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16 pages, 44991 KiB

Open AccessArticle

Ground-Based Oblique-View Photogrammetry and Sentinel-1 Spaceborne RADAR Reflectivity Snow Melt Processes Assessment on an Arctic Glacier

by Jean-Michel Friedt, Éric Bernard and Madeleine Griselin

Remote Sens. 2023, 15(7), 1858; https://doi.org/10.3390/rs15071858 - 30 Mar 2023

Cited by 1 | Viewed by 1690

Abstract

The snowpack evolution during the melt season on an Arctic glacier is assessed using ground-based oblique-view cameras, spaceborne imaging and spaceborne RADAR. The repeated and systematic Synthetic Aperture RADAR (SAR) imaging by the European Space Agency’s Sentinel-1 spaceborne RADARs allows for all-weather, all-illumination [...] Read more.

The snowpack evolution during the melt season on an Arctic glacier is assessed using ground-based oblique-view cameras, spaceborne imaging and spaceborne RADAR. The repeated and systematic Synthetic Aperture RADAR (SAR) imaging by the European Space Agency’s Sentinel-1 spaceborne RADARs allows for all-weather, all-illumination condition monitoring of the snow-covered fraction of the glacier and hence assessing its water production potential. A comparison of the RADAR reflectivity with optical and multispectral imaging highlights the difference between the observed quantities—water content in the former, albedo in the latter—and the complementarity for understanding the snow melt processes. This work highlights the temporal inertia between the visible spring melting of the snowpack and the snow metamorphism. It was found that the snowpack exhibits that approximately 30 days before it starts to fade. Full article

(This article belongs to the Special Issue Remote Sensing in Snow and Glacier Hydrology)

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17 pages, 8077 KiB

Open AccessArticle

Compounded Impacts of Global Warming and Anthropogenic Disturbances on Snowmelt in Northern Baffin Island

by Liming He, H. Peter White and Wenjun Chen

Remote Sens. 2023, 15(2), 313; https://doi.org/10.3390/rs15020313 - 5 Jan 2023

Viewed by 2037

Abstract

Fugitive dust arising from mining operations in the Arctic can be a concern to surrounding communities. The Mary River Mine operation on northwest Baffin Island in the Qikiqtani region, Nunavut, is one example. Yet, the short and long-term impacts of fugitive dust remain [...] Read more.

Fugitive dust arising from mining operations in the Arctic can be a concern to surrounding communities. The Mary River Mine operation on northwest Baffin Island in the Qikiqtani region, Nunavut, is one example. Yet, the short and long-term impacts of fugitive dust remain poorly understood. Dust lowers snow albedo which can contribute to early snowmelt. This influences the spring snowmelt freshet period, significant to the land-atmosphere interactions, hydrology, ecology, and socioeconomic activities in the Arctic. Here, we map dust extents indicated by snow discoloration and examine for areas of early snowmelt using a 21-year MODIS time series snow cover product in 2000–2020. We found an episode of dust plume extended far beyond the reference dust sampler sites from where Nil dustfall is detected. A snow albedo decrease of 0.014 was seen more than 60 km away from the mine site. Incidents of early snowmelt existed extensively and progressively prior to the Mary River Mine operations; however, localized and even earlier snowmelt also appear around Mine’s operations; we estimated that the snow-off date was advanced by one week and three weeks for the background, and areas around the Mine facilities, respectively, during the 21-year period. Furthermore, the area increase in early snowmelt around the Mine facilities correlates to ore production growth. This study demonstrates rapid changes in early snowmelt beyond observed regional trends when additional drivers are introduced. Full article

(This article belongs to the Special Issue Remote Sensing in Snow and Glacier Hydrology)

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18 pages, 6446 KiB

Open AccessArticle

Snow Depth Inversion in Forest Areas from Sentinel-1 Data Based on Phase Deviation Correction

by Yu Li, Xinyue Zhao and Quanhua Zhao

Remote Sens. 2022, 14(23), 5930; https://doi.org/10.3390/rs14235930 - 23 Nov 2022

Cited by 1 | Viewed by 2201

Abstract

At present, snow depth inversion based on active microwave remote sensing is concerned essentially with areas having a relatively simple underlying surface. The existence of forests reduces the sensitivity of microwaves to snow, which often makes the snow depth inversion results uncertain. This [...] Read more.

