Articles | Volume 18, issue 12
https://doi.org/10.5194/tc-18-6027-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-6027-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Dec 2024
Research article |
| 20 Dec 2024
| 20 Dec 2024
Temporal stability of a new 40-year daily AVHRR land surface temperature dataset for the pan-Arctic region
Institute of Geography, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Frank-Michael Göttsche
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Stefan Wunderle
Institute of Geography, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Related authors
Pauline Rivoire, Sonia Dupuis, Antoine Guisan, Pascal Vittoz, and Daniela I. V. Domeisen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3482, https://doi.org/10.5194/egusphere-2024-3482, 2024
Short summary
Short summary
Our study investigates the conditions in temperature, precipitation, humidity, and soil moisture leading to the browning of the European forests in summer. Using a Random Forest model and satellite measurement of vegetation greenness, we identify key conditions that predict forest damage. We conclude that hot and dry conditions in spring and summer are adverse conditions, in particular for broad-leaved trees. The hydro-meteorological conditions during the preceding year can also have an impact.
Pauline Rivoire, Sonia Dupuis, Antoine Guisan, Pascal Vittoz, and Daniela I. V. Domeisen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3482, https://doi.org/10.5194/egusphere-2024-3482, 2024
Short summary
Short summary
Our study investigates the conditions in temperature, precipitation, humidity, and soil moisture leading to the browning of the European forests in summer. Using a Random Forest model and satellite measurement of vegetation greenness, we identify key conditions that predict forest damage. We conclude that hot and dry conditions in spring and summer are adverse conditions, in particular for broad-leaved trees. The hydro-meteorological conditions during the preceding year can also have an impact.
Falu Hong, Wenfeng Zhan, Frank-M. Göttsche, Zihan Liu, Pan Dong, Huyan Fu, Fan Huang, and Xiaodong Zhang
Earth Syst. Sci. Data, 14, 3091–3113, https://doi.org/10.5194/essd-14-3091-2022, https://doi.org/10.5194/essd-14-3091-2022, 2022
Short summary
Short summary
Daily mean land surface temperature (LST) acquired from satellite thermal sensors is crucial for various applications such as global and regional climate change analysis. This study proposed a framework to generate global spatiotemporally seamless daily mean LST products (2003–2019). Validations show that the products outperform the traditional method with satisfying accuracy. Our further analysis reveals that the LST-based global land surface warming rate is 0.029 K yr−1 from 2003 to 2019.
Matthias M. Frey, Frank Hase, Thomas Blumenstock, Darko Dubravica, Jochen Groß, Frank Göttsche, Martin Handjaba, Petrus Amadhila, Roland Mushi, Isamu Morino, Kei Shiomi, Mahesh Kumar Sha, Martine de Mazière, and David F. Pollard
Atmos. Meas. Tech., 14, 5887–5911, https://doi.org/10.5194/amt-14-5887-2021, https://doi.org/10.5194/amt-14-5887-2021, 2021
Short summary
Short summary
In this study, we present measurements of carbon dioxide, methane and carbon monoxide from a recently established site in Gobabeb, Namibia. Gobabeb is the first site observing these gases on the African mainland and improves the global coverage of measurement sites. Gobabeb is a hyperarid desert site, offering unique characteristics. Measurements started 2015 as part of the COllaborative Carbon Column Observing Network. We compare our results with other datasets and find a good agreement.
Jin Ma, Ji Zhou, Frank-Michael Göttsche, Shunlin Liang, Shaofei Wang, and Mingsong Li
Earth Syst. Sci. Data, 12, 3247–3268, https://doi.org/10.5194/essd-12-3247-2020, https://doi.org/10.5194/essd-12-3247-2020, 2020
Short summary
Short summary
Land surface temperature is an important parameter in the research of climate change and many land surface processes. This article describes the development and testing of an algorithm for generating a consistent global long-term land surface temperature product from 20 years of NOAA AVHRR radiance data. The preliminary validation results indicate good accuracy of this new long-term product, which has been designed to simplify applications and support the scientific research community.
Sarah Safieddine, Ana Claudia Parracho, Maya George, Filipe Aires, Victor Pellet, Lieven Clarisse, Simon Whitburn, Olivier Lezeaux, Jean-Noel Thepaut, Hans Hersbach, Gabor Radnoti, Frank Goettsche, Maria Martin, Marie Doutriaux Boucher, Dorothee Coppens, Thomas August, and Cathy Clerbaux
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-185, https://doi.org/10.5194/amt-2019-185, 2019
Preprint withdrawn
Short summary
Short summary
Skin temperature is one of the essential climate variables (ECVs), and is relevant for the current and future understanding of our climate. This work presents a method to retrieve skin temperature from the thermal infrared sounder IASI that provides a global observation of Earth’s surface and atmosphere twice a day. With this method, the first consistent long-term [2007-present] skin temperature record from IASI can be constructed.
G. Masiello, C. Serio, S. Venafra, G. Liuzzi, F. Göttsche, I. F. Trigo, and P. Watts
Atmos. Meas. Tech., 8, 2981–2997, https://doi.org/10.5194/amt-8-2981-2015, https://doi.org/10.5194/amt-8-2981-2015, 2015
Related subject area
Discipline: Snow | Subject: Remote Sensing
Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites
Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements
Simulation of Arctic snow microwave emission in surface-sensitive atmosphere channels
Retrieval of snow and soil properties for forward radiative transfer modeling of airborne Ku-band SAR to estimate snow water equivalent: the Trail Valley Creek 2018/19 snow experiment
Evaluating L-band InSAR snow water equivalent retrievals with repeat ground-penetrating radar and terrestrial lidar surveys in northern Colorado
Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne lidar data
Tower-based C-band radar measurements of an alpine snowpack
Mapping surface hoar from near-infrared texture in a laboratory
Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign
Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022
Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method
Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17
Snow water equivalent retrieval over Idaho – Part 1: Using Sentinel-1 repeat-pass interferometry
Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach
Retrieval of snow water equivalent from dual-frequency radar measurements: using time series to overcome the need for accurate a priori information
Snow accumulation, albedo and melt patterns following road construction on permafrost, Inuvik–Tuktoyaktuk Highway, Canada
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 1: Measurements, processing, and accuracy assessment
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions
Evaluating Snow Microwave Radiative Transfer (SMRT) model emissivities with 89 to 243 GHz observations of Arctic tundra snow
Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments
Evaluation of snow depth retrievals from ICESat-2 using airborne laser-scanning data
How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction?
Exploring the use of multi-source high-resolution satellite data for snow water equivalent reconstruction over mountainous catchments
Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR)
Snowmelt characterization from optical and synthetic-aperture radar observations in the La Joie Basin, British Columbia
Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar
Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends
Towards long-term records of rain-on-snow events across the Arctic from satellite data
Implementing spatially and temporally varying snow densities into the GlobSnow snow water equivalent retrieval
Evaluation of E3SM land model snow simulations over the western United States
Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets
Snow stratigraphy observations from Operation IceBridge surveys in Alaska using S and C band airborne ultra-wideband FMCW (frequency-modulated continuous wave) radar
Brief communication: A continuous formulation of microwave scattering from fresh snow to bubbly ice from first principles
Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing
Automated avalanche mapping from SPOT 6/7 satellite imagery with deep learning: results, evaluation, potential and limitations
Potential of X-band polarimetric synthetic aperture radar co-polar phase difference for arctic snow depth estimation
Snow water equivalent change mapping from slope-correlated synthetic aperture radar interferometry (InSAR) phase variations
Sentinel-1 time series for mapping snow cover depletion and timing of snowmelt in Arctic periglacial environments: case study from Zackenberg and Kobbefjord, Greenland
Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps
Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals
Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements
Brief communication: Evaluation of the snow cover detection in the Copernicus High Resolution Snow & Ice Monitoring Service
Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas
Deriving Arctic 2 m air temperatures over snow and ice from satellite surface temperature measurements
Impact of dynamic snow density on GlobSnow snow water equivalent retrieval accuracy
The retrieval of snow properties from SLSTR Sentinel-3 – Part 1: Method description and sensitivity study
The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation
Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning
Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States
Mapping avalanches with satellites – evaluation of performance and completeness
Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall
The Cryosphere, 18, 5407–5430, https://doi.org/10.5194/tc-18-5407-2024, https://doi.org/10.5194/tc-18-5407-2024, 2024
Short summary
Short summary
This study uses radar imagery from the Sentinel-1 satellite to derive snow depth from increases in the returning energy. These retrieved depths are then compared to nine lidar-derived snow depths across the western United State to assess the ability of this technique to be used to monitor global snow distributions. We also qualitatively compare the changes in underlying Sentinel-1 amplitudes against both the total lidar snow depths and nine automated snow monitoring stations.
