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Remote Sensing of Environmental Changes in Cold Regions Ⅱ
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Remote Sensing of Environmental Changes in Cold Regions Ⅱ

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 30393

Special Issue Editors

Dr. Youngwook Kim

E-Mail Website
Guest Editor
Department of Biology, United Arab Emirates University, Al Ain 15551, UAE
Interests: remote sensing of freeze/thaw status and vegetation dynamics; climate and environmental change impact on terrestrial ecosystems
Dr. Jinyang Du

E-Mail Website
Guest Editor
NTSG, University of Montana, Missoula, MT 59812, USA
Interests: quantitative remote sensing of land surface parameters, including landscape freeze/thaw state; vegetation water content; soil moisture and snow water equivalent for global environmental change studies
Special Issues, Collections and Topics in MDPI journals
Dr. Jennifer D. Watts

E-Mail Website
Guest Editor
Woodwell Climate Research Center, Falmouth, MA 02540, USA
Interests: global wetlands; arctic-boreal regions; remote sensing papers: Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties
Special Issues, Collections and Topics in MDPI journals
Dr. Hui Lu

E-Mail Website
Guest Editor
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Interests: microwave remote sensing; soil moisture; land surface data assimilation; hydrological model; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Lingmei Jiang

E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: microwave emission model; snow and soil parameters retrieval; climatic and environmental change; land surface modelling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paolo Tarolli

E-Mail Website
Guest Editor
Department of Land, Environment, Agriculture and Forestry, University of Padova, viale dell’Università 16, 35020 Legnaro, PD, Italy
Interests: digital terrain analysis; earth surface processes analysis; natural hazards; geomorphometry; lidar; structure from motion; Anthropocene
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cold regions including the northern high latitudes, polar regions, and Tibetan Plateau are highly sensitive to global warming, and are undergoing dramatic changes in ecological, hydrologic, and climatic processes. Yet, studies of these regions are restricted by their limited in-situ measurements, and remote sensing provides key supports for monitoring and interpreting the on-going environmental changes.

This Special Issue, “Remote Sensing of Environmental Changes in Cold Regions II”, will host papers focusing on, but not limited to, the following topics:

  • Long-term monitoring of the dynamic changes of glacier, snow cover, permafrost, water bodies, vegetation, and carbon emissions using multi-year and multi-source remote sensing data;
  • Applying emerging remote sensing techniques (e.g., SmallSat, UAV, lidar, GNSS, and near-nadir SAR imaging techniques) for the enhanced mapping of cold land biophysical/geophysical parameters;
  • Investigating the use of current and future satellite missions such as SMAP, SMOS, SWOT, OCO3, and NISAR in monitoring eco-hydrological and cryospheric parameters;
  • Interpreting remote sensing data based on cloud computation and machine learning techniques for cold region studies;
  • Monitoring, modelling, understanding, and forecasting the interactions among earth system cycles under changing climate.

Related References

  • Kim, Y.; Kimball, J.S.; Glassy, J.; Du, J. An extended global earth system data record on daily landscape freeze-thaw status determined from satellite passive microwave remote sensing. Earth System Science Data, 2017
  • Du, J.; Kimball, J.S.; Duguay, C.; Kim, Y.; Watts, J.D. Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015. The Cryosphere, 2017.
  • Cooley, S.W., Smith, L.C., Stepan, L. and Mascaro, J., 2017. Tracking dynamic northern surface water changes with high-frequency planet CubeSat imagery. Remote Sensing, 9(12), p.1306.
  • Yang, J., Jiang, L., Luojus, K., Pan, J., Lemmetyinen, J., Takala, M. and Wu, S., 2020. Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach. The Cryosphere, 14(6), pp.1763-1778.
  • Endsley, K.A., Kimball, J.S., Reichle, R.H. and Watts, J.D., 2020. Satellite monitoring of global surface soil organic carbon dynamics using the SMAP Level 4 Carbon product. Journal of Geophysical Research: Biogeosciences, p.e2020JG006100.
  • Yao, P., Lu, H., Shi, J., Zhao, T., Yang, K., Cosh, M.H., Gianotti, D.J.S. and Entekhabi, D., 2021. A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019). Scientific Data, 8(1), pp.1-16.
Dr. Youngwook Kim
Dr. Jinyang Du
Dr. Jennifer D. Watts
Dr. Hui Lu
Dr. Lingmei Jiang
Prof. Dr. Paolo Tarolli
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • environmental changes
  • cold region
  • remote sensing
  • land surface parameters
  • machine learning