At present, snow depth inversion based on active microwave remote sensing is concerned essentially with areas having a relatively simple underlying surface. The existence of forests reduces the sensitivity of microwaves to snow, which often makes the snow depth inversion results uncertain. This paper presents a snow depth estimation algorithm for forest areas by introducing a forest phase to characterize the effect of forests on backscattering electromagnetic wave. Firstly, the interferogram is generated with the differential interference of two-pass master-slave Synthetic Aperture Radar (SAR) images, and the real phase under snow cover condition is obtained by phase unwrapping. Secondly, the phase models for forest and non-forest areas are constructed. The effects of forest cover are modeled as forest phase in the forest phase model, which is estimated under the assumption of snow depth consistency on both sides of the boundaries between forest and non-forest areas. Finally, snow depth is estimated by the snow phase-depth model. The correctness of the proposed forest snow depth inversion algorithm was verified by taking the Jiagedaqi area of Greater Xing’an Mountains as the study area and sentinel-1 dual polarization images as the data source. Finally, the snow depth distribution of the study area was obtained with a spatial resolution of 30 m on 7 December 2020. The experimental results show that the snow depth values estimated in Jiagedaqi area are mainly between 40–120 cm, and the average snow depth value is 80.27 cm. Taking the snow depth value of 84.69 cm reckoned from hourly accumulated snowfall in Jiagedaqi as the reference snow depth, the results of the estimated snow depth are relatively consistent and well-founded. With the introduction of the forest phase, the average snow depth values estimated in the forest area increase by 5.98 cm, which reduces the underestimation of the snow depth in forest areas. Full article

(This article belongs to the Special Issue Remote Sensing in Snow and Glacier Hydrology)

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24 pages, 10672 KiB

Open AccessArticle

Glacier Changes in India’s Dhauliganga Catchment over the Past Two Decades

by Nauman Ali, Qinghua Ye, Xueqin Zhang, Xinhui Ji, Yafan Hu, Liping Zhu and Arslan Ali

Remote Sens. 2022, 14(22), 5692; https://doi.org/10.3390/rs14225692 - 10 Nov 2022

Cited by 2 | Viewed by 2729

Abstract

The rapid melting of glaciers has led to severe glacial-hydrological hazards in the Himalayas. An extreme example occurred on 7 February 2021, when a catastrophic mass flow descended from the Ronti glacier at Chamoli, Indian Himalaya, causing widespread devastation, with more than 200 [...] Read more.

The rapid melting of glaciers has led to severe glacial-hydrological hazards in the Himalayas. An extreme example occurred on 7 February 2021, when a catastrophic mass flow descended from the Ronti glacier at Chamoli, Indian Himalaya, causing widespread devastation, with more than 200 people killed or missing, as well as severe damage to four hydropower projects. To disclose what happened to the Ronti glacier over the past several decades, here, we focused on glacier changes in the Dhauliganga catchment in Uttarakhand, India, over the past two decades. Another five glaciers in the catchment were also studied to map the regional detailed glacier changes. Our achievements are summarized as follows. (1) Based on Landsat images, we constructed two glacier inventories for the catchment in 2001 and 2020. We mapped nearly 413 debris-free glaciers in the catchment between 2001 and 2020 and analyzed the glacier area change at basin and altitude levels. (2) Debris-free glacier area decreased from 477.48 ± 35.23 km² in 2001 to 418.52 ± 36.18 km² in 2020, with a reduction of 58.95 km² or 12.35% over the past two decades. (3) The geodetic mass balance was −0.27± 0.10 m w.e.a⁻¹, with a glacier mass change of −0.12 Gt. a⁻¹ from 2000 to 2013. Based on the surface elevation difference between the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) footprints (acquired from 2018 to 2021) and the National Aeronautics and Space Administration (NASA) DEM from 2000 to 2021, the average glacier geodetic mass balance was −0.22 ± 0.005 m w.e.a⁻¹, and glacier mass change was −0.10 Gt a⁻¹. (4) Our results were cross verified by available published elevation difference datasets covering multiple temporal periods, where mass balance was by −0.22 ± 0.002 m w.e.a⁻¹ from 1975 to 2000 and −0.28 ± 0.0001 w.e.a⁻¹ from 2000 to 2020. (5) Glacier 1 and Glacier 2, the two largest glaciers in the catchment, experienced a decreasing melt rate from 2000 to 2020, while Glacier 3, Glacier 4, and Glacier 5 demonstrated an increasing melt rate. However, Glacier 6, also known as the collapsed Ronti glacier, had a negative mass balance of −0.04 m w.e.a⁻¹ from 2000 to 2005 and turned positive from 2005 onward with 0.06 m w.e.a⁻¹ from 2005 to 2010, 0.19 m w.e.a⁻¹ from 2010 to 2015, and 0.32 m w.e.a⁻¹ from 2015 to 2020. We postulate that the Ronti glacier collapsed solely because of the significant mass accumulation observed between 3700 to 5500 m a.s.l. Our study helps to understand the collapsed glacier’s mass changes over the past two decades and highlights the necessity to monitor mass-gaining glaciers from space to forecast the risks of disasters. Full article

