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Dataset of microwave brightness temperature and the freeze-thaw process for medium-to-large lakes in the High Asia Region (2002-2016)

The High Asia region is an area sensitive to global changes in mid-latitude regions and is a hotspot for research. The lakes in the territory are scattered, and the lake freeze-thaw process is one of the key factors sensitive to global change. Due to the large difference in the dielectric constant between ice and water, satellite-borne passive microwave remote sensing is weather insensitive and has a high revisiting rate; thus, it can achieve rapid monitoring of the freeze-thaw state of lakes. According to the area ratio of the lake and the land surface in the sub-pixels of passive microwave radiometer data, this data set represents the lake brightness temperature information of the pixel (sub-pixel level) by applying the hybrid pixel decomposition method in order to monitor the lake freeze-thaw process in the High Asia region. Thus, by adopting a variety of passive microwave data, time series of lake brightness temperature and freeze-thaw status were obtained for a total of 51 medium to large lakes from 2002 to 2016 in the High Asia region. Using cloudless MODIS optical products as validation data, three lakes of different sizes in different regions of High Asia, i.e., Hoh Xil Lake, Dagze Co Lake, and Kusai Lake, were selected for freeze-thaw detection validation. The results indicated that the lake freeze-thaw parameters obtained by microwave and optical remote sensing were highly consistent, and the correlation coefficients reached 0.968 and 0.987. This data set contained the time series brightness temperature of lakes and the freeze-thaw parameters of lake ice, which could be used to further invert the characteristic parameters of lakes and enhance the understanding of lake ice freezing and thawing in the High Asia region. This database will be useful in the assessment of climatic and environmental changes in the High Asia region and in global climatic change response models. The data set consists of two parts: the passive microwave remote sensing brightness temperature data set of 51 lakes in the High Asia region from 2002 to 2016, with an observation interval of 1 to 2 days, and the lake ice freeze-thaw data set obtained by estimation of the lake brightness temperature. The files are the lake brightness temperature data via the nearest neighbour method and pixel decomposition in the form of a .zip file (12 MB) and the lake freeze-thaw data set for 51 lakes in the High Asia region from 2002 to 2016 in the form of an .xls file (0.1 MB).

2019-09-15

Effects of permafrost environment on the construction of Qinghai-Tibet railway and environmental effects data (2002-2004)

The project “The impact of the frozen soil environment on the construction of the Qinghai-Tibet Railway and the environmental effects of the construction” is part of the “Environmental and Ecological Science in West China” programme supported by the National Natural Science Foundation of China. The person in charge of the project is Wei Ma, a researcher at the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences. The project ran from January 2002 to December 2004. Data collected in this project included the following: Monitoring data of the active layer in the Beiluhe River Basin (1) Description of the active layer in the Beiluhe River Basin (2) Subsurface moisture data from the Beiluhe River Basin, 2002.9.28-2003.8.10 (Excel file) * Site 1 - Grassland moisture data * Site 2 – Removed turf moisture data * Site 3 - Natural turf moisture data * Site 4 - Gravel moisture data * Site 5 - Insulation moisture data (3) Subsurface temperature data from the Beiluhe River Basin, 0207-0408 Excel file * Temperature data for the ballast surface * Temperature data for insulation materials * Temperature data for a surface without vegetation * Temperature data for a grassland surface * Temperature data for a grit and pebble surface Data on the impact of construction on the ecological environment were obtained at Fenghuoshan, Tuotuohe, and Wudaoliang. Sample survey included plant type, abundance, community coverage, total coverage, aboveground biomass ratio and soil structure. The moisture content at different depths of the soil was detected using a time domain reflectometer (TDR). A set of soil samples was collected at a depth of 0-100 cm at each sample site. An EKKO100 ground-penetrating radar detector was used to continuously sample 1-1.5 km long sections parallel to the road to determine the upper limit depth of the frozen soil. 3. Predicted data: The temperature of the frozen soil at different depths and times was predicted in response to temperature increases of 1 degree and 2 degrees over the next 50 years based on initial surface temperatures of -0.5, -1.5, -2.5, -3.5, and -4.5 degrees. 4. The frozen soil parameters of the Qinghai-Tibet Railway were as follows: location, railway mileage, total mileage (km), frozen soil type mileage, mileage of zones with an average temperature conducive to permafrost, frozen soil with high temperatures and high ice contents, frozen soils with high temperatures and low ice contents, frozen soils with low temperatures and high ice contents, frozen soils with low temperatures and low ice contents, and melting area.