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Published Papers (11 papers)

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18 pages, 7683 KiB  
Article
Use of Landsat Satellite Images in the Assessment of the Variability in Ice Cover on Polish Lakes
by Mariusz Sojka, Mariusz Ptak and Senlin Zhu
Remote Sens. 2023, 15(12), 3030; https://doi.org/10.3390/rs15123030 - 9 Jun 2023
Cited by 3 | Viewed by 1316
Abstract
Despite several decades of observations of ice cover in Polish lakes, researchers have not broadly applied satellite images to date. This paper presents a temporal and spatial analysis of the variability in the occurrence of ice cover on lakes in the Drawskie Lakeland [...] Read more.
Despite several decades of observations of ice cover in Polish lakes, researchers have not broadly applied satellite images to date. This paper presents a temporal and spatial analysis of the variability in the occurrence of ice cover on lakes in the Drawskie Lakeland in the hydrological years 1984–2022 based on satellite data from Landsat missions 4, 5, 7, 8, and 9. The range of occurrence of ice cover was determined based on the value of the Normalised Difference Snow Index (NDSI) and blue spectral band (ρλblue). The determination of ice cover extent adopted ρλblue  values from 0.033 to 0.120 as the threshold values. The analysis covered 67 lakes with an area from 0.07 to 18.71 km2. A total of 53 images were analysed, 14 and 39 out of which showed full and partial ice cover, respectively. The cluster analysis permitted the designation of two groups of lakes characterised by an approximate range of ice cover. The obtained results were analysed in the context of the morphometric parameters of the lakes. It was evidenced that the range of the ice cover on lakes is determined by the surface area of the lakes; their mean and maximum depth, volume, length, and width; and the height of the location above sea level. The results of analyses of the spatial range of ice cover in subsequent scenes allowed for the preparation of maps of probability of ice cover occurrence that permit the complete determination of its variability within each of the lakes. Monitoring of the spatial variability in ice cover within individual lakes as well as in reference to lakes not subject to traditional observations offers new research possibilities in many scientific disciplines focused on these ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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10 pages, 7924 KiB  
Communication
A Step-Wise Workflow for SAR Remote Sensing of Perennial Heaving Mound/Crater on the Yamal Peninsula, Western Siberia
by Valery Bondur, Tumen Chimitdorzhiev and Aleksey Dmitriev
Remote Sens. 2023, 15(1), 281; https://doi.org/10.3390/rs15010281 - 3 Jan 2023
Viewed by 1877
Abstract
Climate change in the Arctic region is more significant than in other parts of our planet. One of the manifestations of these changes is crater creation with blowouts of a gas, ice and frozen soil mixture. In this context, dynamics studies of long-term [...] Read more.
Climate change in the Arctic region is more significant than in other parts of our planet. One of the manifestations of these changes is crater creation with blowouts of a gas, ice and frozen soil mixture. In this context, dynamics studies of long-term heaving mounds that turn into craters as a result are relevant. A workflow for detecting and assessing anomalous dynamics of heaving mounds in the Arctic regions is proposed. Areas with anomalous increase of ALOS-2 PALSAR-2 synthetic aperture radar (SAR) backscattering intensity are detected in the first stage. These increases take place due to sudden changes in local terrain slopes when the scattering surface (mound slope) turns toward the radar. Radar backscattering intensity also rises due to depolarization at newly formed frost cracks. Validation of the detected anomaly is carried out at the second stage through a comparison of multi-temporal digital elevation models obtained from bistatic radar interferometry TerraSAR-X/TanDEM-X data. At the final stage, the deformations are assessed within the detected areas using differential SAR interferometry (DInSAR) technique by ALOS-2 PALSAR-2 data. The magnitude of the heaving along the line of sight (LOS) was 22–24 cm in the period from January 2019 to January 2020. In general, effectiveness for detecting the perennial heaving mounds and the rate assessment of their increase were demonstrated in the suggested workflow. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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20 pages, 5703 KiB  
Article
Research on Glacier Elevation Variability in the Qilian Mountains of the Qinghai-Tibet Plateau Based on Topographic Correction by Pyramid Registration
by Junze Zeng, Junfeng Xie, Ren Liu, Fan Mo and Xiaomeng Yang
Remote Sens. 2023, 15(1), 62; https://doi.org/10.3390/rs15010062 - 22 Dec 2022
Cited by 5 | Viewed by 1808
Abstract
