Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition

Abstract

Unlike satellite earth observation, multispectral images acquired by Unmanned Aerial Systems (UAS) provide great opportunities to monitor land surface conditions also in cloudy or overcast weather conditions. This is especially relevant for high latitudes where overcast and cloudy days are common. However, multispectral imagery acquired by miniaturized UAS sensors under such conditions tend to present low brightness and dynamic ranges, and high noise levels. Additionally, cloud shadows over space (within one image) and time (across images) are frequent in UAS imagery collected under variable irradiance and result in sensor radiance changes unrelated to the biophysical dynamics at the surface. To exploit the potential of UAS for vegetation mapping, this study proposes methods to obtain robust and repeatable reflectance time series under variable and low irradiance conditions. To improve sensor sensitivity to low irradiance, a radiometric pixel-wise calibration was conducted with a six-channel multispectral camera (mini-MCA6, Tetracam) using an integrating sphere simulating the varying low illumination typical of outdoor conditions at 55 o N latitude. The sensor sensitivity was increased by using individual settings for independent channels, obtaining higher signal-to-noise ratios compared to the uniform setting for all image channels. To remove cloud shadows, a multivariate statistical procedure, Tucker tensor decomposition, was applied to reconstruct images using a four-way factorization scheme that takes advantage of spatial, spectral and temporal information simultaneously. The comparison between reconstructed (with Tucker) and original images showed an improvement in cloud shadow removal. Outdoor vicarious reflectance validation showed that with these methods, the multispectral imagery can provide reliable reflectance at sunny conditions with root mean square deviations of around 3%. The proposed methods could be useful for operational multispectral mapping with UAS under low and variable irradiance weather conditions as those prevalent in northern latitudes.

Comparing methods to estimate perennial ryegrass biomass: canopy height and spectral vegetation indices

“…Given this research-gap, current commercial multispectral cameras and data analysis (Michez et al 2019) have reported poor performances for pasture biomass estimation, both when employing spectral-based (R 2 = 0.35) or canopy based (photogrammetric estimated height) techniques (R 2 = 0.23). This is in spite of the potential reported in previous research (Mutanga and Skidmore 2004) and absence of scaling-up issues (Burkart et al 2014;Wang et al 2019).…”

“…In parallel, Burkart et al (2014) demonstrated the equivalence between spectral observations at two different data acquisition scales: handheld and low-level flight. In a more recent study, Wang et al (2019) presented a brief review on the difference between reflectance measurements from both acquisition-scales, indicating that such differences are negligible. Such findings fulfil a methodological gap for data collection, analysis and performance validation for UAV sensors (i.e.…”

Influence of the Sun Position and Platform Orientation on the Quality of Imagery Obtained from Unmanned Aerial Vehicles

“…The problem of uneven exposure of UAV images is also discussed in [26], however, this method focuses mainly on reducing distortions caused by cloud cover and variable solar irradiance conditions. This is a good method to reduce uneven lighting of images in a photogrammetric block when it is caused by shadows and external conditions during image acquisition.…”

“…So far, the proposed solutions for solar and topographic corrections were based on approaches dedicated to satellite imagery [25]. Another approach suggests using tensor decomposition for solar corrections, however this method focuses mainly on reducing cloud distortion and variable solar irradiance conditions [26]. We notice in this manuscript that solar correction of low-altitude images requires a different approach.…”

Current Practices in UAS-based Environmental Monitoring

“…UAS mapping under variable solar conditions also has an impact on image quality. Wang et al [155] proposed a pixel-wise radiometric and geometric calibration, extending the sensor calibration to such conditions and correcting vignetting effects. Stow et al [114] showed that illumination geometry impacts the retrieval of reflectance values, but using VIs and photogrammetry can mitigate those effects.…”