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
The Cryosphere, 18, 5015–5029, https://doi.org/10.5194/tc-18-5015-2024, https://doi.org/10.5194/tc-18-5015-2024, 2024
Short summary
Short summary
Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
Short summary
Short summary
Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
Short summary
Short summary
This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Randall Bonnell, Daniel McGrath, Jack Tarricone, Hans-Peter Marshall, Ella Bump, Caroline Duncan, Stephanie Kampf, Yunling Lou, Alex Olsen-Mikitowicz, Megan Sears, Keith Williams, Lucas Zeller, and Yang Zheng
The Cryosphere, 18, 3765–3785, https://doi.org/10.5194/tc-18-3765-2024, https://doi.org/10.5194/tc-18-3765-2024, 2024
Short summary
Short summary
Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA, to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small
The Cryosphere, 18, 3495–3512, https://doi.org/10.5194/tc-18-3495-2024, https://doi.org/10.5194/tc-18-3495-2024, 2024
Short summary
Short summary
Automated stations measure snow properties at a single point but are frequently used to validate data that represent much larger areas. We use lidar snow depth data to see how often the mean snow depth surrounding a snow station is within 10 cm of the snow station depth at different scales. We found snow stations overrepresent the area-mean snow depth in ~ 50 % of cases, but the direction of bias at a site is temporally consistent, suggesting a site could be calibrated to the surrounding area.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Mätzler, and Hans Lievens
The Cryosphere, 18, 3177–3193, https://doi.org/10.5194/tc-18-3177-2024, https://doi.org/10.5194/tc-18-3177-2024, 2024
Short summary
Short summary
To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
James Dillon, Christopher Donahue, Evan Schehrer, Karl Birkeland, and Kevin Hammonds
The Cryosphere, 18, 2557–2582, https://doi.org/10.5194/tc-18-2557-2024, https://doi.org/10.5194/tc-18-2557-2024, 2024
Short summary
Short summary
Surface hoar crystals are snow grains that form when vapor deposits on a snow surface. They create a weak layer in the snowpack that can cause large avalanches to occur. Thus, determining when and where surface hoar forms is a lifesaving matter. Here, we developed a means of mapping surface hoar using remote-sensing technologies. We found that surface hoar displayed heightened texture, hence the variability of brightness. Using this, we created surface hoar maps with an accuracy upwards of 95 %.
Steven J. Pestana, C. Chris Chickadel, and Jessica D. Lundquist
The Cryosphere, 18, 2257–2276, https://doi.org/10.5194/tc-18-2257-2024, https://doi.org/10.5194/tc-18-2257-2024, 2024
Short summary
Short summary
We compared infrared images taken by GOES-R satellites of an area with snow and forests against surface temperature measurements taken on the ground, from an aircraft, and by another satellite. We found that GOES-R measured warmer temperatures than the other measurements, especially in areas with more forest and when the Sun was behind the satellite. From this work, we learned that the position of the Sun and surface features such as trees that can cast shadows impact GOES-R infrared images.
Jiahui Xu, Yao Tang, Linxin Dong, Shujie Wang, Bailang Yu, Jianping Wu, Zhaojun Zheng, and Yan Huang
The Cryosphere, 18, 1817–1834, https://doi.org/10.5194/tc-18-1817-2024, https://doi.org/10.5194/tc-18-1817-2024, 2024
Short summary
Short summary
Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
Short summary
Short summary
We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros
The Cryosphere, 18, 747–773, https://doi.org/10.5194/tc-18-747-2024, https://doi.org/10.5194/tc-18-747-2024, 2024
Short summary
Short summary
Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
Short summary
Short summary
The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
Dhiraj Kumar Singh, Srinivasarao Tanniru, Kamal Kant Singh, Harendra Singh Negi, and RAAJ Ramsankaran
The Cryosphere, 18, 451–474, https://doi.org/10.5194/tc-18-451-2024, https://doi.org/10.5194/tc-18-451-2024, 2024
Short summary
Short summary
In situ techniques for snow depth (SD) measurement are not adequate to represent the spatiotemporal variability in SD in the Western Himalayan region. Therefore, this study focuses on the high-resolution mapping of daily snow depth in the Indian Western Himalayan region using passive microwave remote-sensing-based algorithms. Overall, the proposed multifactor SD models demonstrated substantial improvement compared to the operational products. However, there is a scope for further improvement.
Michael Durand, Joel T. Johnson, Jack Dechow, Leung Tsang, Firoz Borah, and Edward J. Kim
The Cryosphere, 18, 139–152, https://doi.org/10.5194/tc-18-139-2024, https://doi.org/10.5194/tc-18-139-2024, 2024
Short summary
Short summary
Seasonal snow accumulates each winter, storing water to release later in the year and modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.
Jennika Hammar, Inge Grünberg, Steven V. Kokelj, Jurjen van der Sluijs, and Julia Boike
The Cryosphere, 17, 5357–5372, https://doi.org/10.5194/tc-17-5357-2023, https://doi.org/10.5194/tc-17-5357-2023, 2023
Short summary
Short summary
Roads on permafrost have significant environmental effects. This study assessed the Inuvik to Tuktoyaktuk Highway (ITH) in Canada and its impact on snow accumulation, albedo and snowmelt timing. Our findings revealed that snow accumulation increased by up to 36 m from the road, 12-day earlier snowmelt within 100 m due to reduced albedo, and altered snowmelt patterns in seemingly undisturbed areas. Remote sensing aids in understanding road impacts on permafrost.
Anssi Rauhala, Leo-Juhani Meriö, Anton Kuzmin, Pasi Korpelainen, Pertti Ala-aho, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4343–4362, https://doi.org/10.5194/tc-17-4343-2023, https://doi.org/10.5194/tc-17-4343-2023, 2023
Short summary
Short summary
Snow conditions in the Northern Hemisphere are rapidly changing, and information on snow depth is important for decision-making. We present snow depth measurements using different drones throughout the winter at a subarctic site. Generally, all drones produced good estimates of snow depth in open areas. However, differences were observed in the accuracies produced by the different drones, and a reduction in accuracy was observed when moving from an open mire area to forest-covered areas.
Leo-Juhani Meriö, Anssi Rauhala, Pertti Ala-aho, Anton Kuzmin, Pasi Korpelainen, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4363–4380, https://doi.org/10.5194/tc-17-4363-2023, https://doi.org/10.5194/tc-17-4363-2023, 2023
Short summary
Short summary
Information on seasonal snow cover is essential in understanding snow processes and operational forecasting. We study the spatiotemporal variability in snow depth and snow processes in a subarctic, boreal landscape using drones. We identified multiple theoretically known snow processes and interactions between snow and vegetation. The results highlight the applicability of the drones to be used for a detailed study of snow depth in multiple land cover types and snow–vegetation interactions.
Kirsty Wivell, Stuart Fox, Melody Sandells, Chawn Harlow, Richard Essery, and Nick Rutter
The Cryosphere, 17, 4325–4341, https://doi.org/10.5194/tc-17-4325-2023, https://doi.org/10.5194/tc-17-4325-2023, 2023
Short summary
Short summary
Satellite microwave observations improve weather forecasts, but to use these observations in the Arctic, snow emission must be known. This study uses airborne and in situ snow observations to validate emissivity simulations for two- and three-layer snowpacks at key frequencies for weather prediction. We assess the impact of thickness, grain size and density in key snow layers, which will help inform development of physical snow models that provide snow profile input to emissivity simulations.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
Short summary
Short summary
As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
César Deschamps-Berger, Simon Gascoin, David Shean, Hannah Besso, Ambroise Guiot, and Juan Ignacio López-Moreno
The Cryosphere, 17, 2779–2792, https://doi.org/10.5194/tc-17-2779-2023, https://doi.org/10.5194/tc-17-2779-2023, 2023
Short summary
Short summary
The estimation of the snow depth in mountains is hard, despite the importance of the snowpack for human societies and ecosystems. We measured the snow depth in mountains by comparing the elevation of points measured with snow from the high-precision altimetric satellite ICESat-2 to the elevation without snow from various sources. Snow depths derived only from ICESat-2 were too sparse, but using external airborne/satellite products results in spatially richer and sufficiently precise snow depths.
Edward H. Bair, Jeff Dozier, Karl Rittger, Timbo Stillinger, William Kleiber, and Robert E. Davis
The Cryosphere, 17, 2629–2643, https://doi.org/10.5194/tc-17-2629-2023, https://doi.org/10.5194/tc-17-2629-2023, 2023
Short summary
Short summary
To test the title question, three snow cover products were used in a snow model. Contrary to previous work, higher-spatial-resolution snow cover products only improved the model accuracy marginally. Conclusions are as follows: (1) snow cover and albedo from moderate-resolution sensors continue to provide accurate forcings and (2) finer spatial and temporal resolutions are the future for Earth observations, but existing moderate-resolution sensors still offer value.
