Abstract
Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. It is, however, a challenge to perform radiometric correction of the images to create reflectance maps (orthomosaics with surface reflectance) and to compute vegetation indices with sufficient accuracy to enable comparisons between data collected at different times and locations. Studies have compared different radiometric correction methods applied to the Sequoia camera, but there is no consensus about a standard method that provides consistent results for all spectral bands and for different flight conditions. In this study, we perform experiments to assess the accuracy of the Parrot Sequoia camera and sunshine sensor to get an indication if the quality of the data collected is sufficient to create accurate reflectance maps. In addition, we study if there is an influence of the atmosphere on the images and suggest a workflow to collect and process images to create a reflectance map. The main findings are that the sensitivity of the camera is influenced by camera temperature and that the atmosphere influences the images. Hence, we suggest letting the camera warm up before image collection and capturing images of reflectance calibration panels at an elevation close to the maximum flying height to compensate for influence from the atmosphere. The results also show that there is a strong influence of the orientation of the sunshine sensor. This introduces noise and limits the use of the raw sunshine sensor data to compensate for differences in light conditions. To handle this noise, we fit smoothing functions to the sunshine sensor data before we perform irradiance normalization of the images. The developed workflow is evaluated against data from a handheld spectroradiometer, giving the highest correlation (R2 = 0.99) for the normalized difference vegetation index (NDVI). For the individual wavelength bands, R2 was 0.80–0.97 for the red-edge, near-infrared, and red bands.
Highlights
IntroductionUnmanned aerial systems (UASs) are used extensively for environmental monitoring, and there has been a sharp increase in the number of studies since 2010 (e.g., [1,2]), with the largest number of publications in the field of agriculture [1]
The actual values could not be directly compared since the Sequoia sunshine sensor data were in an arbitrary unit common to all Sequoia cameras, but the linear correlation between the two sensors was strong with R2 = 0.998 for both the red (Figure 7b) and the red-edge wavelength bands
We suggest using a larger number of reflectance calibration panels with reflectance lower than 20% to have more reflectance values to derive the equation for the empirical line method
Summary
Unmanned aerial systems (UASs) are used extensively for environmental monitoring, and there has been a sharp increase in the number of studies since 2010 (e.g., [1,2]), with the largest number of publications in the field of agriculture [1]. Initially demonstrated the potential to derive biophysical parameters from data collected with sensors carried by UASs, and UAS data have been used extensively for precision agriculture (e.g., [4,5]). UASs are commonly used in forestry [1,6] and for more general environmental monitoring [2]. Major advantages of using UASs for data collection are the Remote Sens.
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