scholarly journals Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China

2018 ◽  
Vol 10 (10) ◽  
pp. 1577 ◽  
Author(s):  
Chao Wang ◽  
Zhengjia Zhang ◽  
Simonetta Paloscia ◽  
Hong Zhang ◽  
Fan Wu ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Winter Season ◽  
Tibet Plateau ◽  
Permafrost Soil ◽  
Permafrost Region ◽  
Backscattering Coefficient ◽  
Sar Images ◽  
Northern Tibet ◽  
Variation Patterns

Global change has significant impact on permafrost region in the Tibet Plateau. Soil moisture (SM) of permafrost is one of the most important factors influencing the energy flux, ecosystem, and hydrologic process. The objectives of this paper are to retrieve the permafrost SM using time-series SAR images, without the need of auxiliary survey data, and reveal its variation patterns. After analyzing the characteristics of time-series radar backscattering coefficients of different landcover types, a two-component SM retrieval model is proposed. For the alpine meadow area, a linear retrieving model is proposed using the TerraSAR-X time-series images based on the assumption that the lowest backscattering coefficient is measured when the soil moisture is at its wilting point and the highest backscattering coefficient represents the water-saturated soil state. For the alpine desert area, the surface roughness contribution is eliminated using the dual SAR images acquired in the winter season with different incidence angles when retrieving soil moisture from the radar signal. Before the model implementation, landcover types are classified based on their backscattering features. 22 TerraSAR-X images are used to derive the soil moisture in Beiluhe, Northern Tibet with different incidence angles. The results obtained from the proposed method have been validated using in-situ soil moisture measurements, thus obtaining RMSE and Bias of 0.062 cm3/cm3 and 4.7%, respectively. The retrieved time-series SM maps of the study area point out the spatial and temporal SM variation patterns of various landcover types.

scholarly journals Deriving Centimeter-Level Coseismic Deformation and Fault Geometries of Small-To-Moderate Earthquakes From Time-Series Sentinel-1 SAR Images

2021 ◽  
Vol 9 ◽  
Author(s):  
Heng Luo ◽  
Teng Wang ◽  
Shengji Wei ◽  
Mingsheng Liao ◽  
Jianya Gong
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Active Tectonics ◽  
Tibet Plateau ◽  
Coseismic Deformation ◽  
Earthquake Catalog ◽  
Sar Images ◽  
Weak Surface ◽  
Phase Screens ◽  
Atmospheric Delays

Small-to-moderate earthquakes (e.g. ≤Mw5.5) occur much more frequently than large ones (e.g. >Mw6.0), yet are difficult to study with InSAR due to their weak surface deformation that are severely contaminated by atmospheric delays. Here we propose a stacking method using time-series SAR images that can effectively suppress atmospheric phase screens and extract weak coseismic deformation in centimeter to sub-centimeter level. Using this method, we successfully derive coseismic surface deformations for three small-to-moderate (Mw∼5) earthquakes in Tibet Plateau and Tienshan region from time-series Sentinel-1 SAR images, with peak line-of-sight deformation ranging from 5–6 mm to 13 mm. We also propose a strategy to downsample interferograms with weak deformation signal based on quadtree mesh obtained from preliminary slip model. With the downsampled datasets, we invert for the centroid locations, fault geometries and slips of these events. Our results demonstrate the potential of using time-series InSAR images to enrich earthquake catalog with geodetic observations for further study of earthquake cycle and active tectonics.

scholarly journals Freeze-Thaw Deformation Cycles and Temporal-Spatial Distribution of Permafrost along the Qinghai-Tibet Railway Using Multitrack InSAR Processing

2021 ◽  
Vol 13 (23) ◽  
pp. 4744
Author(s):  
Jing Wang ◽  
Chao Wang ◽  
Hong Zhang ◽  
Yixian Tang ◽  
Wei Duan ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Surface Deformation ◽  
Anthropogenic Activities ◽  
Composite Index ◽  
Buffer Zone ◽  
Absolute Error ◽  
Tibet Plateau ◽  
Permafrost Region ◽  
Index Model

