Aerosol Optical Depth (AOD) reflects the attenuation of solar radiation to the surface by aerosols. The aerosol type is calculated according to the aerosol optical thickness (AOD). This data set is derived from the latest MODIS aerosol secondary product MOD04_ L2 and MYD04_ L2, where MOD and MYD represent Terra and Aqua satellites respectively. At present, MODIS aerosol retrieval algorithms are Dark Target (DT) and Deep Blue (DB). According to the inversion accuracy of the metadata field table Quality Assurance Confidence (QAC), DT and DB algorithm products are integrated to deal with land, ocean and coast respectively. The index quality is optimal (QAF=3) or suboptimal (QAF=2) or meets the basic needs (QAF=1) to obtain high-resolution AOD products (0.1 degree, daily scale) with full coverage and long time series. According to AOD experience threshold (AOD: 0~0.2, clean type; 0.2~0.6, urban or industrial type; greater than 0.6, sand dust type) The aerosol types are classified into three types: clean type (1), urban or industrial type (2) and sand dust type (3). This dataset provides MOD, MYD and fusion products based on transit time.
0 2022-10-19
Aerosol Optical Depth (AOD) reflects the attenuation of solar radiation to the surface by aerosols. This data set is derived from the latest MODIS aerosol secondary product MOD04_ L2 and MYD04_ L2, where MOD and MYD represent Terra and Aqua satellites respectively. At present, MODIS aerosol retrieval algorithms are Dark Target (DT) and Deep Blue (DB). According to the inversion accuracy of the metadata field table Quality Assurance Confidence (QAC), DT and DB algorithm products are integrated to deal with land, ocean and coast respectively. The index quality is optimal (QAF=3) or suboptimal (QAF=2) or meets the basic needs (QAF=1) to obtain high-resolution AOD products (0.1 degree, daily scale) with full coverage and long time series. This dataset provides MOD, MYD and fusion products based on transit time.
0 2022-10-19
This data includes the distribution data of soil bacteria in Namco region of the Qinghai Tibet Plateau, which can be used to explore the seasonal impact of fencing and grazing on soil microorganisms in Namco region. The sample was collected from May to September 2015, and the soil samples were stored in ice bags and transported back to the Ecological Laboratory of Beijing Institute of Qinghai Tibet Plateau Research; This data is the result of amplification sequencing, using MoBio Powersoil ™ Soil DNA was extracted with DNA isolation kit, and the primers were 515F (5 '- GTGCCAAGCGCCGGTAA-3') and 806R (5'GGACTACNVGGGTWTCTAAT-3 '). The amplified fragments were sequenced by Illumina Miseq PE250. The original data is analyzed by Qiime software, and then the similarity between sequences is calculated, and the sequences with a similarity of more than 97% are clustered into an OTU. The Greengenes reference library is used for sequence alignment to remove the sequence that only appears once in the database. The soil moisture content and soil temperature were measured by a soil hygrometer, and the soil pH was measured by a pH meter (Sartorius PB-10, Germany). The soil nitrate nitrogen (NO3 −) and ammonium nitrogen (NH4+) concentrations were extracted with 2 M KCl (soil/solution, 1:5), and analyzed with a Smartchem200 discrete automatic analyzer. This data set is of great significance to the study of soil microbial diversity in arid and semi-arid grasslands.