As the ‘Third Pole’ of the world, the Qinghai-Tibet Plateau is also known as the Asian Water Tower. The glaciers covering its surface can reflect changes in the global climate and ecological environment. Therefore, the critical need for accurate information regarding the elevation [...] Read more.
As the ‘Third Pole’ of the world, the Qinghai-Tibet Plateau is also known as the Asian Water Tower. The glaciers covering its surface can reflect changes in the global climate and ecological environment. Therefore, the critical need for accurate information regarding the elevation changes of the glaciers on the Qinghai-Tibet Plateau is self-evident. Here we present a method for monitoring the elevation change of the glaciers on the Qinghai-Tibet Plateau that is based on pyramid registration and terrain correction techniques. The registration results show that the average elevation difference in the stable area has been improved to a considerable extent, at least 70%. The elevation difference after registration obeys a Gaussian distribution with a mean of 0. In this study, glaciers in the Qilian Mountains of the Qinghai-Tibet Plateau were used as the experimental objects, and the changes in glacier elevation in the region were monitored over the past three years. The results show that from 2019 to 2021, the glaciers in the western Qilian Mountains thinned significantly, and the glacier elevation change rate was −0.99 ± 0.34 m/year. The changes in glaciers in the southwest and north were relatively minor, with change rates of 0.09 ± 0.94 m/year and −0.08 ± 0.79 m/year, respectively. The change rates of the two glaciers in the middle were 0.74 ± 0.84 m/year and −0.16 ± 0.85 m/year, and the glacier change rate in the northeast was −0.27 ± 0.77 m/year. Finally, combined with meteorological data analysis, it is concluded that the change in glacier elevation is primarily affected by temperature and precipitation. Among these, precipitation accounts for the dominant factor impacting glacier elevation change. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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20 pages, 7570 KiB  
Article
Elevation Change of CookE2 Subglacial Lake in East Antarctica Observed by DInSAR and Time-Segmented PSInSAR
by Jihyun Moon, Hoseung Lee and Hoonyol Lee
Remote Sens. 2022, 14(18), 4616; https://doi.org/10.3390/rs14184616 - 15 Sep 2022
Cited by 1 | Viewed by 1904
Abstract
In this study, elevation change and surface morphology of CookE2, one of the most active subglacial lakes in East Antarctica, were analyzed by using Differential Interferometric Synthetic Aperture Radar (DInSAR) and a newly adapted Time-Segmented Persistent Scatterer Interferometric Synthetic Aperture Radar (TS-PSInSAR) techniques. [...] Read more.
In this study, elevation change and surface morphology of CookE2, one of the most active subglacial lakes in East Antarctica, were analyzed by using Differential Interferometric Synthetic Aperture Radar (DInSAR) and a newly adapted Time-Segmented Persistent Scatterer Interferometric Synthetic Aperture Radar (TS-PSInSAR) techniques. Firstly, several DInSAR pairs were used to study the surface morphology of the subglacial lake during the rapid discharge event in 2007 and the subsequent recharge in 2010 by using ALOS PALSAR data and the continuous recharge from 2018 to 2020 by using Sentinel-1 SAR data. For time-series observation from 2018 to 2020, however, simple integration of DInSAR deviates largely from the satellite altimeter data because errors from the horizontal flow of the surrounding ice field or atmospheric phase accumulate. Conventional PSInSAR deviates from the altimeter data if the LOS displacement exceeds 300 mm, i.e., approximately 1/4 of the slant range resolution of the Sentinel-1 SAR in Interferometric Wide-swath (IW) mode, during the time window. Therefore, a series of Time-Segmented PSInSAR with a 4-month time window could accurately distinguish 1.10 ± 0.01 m/year of highly linear (R2 = 0.99) surface rise rate of CookE2 and 0.63 m/year of horizontal deformation rate of the surrounding ice field from 2018 to 2020. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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25 pages, 5180 KiB  
Article
Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau
by Zixuan Hu, Linna Chai, Wade T. Crow, Shaomin Liu, Zhongli Zhu, Ji Zhou, Yuquan Qu, Jin Liu, Shiqi Yang and Zheng Lu
Remote Sens. 2022, 14(13), 3063; https://doi.org/10.3390/rs14133063 - 25 Jun 2022
Cited by 10 | Viewed by 2310
Abstract
Soil moisture (SM) is an important land-surface parameter. Although microwave remote sensing is recognized as one of the most appropriate methods for retrieving SM, such retrievals often cannot meet the requirements of specific applications because of their coarse spatial resolution and spatiotemporal data [...] Read more.
