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A significant obstacle in the development of phosphor-converted white-laser diodes (pc-wLDs) is associated with the laser-induced luminescence saturation of phosphors. YAG:Ce is a canonical yellow-emitting phosphor. However, there has been no few systematic research of measurement and understanding of the luminescence saturation in YAG:Ce yet. Here, we analyze the luminescence saturation of a single-crystal YAG:Ce with a unique phosphor geometry. A novel characterization method, which enables the optical signals to be fully collected and analyzed, is introduced. The effects of both laser power and laser power density on the luminescence properties and colorimetric properties of the YAG:Ce are studied systematically. The results show that a single-crystal YAG:Ce has a high saturation threshold (over 360 W/mm 2 ) so that only a very slight saturation behavior can be observed at a high power density. Furthermore, a pc-wLDs light source based on the combination of a single laser diode and a YAG:Ce phosphor exhibits remarkable efficiency and stability. Under the excitation of a 3.38 W blue laser (~100 W/mm 2 ), the white light emission with the luminous flux of 465 lm, the luminous 2 / 23 efficacy (LE) of 145 lm/W, and the correlated color temperature (CCT) of 4980 K can be achieved.

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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.