the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Feb 2024
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).
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RC1: 'Comment on essd-2024-55', Oliver Wigmore, 28 Apr 2024
The manuscript presents a dataset comprised of a series of thermal maps/images and RGB images collected from a single observation station looking across a snow-covered mountain area in the French Alps. The paper presents perhaps the most rigorous and best example of calibration routines for thermal imaging over snow surfaces (and perhaps for any land surface thermal imaging) in the current literature. The authors address most of the major sensor limitations of microbolometer thermal sensors; including sensor drift, internal temperature, bias correction, emissivity, etc. This is a technically very challenging task and there are many valuable insights in the workflow presented that will be of great use to the research community. The datasets themselves are likely to provide a useful resource for snow studies and calibration of future satellite missions (if the dataset continues to be collected contemporaneously with these), alongside being a useful dataset in their own right for studies of snow surface energy balance, heterogeneity, etc. Furthermore, the manuscript is well written, and easy to follow.
However, I do have one major comment on the paper. While the authors account for most of the error sources of microbolometer sensors they do not address the potential impact of variable viewing angles and variable distance to target on their measurements/dataset. Studies have shown these can both potentially significantly impact thermal camera measurements. The impact of these on the datasets is likely not apparent in the error assessment against the more accurate IR sensors on the AWS because these are all located at roughly similar distances from the camera and have similar viewing angles. As a result, I believe the reported absolute error values of 0.67K (spring 2023) is probably only representative of areas with similar distance and viewing angle to the AWS stations and is likely worse in other areas of the study site, these limitations should ideally be investigated to confirm or negate their presence. If present the datasets could be improved by addressing these issues, however this is likely to be fairly labour intensive and the dataset and manuscript in their current form are still at the forefront of research in this field. To produce the most accurate and thus useful dataset I suggest the authors identify and address these error sources, however given the high quality of the paper and datasets already I would be satisfied if their potential impacts and consequent limitations were discussed within the paper, at a minimum.
More details on the issues mentioned above and specific comments are included in the attached pdf.
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AC1: 'Reply on RC1', Sara Arioli, 25 Jun 2024
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2024-55/essd-2024-55-AC1-supplement.pdf
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AC1: 'Reply on RC1', Sara Arioli, 25 Jun 2024
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RC2: 'Comment on essd-2024-55', Steven Pestana, 30 Apr 2024
This paper presents a very thorough description of this dataset of thermal infrared imagery, how it was collected, calibrated, and sources of error quantified. The rich dataset will no doubt prove useful for validating the retrieval of land and snow surface temperature from satellite remote sensing observations. The detailed descriptions of instrument setup, lab calibration, and ancillary datasets used will also provide a good roadmap for others to set up similar observations in other locations. There are two things that will make this dataset particularly value, it is of high quality (or rather, sources of error have been investigated and quantified), and it is a rich dataset, providing high temporal and spatial resolution observations within two snow seasons (and sufficient ancillary datasets such as local meteorological variables and internal camera temperatures). However, there are two main points that should be addressed in the paper to better describe or quantify sources of error or uncertainty before it is published: 1) angular emissivity, and 2) the parallax effect on large trees in the image projection.
1) Emissivity:
The authors note the high, near-blackbody, emissivity of snow surfaces with some variation due to grain size, but neglect to address the angular variability of snow emissivity (see Dozier & Warren 1982, which the authors did cite). The difference between observed temperature using a blackbody assumption and the true surface temperature of snow is likely much larger than the errors from other sources that the authors did investigate (e.g. window transmissivity). High spatial resolution DEMs of the study site were used in the projection of the imagery, therefore I suggest that the authors use these same DEMs to estimate snow emissivities given the varying view angles across the study site. Alternatively, an additional map could be provided with the dataset to show the variation in view angle across the study area so that a user of the data can apply their own angular emissivity corrections across the image.
2) Image projection:
Inspecting the TIR imagery, I noticed an area in which there were significant parallax effects due to the images of trees being projected onto a large area of adjacent terrain (see attached Figure 1). It appears that this line of trees (behind a snow fence from the camera’s point of view) occupies a slight rise in terrain before the terrain dips into a ravine or stream valley below. When the TIR images are projected using a bare-ground DEM, the sides of these trees are therefore draped across a large swath of terrain, making it appear that this terrain is the same temperature as that of the sides of these trees. Though the parallax effect causes this same “lay over” of trees elsewhere in the imagery, those instances are of much less concern since the terrain behind the trees rises uphill (therefore the area of terrain they are projected onto is much smaller). Ideally, regions of the image where the land surface is not visible (such as behind large trees) should be masked out like was done for the hill slopes hidden from view, and/or a digital surface model should be used for projection (one that includes the “surface” of large vegetation). Otherwise, it would be sufficient that the paper should at least mention this issue in the images, and call out the one particular region where the effect is most significant. Users of the imagery data can then mask out this region themselves. (And a related minor note, the road passes this same location as this stand of trees such that if an image was taken just as a car was driving by, the projection stretches the car across the terrain creating a narrow streak of very warm temperature in the image. See attached Figure 2. This is understandably not avoidable, but would be worth mentioning in the paper as an artifact of the image projection.)
Lastly, I have one minor comment: Section 4.2 describes quantifying the transmissivity of the germanium window of the TIR camera housing. Are there specifications from the manufacturer of this window that these results can be compared to?
This dataset is of good quality and will be useful for future work. The paper sufficiently describes the setup and methods used such that not only is it useful in interpreting the dataset (except for the two areas identified that should be expanded on), but it is also useful in providing a guide on how to design these sorts of data collections. I hope the authors can address the two points I raised to improve the interpretation and use of their dataset for others.
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AC2: 'Reply on RC2', Sara Arioli, 25 Jun 2024
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2024-55/essd-2024-55-AC2-supplement.pdf
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AC2: 'Reply on RC2', Sara Arioli, 25 Jun 2024
Data sets
Timeseries of the snow surface temperature acquired at the Col du Lautaret (French Alps) during winter 2021-2022 with an uncooled thermal infrared camera Sara Arioli, Ghislain Picard, and Laurent Arnaud https://doi.org/10.57932/d4e105c4-b6a3-4520-b174-3913fbb20cb7
Timeseries of the snow surface temperature acquired at the Col du Lautaret (French Alps) during spring 2023 with an uncooled thermal infrared camera Sara Arioli, Ghislain Picard, and Laurent Arnaud https://doi.org/10.57932/8c782a49-c992-47af-89bb-2684093e2c65
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