The triple pole aerosol type data product is an aerosol type result obtained through a series of data pre-processing, quality control, statistical analysis and comparative analysis processes by comprehensively using MEERA 2 assimilation data and active satellite CALIPSO products. The key of the aerosol type fusion algorithm is to judge the aerosol type of CALIPSO. During the data fusion of aerosol type, the final aerosol type data (12 types in total) and quality control results in the three polar regions are obtained according to the types and quality control of CALIPSO aerosol types and referring to MERRA 2 aerosol types. The data product fully considers the vertical and spatial distribution of aerosols, and has a high spatial resolution (0.625 ° × 0.5 °) and time resolution (month).
We propose an algorithm for ice fissure identification and detection using u-net network, which can realize the automatic detection of ice fissures of Typical Glaciers in Greenland ice sheet. Based on the data of sentinel-1 IW from July and August every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then the representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking two typical glaciers in Greenland (Jakobshavn and Kangerdlussuaq) as examples, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
In order to describe the distribution pattern of genetic diversity of main domesticated animals in the Qinghai Tibet Plateau and its surrounding areas (Pan third pole area), and clarify its related genetic background. In 2020, we extracted the total DNA from 266 global chicken blood, tissue and other DNA tissue samples, built a database and sequenced the whole genome. At the same time, we downloaded the published chicken genome data, and carried out population analysis of 863 chicken genomes, so as to provide basic data for exploring the historical events of domestication, migration and expansion of domestic chickens in the pan third pole region, and further explore the adaptation mechanism of domesticated animals to harsh environments such as drying. Articles related to this data set have been published. All data in this data set can be downloaded online from fastq, BAM, VCF and SNP files.
To explore inorganic hydrochemical characteristics of the upper Yarlung Zangbo River, water samples were collected from the main stream and different tributaries in this region in August 2020. The water was collected with 100mL polyethylene (PE) plastic bottle, and the basic physical and chemical parameters such as pH value (±0.2) and dissolved oxygen (±1%) of the sampling site were measured by multi -parameter water quality monitor (YSI-EX02,USA).,and HCO3- concentration was titrated with 0.025mol/L HCl.The concentrations of Na+, K+, Ca2+, Mg2+, SO42-, NO3- and Cl- ions were analyzed and determined by ion chromatograph (Shenhan CIC-D160, China) in the laboratory. Using Gibbs model, correlation analysis and principal component analysis method, analyzed the one main ion concentration changes, chemical composition characteristics, analytical, and the ion source was designed to reveal inorganic water chemical characteristics of The Tibet plateau glacier melt water runoff, and for plateau typical river water and changing trend forecast provides the basis.
This data is the runoff and evapotranspiration generated by the precipitation in the growing season of the upper reaches of Heihe River from 1992 to 2015. Temporal resolution: year (growingseason), spatial resolution: 0.00833°. The data include precipitation (mm), evapotranspiration (mm), runoff (mm) and soil water content (m3 / m3). The data are obtained by using meteorological, soil and vegetation parameters based on Eagleson eco hydrological model. The simulated rainfall runoff is verified by using the observed runoff data in the growing season of 6 sub basins in the upper reaches of Heihe River (Heihe main stream, Babao River, yeniugou, Liyuan River, Wafangcheng and Hongshui River). The variation range of correlation coefficient (R) is 0.53-0.74, RMSE is 32.46-233.18 mm, and the relative error range is -0.66-0.0005; The difference between simulated evapotranspiration and gleam et is − 115.36 mm to 44.1 mm. The simulation results can provide some reference for hydrological simulation in the upper reaches of Heihe River.
Log and image are unique and important primary data of field research, and also an important part of scientific data. In order to further standardize the collection, collation, warehousing and exchange of expedition logs and image data of the second Comprehensive scientific investigation and research project on the Qinghai-Tibet Plateau, and ensure the operability, organization and standardization of the warehousing of expedition logs and image data, this technical specification is formulated. This specification provides procedures and methods for the collection and collation of investigation logs and image data, including work preparation, field investigation, data collation and other requirements, in order to better serve the storage of investigation data. This specification applies to the collation and storage of log and image data of field investigations organized by the second Comprehensive scientific investigation and research project on the Qinghai-Tibet Plateau, and other relevant data formed by field investigations can also be carried out by reference to this technical specification.
To fully implement the measures for the administration of the scientific data for the "government budget funding for formation of the scientific data shall, in accordance with the open as normal, not open for exception principle, by the competent department to organize the formulation of scientific data resources directory, the directory should be timely access to the national data sharing and data exchange platform, open to society and relevant departments to share, In the spirit of unimpeded military-civilian sharing channels for scientific data, and in accordance with the relevant requirements of relevant exchange standards and specifications, this code is now established for the second Comprehensive scientific investigation and research project on the Qinghai-Tibet Plateau. The main drafting unit of this code: Institute of Geographic Sciences and Natural Resources Research, CAS. Main draftsman of this specification: project group 9 of the second Comprehensive Scientific investigation and research Mission of qinghai-Tibet Plateau.
