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Remote Sensing and Land Surface Process Models for Permafrost Studies

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

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 23302

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Special Issue Editors

Dr. Guojie Hu

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Guest Editor

Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: permafrost; hydrothermal transfer processes; land surface modeling;land-atmosphere interaction; climate change
Special Issues, Collections and Topics in MDPI journals

Dr. Wenxin Zhang

E-Mail Website
Guest Editor

Department of Physical Geography and Ecosystem Science, Lund University, 221 00 Lund, Sweden
Interests: soil thermal regime; vegetation dynamics; biophysical properties of vegetaton; land surface models
Special Issues, Collections and Topics in MDPI journals

Dr. Jie Chen

E-Mail Website
Guest Editor

Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: permafrost remote sensing; thermokarst processes; landslides
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Permafrost is an essential component of the cryosphere and occupies about 22% of the land surface of the Northern Hemisphere. Under global warming and extreme events, extensive degradation of permafrost has been widely observed in recent years, which has made the frozen carbon vulnerable and more easily emitted as methane and carbon dioxide. The hydrothermal processes are complex due to strong land–atmosphere interactions in the permafrost regions. Improved understanding of the mechanisms that drive changes in the permafrost thermal state and the associated environmental impacts is lacking due to the scarce ground monitoring data in permafrost regions. Remote sensing technology and land surface process models have been providing the effective means of understanding permafrost change processes and their impact on the environment, especially for land–atmosphere interactions. A combined multi-source data, remote sensing technology and model approach provide an opportunity to further understand processes and mechanisms in the interactions between permafrost, climate, ecological and hydrological processes.

In order to further summarize the latest progress and research direction of remote sensing technology and model application in permafrost regions, and to promote wide communication on the subject, we conducted a Special Issue with the theme: “Remote Sensing and Land Surface Process Models for Permafrost studies”.

This Special Issue will showcase recent efforts in applying advanced remote sensing techology and land surface process model in permafrost research, including identification of freeze–thaw states, inversion of soil moisture and ground deformation, simulation of hydrothermal processes, permafrost change processes, permafrost interaction with climate, ecology and hydrology, hazard identification and prediction based on remote sensing techology and land surface process models. This subject involves the multidisciplinary intersection of permafrost, atmospheric, hydrology and ecological sciences with remote sensing. It fits well with the research scope of this journal.

This Special Issue invites contributions dealing with the remote sensing techology and land surface process model for permafrost change processes and its environmental effects on different spatial and temporal scales, monitoring their dynamics, exploring the mechanisms of permafrost change process, and improving simulation accuracy based on the integrated use of remotely sensed data and in situ measurements. Review articles are also welcomed. Articles may address, but are not limited to, the following topics:

Basic data in permafrost regions;
Hydrothermal process;
Freeze–thaw states;
Inversion of soil water content;
Ground deformation;
Thermokarst processes;
Improvement of parameterization scheme for permafrost;
Numerical simulation of permafrost change process;
Land–atmosphere interaction;
Hydro-ecological effects of permafrost;
Hazard identification and prediction.

Dr. Guojie Hu
Dr. Wenxin Zhang
Dr. Jie Chen
Guest Editors

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Keywords

permafrost
soil moisture/temperature
numerical simulation
remote sensing
climate change
observation
environmental effects
cold regions

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29 pages, 20072 KiB

Open AccessArticle

Spatiotemporal Patterns and Regional Differences in Soil Thermal Conductivity on the Qinghai–Tibet Plateau

by Wenhao Liu, Ren Li, Tonghua Wu, Xiaoqian Shi, Lin Zhao, Xiaodong Wu, Guojie Hu, Jimin Yao, Dong Wang, Yao Xiao, Junjie Ma, Yongliang Jiao, Shenning Wang, Defu Zou, Xiaofan Zhu, Jie Chen, Jianzong Shi and Yongping Qiao

