Snow pits were observed daily at Altay base station（lon：88.07、lat: 44.73） from November 27, 2015 to March 26, 2016. Parameters include: snow stratification, stratification thickness, density, particle size, temperature. The frequency of observation was daily. The particle size was observed by a microscope with camera, the density was observed by snowfork, snow shovel and Snow Cone, and the temperature was automatically observed by temperature sensor. The observation time was 8:00-10:100 am local time. The snow particle size is observed according to the natural stratification of snow. The snow particles of each layer are collected, and at least 2 photos are taken. The long axis and short axis of at least 10 groups of particles are measured by corresponding software. Unit: mm. The density was observed at equal intervals, snowfork every 5 cm, snow shovel every 10 cm, snow cone to observe the density of the whole snow layer, and the density of each layer was observed three times. The unit is g / cm3. The height of temperature observation is 0cm, 5cm, 10cm, 15cm, 25cm, 35cm, 45cm, 55cm. The recording frequency was once every 1 minute. The unit is OC.
Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).
Sher Muhammad Sher Muhammad
On the basis of RGI6.0, we use remote sensing and geographic information system technology to update the glacier inventory data in Alaska. The updated glacier inventory uses a data source for 2018 Landsat OLI spatial resolution 15m remote sensing image, and the method used is manual interpretation. The results show that the Alaska Glacier inventory includes 27043 glaciers with a total area of 81285km2. The uncertiany of this data is 4.3%. The data will provide important data support for the study of glacier change in Alaska and the regional and global impact of glacier change in the context of global change.
SHANGGUAN Donghui LI Yaojun
This data set uses SMMR (1979-1987), SSM / I (1987-2009) and ssmis (2009-2015) daily brightness temperature data, which is generated by double index (TB V, SG) freeze-thaw discrimination algorithm. The classification results include four types: frozen surface, melted surface, desert and water body. The data covers the source area of three rivers, with a spatial resolution of 25.067525 km. It is stored in geotif format in the form of ease grid projection. Pixel values represent the state of freezing and thawing: 1 for freezing, 2 for thawing, 3 for deserts, 4 for water bodies. Because all TIF files in the dataset describe the scope of Sanjiangyuan National Park, the row and column number information of these files is unchanged, and the excerpt is as follows (where the unit of cellsize is m): ncols 52 nrows 28 cellsize 25067.525 nodata_value 0
This dataset is land surface phenology estimated from 16 days composite MODIS NDVI product (MOD13Q1 collection6) in the Three-River-Source National Park from 2001 to 2018. The spatial resolution is 250m. The variables include Start of Season (SOS) and End of Season (EOS). Two phenology estimating methods were used to MOD13Q1, polynomial fitting based threshold method and double logistic function based inflection method. There are 4 folders in the dataset. CJYYQ_phen is data folder for source region of the Yangtze River in the national park. HHYYQ_phen is data folder for source region of Yellow River in the national park. LCJYYQ_phen is data folder for source region of Lancang River in the national park. SJY_phen is data folder for the whole Three-River-Source region. Data format is geotif. Arcmap or Python+GDAL are recommended to open and process the data.
Tibetan Plateau, located in southwest China, is one of the key areas affecting the Asian monsoon, and it is also an early warning area and sensitive area for global climate change. As the main parameter of surface energy balance, surface temperature represents the degree of energy and water exchange between earth and atmosphere, and is widely used in climatology, hydrology and ecology. The study of land-atmosphere interaction in Qinghai-Xizang Plateau urgently needs long time series and all-weather surface temperature data set with high temporal and spatial resolution. However, the frequent cloud cover characteristics in this area limit the use of the existing satellite thermal infrared remote sensing surface temperature data set. The generation method of this data set is an integrated method of thermal infrared and passive microwave surface temperature based on the time component decomposition model of surface temperature. This method was originally applied to Northeast China and its adjacent areas, and subsequently extended to western China including the Qinghai-Xizang Plateau. The main input data of this method are Aqua MODIS,Aqua AMSR-E,GCOM-W1 AMSR2 and other data, and the auxiliary data include leaf area index (LAI) products provided by satellite remote sensing, surface cover type data and so on. This method makes full use of the steady and unstable components of surface temperature provided by satellite thermal infrared remote sensing and passive microwave remote sensing, as well as the spatial correlation of surface temperature. The obtained all-weather surface temperature has good accuracy and image quality. The time span of the dataset is from 2003 to 2018, the time resolution is 2 times a day, and the spatial resolution is 1 km, which is expected to provide data support for related applications.