The Qinghai-Tibet Railway (QTR) is the railway with the highest elevation and longest distance in the world, spanning more than 1142 km from Golmud to Lhasa across the continuous permafrost region. Due to climate change and anthropogenic activities, geological disasters such as subsidence and thermal melt collapse have occurred in the QTR embankment. To conduct the large-scale permafrost monitoring and geohazard investigation along the QTR, we collected 585 Sentinel-1A images based on the composite index model using the multitrack time-series interferometry synthetic aperture radar (MTS-InSAR) method to retrieve the surface deformation over a 3.15 × 105 km2 area along the QTR. Meanwhile, a new method for permafrost distribution mapping based on InSAR time series deformation was proposed. Finally, the seasonal deformation map and a new map of permafrost distribution along the QTR from Golmud to Lhasa were obtained. The results showed that the estimated seasonal deformation range of the 10 km buffer zone along the QTR was −50–10 mm, and the LOS deformation rate ranged from −30 to 15 mm/yr. In addition, the deformation results were validated by leveling measurements, and the range of absolute error was between 0.1 and 4.62 mm. Most of the QTR was relatively stable. Some geohazard-prone sections were detected and analyzed along the QTR. The permafrost distribution results were mostly consistent with the simulated results of Zou’s method, based on the temperature at the top of permafrost (TTOP) model. This study reveals recent deformation characteristics of the QTR, and has significant scientific implications and applicational value for ensuring the safe operation of the QTR. Moreover, our method, based on InSAR results, provides new insights for permafrost classification on the Qinghai-Tibet Plateau (QTP).

scholarly journals Effects of Rainfall Manipulation on Ecosystem Respiration and Soil Respiration in an Alpine Steppe in Northern Tibet Plateau

2021 ◽  
Vol 9 ◽  
Author(s):  
Xueqin Li ◽  
Yan Yan ◽  
Lijiao Fu
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Soil Respiration ◽  
Water Content ◽  
Ecosystem Respiration ◽  
Tibet Plateau ◽  
Climate Change Scenarios ◽  
Regional Patterns ◽  
Northern Tibet ◽  
Alpine Steppe

The response mechanism of ecosystem respiration (Re) and soil respiration (Rs) to different water conditions is of great significance for understanding the carbon cycle under future changes in the precipitation patterns. We used seven precipitation treatments to investigate the effects of precipitation on Re and Rs on a typical alpine steppe in Northern Tibet. Precipitation was captured and relocated to simulate the precipitation rates of −25, −50, −75, 0 (CK), +25, +50, and +75%. The soil moisture was influenced by all the precipitation treatments. There was a positive linear relationship between the soil moisture and Re, Rs in the study area during the experiment (July–October). Soil volumetric water content (VWC), absolute water content (AWC), soil temperature (ST), aboveground biomass (AGB), bulk density, soil total nitrogen (TN), and alkaline hydrolysis nitrogen (AHN) were the predictors of Re and Rs. The multiple linear regression analysis showed that ST and AWC could explain 90.6% of Rs, and ST, AWC, and AHN could explain 89.4% of Re. Ecosystem respiration was more sensitive to the increased precipitation (+29.5%) whereas Rs was more sensitive to the decreased precipitation (−23.8%). An appropriate increase in water (+25 and +50%) could improve the Re and Rs, but a greater increase (+75%) would not have a significant effect; it could have an effect even lower than those of the first two. Our study highlights the importance of increased precipitation and the disadvantage of decreased precipitation on Re and Rs in an arid region. The precipitation changes will lead to significant changes in the soil properties and AGB, and affect Re and Rs, to change the climate of the alpine steppe in Northern Tibet in the future. These findings contribute to our understanding of the regional patterns of environmental C exchange and soil C flux under the climate change scenarios and highlight the importance of water availability to the regulating ecosystem processes in semi-arid steppe ecosystems. In view of these findings, we urge future researchers to focus on manipulating the precipitation over longer time scales, seasonality, and incorporating more environmental factors to improve our ability to predict and model Re and Rs and feedback from climate change.

scholarly journals ACTIVE LAYER THICKNESS RETRIEVAL OVER THE QINGHAI-TIBET PLATEAU FROM 2000 TO 2020 BASED ON INSAR-MEASURED SUBSIDENCE AND MULTI-LAYER SOIL MOISTURE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 437-442
Author(s):  
T. Chang ◽  
J. Han ◽  
Z. Li ◽  
Y. Wen ◽  
T. Hao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Layer Thickness ◽  
Active Layer ◽  
Surface Deformation ◽  
Tibet Plateau ◽  
Active Layer Thickness ◽  
Permafrost Region ◽  
Radar Images ◽  
Soil Moisture Data ◽  
Qinghai Tibet Plateau

Abstract. Active layer thickness (ALT) is an important index to reflect the stability of permafrost. The retrieval of ALT based on Interferometric Synthetic Aperture Radar (InSAR) technology has been investigated recently in permafrost research. However, most of such studies are carried out in a limited extend and relatively short temporal coverage. The combination of temporal-spatial multi-layer soil moisture data and multi-temporal InSAR is a promising approach for the large-scale characterization of ALT. In this study, we employed Small Baseline Subset Interferometry (SBAS-InSAR) technology to obtain the seasonal surface deformation from radar images of Envisat and Sentinel-1 in a permafrost region of Qinghai-Tibet Plateau (QTP). We attempt to verify and calibrate the temporal-spatial multi-layer soil moisture product in combination with the in-situ data. Based on the land subsidence data and the temporal-spatial multi-layer soil moisture data, we further improve method to retrieve the ALT information. This paper describes the progress so far and point out the future work.