Remote Sens. 2023, 15(4), 1168; https://doi.org/10.3390/rs15041168 - 20 Feb 2023

Cited by 4 | Viewed by 1868

Abstract

The Qinghai–Tibet Plateau is an area known to be sensitive to global climate change, and the problems caused by permafrost degradation in the context of climate warming potentially have far-reaching effects on regional hydrogeological processes, ecosystem functions, and engineering safety. Soil thermal conductivity (STC) is a key input parameter for temperature and surface energy simulations of the permafrost active layer. Therefore, understanding the spatial distribution patterns and variation characteristics of STC is important for accurate simulation and future predictions of permafrost on the Qinghai–Tibet Plateau. However, no systematic research has been conducted on this topic. In this study, based on a dataset of 2972 STC measurements, we simulated the spatial distribution patterns and spatiotemporal variation of STC in the shallow layer (5 cm) of the Qinghai–Tibet Plateau and the permafrost area using a machine learning model. The monthly analysis results showed that the STC was high from May to August and low from January to April and from September to December. In addition, the mean STC in the permafrost region of the Qinghai–Tibet Plateau was higher during the thawing period than during the freezing period, while the STC in the eastern and southeastern regions is generally higher than that in the western and northwestern regions. From 2005 to 2018, the difference between the STC in the permafrost region during the thawing and freezing periods gradually decreased, with a slight difference in the western hinterland region and a large difference in the eastern region. In areas with specific landforms such as basins and mountainous areas, the changes in the STC during the thawing and freezing periods were different or even opposite. The STC of alpine meadow was found to be most sensitive to the changes during the thawing and freezing periods within the permafrost zone, while the STC for bare land, alpine desert, and alpine swamp meadow decreased overall between 2005 and 2018. The results of this study provide important baseline data for the subsequent analysis and simulation of the permafrost on the Qinghai–Tibet Plateau. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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15 pages, 4452 KiB

Open AccessArticle

Calibration of the ESA CCI-Combined Soil Moisture Products on the Qinghai-Tibet Plateau

by Wenjun Yu, Yanzhong Li and Guimin Liu

Remote Sens. 2023, 15(4), 918; https://doi.org/10.3390/rs15040918 - 07 Feb 2023

Cited by 2 | Viewed by 1523

Abstract

Soil moisture (SM) retrieved from satellite and spaceborn sensors provides useful parameters for earth system models (ESMs). The Climate Change Initiative (CCI) SM products released by the European Space Agency have been widely used in many humid/semi-humid climatic regions due to their relatively long-term record. However, the performance of these products in cold and arid regions, such as the Qinghai-Tibetan Plateau (QTP), is largely unknown, necessitating urgent evaluation and calibration in these areas. In this work, we evaluated the reliability and improved the accuracy of the active-passive combined CCI products (CCI-C) using in-situ measured SM contents (SMC) under different underlying surface conditions. First, some conventional models were used to investigate the relationship between the CCI-C and the in-situ observed SMC, yielding similar fitting performances. Next, the random forest method and multiple linear regression were used to contrast the conventional models to calibrate and validate the CCI-C SM product based on the in-situ observed SMC, and the random forest method was found to have the highest accuracy. However, calibration of the CCI-C SM data with the best-performed random forest method based on different spatial zonation methods, e.g., by climate, topography, land cover, and vegetation, resulted in distinct spatial patterns of SM. Compared to a widely-used satellite SM product, namely the Soil Moisture Active Passive (SMAP) SM dataset, the calibrated CCI-C SM data based on climatic and vegetation zonation were larger but had similar spatial patterns. This study also points to the value of the calibrated CCI-C SM product to support land surface studies on the QTP. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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24 pages, 10938 KiB

Open AccessArticle

Distribution and Degradation Processes of Isolated Permafrost near Buried Oil Pipelines by Means of Electrical Resistivity Tomography and Ground Temperature Monitoring: A Case Study of Da Xing’anling Mountains, Northeast China

by Gang Wu, Guoyu Li, Yapeng Cao, Dun Chen, Shunshun Qi, Fei Wang, Kai Gao, Qingsong Du, Xinbin Wang, Hongyuan Jing and Zhenrong Zhang