Soil moisture (SM) is an important land-surface parameter. Although microwave remote sensing is recognized as one of the most appropriate methods for retrieving SM, such retrievals often cannot meet the requirements of specific applications because of their coarse spatial resolution and spatiotemporal data gaps. A range of general models (GMs) for SM analysis topics (e.g., gap-filling, forecasting, and downscaling) have been introduced to address these shortcomings. This work presents a novel strategy (i.e., optimized wavelet-coupled fitting method (OWCM)) to enhance the fitting accuracy of GMs by introducing a wavelet transform (WT) technique. Four separate GMs are selected, i.e., elastic network regression, area-to-area regression kriging, random forest regression, and neural network regression. The fitting procedures are then tested within a downscaling analysis implemented between aggregated Global Land Surface Satellite products (i.e., LAI, FVC, albedo), Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST, and Random Forest Soil Moisture (RFSM) datasets in both the WT space and the regular space. Then, eight fine-resolution SM datasets mapped from the trained GMs and OWCMs are analyzed using direct comparisons with in situ SM measurements and indirect intercomparisons between the aggregated OWCM-/GM-derived SM and RFSM. The results demonstrate that OWCM-derived SM products are generally closer to the in situ SM observations, and better capture in situ SM dynamics during the unfrozen season, compared to the corresponding GM-derived SM product, which shows fewer time changes and more stable trends. Moreover, OWCM-derived SM products represent a significant improvement over corresponding GM-derived SM products in terms of their ability to spatially and temporally match RFSM. Although spatial heterogeneity still substantially impacts the fitting accuracies of both GM and OWCM SM products, the improvements of OWCMs over GMs are significant. This improvement can likely be attributed to the fitting procedure of OWCMs implemented in the WT space, which better captures high- and low-frequency image features than in the regular space. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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20 pages, 4913 KiB  
Article
Recent Changes in Glaciers in the Northern Tien Shan, Central Asia
by Qifei Zhang, Yaning Chen, Zhi Li, Yanyun Xiang, Yupeng Li and Congjian Sun
Remote Sens. 2022, 14(12), 2878; https://doi.org/10.3390/rs14122878 - 16 Jun 2022
Cited by 12 | Viewed by 3855
Abstract
The Tien Shan is regarded as the “Water tower of Central Asia,” being a solid reservoir of freshwater resources and also a natural and early warning indicator of climate change. Research on glaciers is important for the sustainable development and management of water [...] Read more.
The Tien Shan is regarded as the “Water tower of Central Asia,” being a solid reservoir of freshwater resources and also a natural and early warning indicator of climate change. Research on glaciers is important for the sustainable development and management of water resources in Central Asia. This study investigated the spatiotemporal dynamics of glaciers in the northern Tien Shan from 1990 to 2015 using multi-source remote sensing and meteorological data. The results showed that the total area and volume of glaciers in the northern Tien Shan exhibited negative trends, decreasing by 456.43 km2 (16.08%) and 26.14 km3 (16.38%), respectively. The reduction in the total glacier area exhibited an accelerating trend, decreasing by 0.60%/a before 2000, but by 0.71%/a after 2000. Glaciers in the outer northern Tien Shan region, with areas < 2 km2 showed the greatest shrinkage, especially those in the northeastern and southwestern regions. All aspects in the northern Tien Shan exhibited negative trends in the glacier area, especially in the east–west aspects (shrinkage of 24.74–38.37%). Regarding altitude, the termini of glaciers rose continuously from 1990 to 2015, particularly for glaciers below 3700 m, with a total area decrease of 30.37%, and the lower altitude of the glaciers showed a higher area decrease. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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23 pages, 35055 KiB  
Article
An Assessment of Sea Ice Motion Products in the Robeson Channel Using Daily Sentinel-1 Images
by Tingting Liu, Zihan Wang, Mohammed Shokr, Ruibo Lei and Zhaoru Zhang
Remote Sens. 2022, 14(2), 329; https://doi.org/10.3390/rs14020329 - 11 Jan 2022
Cited by 3 | Viewed by 2498
Abstract
Sea ice motion is an essential parameter when determining sea ice deformation, regional advection, and the outflow of ice from the Arctic Ocean. The Robeson Channel, which is located between Ellesmere Island and northwest Greenland, is a narrow but crucial channel for ice [...] Read more.