0 2022-10-14
Plant functional types (PFT) is a combination of large plant species according to the ecosystem function and resource utilization mode of plant species. Each planting functional type shares similar plant attributes, which simplifies the diversity of plant species into the diversity of plant function and structure.The concept of plant-functional has been advocated by ecologists especially ecosystem modelers.The basic assumption is that globally important ecosystem dynamics can be expressed and simulated through limited plant functional types.At present, vegetation-functional model has been widely used in biogeographic model, biogeochemical model, land surface process model and global dynamic vegetation model. For example, the land surface process model of the national center for atmospheric research (NCAR) in the United States has changed the original land cover information into the applied plant-functional map (Bonan et al., 2002).Functional plant has been used in the dynamic global vegetation model (DGVM) to predict the changes of ecosystem structure and function under the global change scenario. 1. Functional classification system of Plant 1 Needleleaf evergreen tree, temperate 2 Needleleaf evergreen tree, boreal 3 Needleleaf deciduous tree 4 Broadleaf evergreen tree, tropical 5 Broadleaf evergreen tree, temperate 6 Broadleaf deciduous tree, tropical 7 Broadleaf deciduous tree, temperate 8 Broadleaf deciduous tree, boreal 9 Broadleaf evergreen shrub, temperate 10 Broadleaf deciduous shrub, temperate 11 Broadleaf deciduous shrub, boreal 12 C3 grass, arctic 13 C3 grass 14 C4 grass 15 Crop 16 Permanent wetlands 17 Urban and built-up lands 18 Snow and ice 19 Barren or sparsely vegetated lands 20 Bodies of water 2. Drawing method China's 1km plant function map is based on the climate rules of land cover and plant function conversion proposed by Bonan et al. (Bonan et al., 2002).Ran et al., 2012).MICLCover land cover map is a blend of 1:100000 data of land use in China in 2000, the Chinese atlas (1:10 00000) the type of vegetation, China 1:100000 glacier map, China 1:10 00000 marshes and MODIS land cover 2001 products (MOD12Q1) released the latest land cover data, using IGBP land cover classification system.The evaluation shows that it may be the most accurate land cover map on the scale of 1km in China.Climate data is China's atmospheric driven data with spatial resolution of 0.1 and temporal resolution of 3 hours from 1981 to 2008 developed by he jie et al. (2010).The data incorporates Princeton land-surface model driven data (Sheffield et al., 2006), gewex-srb radiation data (Pinker et al., 2003), TRMM 3B42 and APHRODITE precipitation data, and observations from 740 meteorological stations and stations under the China meteorological administration.According to the evaluation results of RanYouhua et al. (2010), GLC2000 has a relatively high accuracy in the current global land cover data set, and there is no mixed forest in its classification system. Therefore, the mixed forest in the MICLCover land cover diagram USES GLC2000 (Bartholome and Belward, 2005).The information in xu wenting et al., 2005) was replaced.The data can be used in land surface process model and other related researches.
0 2022-10-17
The 0.1 º aerosol optical thickness dataset (also known as the "Poles AOD Collection 1.0" aerosol optical thickness (AOD) dataset) in the polar regions from 2000 to 2020 was produced by combining Merra-2 mode data and MODIS satellite sensor AOD. The data covers the period from 2000 to 2020, with a daily time resolution, covering the "tri polar" (Antarctic, Arctic and Qinghai Tibet Plateau) region, and a spatial resolution of 0.1 degree. The verification of the measured stations shows that the relative deviation of the data is within 35%, which can effectively improve the coverage and accuracy of AOD in the polar region.
0 2022-10-17
The Tibet-Obs established in 2008 consists of three regional-scale soil moisture (SM) monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15-min in situ measurements collected at a depth of 5 cm by multiple SM monitoring sites of all the networks, and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks.
0 2022-10-14
As the “water tower of Asia”, Tibetan Plateau (TP) are the resource of major rivers in Asia. Black carbon (BC) aerosol emitted from surrounding regions can be transported to the inner TP by atmospheric circulation and consequently deposited in snow, which can significantly influence precipitation and mass balance of glaciers. By drilling and sampling ice cores and snow samples and measuring BC concentration, historical record and spatial distribution can be abtained. It can provide basic dataset to study the effects of BC to the environment and climate over the Tibetan Plateau, as well as the pollutants transport.