Remote Sens. 2023, 15(3), 707; https://doi.org/10.3390/rs15030707 - 25 Jan 2023

Viewed by 1666

Abstract

Human engineering activities and climate warming induce permafrost degradation in the Da Xing’anling Mountains, which may affect the distribution of permafrost and the safety of infrastructure. This study uses the electrical resistivity tomography method, in combination with field surveys and ground temperature monitoring, to investigate the distribution and degradation characteristics of permafrost and influencing factors at a typical monitoring site (MDS304) near the China-Russia Crude Oil Pipeline (CRCOP). The results show that the isolated permafrost in this area is vulnerable to further degradation because of warm oil pipelines and thermal erosion of rivers and ponds. The isolated permafrost is degrading in three directions at the MDS304 site. Specifically, the boundary between permafrost and talik is on both sides of the CRCOP, and permafrost is distributed as islands along a cross-section with a length of about 58–60 m. At present, the vertical hydrothermal influence range of the CRCOP increased to about 10–12 m. The active layer thickness has increased at a rate of 2.0 m/a from about 2.4–6.8 m to 2.5–10.8 m from 2019 to 2021 along this cross-section. Permafrost degradation on the side of the CRCOP’s second line is more visible due to the river’s lateral thermal erosion, where the talik boundary has moved eastward about 12 m during 2018–2022 at a rate of 3.0 m/a. It is 2.25 times the westward moving speed of the talik boundary on one side of the CRCOP’s first line. In contrast, the talik boundary between the CRCOP’s first line and the G111 highway also moves westward by about 4 m in 2019–2022. Moreover, the maximum displacement of the CRCOP’s second line caused by the thawing of frozen soil has reached up to 1.78 m. The degradation of permafrost may threaten the long-term stability of the pipeline. Moreover, the research results can provide a useful reference for decision-makers to reduce the risk of pipeline freeze-thaw hazards. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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18 pages, 5314 KiB

Open AccessArticle

Evaluating the Impact of Soil Enthalpy upon the Thawing Process of the Active Layer in Permafrost Regions of the Qinghai–Tibet Plateau Using CLM5.0

by Shenning Wang, Ren Li, Tonghua Wu, Lin Zhao, Xiaodong Wu, Guojie Hu, Jimin Yao, Junjie Ma, Wenhao Liu, Yongliang Jiao, Yao Xiao, Shuhua Yang, Jianzong Shi and Yongping Qiao

Remote Sens. 2023, 15(1), 249; https://doi.org/10.3390/rs15010249 - 31 Dec 2022

Cited by 2 | Viewed by 2307

Abstract

The hydrothermal dynamics of the active layer is a key issue in the study of surface processes in permafrost regions. Even though the soil energy budget is controlled by thermal conduction and latent heat transfer, few studies have focused on their effects upon the active layer thickness (ALT). In the present study, the community land model (CLM) version 5.0 is used to simulate the soil temperature and moisture of the active layers at the Tanggula (TGL) and Beiluhe (BLH) stations in permafrost regions of the Qinghai–Tibet Plateau based on the theory of soil enthalpy in order to estimate the soil energy state and analyze the energy changes in the active layer during freezing and thawing. The results indicate that the soil enthalpy has significant seasonal variation characteristics, which accurately reflected the freezing and thawing processes of the active layer. The change in soil enthalpy is significantly related to the thawing depth of the active layer in TGL and BLH, and its changing process can be expressed as an exponential relationship. Near the surface, the variation of the energy due to temperature gradient and actual evaporation can also be expressed as an exponential relationship. The promoting effect of heat conduction on the ALT is greater than the inhibiting effect of latent heat transfer, with the energy contribution from the phase change accounting for about 20–40% of the energy due to the temperature gradient. The thawing depth increases by 14.16–18.62 cm as the energy due to the temperature gradient increases by 1 MJ/m² and decreases by 2.75–7.16 cm as the energy due to the phase change increases by 1 MJ/m². Thus, the present study quantifies the effects of soil energy upon the ALT and facilitates an understanding of the hydrothermal processes in soils in permafrost regions. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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18 pages, 4001 KiB

Open AccessArticle

Ground Surface Freezing and Thawing Index Distribution in the Qinghai-Tibet Engineering Corridor and Factors Analysis Based on GeoDetector Technique

by Shen Ma, Jingyi Zhao, Ji Chen, Shouhong Zhang, Tianchun Dong, Qihang Mei, Xin Hou and Guojun Liu

Remote Sens. 2023, 15(1), 208; https://doi.org/10.3390/rs15010208 - 30 Dec 2022

Cited by 4 | Viewed by 1967

Abstract

The land surface temperature obtained from remote sensing was widely used in the simulation of permafrost mapping instead of air temperature with the rapid development of remote sensing technology. The land surface freezing and thawing index (LFI and LTI), which is commonly regarded as the ground surface freezing and thawing index (GFI and GTI), can produce certain errors in the simulation of permafrost distribution on the Qinghai–Tibet Plateau. This paper improved the accuracy of the thermal condition of the surface soil in the Qinghai–Tibet Engineering Corridor (QTEC) by calculating the LFI (or LTI) and N-factors. The environmental factors affecting the spatial distribution of the GFI and GTI were detected by the GeoDetector model. Finally, the multiple linear relationships between the GFI (or GTI) and the environmental factors were established. The results from 25 monitoring sites in the QTEC show that the N_f (ratio of GFI to LFI) is 1.088, and the N_t (ratio of GTI to LTI) is 0.554. The explanatory power of the interaction between elevation and latitude for the GFI and GTI is 79.3% and 85.6%, respectively. The multiple linear regression model with six explanatory variables established by GFI (or GTI) has good accuracy. This study can provide relatively accurate upper boundary conditions for the simulation of permafrost distribution in the QTEC region. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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21 pages, 7266 KiB