ZHOU Ji ZHANG Xiaodong LIU Shaomin
The fraction snow cover (FSC) is the ratio of the snow cover area SCA to the pixel space. The data set covers the Arctic region (35 ° to 90 ° north latitude). Using Google Earth engine platform, the initial data is the global surface reflectance product with a resolution of 1000m with mod09ga, and the data preparation time is from February 24, 2000 to November 18, 2019. The methods are as follows: in the training sample area, the reference data set of FSC is prepared by using Landsat 8 surface reflectance data and snomap algorithm, and the data set is taken as the true value of FSC in the training sample area, so as to establish the linear regression model between FSC in the training sample area and NDSI based on MODIS surface reflectance products. Using this model, MODIS global surface reflectance product is used as input to prepare snow area ratio time series data in the Arctic region. The data set can provide quantitative information of snow distribution for regional climate simulation and hydrological model.
MA Yuan LI Hongyi
Based on the multispectral remote sensing data of 210 Landsat 8 oli satellites, corrected and inlaid as false color composite image (RGB: 654), the method of artificial visual interpretation is adopted, and the result of band ratio method is referred, combined with SRTM DEM v4.1 data and Google data The images of earth and hj1a / 1b satellites in different seasons of the same year, excluding the influence of mountain shadow and seasonal snow, referring to the first and second glacial cataloguing data in China, excluding the steep cliffs and exposed bedrock in non glacial areas, comprehensively extracting the thematic vector data of net glaciers, excluding the surface moraine coverage area with unclear glacier end position, and the accuracy of glacial boundary digitization is half Pixel (15m). Through comparative analysis, it can be seen that the mountain glacier data extracted based on multi data sources, reference to multi method results and integration of expert experience and knowledge is more accurate.
This dataset was derived from long-term daily snow depth in China based on the boundary of the three-river-source area. The snow depth ranges from 0 to 100 cm, and the temporal coverage is from January 1 1980 to December 31 2018. The spatial and temporal resolutions are 0.25o and daily, respectively. Snow depth was produced from satellite passive microwave remote sensing data which came from three different sensors that are SMMR, SSM/I and SSMI/S. Considering the systematic bias among these sensors, the inter-sensor calibrations were performed to obtain temporal consistent passive microwave remote sensing data. And the long-term daily snow depth in China were produced from this consistent data based on the spectral gradient method.
The permafrost stability map was created based on the classification system proposed by Guodong Cheng (1984), which mainly depended on the inter-annual variation of deep soil temperature. By using the geographical weighted regression method, many auxiliary data was fusion in the map, such as average soil temperature, snow cover days, GLASS LAI, soil texture and organic from SoilGrids250, soil moisture products from CLDAS of CMA, and FY2/EMSIP precipitation products. The permafrost stability data spatial resolution is 1km and represents the status around 2010. The following table is the permafrost stability classification system. The data format is Arcgis Raster.
The dataset was produced based on MODIS data. Parameters and algorithm were revised to be suitable for the land cover type in the Three-River-Source Regions. By using the Markov de-cloud algorithm, SSM/I snow water equivalent data was fused to the result. Finally, high accuracy daily de-cloud snow cover data was produced. The data value is 0(no snow) or 1(snow). The spatial resolution is 500m, the time period is from 2000-2-24 to 2018-12-31. Data format is geotiff, Arcmap or python+GDAL were recommended to open and process the data.