Valentina Premier, Carlo Marin, Giacomo Bertoldi, Riccardo Barella, Claudia Notarnicola, and Lorenzo Bruzzone
The Cryosphere, 17, 2387–2407, https://doi.org/10.5194/tc-17-2387-2023, https://doi.org/10.5194/tc-17-2387-2023, 2023
Short summary
Short summary
The large amount of information regularly acquired by satellites can provide important information about SWE. We explore the use of multi-source satellite data, in situ observations, and a degree-day model to reconstruct daily SWE at 25 m. The results show spatial patterns that are consistent with the topographical features as well as with a reference product. Being able to also reproduce interannual variability, the method has great potential for hydrological and ecological applications.
Jack Tarricone, Ryan W. Webb, Hans-Peter Marshall, Anne W. Nolin, and Franz J. Meyer
The Cryosphere, 17, 1997–2019, https://doi.org/10.5194/tc-17-1997-2023, https://doi.org/10.5194/tc-17-1997-2023, 2023
Short summary
Short summary
Mountain snowmelt provides water for billions of people across the globe. Despite its importance, we cannot currently measure the amount of water in mountain snowpacks from satellites. In this research, we test the ability of an experimental snow remote sensing technique from an airplane in preparation for the same sensor being launched on a future NASA satellite. We found that the method worked better than expected for estimating important snowpack properties.
Sara E. Darychuk, Joseph M. Shea, Brian Menounos, Anna Chesnokova, Georg Jost, and Frank Weber
The Cryosphere, 17, 1457–1473, https://doi.org/10.5194/tc-17-1457-2023, https://doi.org/10.5194/tc-17-1457-2023, 2023
Short summary
Short summary
We use synthetic-aperture radar (SAR) and optical observations to map snowmelt timing and duration on the watershed scale. We found that Sentinel-1 SAR time series can be used to approximate snowmelt onset over diverse terrain and land cover types, and we present a low-cost workflow for SAR processing over large, mountainous regions. Our approach provides spatially distributed observations of the snowpack necessary for model calibration and can be used to monitor snowmelt in ungauged basins.
Vasana Dharmadasa, Christophe Kinnard, and Michel Baraër
The Cryosphere, 17, 1225–1246, https://doi.org/10.5194/tc-17-1225-2023, https://doi.org/10.5194/tc-17-1225-2023, 2023
Short summary
Short summary
This study highlights the successful usage of UAV lidar to monitor small-scale snow depth distribution. Our results show that underlying topography and wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. This emphasizes the importance of including and better representing these processes in physically based models for accurate snowpack estimates.
Ruben Urraca and Nadine Gobron
The Cryosphere, 17, 1023–1052, https://doi.org/10.5194/tc-17-1023-2023, https://doi.org/10.5194/tc-17-1023-2023, 2023
Short summary
Short summary
We evaluate the fitness of some of the longest satellite (NOAA CDR, 1966–2020) and reanalysis (ERA5, 1950–2020; ERA5-Land, 1950–2020) products currently available to monitor the Northern Hemisphere snow cover trends using 527 stations as the reference. We found different artificial trends and stepwise discontinuities in all the products that hinder the accurate monitoring of snow trends, at least without bias correction. The study also provides updates on the snow cover trends during 1950–2020.
Annett Bartsch, Helena Bergstedt, Georg Pointner, Xaver Muri, Kimmo Rautiainen, Leena Leppänen, Kyle Joly, Aleksandr Sokolov, Pavel Orekhov, Dorothee Ehrich, and Eeva Mariatta Soininen
The Cryosphere, 17, 889–915, https://doi.org/10.5194/tc-17-889-2023, https://doi.org/10.5194/tc-17-889-2023, 2023
Short summary
Short summary
Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. In extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record. Retrieval is most robust in the tundra biome, where records can be used to identify extremes and the results can be applied to impact studies at regional scale.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
Short summary
Short summary
Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
Short summary
Short summary
We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Timbo Stillinger, Karl Rittger, Mark S. Raleigh, Alex Michell, Robert E. Davis, and Edward H. Bair
The Cryosphere, 17, 567–590, https://doi.org/10.5194/tc-17-567-2023, https://doi.org/10.5194/tc-17-567-2023, 2023
Short summary
Short summary
Understanding global snow cover is critical for comprehending climate change and its impacts on the lives of billions of people. Satellites are the best way to monitor global snow cover, yet snow varies at a finer spatial resolution than most satellite images. We assessed subpixel snow mapping methods across a spectrum of conditions using airborne lidar. Spectral-unmixing methods outperformed older operational methods and are ready to to advance snow cover mapping at the global scale.
Jilu Li, Fernando Rodriguez-Morales, Xavier Fettweis, Oluwanisola Ibikunle, Carl Leuschen, John Paden, Daniel Gomez-Garcia, and Emily Arnold
The Cryosphere, 17, 175–193, https://doi.org/10.5194/tc-17-175-2023, https://doi.org/10.5194/tc-17-175-2023, 2023
Short summary
Short summary
Alaskan glaciers' loss of ice mass contributes significantly to ocean surface rise. It is important to know how deeply and how much snow accumulates on these glaciers to comprehend and analyze the glacial mass loss process. We reported the observed seasonal snow depth distribution from our radar data taken in Alaska in 2018 and 2021, developed a method to estimate the annual snow accumulation rate at Mt. Wrangell caldera, and identified transition zones from wet-snow zones to ablation zones.
Ghislain Picard, Henning Löwe, and Christian Mätzler
The Cryosphere, 16, 3861–3866, https://doi.org/10.5194/tc-16-3861-2022, https://doi.org/10.5194/tc-16-3861-2022, 2022
Short summary
Short summary
Microwave satellite observations used to monitor the cryosphere require radiative transfer models for their interpretation. These models represent how microwaves are scattered by snow and ice. However no existing theory is suitable for all types of snow and ice found on Earth. We adapted a recently published generic scattering theory to snow and show how it may improve the representation of snows with intermediate densities (~500 kg/m3) and/or with coarse grains at high microwave frequencies.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 16, 3517–3530, https://doi.org/10.5194/tc-16-3517-2022, https://doi.org/10.5194/tc-16-3517-2022, 2022
Short summary
Short summary
Knowing where avalanches occur is very important information for several disciplines, for example avalanche warning, hazard zonation and risk management. Satellite imagery can provide such data systematically over large regions. In our work we propose a machine learning model to automate the time-consuming manual mapping. Additionally, we investigate expert agreement for manual avalanche mapping, showing that our network is equally as good as the experts in identifying avalanches.
Joëlle Voglimacci-Stephanopoli, Anna Wendleder, Hugues Lantuit, Alexandre Langlois, Samuel Stettner, Andreas Schmitt, Jean-Pierre Dedieu, Achim Roth, and Alain Royer
The Cryosphere, 16, 2163–2181, https://doi.org/10.5194/tc-16-2163-2022, https://doi.org/10.5194/tc-16-2163-2022, 2022
Short summary
Short summary
Changes in the state of the snowpack in the context of observed global warming must be considered to improve our understanding of the processes within the cryosphere. This study aims to characterize an arctic snowpack using the TerraSAR-X satellite. Using a high-spatial-resolution vegetation classification, we were able to quantify the variability in snow depth, as well as the topographic soil wetness index, which provided a better understanding of the electromagnetic wave–ground interaction.
Jayson Eppler, Bernhard Rabus, and Peter Morse
The Cryosphere, 16, 1497–1521, https://doi.org/10.5194/tc-16-1497-2022, https://doi.org/10.5194/tc-16-1497-2022, 2022
Short summary
Short summary
We introduce a new method for mapping changes in the snow water equivalent (SWE) of dry snow based on differences between time-repeated synthetic aperture radar (SAR) images. It correlates phase differences with variations in the topographic slope which allows the method to work without any "reference" targets within the imaged area and without having to numerically unwrap the spatial phase maps. This overcomes the key challenges faced in using SAR interferometry for SWE change mapping.