Sea ice motion is an essential parameter when determining sea ice deformation, regional advection, and the outflow of ice from the Arctic Ocean. The Robeson Channel, which is located between Ellesmere Island and northwest Greenland, is a narrow but crucial channel for ice outflow. Only three Eulerian sea ice motion products derived from ocean/sea ice reanalysis are available: GLORYS12V1, PSY4V3, and TOPAZ4. In this study, we used Lagrangian ice motion in the Robeson Channel derived from Sentinel-1 images to assess GLORYS12V1, PSY4V3, and TOPAZ4. The influence of the presence of ice arches, and wind and tidal forcing on the accuracies of the reanalysis products was also investigated. The results show that the PSY4V3 product performs the best as it underestimates the motion the least, whereas TOPAZ4 grossly underestimates the motion. This is particularly true in regimes of free drift after the formation of the northern arch. In areas with slow ice motion or grounded ice floes, the GLORYS12V1 and TOPAZ4 products offer a better estimation. The spatial distribution of the deviation between the products and ice floe drift is also presented and shows a better agreement in the Robeson Channel compared to the packed ice regime north of the Robeson Channel. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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16 pages, 6582 KiB  
Article
Distribution and Evolution of Supraglacial Lakes in Greenland during the 2016–2018 Melt Seasons
by Jinjing Hu, Huabing Huang, Zhaohui Chi, Xiao Cheng, Zixin Wei, Peimin Chen, Xiaoqing Xu, Shengliang Qi, Yifang Xu and Yang Zheng
Remote Sens. 2022, 14(1), 55; https://doi.org/10.3390/rs14010055 - 23 Dec 2021
Cited by 10 | Viewed by 4686
Abstract
In recent decades, the melting of the Greenland Ice Sheet (GrIS) has become one of the major causes of global sea-level rise. Supraglacial lakes (SGLs) are typical hydrological features produced on the surface of the GrIS during the melt seasons. The existence and [...] Read more.
In recent decades, the melting of the Greenland Ice Sheet (GrIS) has become one of the major causes of global sea-level rise. Supraglacial lakes (SGLs) are typical hydrological features produced on the surface of the GrIS during the melt seasons. The existence and evolution of SGLs play an important role in the melting process of the ice sheet surface. To understand the distribution and recent changes of SGLs in Greenland, this study developed a random forest (RF) algorithm incorporating the texture and morphological features to automatically identify SGLs based on the Google Earth Engine (GEE) platform. Sentinel-2 imagery was used to map the SGLs inventory in Greenland during the 2016–2018 melt seasons and to explore the spatial and temporal variability characteristics of SGLs. Our results show changes in SGLs from 2016 to 2018, with the total area decreasing by ~1152.22 km2 and the number increasing by 1134; SGLs are mainly distributed in western Greenland (SW, CW, NW) and northeastern Greenland (NE), where the NE region has the largest number of observed SGLs and the largest SGL was with the surface area of 16.60 km2 (2016). SGLs were found to be most active in the area with the elevation of 800–1600 m and the slope of 0–5°, and showed a phenomenon of retreating to lower elevation areas and developing to steeper slope areas. Our work provided a method for rapid inventory of SGLs. This study will help monitor the mass balance of the GrIS and predict future rapid ice loss from Greenland. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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15 pages, 6424 KiB  
Article
A Pixel-Based Vegetation Greenness Trend Analysis over the Russian Tundra with All Available Landsat Data from 1984 to 2018
by Caixia Liu, Huabing Huang and Fangdi Sun
Remote Sens. 2021, 13(23), 4933; https://doi.org/10.3390/rs13234933 - 4 Dec 2021
Cited by 14 | Viewed by 3350
Abstract
As Arctic warming continues, its impact on vegetation greenness is complex, variable and inherently scale-dependent. Studies with multiple spatial resolution satellite observations, with 30 m resolution included, on tundra greenness have been implemented all over the North American tundra. However, finer resolution studies [...] Read more.
As Arctic warming continues, its impact on vegetation greenness is complex, variable and inherently scale-dependent. Studies with multiple spatial resolution satellite observations, with 30 m resolution included, on tundra greenness have been implemented all over the North American tundra. However, finer resolution studies on the greenness trends in the Russian tundra have only been carried out at a limited local or regional scale and the spatial heterogeneity of the trend remains unclear. Here, we analyzed the fine spatial resolution dataset Landsat archive from 1984 to 2018 over the entire Russian tundra and produced pixel-by-pixel greenness trend maps with the support of Google Earth Engine (GEE). The entire Russian tundra was divided into six geographical regions based on World Wildlife Fund (WWF) ecoregions. A Theil–Sen regression (TSR) was used for the trend identification and the changed pixels with a significance level p < 0.05 were retained in the final results for a subsequent greening/browning trend analysis. Our