Open AccessArticle

Analysis of Permafrost Distribution and Change in the Mid-East Qinghai–Tibetan Plateau during 2012–2021 Using the New TLZ Model

by Zhijian Zhao and Hideyuki Tonooka

Remote Sens. 2022, 14(24), 6350; https://doi.org/10.3390/rs14246350 - 15 Dec 2022

Viewed by 1322

Abstract

The monitoring of permafrost is important for assessing the effects of global environmental changes and maintaining and managing social infrastructure, and remote sensing is increasingly being used for this wide-area monitoring. However, the accuracy of the conventional method in terms of temperature factor and soil factor needs to be improved. To address these two issues, in this study, we propose a new model to evaluate permafrost with a higher accuracy than the conventional methods. In this model, the land surface temperature (LST) is used as the upper temperature of the active layer of permafrost, and the temperature at the top of permafrost (TTOP) is used as the lower temperature. The TTOP value is then calculated by a modified equation using precipitation–evapotranspiration (PE) factors to account for the effect of soil moisture. This model, referred to as the TTOP-LST zero-curtain (TLZ) model, allows us to analyze subsurface temperatures for each layer of the active layer, and to evaluate the presence or absence of the zero-curtain effect through a time series analysis of stratified subsurface temperatures. The model was applied to the Qinghai–Tibetan Plateau and permafrost was classified into seven classes based on aspects such as stability and seasonality. As a result, it was possible to map the recent deterioration of permafrost in this region, which is thought to be caused by global warming. A comparison with the mean annual ground temperature (MAGT) model using local subsurface temperature data showed that the average root mean square error (RMSE) value of subsurface temperatures at different depths was 0.19 degrees C, indicating the validity of the TLZ model. A similar analysis based on the TLZ model is expected to enable detailed permafrost analysis in other areas. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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19 pages, 2822 KiB

Open AccessArticle

Evaluation of the Performance of CLM5.0 in Soil Hydrothermal Dynamics in Permafrost Regions on the Qinghai–Tibet Plateau

by Shuhua Yang, Ren Li, Lin Zhao, Tonghua Wu, Xiaodong Wu, Yuxin Zhang, Jianzong Shi and Yongping Qiao

Remote Sens. 2022, 14(24), 6228; https://doi.org/10.3390/rs14246228 - 08 Dec 2022

Cited by 3 | Viewed by 1601

Abstract

Soil hydrothermal dynamics are crucial processes for understanding the internal physical conditions of the active layer in permafrost regions. It is very difficult to obtain data in permafrost regions, especially on the Qinghai–Tibet Plateau (QTP). Land surface modes (LSMs) provide an effective tool for soil hydrothermal dynamics. However, it is necessary to evaluate the simulation performance before using them. Here, we used two in situ sites along with the latest version of the Community Land Model (CLM5.0) to evaluate the simulated performance in the soil hydrothermal parameters of the model in permafrost regions on the QTP. Meanwhile, the effects of soil properties, thermal roughness length, and the freeze–thaw process on the simulation results were investigated. The results showed that CLM5.0 can capture the dynamic changes in soil hydrothermal changes well in permafrost regions on the QTP. Soil moisture and thermal conductivity were more sensitive to soil properties and the freeze–thaw process, while the thermal roughness length had a greater effect on soil temperature. Notably, although we improved the soil properties and thermal roughness length, there were still some errors, especially in the soil moisture and soil thermal conductivity. It may be caused by inappropriate hydrothermal parameterizations of the model, especially the soil thermal conductivity, hydraulic conductivity, unfrozen water scheme, and snow schemes. There is an urgent need for collaboration between experts in permafrost science, hydrological science, and modelers to develop the appropriate schemes for permafrost regions and enhance the LSMs. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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20 pages, 4579 KiB

Open AccessArticle

Modelling Permafrost Characteristics and Its Relationship with Environmental Constraints in the Gaize Area, Qinghai-Tibet Plateau, China

by Yudan Wang, Hao Chen, Zhuotong Nan and Zhihai Shang

Remote Sens. 2022, 14(21), 5610; https://doi.org/10.3390/rs14215610 - 07 Nov 2022