Sebastian Buchelt, Kirstine Skov, Kerstin Krøier Rasmussen, and Tobias Ullmann
The Cryosphere, 16, 625–646, https://doi.org/10.5194/tc-16-625-2022, https://doi.org/10.5194/tc-16-625-2022, 2022
Short summary
Short summary
In this paper, we present a threshold and a derivative approach using Sentinel-1 synthetic aperture radar time series to capture the small-scale heterogeneity of snow cover (SC) and snowmelt. Thereby, we can identify start of runoff and end of SC as well as perennial snow and SC extent during melt with high spatiotemporal resolution. Hence, our approach could support monitoring of distribution patterns and hydrological cascading effects of SC from the catchment scale to pan-Arctic observations.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
Short summary
Short summary
Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Julien Meloche, Alexandre Langlois, Nick Rutter, Alain Royer, Josh King, Branden Walker, Philip Marsh, and Evan J. Wilcox
The Cryosphere, 16, 87–101, https://doi.org/10.5194/tc-16-87-2022, https://doi.org/10.5194/tc-16-87-2022, 2022
Short summary
Short summary
To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.
Christopher Donahue, S. McKenzie Skiles, and Kevin Hammonds
The Cryosphere, 16, 43–59, https://doi.org/10.5194/tc-16-43-2022, https://doi.org/10.5194/tc-16-43-2022, 2022
Short summary
Short summary
The amount of water within a snowpack is important information for predicting snowmelt and wet-snow avalanches. From within a controlled laboratory, the optimal method for measuring liquid water content (LWC) at the snow surface or along a snow pit profile using near-infrared imagery was determined. As snow samples melted, multiple models to represent wet-snow reflectance were assessed against a more established LWC instrument. The best model represents snow as separate spheres of ice and water.
Zacharie Barrou Dumont, Simon Gascoin, Olivier Hagolle, Michaël Ablain, Rémi Jugier, Germain Salgues, Florence Marti, Aurore Dupuis, Marie Dumont, and Samuel Morin
The Cryosphere, 15, 4975–4980, https://doi.org/10.5194/tc-15-4975-2021, https://doi.org/10.5194/tc-15-4975-2021, 2021
Short summary
Short summary
Since 2020, the Copernicus High Resolution Snow & Ice Monitoring Service has distributed snow cover maps at 20 m resolution over Europe in near-real time. These products are derived from the Sentinel-2 Earth observation mission, with a revisit time of 5 d or less (cloud-permitting). Here we show the good accuracy of the snow detection over a wide range of regions in Europe, except in dense forest regions where the snow cover is hidden by the trees.
Xiaodan Wu, Kathrin Naegeli, Valentina Premier, Carlo Marin, Dujuan Ma, Jingping Wang, and Stefan Wunderle
The Cryosphere, 15, 4261–4279, https://doi.org/10.5194/tc-15-4261-2021, https://doi.org/10.5194/tc-15-4261-2021, 2021
Short summary
Short summary
We performed a comprehensive accuracy assessment of an Advanced Very High Resolution Radiometer global area coverage snow-cover extent time series dataset for the Hindu Kush Himalayan (HKH) region. The sensor-to-sensor consistency, the accuracy related to snow depth, elevations, land-cover types, slope, and aspects, and topographical variability were also explored. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006.
Pia Nielsen-Englyst, Jacob L. Høyer, Kristine S. Madsen, Rasmus T. Tonboe, Gorm Dybkjær, and Sotirios Skarpalezos
The Cryosphere, 15, 3035–3057, https://doi.org/10.5194/tc-15-3035-2021, https://doi.org/10.5194/tc-15-3035-2021, 2021
Short summary
Short summary
The Arctic region is responding heavily to climate change, and yet, the air temperature of Arctic ice-covered areas is heavily under-sampled when it comes to in situ measurements. This paper presents a method for estimating daily mean 2 m air temperatures (T2m) in the Arctic from satellite observations of skin temperature, providing spatially detailed observations of the Arctic. The satellite-derived T2m product covers clear-sky snow and ice surfaces in the Arctic for the period 2000–2009.
Pinja Venäläinen, Kari Luojus, Juha Lemmetyinen, Jouni Pulliainen, Mikko Moisander, and Matias Takala
The Cryosphere, 15, 2969–2981, https://doi.org/10.5194/tc-15-2969-2021, https://doi.org/10.5194/tc-15-2969-2021, 2021
Short summary
Short summary
Information about snow water equivalent (SWE) is needed in many applications, including climate model evaluation and forecasting fresh water availability. Space-borne radiometer observations combined with ground snow depth measurements can be used to make global estimates of SWE. In this study, we investigate the possibility of using sparse snow density measurement in satellite-based SWE retrieval and show that using the snow density information in post-processing improves SWE estimations.
Linlu Mei, Vladimir Rozanov, Christine Pohl, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2757–2780, https://doi.org/10.5194/tc-15-2757-2021, https://doi.org/10.5194/tc-15-2757-2021, 2021
Short summary
Short summary
This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 1 of two companion papers and shows the method description and sensitivity study. The paper investigates the major factors, including the assumptions of snow optical properties, snow particle distribution and atmospheric conditions (cloud and aerosol), impacting snow property retrievals from satellite observation.
Linlu Mei, Vladimir Rozanov, Evelyn Jäkel, Xiao Cheng, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2781–2802, https://doi.org/10.5194/tc-15-2781-2021, https://doi.org/10.5194/tc-15-2781-2021, 2021
Short summary
Short summary
This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 2 of two companion papers and shows the results and validation. The paper performs the new retrieval algorithm on the Sea and Land
Surface Temperature Radiometer (SLSTR) instrument and compares the retrieved snow properties with ground-based measurements, aircraft measurements and other satellite products.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, https://doi.org/10.5194/tc-15-2187-2021, 2021
Short summary
Short summary
We describe the relationships between snow depth, vegetation canopy, and local-scale processes during the snow accumulation period using terrestrial laser scanning (TLS). In addition to topography and wind, our findings suggest the importance of fine-scale tree structure, species type, and distributions on snow depth. Snow depth increases from the canopy edge toward the open areas, but wind and topographic controls may affect this trend. TLS data are complementary to wide-area lidar surveys.
Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, Elizabeth A. Burakowski, Christina Herrick, and Eunsang Cho
The Cryosphere, 15, 1485–1500, https://doi.org/10.5194/tc-15-1485-2021, https://doi.org/10.5194/tc-15-1485-2021, 2021
Short summary
Short summary
This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m2 resolution snow depth map, generated by subtracting snow-off from snow-on lidar-derived digital terrain models, consistently had 0.5 to 1 cm precision in the field, with a considerable reduction in accuracy in the forest. Performance depends on the point cloud density and the ground surface variability and vegetation.
Elisabeth D. Hafner, Frank Techel, Silvan Leinss, and Yves Bühler
The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, https://doi.org/10.5194/tc-15-983-2021, 2021
Short summary
Short summary
Satellites prove to be very valuable for documentation of large-scale avalanche periods. To test reliability and completeness, which has not been satisfactorily verified before, we attempt a full validation of avalanches mapped from two optical sensors and one radar sensor. Our results demonstrate the reliability of high-spatial-resolution optical data for avalanche mapping, the suitability of radar for mapping of larger avalanches and the unsuitability of medium-spatial-resolution optical data.