2019-09-14

The ground temperature distribution Map of the Tibet engineering corridor (2010-2015)

The GIPL2.0 frozen soil model was used to simulate the average ground temperature distribution map of the Qinghai-Tibet Engineering Corridor. The model required to synthesize temperature data set of time series. In addition, the temperature data were divided into two phases according to the time spans, which were 1980-2009 and 2010-2015. The data of the first phase were from the Chinese meteorological driving data set (http://dam. Itpcas.ac.cn/rs/?q=data#CMFD_0.1), the data of the second phase were the application of MODIS surface temperature products (MOD11A1/A2 and MYD11A1/A2) with a spatial resolution of 1 km. In addition, the soil type data required by the model came from the China Soil Database (V1.1) and have a resolution of 1 km. At the same time, the topography was also considered. The research area was classified into 88 types based on the measured soil thermophysical parameters and land cover types, and then the simulation was performed. The annual average ground temperature simulation results were compared with the field measured data, and the results showed that they were highly consistent. The simulation results show that the annual average ground temperature is lower than -2.0 °C in high mountain areas such as Kunlun Mountain and Tanggula Mountain, while that in the higher river valleys such as Tuotuohe is above 0 °C. In the high plain areas (such as Beiluhe Basin and Wudaoliang Basin), the annual average ground temperatures are between -2.0 °C and 0 °C. If taking an annual average ground temperature lower than 0 °C as the threshold for the presence or absence of permafrost, the permafrost of the Qinghai-Tibet Engineering Corridor accounts for 78.9% of the entire area. In the meantime, according to the different ground temperatures, the frozen soils of the Qinghai-Tibet Engineering Corridor are divided into four types: low-temperature stable permafrost, low-temperature basically stable permafrost, high-temperature unstable permafrost and high-temperature extremely unstable permafrost.

2019-09-13

Distribution map of frozen ground in China based on Map of Snow, Ice and Frozen Ground in China (1998)

The scanned picture of the Map of Snow Ice and Frozen Ground in China (1:4,000,000) (Shi Yafeng, Meidesheng, 1988) is geometrically corrected and then digitized in the data set, and by taking altitude and latitude into account in combination with the continuity of permafrost, the frozen soil is divided into the predominant permafrost of high-latitude permafrost, island talik permafrost and island permafrost; high-altitude permafrost and mountain permafrost (including Altai, Tianshan Mountain, Qilian Mountain, Hengduan, the Himalayas and Taibai Mountain in East China, Huanggangliang and Changbai Mountain), and the plateau permafrost (the Tibetan Plateau), which is divided into predominant permafrost and island permafrost; and seasonal frozen soil, instantaneous frozen soil and nonfrozen areas.

2019-09-12

Dataset on the freeze-thaw process in Antarctic and Arctic ice sheets (1978-2015)

This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.

2019-09-12

Observation data of temperature and rainfall in permafrost regions of Qinghai-Tibet Engineering Corridor (1956-2012)