River lake ice phenology is sensitive to climate change and is an important indicator of climate change. 308 excel file names correspond to Lake numbers. Each excel file contains six columns, including daily ice coverage information of corresponding lakes from July 2002 to June 2018. The attributes of each column are: date, lake water coverage, lake water ice coverage, cloud coverage, lake water coverage and lake ice coverage after cloud treatment. Generally, the ice cover area ratio of 0.1 and 0.9 is used as the basis to distinguish the lake ice phenology. The excel file contained in the data set can further obtain four lake ice phenological parameters: Fus, fue, bus, bue, and 92 lakes. Two parameters, Fus and bue, can be obtained.
The near-surface freeze-thaw affects the water and energy exchanges mode and efficiency between the land and atmosphere. The transition of the freeze/thaw state affects the pattern of runoff concentration, which has an important impact on regional and global water cycle. Based on the remote sensing data of AMSR-E/2 passive microwave sensors and MODIS optical sensor, this data set uses the discriminant function algorithm and its downscaling method to produce a global mapping of near-surface freeze-thaw states with higher spatial resolution. This product covers the time period from 2002 to 2018 (daily), and spatial coverage is global scale (spatial resolution of 0.05°). It can be used to analyze the start/end time of global near-surface freeze/thaw states, the duration of freezing/thawing and their changing trends, and provide data support for studying the mechanism of water cycle and energy exchanges in the context of global change.
There are many lakes in the Qinghai Tibet Plateau. The glacial phenology and duration of lakes in this region are very sensitive to regional and global climate change, so they are used as the key indicators of climate change research, especially the comparative study of the three polar environmental changes of the earth. However, due to its poor natural environment and sparse population, there is a lack of conventional field measurement of lake ice phenology. The lake ice was monitored with a resolution of 500 meters by using the normalized difference snow index (NDSI) data of MODIS. The traditional snow map algorithm is used to detect the lake daily ice amount and coverage under the condition of sunny days, and the lake daily ice amount and coverage under the condition of cloud cover are re determined through a series of steps based on the spatiotemporal continuity of the lake surface conditions. Through time series analysis, 308 lakes larger than 3km2 are identified as effective records of lake ice range and coverage, forming a daily lake ice range and coverage data set, including 216 lakes.
This data set provides daily snow thickness distribution data of China from October 24, 1978 to December 31, 2012, with a spatial resolution of 25km.The original data used for the inversion of the snow depth data set came from SMMR (1978-1987), SSM/I (1987-2008) and amsr-e (2002-2012) daily passive microwave bright temperature data processed by the national snow and ice data center (NSIDC).As the three sensors are mounted on different platforms, there is a certain system inconsistency in the obtained data.The time consistency of bright temperature data is improved by cross calibration of bright temperature of different sensors.Then, based on Chang algorithm, Dr. Che tao is used to carry out snow depth inversion.Refer to the data description document for specific inversion methods.
CHE Tao LI Xin DAI Liyun
This dataset includes component temperatures measured by the thermal imager at the Mixed Forest and Sidaoqiao stations between 23 July and 18 August, 2014. The Mixed Forest (101.1335 °E, 41.9903 °N, 874 m.a.s.l.) and Sidaoqiao (101.1374 °E, 42.0012 °N, 873 m.a.s.l.) stations were located in the downstream of the Heihe River basin, Dalaihubu Town, Ejin Banner, Inner Mongolia. At the Mixed Forest station, a Testo 890-2 thermal imager (Testo Inc., Germany) with a resolution of 640 × 480 pixels was employed to acquire brightness temperature images. The imager was manually operated from a 10-m height platform of the tower between 10:00-16:00 (China Standard Time, CST) with an observation interval of 1-h on cloudless days. On August 4th observations were acquired between 11:00 and 17:00 at an interval of 10-min to match observations acquired with an airborne TIR imager. The ground based imager was pointed to five viewing directions (southeast-SE, east-E, northeast-NE, northwest-NW, and southwest-SW) and was inclined 25°–45° below the horizon depending on viewing direction. At Sidaoqiao station, a Testo 875-2i imager (Testo Inc., Germany) with a resolution of 160 × 120 pixels was manually operated from a 10-m high platform to acquire brightness temperature images in directions SW, SE, NE, and NW. Depending on the targets in each viewing direction, the imager was inclined to 30°–45° below the horizon. Observations at Sidaoqiao and Mixed Forest stations were almost synchronous. Furthermore, visible images were taken simultaneously with the aforementioned two TIR imagers (2048 × 1536 pixels for Testo 890-2 and 640 × 480 pixels for Testo 875-2i).