Cited articles
Arctic Monitoring and Assessment Programme (AMAP): Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policy-makers, AMAP, 2021. a
Bartsch, A., Strozzi, T., and Nitze, I.: Permafrost Monitoring from Space, Surv. Geophys., 44, 1579–1613, https://doi.org/10.1007/s10712-023-09770-3, 2023. a
Bartsch, A., Efimova, A., Widhalm, B., Muri, X., von Baeckmann, C., Bergstedt, H., Ermokhina, K., Hugelius, G., Heim, B., and Leibman, M.: Circumarctic land cover diversity considering wetness gradients, Hydrol. Earth Syst. Sci., 28, 2421–2481, https://doi.org/10.5194/hess-28-2421-2024, 2024. a
Batbaatar, J., Gillespie, A. R., Sletten, R. S., Mushkin, A., Amit, R., Trombotto Liaudat, D., Liu, L., and Petrie, G.: Toward the Detection of Permafrost Using Land-Surface Temperature Mapping, Remote Sens., 12, 695, https://doi.org/10.3390/rs12040695, 2020. a
Berge, J., Daase, M., Renaud, P. E., Ambrose, W. G., Darnis, G., Last, K. S., Leu, E., Cohen, J. H., Johnsen, G., Moline, M. A., Cottier, F., Varpe, O., Shunatova, N., Bałazy, P., Morata, N., Massabuau, J. C., Falk-Petersen, S., Kosobokova, K., Hoppe, C. J., Węsławski, J. M., Kukliński, P., Legeżyńska, J., Nikishina, D., Cusa, M., Kędra, M., Włodarska-Kowalczuk, M., Vogedes, D., Camus, L., Tran, D., Michaud, E., Gabrielsen, T. M., Granovitch, A., Gonchar, A., Krapp, R., and Callesen, T. A.: Unexpected levels of biological activity during the polar night offer new perspectives on a warming arctic, Curr. Biol., 25, 2555–2561, https://doi.org/10.1016/j.cub.2015.08.024, 2015. a
Borbas, E., Seemann, S. W., Huang, H.-L., Li, J. Y., and Menzel, W. P.: Global profile training database for satellite regression retrievals with estimates of skin temperature and emissivity, in: Proceedings of the International TOVS Study Conference-XIV, Beijing, China, 25–31 May 2005, https://cimss.ssec.wisc.edu/training_data/data/borbas_trainingData_poster_ITSC14.pdf (last access: 17 December 2024), 2005. a
Brugnara, Y., Good, E., Squintu, A. A., van der Schrier, G., and Brönnimann, S.: The EUSTACE global land station daily air temperature dataset, Geosci. Data J., 6, 189–204, https://doi.org/10.1002/gdj3.81, 2019. a, b
Carlson, T. N. and Ripley, D. A.: On the relation between NDVI, fractional vegetation cover, and leaf area index, Remote Sens. Environ., 62, 241–252, https://doi.org/10.1016/S0034-4257(97)00104-1, 1997. a
Cheng, J., Liang, S., Yao, Y., and Zhang, X.: Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation, IEEE Geosci. Remote Sens. Lett., 10, 401–405, https://doi.org/10.1109/LGRS.2012.2206367, 2013. a
Chevallier, F., Chédin, A., Cheruy, F., and Morcrette, J.-J.: TIGR-like atmospheric-profile databases for accurate radiative-flux computation, Q. J. R. Meteorol. Soc., 126, 777–785, https://doi.org/10.1002/qj.49712656319, 2000. a
Christensen, T. R., Johansson, T., Åkerman, H. J., Mastepanov, M., Malmer, N., Friborg, T., Crill, P., and Svensson, B. H.: Thawing sub-arctic permafrost: Effects on vegetation and methane emissions, Geophys. Res. Lett., 31, L04501, https://doi.org/10.1029/2003GL018680, 2004. a
Chylek, P., Folland, C., Klett, J. D., Wang, M., Hengartner, N., Lesins, G., and Dubey, M. K.: Annual Mean Arctic Amplification 1970–2020: Observed and Simulated by CMIP6 Climate Models, Geophys. Res. Lett., 49, e2022GL099371, https://doi.org/10.1029/2022GL099371, 2022. a
Coll, C., Hook, S. J., and Galve, J. M.: Land surface temperature from the advanced along-track scanning radiometer: Validation over inland waters and vegetated surfaces, IEEE T. Geosci. Remote, 47, 350–360, https://doi.org/10.1109/TGRS.2008.2002912, 2009. a
Copernicus Climate Change Service, Climate Data Store: Land cover classification gridded maps from 1992 to present derived from satellite observation, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.006f2c9a (last access: 1 March 2024), 2019. a, b
Copernicus digital elevation model (DEM) GLO-90: https://doi.org/10.5270/ESA-c5d3d65, last access: 1 March 2024. a
Cox, C. J. and Halliwell, D.: Basic measurements of radiation at station Alert (2004-08–2014-06), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.932867, 2021. a
Crameri, F.: Scientific colour maps (8.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.8409685, 2023. a
Dada, L., Angot, H., Beck, I., Baccarini, A., Quéléver, L. L. J., Boyer, M., Laurila, T., Brasseur, Z., Jozef, G., de Boer, G., Shupe, M. D., Henning, S., Bucci, S., Dütsch, M., Stohl, A., Petäjä, T., Daellenbach, K. R., Jokinen, T., and Schmale, J.: A central arctic extreme aerosol event triggered by a warm air-mass intrusion, Nat. Commun., 13, 5290, https://doi.org/10.1038/s41467-022-32872-2, 2022. a
de Araújo, C. S. P., Campos e Silva, I. A., Ippolito, M., and Guedes Cabral de Almeida, C. D.: Evaluation of air temperature estimated by ERA5-Land reanalysis using surface data in Pernambuco, Brazil, Environ. Monit. Assess., 194, 381, https://doi.org/10.1007/s10661-022-10047-2, 2022. a
Devasthale, A. and Karlsson, K.-G.: Decadal Stability and Trends in the Global Cloud Amount and Cloud Top Temperature in the Satellite-Based Climate Data Records, Remote Sens., 15, 3819, https://doi.org/10.3390/rs15153819, 2023. a
Dupuis, S., Wunderle, S., and Göttsche, F.: 40-year monthly mean AVHRR GAC Land Surface Temperature data for the Pan-Arctic region (Pan-Arctic AVHRR LST), Zenodo [data set], https://doi.org/10.5281/zenodo.13361744, 2024. a
Ermida, S. L. and Trigo, I. F.: Clear-sky profile database for the development of Land Surface Temperature algorithms (v0.0.0) [data set], Zenodo, https://doi.org/10.5281/zenodo.5779543, 2021. a
Ermida, S. L., Trigo, I. F., DaCamara, C. C., Göttsche, F. M., Olesen, F. S., and Hulley, G.: Validation of remotely sensed surface temperature over an oak woodland landscape – The problem of viewing and illumination geometries, Remote Sens. Environ., 148, 16–27, https://doi.org/10.1016/j.rse.2014.03.016, 2014. a
Ermida, S. L., Jiménez, C., Prigent, C., Trigo, I. F., and DaCamara, C. C.: Inversion of AMSR-E observations for land surface temperature estimation: 2. Global comparison with infrared satellite temperature, J. Geophys. Res., 122, 3348–3360, https://doi.org/10.1002/2016JD026148, 2017. a
EUMETSAT: AVHRR Fundamental Data Record – Release 1 – Multimission, EUMETSAT [data set], https://doi.org/10.15770/EUM_SEC_CLM_0060, 2023a. a, b
EUMETSAT: PyGAC FDR ATBD, Tech. rep., EUMETSAT, ISBN 4961518077, https://doi.org/10.15770/EUM_SEC_CLM_0060, 2023b. a
EUMETSAT: PyGAC AVHRR FDR Release 1 Product Users Guide, Tech. rep., EUMETSAT, https://doi.org/10.15770/EUM_SEC_CLM_0060, 2023c. a, b
EUMETSAT: PyGAC AVHRR FDR Release 1 Validation Report, Tech. rep., EUMETSAT, https://doi.org/10.15770/EUM_SEC_CLM_0060, 2023d. a, b
EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF): Product User Manual CM SAF Cloud, Albedo, Radiation data record, AVHRR-based, Edition 3 (CLARA-A3) Cloud Products, Tech. rep., EUMETSAT CM SAF, https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003, 2023. a, b
Freitas, S. C., Trigo, I. F., Bioucas-Dias, J. M., and Göttsche, F. M.: Quantifying the uncertainty of land surface temperature retrievals from SEVIRI/Meteosat, IEEE T. Geosci. Remote, 48, 523–534, https://doi.org/10.1109/TGRS.2009.2027697, 2010. a, b
Ghent, D., Dodd, E., Veal, K., Perry, M., Jimenez, C., and Ermida, S.: Algorithm Theoretical Basis Document: WP2.1 – DEL-LST-CCI-D2.2-ATBD, Tech. rep., LST_CCI, https://admin.climate.esa.int/media/documents/LST-CCI-D2.2-ATBD_-_i4r0_-_Algorithm_Theoretical_Basis_Document.pdf (last access: 17 December 2024), 2023. a
Gleason, A. C., Prince, S. D., Goetz, S. J., and Small, J.: Effects of orbital drift on land surface temperature measured by AVHRR thermal sensors, Remote Sens. Environ., 79, 147–165, https://doi.org/10.1016/S0034-4257(01)00269-3, 2002. a