Cited by 3 | Viewed by 1446

Abstract

The impact of environmental constraints on permafrost distribution and characteristics of the remote western Qinghai-Tibetan Plateau (QTP) were seldom reported. Using augmented Noah land surface model, this study aims to elaborate the permafrost characteristics and their relationship with key environmental constraints in the Gaize, a transitional area with mosaic distribution of permafrost and seasonally frozen ground in the western QTP. There were two soil parameter schemes, two thermal roughness schemes, and three vegetation parameter schemes with optimal minimum stomatal resistance established using MODIS NDVI, turbulent flux, and field survey data. Forcing data were extracted from the China Meteorological Forcing Dataset (CMFD) and downscaled to 5 km × 5 km resolution. Results show that the error of simulated mean annual ground temperatures (MAGT) were less than 1.0 °C for nine boreholes. The Kappa coefficiency between three types of permafrost and three types of vegetation is 0.654, which indicates the close relationship between the presence of certain vegetation types and the occurrence of certain permafrost types in the Gaize. Permafrost distribution and characteristics of the Gaize are jointly influenced by both altitude and vegetation. The relationship of permafrost with environmental constraints over the Gaize is significantly different from that of the West Kunlun, a western, predominantly permafrost-distributed area. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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15 pages, 3815 KiB

Open AccessArticle

A Calculation Model for Ground Surface Temperature in High-Altitude Regions of the Qinghai-Tibet Plateau, China

by Mingtang Chai, Nan Li, Furong Liu, Yu Gao, Yanhu Mu and Wei Ma

Remote Sens. 2022, 14(20), 5219; https://doi.org/10.3390/rs14205219 - 18 Oct 2022

Cited by 1 | Viewed by 1506

Abstract

As a major parameter in the energy balance of the ground surface, temperature represents the level of exchange of energy and moisture between the ground and air. The Qinghai-Tibet Plateau (QTP) has the permafrost region with the highest altitude and the largest area in low–middle latitude of the world. The variation in ground surface temperature has an impact on the existence and development of the permafrost. Therefore, the analysis of the ground surface temperature in the QTP is significant to reflect the energy exchange in permafrost regions. This paper collected solar radiation data and calculated the conversion coefficient from total solar radiation to long-wave radiation of the ground surface on different underlying surfaces. The ground surface temperature was inversely calculated and modified based on the reception of solar radiation on different underlying surfaces. A simplified calculation model of ground surface temperature was built to reflect the ground surface temperature on different underlying surfaces of the QTP. The calculation results were compared with MODIS and showed good fitness, providing a systematic and reliable method for calculating the ground surface temperature on the QTP. The above model plays a significant role in the estimation of soil moisture, ground surface energy and water balance. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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16 pages, 3347 KiB

Open AccessArticle

Soil Texture and Its Relationship with Environmental Factors on the Qinghai–Tibet Plateau

by Yadong Liu, Xiaodong Wu, Tonghua Wu, Lin Zhao, Ren Li, Wangping Li, Guojie Hu, Defu Zou, Jie Ni, Yizhen Du, Mengjuan Wang, Zhihong Li, Xianhua Wei and Xuchun Yan

Remote Sens. 2022, 14(15), 3797; https://doi.org/10.3390/rs14153797 - 06 Aug 2022

Cited by 12 | Viewed by 2292

Abstract

Soil texture data are the basic input parameters for many Earth System Models. As the largest middle–low altitude permafrost regions on the planet, the land surface processes on the Qinghai–Tibet Plateau can affect regional and even global water and energy cycles. However, the spatial distribution of soil texture data on the plateau is largely unavailable due to the difficulty of obtaining field data. Based on collection data from field surveys and environmental factors, we predicted the spatial distribution of clay, silt, and sand contents at a 1 km resolution, from 0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm soil depth layers. The random forest models were constructed to predict the soil texture according to the relationships between environmental factors and soil texture data. The results showed that the soil particles of the QTP are dominated by sand, which accounts for more than 70% of the total particles. As for the spatial distribution, silt and clay contents are high in the southeast plateau, and low values of silt and clay mainly appeared in the northwest plateau. Climate and NDVI values are the most important factors that affect the spatial distribution of soil texture on the QTP. The results of this study provide the soil texture data at different depths for the whole plateau at a spatial resolution of 1 km, and the dataset can be used as an input parameter for many Earth System Models. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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32 pages, 6506 KiB

