Characteristics of Freeze–Thaw Cycles in an Endorheic Basin on the Qinghai-Tibet Plateau Based on SBAS-InSAR Technology
Abstract
:1. Introduction
2. Study Area and Datasets
2.1. Study Area
2.2. Field Observation Data
2.3. Remote Sensing Data
3. Methods
3.1. SBAS-InSAR Processing
3.2. Seasonal Deformation Characteristics Extraction
3.3. Influencing Factor Analysis
4. Results
4.1. Comparison with Field Observation Data
4.1.1. Validation with Leveling Data
4.1.2. Deformation Time Series at Borehole Sites
4.2. Spatial Distribution of Seasonal Deformation
4.3. Time Characteristics of Seasonal Deformation Processes
4.4. Relationship between Amplitude and Time of Seasonal Deformation
4.5. Influencing Factors of Seasonal Deformation Amplitude
5. Discussion
5.1. The Amplitude of Seasonal Deformation
5.1.1. Comparison of Seasonal Deformation Amplitude with Values in Historical Studies
- (1)
- Wavelength. The longer the wavelength of the radar, the stronger the penetrating ability, and the less susceptible it is to vegetation [61,113]. The vegetation in permafrost regions of QTP is mainly alpine grassland and alpine desert [94,114]. Previous research results show that, compared with permafrost regions around the Arctic, the wavelength of radar signal has relatively little influence on obtaining the surface deformation of permafrost regions of the QTP [62,115].
- (2)
- MT-InSAR algorithm. The Stamps-InSAR and SBAS-InSAR algorithms are based on permanent scatterers [116] and distributed scatterers [117]. They have been widely used to obtain ground deformation in permafrost regions [60,78,118,119], and their reliability and accuracy have been proved. Because of the requirements for the scattering characteristics in the permafrost region, the SDA obtained by the two algorithms may be different.
- (3)
- Model. Different models may lead to differences in the calculated SDA. The seasonal deformation is caused by the F-T cycle in AL and is determined by many factors, such as topography, soil texture, and soil moisture [62,66,68,71,74]. Both the physical model and statistical model have some shortcomings in obtaining seasonal deformation of AL in permafrost areas [95,119,120].
- (4)
- Monitoring period. For Sentinel-1 SAR data with the same wavelength and MT-InSAR algorithm, the SDA obtained by SBAS-InSAR [71,75,112,121] is somewhat different, and the SDA increases with period generally. Previous studies have shown that permafrost in the study area shows an evident degradation trend [93], which will lead to the thawing of ground ice at the bottom of the AL and the increase in the ALT [18]. An increase in the soil water content in the AL leads to an increase in the SDA.
5.1.2. Influencing Factors of Seasonal Deformation Amplitude
5.2. Time Characteristics of Seasonal Deformation
5.3. Relationship between Amplitude and Time of Seasonal Deformation
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Borehole | Vegetation | ALT (m) | Volume Water Content (%) | Soil Texture in the AL |
---|---|---|---|---|
HL01 | AS | 1.91 | 21.3 | Lacking |
HL02 | AS | 2.03 | 19.5 | 0~0.3 m, clay content 20%; 0.3~1.0 m, sand content 30%; 1~2.2 m, clay content 30%. |
HL03 | AS | 1.82 | 20.8 | 0~0.5 m, sandy soil with gravel content of 20%. 0.5~1.2 m, sandy loam with gravel content of 10%. 1.2~1.8 m, clay, clay content of 40%. |
HL04 | ASM | 1.64 | 9.5 | 0~0.2 m, gravelly sandy soil, gravel content 30%; 0.2~0.9 m, gravel soil, gravel content 30%; 0.9~1.6 m, clay, clay content 45%. |
Data | Grid Size (m) | Time (Year) | Data Source |
---|---|---|---|