(This article belongs to the Special Issue Remote Sensing in Snow and Glacier Hydrology)

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18 pages, 4414 KiB

Open AccessArticle

Validation of Cloud-Gap-Filled Snow Cover of MODIS Daily Cloud-Free Snow Cover Products on the Qinghai–Tibetan Plateau

by Yecheng Yuan, Baolin Li, Xizhang Gao, Wei Liu, Ying Li and Rui Li

Remote Sens. 2022, 14(22), 5642; https://doi.org/10.3390/rs14225642 - 8 Nov 2022

Cited by 6 | Viewed by 1826

Abstract

Accurate daily snow cover extent is a significant input for hydrological applications in the Qinghai–Tibetan Plateau (QTP). Although several Moderate Resolution Imaging Spectroradiometer (MODIS) daily cloud-free snow cover products over the QTP are openly accessible, the cloud-gap-filled snow cover from these products has [...] Read more.

Accurate daily snow cover extent is a significant input for hydrological applications in the Qinghai–Tibetan Plateau (QTP). Although several Moderate Resolution Imaging Spectroradiometer (MODIS) daily cloud-free snow cover products over the QTP are openly accessible, the cloud-gap-filled snow cover from these products has not yet been validated. This study assessed the accuracy of cloud-gap-filled snow cover from three open accessible MODIS daily products based on snow maps retrieved from Landsat TM images. The F1-score (FS) from daily cloud-free MODIS snow cover for the combined MOD10A1F and MYD10A1F (SC1) was 64.4%, which was 7.4% points and 5.3% points higher than the other two commonly used products (SC2 and SC3), respectively. The superior accuracies from SC1 were more evident in regions with altitudes lower than 5000 m, with a weighted average FS by the area percentage of the altitude regions of 58.3%, which was 6.9% points and 9.1% points higher than SC2 and SC3. The improved SC1 accuracies also indicated regional clustering characteristics with higher FS values compared to SC2 and SC3. The lower accuracies of cloud-gap-filled snow cover from SC2 and SC3 were mainly due to the limitation in determining snow cover based on the method of the inferred snow line and the overestimation of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) snow water equivalent (SWE). These results indicate that the temporal filter approach used in SC1 is a good solution to produce daily cloud-gap-filled snow cover data for the QTP because of its higher accuracy and simple computation. The findings can be helpful for the selection of cloud-removal algorithms for determining snow cover dynamics and phenological parameters on the QTP. Full article

(This article belongs to the Special Issue Remote Sensing in Snow and Glacier Hydrology)

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28 pages, 5464 KiB

Open AccessArticle

Interpreting Sentinel-1 SAR Backscatter Signals of Snowpack Surface Melt/Freeze, Warming, and Ripening, through Field Measurements and Physically-Based SnowModel

by Jewell Lund, Richard R. Forster, Elias J. Deeb, Glen E. Liston, S. McKenzie Skiles and Hans-Peter Marshall

Remote Sens. 2022, 14(16), 4002; https://doi.org/10.3390/rs14164002 - 17 Aug 2022

Cited by 12 | Viewed by 3555

Abstract

The transition of a cold winter snowpack to one that is ripe and contributing to runoff is crucial to gauge for water resource management, but is highly variable in space and time. Snow surface melt/freeze cycles, associated with diurnal fluctuations in radiative inputs, [...] Read more.