The Qinghai Tibet Plateau is a sensitive region of global climate change. Land surface temperature (LST), as the main parameter of land surface energy balance, characterizes the degree of energy and water exchange between land and atmosphere, and is widely used in the research of meteorology, climate, hydrology, ecology and other fields. In order to study the land atmosphere interaction over the Qinghai Tibet Plateau, it is urgent to develop an all-weather land surface temperature data set with long time series and high spatial-temporal resolution. However, due to the frequent cloud coverage in this region, the use of existing satellite thermal infrared remote sensing land surface temperature data sets is greatly limited. Compared with the previous version released in 2019, Western China Daily 1km spatial resolution all-weather land surface temperature data set (2003-2018) V1, this data set (V2) adopts a new preparation method, namely satellite thermal infrared remote sensing reanalysis data integration method based on new land surface temperature time decomposition model. The main input data of the method are Aqua MODIS LST products and GLDAS data, and the auxiliary data include vegetation index and surface albedo provided by satellite remote sensing. This method makes full use of the high frequency and low frequency components of land surface temperature and the spatial correlation of land surface temperature provided by satellite thermal infrared remote sensing and reanalysis data. The evaluation results show that this data set has good image quality and accuracy, which is not only seamless in space, but also highly consistent with the amplitude and spatial distribution of 1 km daily Aqua MODIS LST products widely used in current academic circles. When MODIS LST was used as the reference value, the mean deviation (MBE) of the data set in daytime and nighttime was -0.28 K and -0.29 K respectively, and the standard deviation (STD) of the deviation was 1.25 K and 1.36 K respectively. The test results based on the measured data of six stations in the Qinghai Tibet Plateau and Heihe River Basin show that under clear sky conditions, the data set is highly consistent with the measured LST in daytime / night, and its MBE is -0.42-0.25 K / - 0.35-0.19 K; The root mean square error (RMSE) was 1.03 ~ 2.28 K / 1.05 ~ 2.05 K; Under the condition of non clear sky, the MBE of this data set in daytime / night is -0.55 ~ 1.42 K / - 0.46 ~ 1.27 K; The RMSE was 2.24-3.87 K / 2.03-3.62 K. Compared with the V1 version of the data, the two kinds of all-weather land surface temperature show the characteristics of seamless (i.e. no missing value) in the spatial dimension, and in most areas, the spatial distribution and amplitude of the two kinds of all-weather land surface temperature are highly consistent with MODIS land surface temperature. However, in the region where the brightness temperature of AMSR-E orbital gap is missing, the V1 version of land surface temperature has a significant systematic underestimation. The mass of trims land surface temperature is close to that of V1 version outside AMSR-E orbital gap, while the mass of trims is more reliable inside the orbital gap. Therefore, it is recommended that users use V2 version. The time span of this data set is from 2000 to 2021 and will be updated continuously; The time resolution is twice a day (corresponding to the two transit times of aqua MODIS in the daytime and at night); The spatial resolution is 1 km. In order to facilitate the majority of colleagues to carry out targeted research around the Qinghai Tibet Plateau and its adjacent areas, and reduce the workload of data download and processing, the coverage of this data set is limited to Western China and its surrounding areas (72 ° E-104 ° E，20 ° N-45 ° N）。 Therefore, this dataset is abbreviated as trims lst-tp (thermal and reality integrating modem resolution spatial seamless LST – Tibetan Plateau) for user's convenience.
Land surface temperature (LST) is one of the important parameters of the interface between the earth's surface and atmosphere. It is not only the direct reflection of the interaction between the surface and the atmosphere, but also has a complex feedback effect on the earth atmosphere process. Therefore, land surface temperature is not only a sensitive indicator of climate change and an important prerequisite for mastering the law of climate change, but also a direct input parameter of many models, which has been widely used in many fields, such as meteorology, climate, environmental ecology, hydrology and so on. With the deepening and refinement of Geosciences and related fields, there is an urgent need for all weather LST based on satellite remote sensing. The generation principle of this dataset is a satellite thermal infrared remote sensing reanalysis data integration method based on a new land surface temperature time decomposition model. The main input data of the method are Aqua MODIS LST products and GLDAS data, and the auxiliary data include vegetation index and surface albedo provided by satellite remote sensing. The method makes full use of the high-frequency and low-frequency components of land surface temperature and the spatial correlation of land surface temperature provided by satellite thermal infrared remote sensing and reanalysis data, and finally reconstructs a high-quality all-weather land surface temperature data set. The evaluation results show that this data set has good image quality and accuracy, which is not only seamless in space, but also highly consistent with the amplitude and spatial distribution of 1 km daily Aqua MODIS LST products widely used in current academic circles. When MODIS LST is used as reference, the mean deviation (MBE) of the data set is 0.08k to 0.16k, and the standard deviation of deviation (STD) is 1.12k to 1.46k. Compared with the daily 1km AATSR LST product released by ESA, the MBE and STD of the product are -0.21k to 0.25k and 1.27k to 1.36k during the day and night. Based on the measured data of 15 stations in Heihe River Basin, Northeast China, North China and South China, the test results show that the MBE is -0.06k to -1.17k, and the RMSE is 1.52k to 3.71k, and there is no significant difference between clear sky and non clear sky. The time resolution of this data set is twice a day, the spatial resolution is 1km, and the time span is from 2000 to 2021; The spatial scope includes the main areas of China's land (including Hong Kong, Macao and Taiwan, excluding the islands in the South China Sea) and the surrounding areas (72 ° E-135 ° E，19 ° N-55 ° N）。 This dataset is abbreviated as trims LST (thermal and reality integrating modem resolution spatial sealing LST) for users to use. It should be noted that the spatial subset of trims LST, trims lst-tp (1 km daily land surface temperature data set in Western China, trims lst-tp; 2000-2021) V2) has also been released in the national Qinghai Tibet Plateau scientific data center to reduce the workload of data download and processing for relevant users.
In 1970, land use was visually interpreted from MSS images, with an overall interpretation accuracy of more than 90%. Land classification was carried out in accordance with the land use classification system of the Chinese Academy of Sciences. For detailed classification rules, please read the data description document. The 2005 and 2015 data sets were collected from the European Space Agency (ESA) Data acquisition of global land cover types includes five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) and Xinjiang, China. There are 22 land use types in the data set. The IPCC land use classification system is adopted. Please refer to the documentation for specific classification details.