DEM | 30 | / | http://gdex.cr.usgs.gov/gdex/ (accessed on 1 April 2020) |
Vegetation form | 1000 | 2009~2013 | [94] |
NDVI | 250 | 2017~2019 | http://data.tpdc.ac.cn (accessed on 1 January 2021). |
Soil organic carbon | 1000 | 2009~2019 | [98] |
Soil texture | 1000 | 1980s | [99] |
Mean annual air temperature | 1000 | 2017~2019 | [100] |
Mean annual precipitation | 1000 | 2017~2019 | [100] |
ALT | 1000 | 2000~2015 | [101] |
MAGT | 1000 | 2005~2015 | [101] |
Ground ice content | 1000 | 2009~2015 | [18] |
DDT | 1000 | 2017~2019 | MODIS LST |
DDF | 1000 | 2017~2019 | MODIS LST |
Date (DDMMYYYY) | Time Interval (Days) | |
---|---|---|
SBAS-InSAR | Leveling | |
14 January 2017 | 9 January 2017 | 5 |
9 March 2017 | 5 March 2017 | 4 |
20 May 2017 | 24 May 2017 | 4 |
10 August 2017 | 12 August 2017 | 2 |
22 December 2017 | 18 December 2017 | 4 |
27 May 2018 | 29 May 2018 | 2 |
19 August 2018 | 15 August 2018 | 4 |
6 October 2018 | 4 October 2018 | 2 |
10 January 2019 | 9 January 2019 | 1 |
28 April 2019 | 28 April 2019 | 0 |
1 October 2019 | 24 September 2019 | 7 |
SDA | Date of MFH | Date of MTS | Thawing Duration | |
---|---|---|---|---|
SDA | 1 | |||
Date of MFH | −0.27 | 1 | ||
Date of MTS | 0.43 | −0.54 | 1 | |
Thawing duration | −0.03 | −0.25 | 0.68 | 1 |
SDA (mm) | Data | Time | MT-InSAR Algorithm | Source |
---|---|---|---|---|
0~20 | ALOS PALSAR | 2007~2009 | SBAS-InSAR | [110] |
0~20 | ENVISAT ASAR | 2006~2009 | SBAS-InSAR | [85] |
0~40 | Sentinel-1 | 2017~2018 | StaMPS-InSAR | [111] |
0~30 | Sentinel-1 | 2017~2018 | SBAS-InSAR | [75] |
0~50 | Sentinel-1 | 2014~2019 | SBAS-InSAR | [112] |
0~60 | Sentinel-1 | 2017~2020 | SBAS-InSAR | [71] |
0~60 | Sentinel-1 | 2017~2019 | SBAS-InSAR | This study |
Source | Data | Time | MT-InSAR Algorithm | The DOY of MTS | |
---|---|---|---|---|---|
[78] | Sentinel-1 | June to November 2017 | SBAS-InSAR | Adventdalen | 251~269 |
Kapp Linné | 287~305 | ||||
Ny-Ålesund | 287~299 | ||||
This study | Sentinel-1 | 2017~2019 | SBAS-InSAR | 237~263 |
Acronyms | Full Name |
---|---|
AL | Active layer |
ALT | Active layer thickness |
DDF | Degree days of freezing |
DDT | Degree days of thawing |
DOY | Date of year |
F-T | Freeze–thaw |
InSAR | Interferometric synthetic aperture radar |
LST | Land surface temperature |
MAGT | Mean annual ground temperature |
MFH | Maximum frost heave |
MTS | Maximum thaw subsidence |
MT-InSAR | Multi-temporal Interferometric synthetic aperture radar |
QTP | Qinghai-Tibet Plateau |
SBAS-InSAR | Small baseline subset-interferometric synthetic aperture radar |
SDA | Seasonal deformation amplitude |
ZL-YL | Zonag lake-Yanhu lake |
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Zhou, H.; Zhao, L.; Wang, L.; Xing, Z.; Zou, D.; Hu, G.; Xie, C.; Pang, Q.; Liu, G.; Du, E.; et al. Characteristics of Freeze–Thaw Cycles in an Endorheic Basin on the Qinghai-Tibet Plateau Based on SBAS-InSAR Technology. Remote Sens. 2022, 14, 3168. https://doi.org/10.3390/rs14133168
Zhou H, Zhao L, Wang L, Xing Z, Zou D, Hu G, Xie C, Pang Q, Liu G, Du E, et al. Characteristics of Freeze–Thaw Cycles in an Endorheic Basin on the Qinghai-Tibet Plateau Based on SBAS-InSAR Technology. Remote Sensing. 2022; 14(13):3168. https://doi.org/10.3390/rs14133168
Chicago/Turabian StyleZhou, Huayun, Lin Zhao, Lingxiao Wang, Zanpin Xing, Defu Zou, Guojie Hu, Changwei Xie, Qiangqiang Pang, Guangyue Liu, Erji Du, and et al. 2022. "Characteristics of Freeze–Thaw Cycles in an Endorheic Basin on the Qinghai-Tibet Plateau Based on SBAS-InSAR Technology" Remote Sensing 14, no. 13: 3168. https://doi.org/10.3390/rs14133168