The data set includes the trends of annual average temperature and rainfall changes at the three meteorological stations in the permafrost section of the Qinghai-Tibet Engineering Corridor over the past 50 years. According to the recorded data, the annual average temperature is experiencing a gradually rising process. The annual average temperature change over the past 56 years in Wudaoliang and Tuotuohe has a good correlation (r2=0.83). In 1957, the average annual temperatures of Wudaoliang and Tuotuohe were -6.6 °C and -5.1 °C, respectively. By 2012, the temperatures of the two stations were -4.6 and -3.1 °C, and the total temperature has risen by approximately 2 °C. The annual average temperature rises by 0.03-0.04 °C. The annual average temperature changes over the past 47 years in Wudaoliang and Anduo also have a good correlation (r2=0.84). In 1966, the average annual temperature in Anduo was -3.0 °C. By 2012, the temperature has risen to -1.8 °C, corresponding to a total temperature rise of approximately 1.2 °C and an annual average temperature rise of 0.02-0.03 °C. The annual average temperature in Wudaoliang and Tuotuohe rose slightly faster than that in Anduo. However, the change in rainfall was more volatile than that of temperature. The correlation between the rainfall change in Wudaoliang and Tuotuohe over the past 56 years is relatively poor (r2=0.60). In 1957, the annual rainfall amounts in Wudaoliang and Tuotuohe were 302 and 309 mm, respectively. By 2012, the annual rainfall amounts at the two stations were 426 and 332 mm. Thus, the rainfall in Wudaoliang had increased by 124 mm, with an annual rainfall increase of approximately 2 mm. In contrast, the annual rainfall in Tuotuohe only increased by 0.4 mm. The correlation between the rainfall change in Wudaoliang and Anduo over the past 47 years is also poor (r2=0.35). In 1966, and 2012, the annual average rainfall amounts in Anduo were 354 and 404 mm. The total increase was approximately 50 mm, and the annual average increase was 1 mm. The annual rainfall in Wudaoliang increased the fastest. The observation data from the three meteorological stations reveal climate changes in the permafrost sections of the Qinghai-Tibet Engineering Corridor. Judging from the overall trend of temperature and rainfall changes, the temperature in the northern and central parts of the corridor has increased rapidly over the past 50 years, exceeding the global average of 0.02 °C/a (IPCC). The rainfall increase in the northern part of the corridor is also obvious, especially the rate of rainfall increase at the Wudaoliang meteorological station. Increases in both temperature and rainfall have a great impact on accelerating the spatial variation in permafrost, and they are the leading cause of permafrost degradation on the Tibetan Plateau.

2019-09-12

Geocryological regionalization and classification map of the frozen soil in China (1:10,000,000) (2000)

These data are digitized for the Geocryological Regionalization and Classification Map of the Frozen Soil in China (1:10 million) (Guoqing Qiu et al., 2000; Youwu Zhou et al., 2000), adopting a geocryological regionalization and classification dual series system. The geocryological regionalization system and classification system are used on the same map to reflect the commonality and individuality of the formation and distribution of frozen soil at each level. The geocryological regionalization system consists of three regions of frozen soil: (1) the frozen soil region of eastern China; (2) the frozen soil region of northwestern China; and (3) the frozen soil region of southwestern China (Tibetan Plateau). Based on the three large regions, 16 regions and several subregions are further divided. In the division of the geocryological boundary in the frozen soil area, the boundary between major regions I and III mainly consults the results of Bingyuan Li (1987). The boundary between major regions II and III is the northern boundary of the Tibetan Plateau, which is the Kunlun Mountains-Altun Mountains-Northern Qilian Mountains and the piedmont line. The boundary between major regions I and II is in the area of Helan Mountain-Langshan Mountain. The boundary of the secondary region is divided by the geomorphological conditions in regions II and III. However, in region I, it is mainly divided by the ratio of the annual temperature range A to the annual mean temperature T, and the frozen depths of various regions are taken into consideration. The classification system is divided into 8 types based on the continuity of frozen soil, the time of existence of frozen soil and the seasonal frozen depth. The various classifications of boundaries are mainly taken from the "Map of Snow, Ice and Frozen Ground in China" (1:4 million) (Yafeng Shi et al., 1988) and consult some new materials, whereas the seasonal frozen soil boundary is mainly based on the weather station data. The definitions of each classification are as follows: (1) Large permafrost: the continuous coefficient is 90%-70%; (2) Large-island permafrost: the continuous coefficient is 70%-30%; (3) Sparse island-shaped permafrost: the continuous coefficient is <30%; (4) Permafrost in the mountains; (5) Medium-season seasonal frozen soil: the maximum seasonal frozen depth that can be reached is >1 m; (6) Shallow seasonal frozen soil: the maximum seasonal frozen depth that can be reached is <1 m; (7) Short-term frozen soil: less than one month of storage time; and (8) Nonfrozen soil. According to the data, China's permafrost areas sum to approximately 2.19 × 106 km², accounting for 22.83% of China's territory. Among those areas, the mountain permafrost is found over 0.42×106 km2, which is 4.39% of the territory of China. The seasonal frozen soil area is approximately 4.76×106 km², accounting for 49.6% of China's territory, and the instantaneous frozen soil area is approximately 1.86×106 km², i.e., 19.33% of China's territory. For more information, please see the references (Youwu Zhou et al., 2000).