Good, E. J.: An in situ-based analysis of the relationship between land surface “skin” and screen-level air temperatures, J. Geophys. Res., 121, 8801–8819, https://doi.org/10.1002/2016JD025318, 2016. a, b
Grünberg, I., Wilcox, E. J., Zwieback, S., Marsh, P., and Boike, J.: Linking tundra vegetation, snow, soil temperature, and permafrost, Biogeosciences, 17, 4261–4279, https://doi.org/10.5194/bg-17-4261-2020, 2020. a
Guillevic, P., Bork-Unkelbach, A., Gottsche, F. M., Hulley, G., Gastellu-Etchegorry, J. P., Olesen, F. S., and Privette, J. L.: Directional viewing effects on satellite land surface temperature products over sparse vegetation canopies-a multisensor analysis, IEEE Geosci. Remote S., 10, 1464–1468, https://doi.org/10.1109/LGRS.2013.2260319, 2013. a
Guillevic, P., Göttsche, F., Nickeson, J., Hulley, G., Ghent, D., Yu, Y., Trigo, I., Hook, S., Sobrino, J. A., Remedios, J., Román, M., and Camacho, F.: Land Surface Temperature Product Validation Best Practice Protocol. Version 1.1, Tech. rep., Committee on Earth Observation Satellites Working Group on Calibration and Validation Land Product Validation Subgroup, https://doi.org/10.5067/doc/ceoswgcv/lpv/lst.001, 2018. a, b
Göttsche, F., Olesen, F., Trigo, I., Bork-Unkelbach, A., and Martin, M.: Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Africa, Remote Sens., 8, 410, https://doi.org/10.3390/rs8050410, 2016. a, b
Hall, D. K., Comiso, J. C., Digirolamo, N. E., Shuman, C. A., Key, J. R., and Koenig, L. S.: A satellite-derived climate-quality data record of the clear-sky surface temperature of the greenland ice sheet, J. Climate, 25, 4785–4798, https://doi.org/10.1175/JCLI-D-11-00365.1, 2012. a, b
Hammar, J., Grünberg, I., Kokelj, S. V., van der Sluijs, J., and Boike, J.: Snow accumulation, albedo and melt patterns following road construction on permafrost, Inuvik–Tuktoyaktuk Highway, Canada, The Cryosphere, 17, 5357–5372, https://doi.org/10.5194/tc-17-5357-2023, 2023. a
Hocking, J., Roquet, P., and Brunel, P.: Python/C/C++ wrapper for RTTOV v13, NWP SAF (EUMETSAT), https://nwp-saf.eumetsat.int/site/download/documentation/rtm/docs_rttov13/rttov-wrapper.pdf (last access: 17 December 2024), 2021. a
Hulley, G., Veraverbeke, S., and Hook, S.: Thermal-based techniques for land cover change detection using a new dynamic MODIS multispectral emissivity product (MOD21), Remote Sens. Environ., 140, 755–765, https://doi.org/10.1016/j.rse.2013.10.014, 2014. a, b, c
Hulley, G. C., Hook, S. J., Abbott, E., Malakar, N., Islam, T., and Abrams, M.: The ASTER Global Emissivity Dataset (ASTER GED): Mapping Earth's emissivity at 100 meter spatial scale, Geophys. Res. Lett., 42, 7966–7976, https://doi.org/10.1002/2015GL065564, 2015. a
Hussain, M. and Mahmud, I.: pyMannKendall: a python package for non parametric Mann Kendall family of trend tests, J. Open Source Softw., 4, 1556, https://doi.org/10.21105/joss.01556, 2019. a
Ignatov, A., Laszlo, I., Harrod, E. D., Kidwell, K. B., and Goodrum, G. P.: Equator crossing times for NOAA, ERS and EOS sun-synchronous satellites, Int. J. Remote Sens., 25, 5255–5266, https://doi.org/10.1080/01431160410001712981, 2004. a
IPCC: 2021, Summary for Policymakers, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., 3–32, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/9781009157896.001, 2021. a
Jiménez, C., Prigent, C., Ermida, S. L., and Moncet, J. L.: Inversion of AMSR-E observations for land surface temperature estimation: 1. Methodology and evaluation with station temperature, J. Geophys. Res., 122, 3330–3347, https://doi.org/10.1002/2016JD026144, 2017. a
Julien, Y. and Sobrino, J. A.: Toward a Reliable Correction of NOAA AVHRR Orbital Drift, Front. Remote Sens., 3, 851933, https://doi.org/10.3389/frsen.2022.851933, 2022. a
Karlsson, K.-G., Riihelä, AkuandTrentmann, J., Stengel, Martin andSolodovnik, I., Meirink, J. F., Devasthale, A., Jääskeläinen, E., Kallio-Myers, V., Eliasson, S., Benas, N., Johansson, E., Stein, D., Finkensieper, S., Håkansson, N., Akkermans, T., Clerbaux, N., Selbach, N., Schröder, M., and Hollmann, R.: CLARA-A3: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data – Edition 3, Satellite Application Facility on Climate Monitoring (CM SAF), [data set], https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003, 2023a. a
Karlsson, K.-G., Stengel, M., Meirink, J. F., Riihelä, A., Trentmann, J., Akkermans, T., Stein, D., Devasthale, A., Eliasson, S., Johansson, E., Håkansson, N., Solodovnik, I., Benas, N., Clerbaux, N., Selbach, N., Schröder, M., and Hollmann, R.: CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023, Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, 2023b. a, b, c, d, e
Key, J., Wang, X., Liu, Y., Dworak, R., and Letterly, A.: The AVHRR Polar Pathfinder Climate Data Records, Remote Sens., 8, 167, https://doi.org/10.3390/rs8030167, 2016. a, b
Kidwell, K. B.: NOAA Polar Orbiter Data Users Guide, Tech. rep., National Climatic Data Center (U.S.). National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, National Climatic Data Center, Satellite Data Services Division, https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.star.nesdis.noaa.gov/mirs/documents/0.0_NOAA_KLM_Users_Guide.pdf (last access: 17 December 2024), 1995. a
Kustov, V.: Basic and other measurements of radiation at station Cape Baranova (2016-09), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.892295, 2018. a
Kustov, V.: Basic and other measurements of radiation at station Tiksi (2010-06 et seq), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.959213, 2023. a
Larocca, L. J., Lea, J. M., Erb, M. P., McKay, N. P., Phillips, M., Lamantia, K. A., and Kaufman, D. S.: Arctic glacier snowline altitudes rise 150 m over the last 4 decades, The Cryosphere, 18, 3591–3611, https://doi.org/10.5194/tc-18-3591-2024, 2024. a
Latifovic, R., Pouliot, D., and Dillabaugh, C.: Identification and correction of systematic error in NOAA AVHRR long-term satellite data record, Remote Sens. Environ., 127, 84–97, https://doi.org/10.1016/j.rse.2012.08.032, 2012. a
Li, J.-H., Li, Z.-L., Liu, X., and Duan, S.-B.: A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by Advanced Very High Resolution Radiometer observations from 1981 to 2021, Earth Syst. Sci. Data, 15, 2189–2212, https://doi.org/10.5194/essd-15-2189-2023, 2023a. a, b
Li, K., Guan, K., Jiang, C., Wang, S., Peng, B., and Cai, Y.: Evaluation of Four New Land Surface Temperature (LST) Products in the U.S. Corn Belt: ECOSTRESS, GOES-R, Landsat, and Sentinel-3, IEEE J. Sel. Top. Appl., 14, 9931–9945, https://doi.org/10.1109/JSTARS.2021.3114613, 2021. a
Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., and Sobrino, J. A.: Satellite-derived land surface temperature: Current status and perspectives, Remote Sens. Environ., 131, 14–37, https://doi.org/10.1016/j.rse.2012.12.008, 2013. a, b, c
Li, Z.-L., Wu, H., Duan, S.-B., Zhao, W., Ren, H., Liu, X., Leng, P., Tang, R., Ye, X., Zhu, J., Sun, Y., Si, M., Liu, M., Li, J., Zhang, X., Shang, G., Tang, B.-H., Yan, G., and Zhou, C.: Satellite remote sensing of global land surface temperature: Definition, methods, products, and applications, Rev. Geophys., 61, e2022RG000777, https://doi.org/10.1029/2022RG000777, 2023b. a, b
Lieberherr, G. and Wunderle, S.: Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes, Remote Sens., 10, 990, https://doi.org/10.3390/rs10070990, 2018. a
Lieberherr, G., Riffler, M., and Wunderle, S.: Performance Assessment of Tailored Split-Window Coefficients for the Retrieval of Lake Surface Water Temperature from AVHRR Satellite Data, Remote Sens., 9, 1334, https://doi.org/10.3390/rs9121334, 2017. a