The transition of a cold winter snowpack to one that is ripe and contributing to runoff is crucial to gauge for water resource management, but is highly variable in space and time. Snow surface melt/freeze cycles, associated with diurnal fluctuations in radiative inputs, are hallmarks of this transition. C-band synthetic aperture radar (SAR) reliably detects meltwater in the snowpack. Sentinel-1 (S1) C-band SAR offers consistent acquisition patterns that allow for diurnal investigations of melting snow. We used over 50 snow pit observations from 2020 in Grand Mesa, Colorado, USA, to track temperature and wetness in the snowpack as a function of depth and time during snowpack phases of warming, ripening, and runoff. We also ran the physically-based SnowModel, which provided a spatially and temporally continuous independent indication of snowpack conditions. Snowpack phases were identified and corroborated by comparing field measurements with SnowModel outputs. Knowledge of snowpack warming, ripening, and runoff phases was used to interpret diurnal changes in S1 backscatter values. Both field measurements and SnowModel simulations suggested that S1 SAR was not sensitive to the initial snowpack warming phase on Grand Mesa. In the ripening and runoff phases, the diurnal cycle in S1 SAR co-polarized backscatter was affected by both surface melt/freeze as well as the conditions of the snowpack underneath (ripening or ripe). The ripening phase was associated with significant increases in morning backscatter values, likely due to volume scattering from surface melt/freeze crusts, as well as significant decreases in evening backscatter values associated with snowmelt. During the runoff phase, both morning and evening backscatter decreased compared to reference values. These unique S1 diurnal signatures, and their interpretations using field measurements and SnowModel outputs, highlight the capacities and limitations of S1 SAR to understand snow surface states and bulk phases, which may offer runoff forecasting or energy balance model validation or parameterization, especially useful in remote or sparsely-gauged alpine basins. Full article

(This article belongs to the Special Issue Remote Sensing in Snow and Glacier Hydrology)

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23 pages, 7891 KiB

Open AccessArticle

Monitoring the Ice Thickness in High-Order Rivers on the Tibetan Plateau with Dual-Polarized C-Band Synthetic Aperture Radar

by Huan Zhang, Hongyi Li and Haojie Li

Remote Sens. 2022, 14(11), 2591; https://doi.org/10.3390/rs14112591 - 27 May 2022

Cited by 5 | Viewed by 1942

Abstract

River ice on the Tibetan Plateau has important impacts on the ecosystem and hydrology. High-resolution Synthetic Aperture Radar (SAR) is an important tool for monitoring the thickness of river ice in high-altitude areas without ground data. However, due to the complex topography and [...] Read more.

River ice on the Tibetan Plateau has important impacts on the ecosystem and hydrology. High-resolution Synthetic Aperture Radar (SAR) is an important tool for monitoring the thickness of river ice in high-altitude areas without ground data. However, due to the complex topography and narrow width, it remains challenging to monitor the ice thickness of high-order rivers (high-level branches in the plateau river system) on the Tibetan Plateau using SAR. Therefore, this paper focuses on inverting the ice thickness by utilizing dual-polarized C-band radar data. We select a typical watershed in the northeastern Tibetan Plateau, namely, the Babao River basin (including the Babao River and Binggou River), as the study area. The results show the following: (1) Dual-polarized C-band radar data have the potential to monitor the ice thickness of high-order rivers. The RMSEs of the Babao and Binggou Rivers are 0.109 m and 0.258 m, respectively. (2) Ascending and descending orbit radar images perform differently in retrieving the ice thicknesses of rivers with different directions. (3) The thickness of river ice affects the inversion accuracy. (4) Polarization parameters have varying explanatory capacities depending on the river characteristics. Our findings can provide a reference for the subsequent development of highly generalizable river ice inversion equations using dual-polarized radar data. Full article

(This article belongs to the Special Issue Remote Sensing in Snow and Glacier Hydrology)

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22 pages, 4559 KiB

Open AccessArticle

An Accuracy Assessment of Snow Depth Measurements in Agro-Forested Environments by UAV Lidar

by Vasana Dharmadasa, Christophe Kinnard and Michel Baraër

Remote Sens. 2022, 14(7), 1649; https://doi.org/10.3390/rs14071649 - 30 Mar 2022

Cited by 15 | Viewed by 3302

Abstract

This study assesses the performance of UAV lidar system in measuring high-resolution snow depths in agro-forested landscapes in southern Québec, Canada. We used manmade, mobile ground control points in summer and winter surveys to assess the absolute vertical accuracy of the point cloud. [...] Read more.