2019-09-11

Map of the frozen soil in the Tibetan Plateau (2003)

The Tibetan Plateau is known as “The World’s Third Pole” and “The Water Tower of Asia”. A relatively accurate map of the frozen soil in the Tibetan Plateau is therefore significant for local cold region engineering and environmental construction. Thus, to meet the engineering and environmental needs, a decision tree was established based on multi-source remote sensing data (elevation, MODIS surface temperature, vegetation index and soil moisture) to divide the permafrost and seasonally frozen soil of the Tibetan Plateau. The data are in grid format, DN=1 stands for permafrost, and DN=2 stands for seasonally frozen soil. The elevation data are from the 1 km x 1 km China DEM (digital elevation model) data set (http://westdc.westgis.ac.cn); the surface temperature is the yearly average data based on daily data estimated by Bin Ouyang and others using the Sin-Linear method. The estimation of the daily average surface temperature was based on the application of the Sin-Linear method to MODIS surface products, and to reduce the time difference with existing frozen soil maps, the surface temperature of the study area in 2003 was used as the information source for the classification of frozen soil. Vegetation information was extracted from the 16-day synthetic product data of Aqua and Terra (MYD13A1 and MOD13A1) in 2003. Soil moisture values were obtained from relatively high-quality ascending pass data collected by AMSR-E in May 2003. Therefore, based on the above data, the classification threshold of the decision tree was obtained using the Map of Frozen Soil in the Tibetan Plateau (1:3000000) and Map of the Glaciers, Frozen Soil and Deserts in China (1:4000000) as the a priori information. Based on the prosed method, the frozen soil types on the Tibetan Plateau were classified. The classification results were then verified and compared with the surveyed maps of frozen soil in the West Kunlun Mountains, revised maps, maps of hot springs and other existing frozen soil maps related to the Tibetan Plateau. Based on the Tibetan Plateau frozen soil map generated from the multi-source remote sensing information, the permafrost area accounts for 42.5% (111.3 × 104 km²), and the seasonally frozen soil area accounts for 53.8% (140.9 × 104 km²) of the total area of the Tibetan Plateau. This result is relatively consistent with the prior map (the 1:3000000 Map of Frozen Soil in the Tibetan Plateau). In addition, the overall accuracy and Kappa coefficient of the different frozen soil maps show that the frozen soil maps compiled or simulated by different methods are basically consistent in terms of the spatial distribution pattern, and the inconsistencies are mainly in the boundary areas between permafrost areas and seasonally frozen soil areas.

2019-05-31

WATER: Dataset of ground truth measurements synchronizing with Envisat ASAR in the A'rou foci experimental area on Oct. 17, 2007 during the pre-observation period

The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No.2 quadrate of the A'rou foci experimental area on Oct. 17, 2007 during the pre-observation period. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 23:04 BJT. The quadrate was divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. 25 sampling points were chosen, including centers and corners of each subsites. Simultaneous with the satellite overpass, numerous ground data were collected, soil volumetric moisture by ML2X; soil volumetric moisture, soil conductivity, soil temperature, and the real part of soil complex permittivity by WET soil moisture sensor; the surface radiative temperature by the hand-held infrared thermometer; soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). Meanwhile, vegetation parameters as height, coverage and water content were also observed. Surface roughness was detailed in the "WATER: Surface roughness dataset in the A'rou foci experimental area". Those provide reliable ground data for retrieval and validation of soil moisture and freeze/thaw status from active remote sensing approaches.

2019-05-23

WATER: Dataset of ground truth measurements synchronizing with Envisat ASAR in the A'rou foci experimental area on Oct. 18, 2007 during the pre-observation period

The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No. 1 and 2 quadrates of the A'rou foci experimental area on Oct. 18, 2007 during the pre-observation period. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:17 BJT. Both the quadrates were divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. 25 sampling points were chosen, including centers and corners of each subsites. Simultaneous with the satellite overpass, numerous ground data were collected, soil volumetric moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity by the WET soil moisture sensor; the surface radiative temperature by the hand-held infrared thermometer; soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). Meanwhile, vegetation parameters as height, coverage and water content were also observed. Surface roughness was detailed in the "WATER: Surface roughness dataset in the A'rou foci experimental area". Those provide reliable ground data for retrieval and validation of soil moisture and freeze/thaw status from active remote sensing approaches.

2019-05-23