Time series of alpine snow surface radiative temperature maps from high precision thermal infrared imaging

Abstract. The surface temperature of the snow cover is a key variable, as it provides information about the current state of the snowpack, helps predict its future evolution, and enhances estimations of the snow water equivalent. Although satellites are often used to measure surface temperature and despite the difficulty to retrieve accurate surface temperature from space, calibration-validation datasets over snow-covered areas are scarce. We present a dataset of extensive measurements of the radiative surface temperature of snow acquired with an uncooled Thermal Infrared (TIR) camera. The set accuracy goal is 0.7 K, which is the radiometric accuracy of the TIR sensor of the future CNES/ISRO TRISHNA mission. TIR images have been acquired over two winter seasons, November 2021 to May 2022 and February to May 2023 at the Col du Lautaret, 2057 m a.s.l. in the French Alps. During the first season, the camera operated in the off-the-shelf configuration, with a rough thermal regulation (7 °C–39 °C). An improved setup with a stabilized internal temperature was developed for the second campaign, and comprehensive laboratory experiments were carried out in order to characterize the physical properties of the TIR camera components and its calibration. A thorough processing including radiometric processing, orthorectification and a filter for poor visibility conditions due to fog or snowfall have been performed. The result is two winter season timeseries of 130,019 maps of the surface radiative temperature of snow with meter-scale resolution over an area of 0.5 km². The validation is performed against precision TIR radiometers. We found an absolute accuracy (MAE) of 1.28 K during winter 2021–2022 and 0.67 K for spring 2023. The efforts to stabilize the internal temperature of the TIR camera therefore led to a notable improvement of the accuracy. Although some uncertainties persist, particularly the temperature overestimation during melt, this dataset represents a major advance in the capacity to monitor and map surface temperature in mountainous areas, and to calibrate-validate satellite measurements over snow-covered areas of complex topography. The complete dataset is at https://doi.org/10.57932/d4e105c4-b6a3-4520-b174-3913fbb20cb7 (Arioli et al., 2024a) and https://doi.org/10.57932/8c782a49-c992-47af-89bb-2684093e2c65 (Arioli et al., 2024b).