This study assesses the performance of UAV lidar system in measuring high-resolution snow depths in agro-forested landscapes in southern Québec, Canada. We used manmade, mobile ground control points in summer and winter surveys to assess the absolute vertical accuracy of the point cloud. Relative accuracy was determined by a repeat flight over one survey block. Estimated absolute and relative errors were within the expected accuracy of the lidar (~5 and ~7 cm, respectively). The validation of lidar-derived snow depths with ground-based measurements showed a good agreement, however with higher uncertainties observed in forested areas compared with open areas. A strip alignment procedure was used to attempt the correction of misalignment between overlapping flight strips. However, the significant improvement of inter-strip relative accuracy brought by this technique was at the cost of the absolute accuracy of the entire point cloud. This phenomenon was further confirmed by the degraded performance of the strip-aligned snow depths compared with ground-based measurements. This study shows that boresight calibrated point clouds without strip alignment are deemed to be adequate to provide centimeter-level accurate snow depth maps with UAV lidar. Moreover, this study provides some of the earliest snow depth mapping results in agro-forested landscapes based on UAV lidar. Full article

(This article belongs to the Special Issue Remote Sensing in Snow and Glacier Hydrology)

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20 pages, 4684 KiB

Open AccessArticle

Surface Characteristics, Elevation Change, and Velocity of High-Arctic Valley Glacier from Repeated High-Resolution UAV Photogrammetry

by Kristaps Lamsters, Jurijs Ješkins, Ireneusz Sobota, Jānis Karušs and Pēteris Džeriņš

Remote Sens. 2022, 14(4), 1029; https://doi.org/10.3390/rs14041029 - 21 Feb 2022

Cited by 16 | Viewed by 4329

Abstract

Unmanned Aerial Vehicles (UAVs) are being increasingly used in glaciology demonstrating their potential for the generation of high-resolution digital elevation models (DEMs) that can be further used for the evaluation of glacial processes in detail. Such investigations are especially important for the evaluation [...] Read more.

Unmanned Aerial Vehicles (UAVs) are being increasingly used in glaciology demonstrating their potential for the generation of high-resolution digital elevation models (DEMs) that can be further used for the evaluation of glacial processes in detail. Such investigations are especially important for the evaluation of surface changes of small valley glaciers, which are not well-represented in lower-resolution satellite-derived products. In this study, we performed two UAV surveys at the end of the ablation season in 2019 and 2021 on Waldemarbreen, a High-Arctic glacier in NW Svalbard. We derived the mean annual glacier surface velocity of 5.3 m. The estimated mean glacier surface elevation change from 2019 to 2021 was −1.46 m a⁻¹ which corresponds to the geodetic mass balance (MB) of −1.33 m w.e. a⁻¹. The glaciological MB for the same period was −1.61 m w.e. a⁻¹. Our survey includes all Waldemarbreen and demonstrates the efficiency of high-resolution DEMs produced from UAV photogrammetry for the reconstruction of changes in glacier surface elevation and velocity. We suggest that glaciological and geodetic MB methods should be used complementary to each other. Full article

(This article belongs to the Special Issue Remote Sensing in Snow and Glacier Hydrology)

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22 pages, 4040 KiB

Open AccessArticle

Multi-Temporal Variations in Surface Albedo on Urumqi Glacier No.1 in Tien Shan, under Arid and Semi-Arid Environment

by Xiaoying Yue, Zhongqin Li, Huilin Li, Feiteng Wang and Shuang Jin

Remote Sens. 2022, 14(4), 808; https://doi.org/10.3390/rs14040808 - 9 Feb 2022

Cited by 8 | Viewed by 2247

Abstract

Surface albedo exerts substantial control over the energy available for glacier melting. For Urumqi Glacier No.1 in the Tien Shan Mountains, China, represented as a “summer accumulation” glacier, the variations in albedo driven by surface processes are complex and still poorly understood. In [...] Read more.

Surface albedo exerts substantial control over the energy available for glacier melting. For Urumqi Glacier No.1 in the Tien Shan Mountains, China, represented as a “summer accumulation” glacier, the variations in albedo driven by surface processes are complex and still poorly understood. In this study, we examined the interannual trends in ablation-period albedo from 2000 to 2021 using MOD10A1 products, evaluated the variation in bare-ice albedo retrieved from 13 end-of-summer Landsat images obtained between 2002 and 2019, and investigated the seasonal variation and diurnal cycle of surface albedo collected near the equilibrium line of the glacier by an AWS from September 2018 to August 2021. During the period of 2000–2021, the average ablation-period albedo presented a slight but not statistically significant downward trend, with a total decrease of 1.87%. Specifically, the decrease in glacier albedo was quicker in July than that in August, and there was a slight increase in May and June. The blackening phenomenon was shown on the east branch glacier, but not on the west branch glacier. For seasonal variability, a bimodal pattern was demonstrated, different from the unimodal seasonal variation in other midlatitude glaciers. The albedo peaks occurred in December and April or May. Under clear sky conditions, the diurnal cycle presented three patterns: a symmetric pattern, an asymmetric pattern, and a progressive decreasing pattern. Air temperature and solid precipitation are the main drivers of variations in glacier albedo, but in different periods of the ablation season, two climate variables affect albedo to varying degrees. The effect of surface albedo reduction enhanced glacier melting by about 20% over the past 20 years. The short-term increase in albedo caused by summer snowfall can considerably reduce glacier melting by as much as 80% in June. Full article