Open AccessReview

Bibliometric Analysis of the Permafrost Research: Developments, Impacts, and Trends

by Qingsong Du, Guoyu Li, Dun Chen, Yu Zhou, Shunshun Qi, Fei Wang, Yuncheng Mao, Jun Zhang, Yapeng Cao, Kai Gao, Gang Wu, Chunqing Li and Yapeng Wang

Remote Sens. 2023, 15(1), 234; https://doi.org/10.3390/rs15010234 - 31 Dec 2022

Cited by 3 | Viewed by 2320

Abstract

Permafrost is a significant part of the cryosphere, which has gained increasing attention from scientists, policy-makers, and the general public due to global warming, environmental degradation, water shortages, and intense human activities. Although many permafrost research review articles have been published, these studies were predominantly limited to either one subject or one field, while systematic studies about permafrost based on bibliometric analysis methods remain limited. We aim to fill this gap by conducting a bibliometric analysis of 13,697 articles in the field of permafrost research from 1942 to 2021, collected from the Web of Science core collection database. The results indicate that permafrost research is a typically multi-author, multi-country, and multi-institution cooperative field, involved in many research fields. The cumulative number of publications has presented an exponential increase over the past 80 years, with an average annual growth rate of 10.40%. Since 2000, China has seen a rapid growth in the number of publications per year, surpassing the USA in 2016 and leading in the years since then. In addition, the authors from China have great contributions in publications, and there is good room for permafrost development in the future according to the authors’ M-index ranking. After the analysis of authors’ keywords, we found that, compared to the conventional methods, machine learning and interferometric synthetic aperture radar (InSAR) are new technological approaches introduced in recent years, and the Qinghai–Tibet Plateau has become a popular study area. The results presented here can help related researchers, scholars, and students in the field to better understand the past developments, current status, and future trends of permafrost research. Furthermore, this paper presents and expands the general process of the bibliometric method used in permafrost studies, which can provide researchers with new inspirations and improve discipline research approach. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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13 pages, 5941 KiB

Open AccessTechnical Note

Spatiotemporal Variations of Soil Temperature at 10 and 50 cm Depths in Permafrost Regions along the Qinghai-Tibet Engineering Corridor

by Mengdi Jiao, Lin Zhao, Chong Wang, Guojie Hu, Yan Li, Jianting Zhao, Defu Zou, Zanpin Xing, Yongping Qiao, Guangyue Liu, Erji Du, Minxuan Xiao and Yingxu Hou

Remote Sens. 2023, 15(2), 455; https://doi.org/10.3390/rs15020455 - 12 Jan 2023

Cited by 2 | Viewed by 1612

Abstract

Soil temperature plays an essential role in the permafrost thermal state and degradation process. Especially the soil temperatures at 10 cm and 50 cm depths in the active layer, which are much easier to be observed in situ, have great effects on the surface water cycles and vegetation, and could be used as the upper boundary for permafrost models to simulate the thermal state of the permafrost and active layer thicknesses. However, due to the limitations of the observation data, there are still large uncertainties in the soil temperature data, including at these two depths, in the permafrost region of Qinghai–Tibet Plateau (QTP). In this study, we evaluated and calibrated the applicability of four daily shallow soil temperature datasets (i.e., MERRA-2, GLDAS-Noah, ERA5-Land, and CFSR) by using the in situ soil temperature data from eight observation sites from 2004 to 2018 in the permafrost region along the Qinghai–Tibet Engineering Corridor. The results revealed that there were different uncertainties for all four sets of reanalysis data, which were the largest (Bias = −2.44 °C) in CFSR and smallest (Bias= −0.43 °C) in GLDAS-Noah at depths of 10 cm and 50 cm. Overall, the reanalysis datasets reflect the trends of soil temperature, and the applicability of reanalysis data at 50 cm depth is better than at 10 cm depth. Furthermore, the GLDAS-Noah soil temperatures were recalibrated based on our observations using multiple linear regression and random forest models. The accuracy of the corrected daily soil temperature was significantly improved, and the RMSE was reduced by 1.49 °C and 1.28 °C at the depth of 10 cm and 50 cm, respectively. The random forest model performed better in the calibration of soil temperature data from GLDAS-Noah. Finally, the warming rates of soil temperature were analyzed, which were 0.0994 °C/a and 0.1005 °C/a at 10 cm and 50 cm depth from 2004 to 2018, respectively. Full article

(This article belongs to the Special Issue Remote Sensing and Land Surface Process Models for Permafrost Studies)

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