).…”

Guidelines for calibration of uncrewed aircraft systems imagery

“…Several researchers (Kruse and others, 1990;Ben-Dor and others, 1994;Dwyer and others, 1995;Ferrier and Wadge, 1996) have used this two-point method. They have generated reasonable calibrations; however, studies (Fawcett and Anderson, 2019;Mamaghani and Salvaggio, 2019) have documented the benefits of ELM-based calibration quantitatively.…”