(This article belongs to the Special Issue Remote Sensing in Snow and Glacier Hydrology)

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18 pages, 2579 KiB

Open AccessArticle

Exploring the Spatiotemporal Coverage of Terrestrial Snow Mass Using a Suite of Satellite Constellation Configurations

by Lizhao Wang, Barton A. Forman and Edward Kim

Remote Sens. 2022, 14(3), 633; https://doi.org/10.3390/rs14030633 - 28 Jan 2022

Cited by 3 | Viewed by 2484

Abstract

Terrestrial snow is a vital freshwater resource for more than 1 billion people. Remotely-sensed snow observations can be used to retrieve snow mass or integrated into a snow model estimate; however, optimally leveraging remote sensing observations of snow is challenging. One reason is [...] Read more.

Terrestrial snow is a vital freshwater resource for more than 1 billion people. Remotely-sensed snow observations can be used to retrieve snow mass or integrated into a snow model estimate; however, optimally leveraging remote sensing observations of snow is challenging. One reason is that no single sensor can accurately measure all types of snow because each type of sensor has its own unique limitations. Another reason is that remote sensing data is inherently discontinuous across time and space, and that the revisit cycle of remote sensing observations may not meet the requirements of a given snow applications. In order to quantify the feasible availability of remotely-sensed observations across space and time, this study simulates the sensor coverage for a suite of hypothetical snow sensors as a function of different orbital configurations and sensor properties. The information gleaned from this analysis coupled with a dynamic snow binary map is used to evaluate the efficiency of a single sensor (or constellation) to observe terrestrial snow on a global scale. The results show the efficacy achievable by different sensors over different snow types. The combination of different orbital and sensor configurations is explored to requirements of remote sensing missions that have 1-day, 3-day, or 30-day repeat intervals. The simulation results suggest that 1100 km, 550 km, and 200 km are the minimum required swath width for a polar-orbiting sensor to meet snow-related applications demanding a 1-day, 3-day, and 30-day repeat cycles, respectively. The results of this paper provide valuable input for the planning of a future global snow mission. Full article

(This article belongs to the Special Issue Remote Sensing in Snow and Glacier Hydrology)

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15 pages, 2006 KiB

Open AccessTechnical Note

Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica

by Zhiwei Chen, Rong Jin, Liqiang Zhang, Ke Chen and Qingxia Li

Remote Sens. 2023, 15(5), 1396; https://doi.org/10.3390/rs15051396 - 1 Mar 2023

Viewed by 1296

Abstract

The snow temperature above the ice sheet is one of the basic characteristic parameters of the ice sheet, which plays an important role in the study of the global climate. Because infrared and microwaves with different frequencies have different penetration depths in snow, [...] Read more.

The snow temperature above the ice sheet is one of the basic characteristic parameters of the ice sheet, which plays an important role in the study of the global climate. Because infrared and microwaves with different frequencies have different penetration depths in snow, it is possible to retrieve the snow temperature profiles by combining microwave and infrared brightness temperatures. This paper proposes a conjoint inversion algorithm to retrieve snow temperature profiles by combining multi-frequency microwave brightness temperature (BT) with infrared BT, in which different weight functions of microwave BT at different frequencies are adopted, and the atmosphere influence has also been corrected. The snow temperature profile data are retrieved based on AMSR2 microwave BT data and MODIS infrared BT data in 2017 and 2018, which are evaluated by comparing with the measured snow temperature at Dome-C station. The results confirm that the inverted snow temperature profiles are consistent with the field observation data from the Dome-C station. Multi-frequency microwave brightness temperature can be used to invert the snow temperature profiles; however, the inverted snow surface temperature is more accurate by combining the infrared BT with the microwave BT in the conjoint inversion algorithm. Full article

(This article belongs to the Special Issue Remote Sensing in Snow and Glacier Hydrology)

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