The multi-decadal lake number and area changes in China during 1960s–2015 are derived from historical topographic maps and >3831 Landsat satellite images, including lakes as fine as ≥1 km2 in size. The total area of lakes in China has increased by 5858.06 km2 (9%) between 1960s and 2015, and with heterogeneous spatial variations. Lake area changes in the Tibetan Plateau, Xinjiang, and Northeast Plain and Mountain regions reveal significant increases of 5676.75, 1417.15, 1134.87 km2 (≥15%), respectively, but the Inner-Mongolian Plateau shows an obvious decrease of 1223.76 km2 (22%). We find that 141 new lakes have appeared predominantly in the arid western China; but 333 lakes, mainly located in the humid eastern China, have disappeared over the past five decades.
This dataset includes component temperatures measured by the thermal infrared (TIR) radiometers at the Mixed Forest and Sidaoqiao stations between 22 July, 2014 and 19 July, 2016. The Mixed Forest (101.1335 °E, 41.9903 °N, 874 m.a.s.l.) and Sidaoqiao (101.1374 °E, 42.0012 °N, 873 m.a.s.l.) stations were located in the downstream of the Heihe River basin, Dalaihubu Town, Ejin Banner, Inner Mongolia. At the Mixed Forest station, two TIR radiometers (SI-111, Apogee Instruments Inc., USA) connected to a data logger (CR800, Campbell Scientific Inc., USA) measured component temperatures of the sunlit canopy and shaded canopy. TIR radiometers were mounted horizontally at 5 m height on iron rods just south and north of a tree and pointed to its canopy. The distance from the sensor to the canopy was ~1 m. At the Sidaoqiao station, two SI-111 TIR radiometers connected to a CR800 data logger measured component temperatures of the soil and shrub. The first sensor pointed from 2 m height under a viewing zenith angle of 45° to bare soil; the second sensor was mounted at 1-m height and pointed horizontally into the shrub canopy.
Global warming and human activities have led to the degradation of permafrost and the collapse of permafrost, which have seriously affected the construction of permafrost projects and the ecological environment. Based on high-resolution satellite images, the permafrost of oboling in Heihe River Basin of Qinghai Tibet Plateau is taken as the research area, and the object-oriented classification technology of machine learning is used to extract the thermal collapse information in the research area. The results show that from 2009 to 2019, the number of thermal collapse increased from 12 to 16, and the total area increased from 14718.9 square meters to 28579.5 square meters, nearly twice. The combination of high spatial resolution remote sensing and object-oriented classification method has a broad application prospect in the monitoring of thermal thawing and collapse of frozen soil.
The “Long-term series of daily global snow depth” was produced using the passive microwave remote sensing data. The temporal range is 1979~2017, and the coverage is the global land. The spatial resolutions is 25,067.53 m and the temporal resolution is daily. A dynamic brightness temperature gradient algorithm was used to derive snow depth. In this algorithm, the spatial and temporal variations of snow characteristics were considered and the spatial and seasonal dynamic relationships between the temperature difference between 18 GHz and 36 GHz and the measured snow depth were established. The long-term sequence of satellite-borne passive microwave brightness temperature data used to derive snow depth came from three sensors (SMMR, SSM/I and SSMI/S), and there is a certain system inconsistency among them. So, the inter-sensor calibration was performed to improve the temporal consistency of these brightness temperature data before snow depth derivation. The accuracy analysis shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file is a 1383*586 snow depth matrix, and each snow depth represents a 25,067.53m* 25,067.53m grid. The projection of this data is EASE-Grid, and following is the file header which describes the projection detail. File header: ncols 1383 nrows 586 xllcorner -17334193.54 yllcorner -7344787.75 cellsize 25,067.53 NODATA_value -1
CHE Tao LI Xin DAI Liyun
Contact SupportNorthwest Institute of Eco-Environment and Resources, CAS 0931-4967287 email@example.com
LinksNational Tibetan Plateau Data Center