A first assessment of satellite and reanalysis estimates of surface and root-zone soil moisture over the permafrost region of Qinghai-Tibet Plateau

2021 ◽  
Vol 265 ◽  
pp. 112666
Author(s):  
Zanpin Xing ◽  
Lei Fan ◽  
Lin Zhao ◽  
Gabrielle De Lannoy ◽  
Frédéric Frappart ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Tibet Plateau ◽  
Permafrost Region ◽  
Qinghai Tibet Plateau

scholarly journals Rebuilding a Microwave Soil Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness Temperature and SMAP over the Qinghai–Tibet Plateau, China

2019 ◽  
Vol 11 (6) ◽  
pp. 683 ◽  
Author(s):  
Yuquan Qu ◽  
Zhongli Zhu ◽  
Linna Chai ◽  
Shaomin Liu ◽  
Carsten Montzka ◽  
...  
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Soil Moisture ◽  
Random Forest ◽  
Brightness Temperature ◽  
Land Surface ◽  
High Accuracy ◽  
Tibet Plateau ◽  
Qinghai Tibet Plateau

Time series of soil moisture (SM) data in the Qinghai–Tibet plateau (QTP) covering a period longer than one decade are important for understanding the dynamics of land surface–atmosphere feedbacks in the global climate system. However, most existing SM products have a relatively short time series or show low performance over the challenging terrain of the QTP. In order to improve the spaceborne monitoring in this area, this study presents a random forest (RF) method to rebuild a high-accuracy SM product over the QTP from 19 June 2002 to 31 March 2015 by adopting the advanced microwave scanning radiometer for earth observing system (AMSR-E), and the advanced microwave scanning radiometer 2 (AMSR2), and tracking brightness temperatures with latitude and longitude using the International Geosphere–Biospheres Programme (IGBP) classification data, the digital elevation model (DEM) and the day of the year (DOY) as spatial predictors. Brightness temperature products (from frequencies 10.7 GHz, 18.7 GHz and 36.5 GHz) of AMSR2 were used to train the random forest model on two years of Soil Moisture Active Passive (SMAP) SM data. The simulated SM values were compared with third year SMAP data and in situ stations. The results show that the RF model has high reliability as compared to SMAP, with a high correlation (R = 0.95) and low values of root mean square error (RMSE = 0.03 m3/m3) and mean absolute percent error (MAPE = 19%). Moreover, the random forest soil moisture (RFSM) results agree well with the data from five in situ networks, with mean values of R = 0.75, RMSE = 0.06 m3/m3, and bias = −0.03 m3/m3 over the whole year and R = 0.70, RMSE = 0.07 m3/m3, and bias = −0.05 m3/m3 during the unfrozen seasons. In order to test its performance throughout the whole region of QTP, the three-cornered hat (TCH) method based on removing common signals from observations and then calculating the uncertainties is applied. The results indicate that RFSM has the smallest relative error in 56% of the region, and it performs best relative to the Japan Aerospace Exploration Agency (JAXA), Global Land Data Assimilation System (GLDAS), and European Space Agency’s Climate Change Initiative (ESA CCI) project. The spatial distribution shows that RFSM has a similar spatial trend as GLDAS and ESA CCI, but RFSM exhibits a more distinct spatial distribution and responds to precipitation more effectively than GLDAS and ESA CCI. Moreover, a trend analysis shows that the temporal variation of RFSM agrees well with precipitation and LST (land surface temperature), with a dry trend in most regions of QTP and a wet trend in few north, southeast and southwest regions of QTP. In conclusion, a spatiotemporally continuous SM product with a high accuracy over the QTP was obtained.

scholarly journals Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine

2021 ◽  
Vol 256 ◽  
pp. 112318
Author(s):  
Dong Liang ◽  
Huadong Guo ◽  
Lu Zhang ◽  
Yun Cheng ◽  
Qi Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Google Earth ◽  
Sar Images ◽  
Google Earth Engine ◽  
The Antarctic
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