results indicated that: (1) the number of valid Landsat observations was spatially varied. The Western and Eastern European Tundras (WET and EET) had denser observations than other regions, which enabled a trend analysis during the whole study period from 1984 to 2018; (2) the most significant greening occurred in the Yamal-Gydan tundra (WET), Bering tundra and Chukchi Peninsula tundra (CT) during 1984–2018. The EET had a greening trend of 2.3% and 6.6% and the WET of 3.4% and 18% during 1984–1999 and 2000–2018, respectively. The area of browning trend was relatively low when we first masked the surface water bodies out before the trend analysis; and (3) the Landsat-based greenness trend was broadly similar to the AVHRR-based trend over the entire region but AVHRR retrieved more browning areas due to spectral mixing adjacent effects. Higher resolution images and field measurement studies are strongly needed to understand the vegetation trend over the Russian tundra ecosystem. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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17 pages, 6028 KiB  
Article
Diversity of Remote Sensing-Based Variable Inputs Improves the Estimation of Seasonal Maximum Freezing Depth
by Bingquan Wang and Youhua Ran
Remote Sens. 2021, 13(23), 4829; https://doi.org/10.3390/rs13234829 - 28 Nov 2021
Cited by 8 | Viewed by 2109
Abstract
The maximum soil freezing depth (MSFD) is an important indicator of the thermal state of seasonally frozen ground. Its variation has important implications for the water cycle, ecological processes, climate and engineering stability. This study tested three aspects of data-driven predictions of MSFD [...] Read more.
The maximum soil freezing depth (MSFD) is an important indicator of the thermal state of seasonally frozen ground. Its variation has important implications for the water cycle, ecological processes, climate and engineering stability. This study tested three aspects of data-driven predictions of MSFD in the Qinghai-Tibet Plateau (QTP), including comparison of three popular statistical/machine learning techniques, differences between remote sensing variables and reanalysis data as input conditions, and transportability of the model built by reanalysis data. The results show that support vector regression (SVR) performs better than random forest (RF), k-nearest neighbor (KNN) and the ensemble mean of the three models. Compared with the climate predictors, the remote sensing predictors are helpful for improving the simulation accuracy of the MSFD at both decadal and annual scales (at the annual and decadal scales, the root mean square error (RMSE) is reduced by 2.84 and 1.99 cm, respectively). The SVR model with climate predictor calibration using the in situ MSFD at the baseline period (2001–2010) can be used to simulate the MSFD over historical periods (1981–1990 and 1991–2000). This result indicates the good transferability of the well-trained machine learning model and its availability to simulate the MSFD of the past and the future when remote sensing predictors are not available. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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11 pages, 2257 KiB  
Technical Note
Development of Snow Cover Frequency Maps from MODIS Snow Cover Products
by George Riggs, Dorothy Hall, Carrie Vuyovich and Nicolo DiGirolamo
Remote Sens. 2022, 14(22), 5661; https://doi.org/10.3390/rs14225661 - 9 Nov 2022
Cited by 6 | Viewed by 2428
Abstract
With a decade scale record of global snow cover extent (SCE) at up to 500 m from the Moderate-resolution Imaging Spectroradiometer (MODIS), the dynamics of snow cover can be mapped at local to global scales. We developed daily snow cover frequency maps from [...] Read more.
With a decade scale record of global snow cover extent (SCE) at up to 500 m from the Moderate-resolution Imaging Spectroradiometer (MODIS), the dynamics of snow cover can be mapped at local to global scales. We developed daily snow cover frequency maps from 2001–2020 using a ~5 km resolution MODIS snow cover map. For each day of the year the maps show the frequency of snow cover for the 20-year period on a per-grid cell basis. Following on from other work to develop snow frequency maps using MODIS snow cover products, we include spatial filtering to reduce errors caused by ‘false snow’ that occurs primarily due to cloud-snow confusion. On our snow frequency maps, there were no regions or time periods with a noticeable absence of snow where snow was expected. In one example, the MODIS derived frequency of snow cover on 25 December compares well with NOAA’s historical probability of snow on the same day. Though the MODIS derived snow frequency and NOAA probabilities are computed from very different data sources, they compare well. Though this preliminary research is promising, much future evaluation is needed. Full article
(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)
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