Luojus, K., Moisander, M., Pulliainen, J., Takala, M., Lemmetyinen, J., Derksen, C., Mortimer, C., Schwaizer, G., Nagler, T., and Venäläinen, P.: ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979–2020), version 2.0, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/4647cc9ad3c044439d6c643208d3c494, 2022. a, b
Lund, M., Stiegler, C., Abermann, J., Citterio, M., Hansen, B. U., and van As, D.: Spatiotemporal variability in surface energy balance across tundra, snow and ice in Greenland, Ambio, 46, 81–93, https://doi.org/10.1007/s13280-016-0867-5, 2017. a
Martin, M. A., Ghent, D., Pires, A. C., Göttsche, F.-M., Cermak, J., and Remedios, J. J.: Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years, Remote Sens., 11, 479, https://doi.org/10.3390/rs11050479, 2019. a, b
Martins, J. P. A., Trigo, I. F., Ghilain, N., Jimenez, C., Göttsche, F.-M., Ermida, S. L., Olesen, F.-S., Gellens-Meulenberghs, F., and Arboleda, A.: An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations, Remote Sens., 11, 3044, https://doi.org/10.3390/rs11243044, 2019. a
Masuda, K., Takashima, T., and Takayama, Y.: Emissivity of pure and sea waters for the model sea surface in the infrared window regions, Remote Sens. Environ., 24, 313–329, https://doi.org/10.1016/0034-4257(88)90032-6, 1988. a
Maturilli, M.: Basic and other measurements of radiation at station Ny-Ålesund (2006-05 et seq), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.914927, 2020. a
Maturilli, M., Hanssen-Bauer, I., Neuber, R., Rex, M., and Edvardsen, K.: The Atmosphere Above Ny-Ålesund: Climate and Global Warming, Ozone and Surface UV Radiation, pp. 23–46, Springer International Publishing, https://doi.org/10.1007/978-3-319-46425-1_2, 2019. a, b
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An overview of the global historical climatology network-daily database, J. Atmos. Ocean. Tech., 29, 897–910, https://doi.org/10.1175/JTECH-D-11-00103.1, 2012. a
Mildrexler, D. J., Zhao, M., Cohen, W. B., Running, S. W., Song, X. P., and Jones, M. O.: Thermal anomalies detect critical global land surface changes, J Appl. Meteorol. Clim., 57, 391–411, https://doi.org/10.1175/JAMC-D-17-0093.1, 2018. a
Miner, K. R., Turetsky, M. R., Malina, E., Bartsch, A., Tamminen, J., McGuire, A. D., Fix, A., Sweeney, C., Elder, C. D., and Miller, C. E.: Permafrost carbon emissions in a changing Arctic, Nat. Rev. Earth Environ., 3, 55–67, https://doi.org/10.1038/s43017-021-00230-3, 2022. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
Niclòs, R., Valor, E., Caselles, V., Coll, C., and Sánchez, J. M.: In situ angular measurements of thermal infrared sea surface emissivity – Validation of models, Remote Sens. Environ., 94, 83–93, https://doi.org/10.1016/j.rse.2004.09.002, 2005. a
Nielsen-Englyst, P., Høyer, J. L., Madsen, K. S., Tonboe, R. T., Dybkjær, G., and Skarpalezos, S.: Deriving Arctic 2 m air temperatures over snow and ice from satellite surface temperature measurements, The Cryosphere, 15, 3035–3057, https://doi.org/10.5194/tc-15-3035-2021, 2021. a
Nitze, I., Grosse, G., Jones, B. M., Romanovsky, V. E., and Boike, J.: Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic, Nature Commun., 9, 5423, https://doi.org/10.1038/s41467-018-07663-3, 2018. a
Obu, J., Westermann, S., Kääb, A., and Bartsch, A.: Ground Temperature Map, 2000-2016, Northern Hemisphere Permafrost, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.888600, 2018. a
Obu, J., Westermann, S., Bartsch, A., Berdnikov, N., Christiansen, H. H., Dashtseren, A., Delaloye, R., Elberling, B., Etzelmüller, B., Kholodov, A., Khomutov, A., Kääb, A., Leibman, M. O., Lewkowicz, A. G., Panda, S. K., Romanovsky, V., Way, R. G., Westergaard-Nielsen, A., Wu, T., Yamkhin, J., and Zou, D.: Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale, Earth-Sci. Rev., 193, 299–316, https://doi.org/10.1016/j.earscirev.2019.04.023, 2019. a, b, c, d
Olefeldt, D., Hovemyr, M., Kuhn, M. A., Bastviken, D., Bohn, T. J., Connolly, J., Crill, P., Euskirchen, E. S., Finkelstein, S. A., Genet, H., Grosse, G., Harris, L. I., Heffernan, L., Helbig, M., Hugelius, G., Hutchins, R., Juutinen, S., Lara, M. J., Malhotra, A., Manies, K., McGuire, A. D., Natali, S. M., O'Donnell, J. A., Parmentier, F.-J. W., Räsänen, A., Schädel, C., Sonnentag, O., Strack, M., Tank, S. E., Treat, C., Varner, R. K., Virtanen, T., Warren, R. K., and Watts, J. D.: The Boreal–Arctic Wetland and Lake Dataset (BAWLD), Earth Syst. Sci. Data, 13, 5127–5149, https://doi.org/10.5194/essd-13-5127-2021, 2021. a, b
Palmtag, J., Obu, J., Kuhry, P., Richter, A., Siewert, M. B., Weiss, N., Westermann, S., and Hugelius, G.: A high spatial resolution soil carbon and nitrogen dataset for the northern permafrost region based on circumpolar land cover upscaling, Earth Syst. Sci. Data, 14, 4095–4110, https://doi.org/10.5194/essd-14-4095-2022, 2022. a
Pearson, R. K.: Outliers in process modeling and identification, IEEE T. Contr. Syst. T., 10, 55–63, https://doi.org/10.1109/87.974338, 2002. a
Peres, L. F. and DaCamara, C. C.: Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI, IEEE T. Geosci. Remote, 43, 1834–1844, https://doi.org/10.1109/TGRS.2005.851172, 2005. a
Prata, A. J.: Land surface temperatures derived from the advanced very high resolution radiometer and the along-track scanning radiometer: 2. Experimental results and validation of AVHRR algorithms, J. Geophys. Res., 99, 13025–13058, https://doi.org/10.1029/94JD00409, 1994. a
Price, J. C.: Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer, J. Geophys. Res.-Atmos., 89, 7231–7237, https://doi.org/10.1029/JD089iD05p07231, 1984. a, b
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed nearly four times faster than the globe since 1979, Nat. Commun. Earth Environ., 3, 168, https://doi.org/10.1038/s43247-022-00498-3, 2022. a
Rantanen, M., Kämäräinen, M., Niittynen, P., Phoenix, G. K., Lenoir, J., Maclean, I., Luoto, M., and Aalto, J.: Bioclimatic atlas of the terrestrial Arctic, Nat. Sci. Data, 10, 40, https://doi.org/10.1038/s41597-023-01959-w, 2023. a, b
Rasmussen, M. O., Gottsche, F.-M., Olesen, F.-S., and Sandholt, I.: Directional Effects on Land Surface Temperature Estimation From Meteosat Second Generation for Savanna Landscapes, IEEE T. Geosci. Remote, 49, 4458–4468, https://doi.org/10.1109/TGRS.2011.2144604, 2011. a
Rayner, N. A., Auchmann, R., Bessembinder, J., Brönnimann, S., Brugnara, Y., Capponi, F., Carrea, L., Dodd, E. M., Ghent, D., Good, E., Høyer, J. L., Kennedy, J. J., Kent, E. C., Killick, R. E., van der Linden, P., Lindgren, F., Madsen, K. S., Merchant, C. J., Mitchelson, J. R., Morice, C. P., Nielsen-Englyst, P., Ortiz, P. F., Remedios, J. J., van der Schrier, G., Squintu, A. A., Stephens, A., Thorne, P. W., Tonboe, R. T., Trent, T., Veal, K. L., Waterfall, A. M., Winfield, K., Winn, J., and Woolway, R. I.: The EUSTACE project: Delivering global, daily information on surface air temperature, B. Am. Meteorol. Soc., 101, E1924–E1947, https://doi.org/10.1175/BAMS-D-19-0095.1, 2020. a
Reiners, P., Asam, S., Frey, C., Holzwarth, S., Bachmann, M., Sobrino, J., Göttsche, F. M., Bendix, J., and Kuenzer, C.: Validation of avhrr land surface temperature with modis and in situ lst – a timeline thematic processor, Remote Sens., 13, 3473, https://doi.org/10.3390/rs13173473, 2021. a, b, c, d
Reiners, P., Sobrino, J., and Kuenzer, C.: Satellite-Derived Land Surface Temperature Dynamics in the Context of Global Change – A Review, Remote Sens., 15, 1857, https://doi.org/10.3390/rs15071857, 2023. a
Riffler, M., Lieberherr, G., and Wunderle, S.: Lake surface water temperatures of European Alpine lakes (1989–2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set, Earth Syst. Sci. Data, 7, 1–17, https://doi.org/10.5194/essd-7-1-2015, 2015. a
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018. a
Sharifnezhadazizi, Z., Norouzi, H., Prakash, S., Beale, C., and Khanbilvardi, R.: A Global Analysis of Land Surface Temperature Diurnal Cycle Using MODIS Observations, J. Appl. Meteor. Climatol., 58, 1279–1291, https://doi.org/10.1175/JAMC-D-18-0256.1, 2019. a
Sobrino, J., Raissouni, N., and Li, Z.-L.: A Comparative Study of Land Surface Emissivity Retrieval from NOAA Data, Remote Sens. Environ., 75, 256–266, https://doi.org/10.1016/S0034-4257(00)00171-1, 2001. a
Sobrino, J. A. and Raissouni, N.: Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco, Int. J. Remote Sens., 21, 353–366, https://doi.org/10.1080/014311600210876, 2000. a
Sobrino, J. A., Jiménez-Muñoz, J. C., Labed-Nachbrand, J., and Nerry, F.: Surface emissivity retrieval from Digital Airborne Imaging Spectrometer data, J. Geophys. Res., 107, ACL 24–1–ACL 24–13, https://doi.org/10.1029/2002JD002197, 2002. a
Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J., Plaza, A., and Martínez, P.: Land surface emissivity retrieval from different VNIR and TIR sensors, IEEE T. Geosci. Remote, 46, 316–327, https://doi.org/10.1109/TGRS.2007.904834, 2008. a, b
Sulla-Menashe, D. and Friedl, M. A.: User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product, Tech. rep., USGS, https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pdf (last access: 17 December 2024), 2018. a
Thiebault, K. and Young, S.: Snow cover change and its relationship with land surface temperature and vegetation in northeastern North America from 2000 to 2017, Int. J. Remote Sens., 41, 8453–8474, https://doi.org/10.1080/01431161.2020.1779379, 2020. a
Treat, C. C., Virkkala, A., Burke, E., Bruhwiler, L., Chatterjee, A., Fisher, J. B., Hashemi, J., Parmentier, F. W., Rogers, B. M., Westermann, S., Watts, J. D., Blanc‐Betes, E., Fuchs, M., Kruse, S., Malhotra, A., Miner, K., Strauss, J., Armstrong, A., Epstein, H. E., Gay, B., Goeckede, M., Kalhori, A., Kou, D., Miller, C. E., Natali, S. M., Oh, Y., Shakil, S., Sonnentag, O., Varner, R. K., Zolkos, S., Schuur, E. A., and Hugelius, G.: Permafrost Carbon: Progress on Understanding Stocks and Fluxes Across Northern Terrestrial Ecosystems, J. Geophys. Res.-Biogeo., 129, e2023JG007638, https://doi.org/10.1029/2023JG007638, 2024. a
Trigo, I., Monteiro, I. T., Olesen, F., and Kabsch, E.: An assessment of remotely sensed land surface temperature, J. Geophys. Res., 113, D17108, https://doi.org/10.1029/2008JD010035, 2008a. a
Trigo, I., Peres, L. F., DaCamara, C. C., and Freitas, S. C.: Thermal land surface emissivity retrieved from SEVIRI/Meteosat, IEEE T. Geosci. Remote, 46, 307–315, https://doi.org/10.1109/TGRS.2007.905197, 2008b. a, b
Trigo, I., Dacamara, C. C., Viterbo, P., Roujean, J. L., Olesen, F., Barroso, C., Camacho-De-Coca, F., Carrer, D., Freitas, S. C., García-Haroj, J., Geiger, B., Gellens-Meulenberghs, F., Ghilain, N., Meliá, J., Pessanha, L., Siljamo, N., and Arboleda, A.: The satellite application facility for land surface analysis, Int. J. Remote Sens., 32, 2725–2744, https://doi.org/10.1080/01431161003743199, 2011. a
Trigo, I., Freitas, S., Bioucas-Dias, J., Barroso, C., Monteiro, I., Viterbo, P., and Martins, J. P.: Algorithm Theoretical Basis Document for Land Surface Temperature (LST), PRODUCTS: LSA-002 (ELST), Tech. rep., EUMETSAT LSA SAF, https://nextcloud.lsasvcs.ipma.pt/s/NsW275gpDAfekzc?dir=undefined&path=%2FATBD-Algorithm_Theoretial_Basis_Document&openfile=27090 (last access: 17 December 2024), 2017. a
Urraca, R. and Gobron, N.: Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends, The Cryosphere, 17, 1023–1052, https://doi.org/10.5194/tc-17-1023-2023, 2023. a
Vandecrux, B., Fausto, R. S., Box, J. E., Covi, F., Hock, R., Rennermalm, Å. K., Heilig, A., Abermann, J., van As, D., Bjerre, E., Fettweis, X., Smeets, P. C. J. P., Kuipers Munneke, P., van den Broeke, M. R., Brils, M., Langen, P. L., Mottram, R., and Ahlstrøm, A. P.: Recent warming trends of the Greenland ice sheet documented by historical firn and ice temperature observations and machine learning, The Cryosphere, 18, 609–631, https://doi.org/10.5194/tc-18-609-2024, 2024. a
Walton, C. C., Sullivan, J. T., Rao, C. R. N., and Weinreb, M. P.: Corrections for detector nonlinearities and calibration inconsistencies of the infrared channels of the advanced very high resolution radiometer, J. Geophys. Res., 103, 3323–3337, https://doi.org/10.1029/97JC02018, 1998. a
Wan, Z. and Dozier, J.: A generalized split-window algorithm for retrieving land-surface temperature from space, IEEE T. Geosci. Remote, 34, 892–905, https://doi.org/10.1109/36.508406, 1996. a, b
Wang, A. and Zeng, X.: Range of monthly mean hourly land surface air temperature diurnal cycle over high northern latitudes, J. Geophys. Res., 119, 5836–5844, https://doi.org/10.1002/2014JD021602, 2014. a
Wang, L., Arora, V. K., Bartlett, P., Chan, E., and Curasi, S. R.: Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model, Biogeosciences, 20, 2265–2282, https://doi.org/10.5194/bg-20-2265-2023, 2023. a
Waring, A. M., Ghent, D., Perry, M., Anand, J. S., Veal, K. L., and Remedios, J.: Regional climate trend analyses for Aqua MODIS land surface temperatures, Int. J. Remote Sens., 44, 4989–5032, https://doi.org/10.1080/01431161.2023.2240522, 2023. a, b
Westermann, S., Lüers, J., Langer, M., Piel, K., and Boike, J.: The annual surface energy budget of a high-arctic permafrost site on Svalbard, Norway, The Cryosphere, 3, 245–263, https://doi.org/10.5194/tc-3-245-2009, 2009. a
Westermann, S., Langer, M., and Boike, J.: Systematic bias of average winter-time land surface temperatures inferred from MODIS at a site on Svalbard, Norway, Remote Sens. Environ., 118, 162–167, https://doi.org/10.1016/j.rse.2011.10.025, 2012. a, b, c
Westermann, S., Peter, M., Langer, M., Schwamborn, G., Schirrmeister, L., Etzelmüller, B., and Boike, J.: Transient modeling of the ground thermal conditions using satellite data in the Lena River delta, Siberia, The Cryosphere, 11, 1441–1463, https://doi.org/10.5194/tc-11-1441-2017, 2017. a
World Meteorological Organization (WMO) : Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC, Tech. Rep. GOOS-184, GTOS-76, WMO-TD/No. 1523, WMO, Geneva, Switzerland, 2010. a
Xiao, X., Naegeli, K., Neuhaus, C., Salberg, A.-B., Schwaizer, G., Wiesmann, A., Wunderle, S., and Nagler, T.: ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1979–2022), version 3.0, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/7491427f8c3442ce825ba5472c224322, 2024. a, b
Yang, J., Zhou, J., Göttsche, F. M., Long, Z., Ma, J., and Luo, R.: Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data, Int. J. Appl. Earth Obs., 91, 102136, https://doi.org/10.1016/j.jag.2020.102136, 2020. a, b
Zhang, X., Zhou, J., Liang, S., and Wang, D.: A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature, Remote Sens. Environ., 260, 112437, https://doi.org/10.1016/j.rse.2021.112437, 2021. a
Zhao, P. and He, Z.: A First Evaluation of ERA5-Land Reanalysis Temperature Product Over the Chinese Qilian Mountains, Front. Earth Sci., 10, 907730, https://doi.org/10.3389/feart.2022.90773, 2022. a
Zhou, J., Liang, S., Cheng, J., Wang, Y., and Ma, J.: The GLASS Land Surface Temperature Product, IEEE J. Sel. Top. Appl. Earth Obs., 12, 493–507, https://doi.org/10.1109/JSTARS.2018.2870130, 2019. a
Short summary
The Arctic has experienced pronounced warming the last few decades. This warming threatens ecosystems, vegetation dynamics, snow cover duration, and permafrost. Traditional monitoring methods like stations and climate models lack the detail needed. Land surface temperature (LST) data derived from satellites offer high spatial and temporal coverage, perfect for studying changes in the Arctic. In particular, LST information from AVHRR provides a 40-year record, valuable for analysing trends.
The Arctic has experienced pronounced warming the last few decades. This warming threatens...