0 2022-10-12
This data set contains sequence data of the number variation of livestock in the major cities and counties of the Tibetan Plateau from 1970 to 2006. It is used to study the social and economic changes of the Tibetan Plateau. The table has ten fields. Field 1: Year Interpretation: Year of the data Field 2: Province Interpretation: The province from which the data were obtained Field 3: City/Prefecture Interpretation: The city or prefecture from which the data were obtained Field 4: County Interpretation: The name of the county Field 5: Large livestock (10,000) Interpretation: The number of large livestock such as cattle, horses, mules, donkeys, and camels. Field 6: Cattle herd (10,000) Interpretation: Number of cattle Field 7: Equine animals(10,000) Interpretation: The number of equine animals such as horses, mules and donkeys. Field 8: Horses (10,000) Interpretation: The number of horses Field 9: Sheep (10,000) Interpretation: The number of sheep Field 10: Data Sources Interpretation: Source of Data The data come from the statistical yearbook and county annals. Some are listed as follows. [1] Gansu Yearbook Editorial Committee. Gansu Yearbook [J]. Beijing: China Statistics Press, 1984, 1988-2009 [2] Statistical Bureau of Yunnan Province. Yunnan Statistical Yearbook [J]. Beijing: China Statistics Press, 1988-2009 [3] Statistical Bureau of Sichuan Province, Sichuan Survey Team. Sichuan Statistical Yearbook [J]. Beijing: China Statistics Press, 1987-1991, 1996-2009 [4] Statistical Bureau of Xinjiang Uighur Autonomous Region . Xinjiang Statistical Yearbook [J]. Beijing: China Statistics Press, 1989-1996, 1998-2009 [5] Statistical Bureau of Tibetan Autonomous Region. Tibet Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-2009 [6] Statistical Bureau of Qinghai Province. Qinghai Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-1994, 1996-2008. [7] County Annals Editorial Committee of Huzhu Tu Autonomous County. County Annals of Huzhu Tu Autonomous County [J]. Qinghai: Qinghai People's Publishing House, 1993 [8] Haiyan County Annals Editorial Committee. Haiyan County Annals[J]. Gansu: Gansu Cultural Publishing House, 1994 [9] Menyuan County Annals Editorial Committee. Menyuan County Annals[J]. Gansu: Gansu People's Publishing House, 1993 [10] Guinan County Annals Editorial Committee. Guinan County Annals [J]. Shanxi: Shanxi People's Publishing House, 1996 [11] Guide County Annals Editorial Committee. Guide County Annals[J]. Shanxi: Shanxi People's Publishing House, 1995 [12] Jianzha County Annals Editorial Committee. Jianzha County Annals [J]. Gansu: Gansu People's Publishing House, 2003 [13] Dari County Annals Editorial Committee. Dari County Annals [J]. Shanxi: Shanxi People's Publishing House, 1993 [14] Golmud City Annals Editorial Committee. Golmud City Annals [J]. Beijing: Fangzhi Publishing House, 2005 [15] Delingha City Annals Editorial Committee. Delingha City Annals [J]. Beijing: Fangzhi Publishing House, 2004 [16] Tianjun County Annals Editorial Committee. Tianjun County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995 [17] Naidong County Annals Editorial Committee. Naidong County Annals [J]. Beijing: China Tibetology Press, 2006 [18] Gulang County Annals Editorial Committee. Gulang County Annals [J]. Gansu: Gansu People's Publishing House, 1996 [19] County Annals Editorial Committee of Akesai Kazak Autonomous County. County Annals of Akesai Kazakh Autonomous County [J]. Gansu: Gansu People's Publishing House, 1993 [20] Minxian County Annals Editorial Committee. Minxian County Annals [J]. Gansu: Gansu People's Publishing House, 1995 [21] Dangchang County Annals Editorial Committee. Dangchang County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995 [22] Dangchang County Annals Editorial Committee. Dangchang County Annals(Sequel) (1985-2005) [J]. Gansu: Gansu Cultural Publishing House, 2006 [23] Wenxian County Annals Editorial Committee. Wenxian County Annals[J]. Gansu: Gansu Cultural Publishing House, 1997 [24] Kangle County Annals Editorial Committee. Kangle County Annals [J]. Shanghai: Sanlian Bookstore. 1995 [25] County Annals Editorial Committee of Jishishan (Baoan, Dongxiang, Sala) Autonomous County. County Annals of Jishishan (Baoan, Dongxiang, Sala) Autonomous County[J], Gansu: Gansu Cultural Publishing House, 1998 [26] Luqu County Annals Editorial Committee. Luqu County Annals [J]. Gansu: Gansu People's Publishing House, 2006 [27] Zhouqu County Annals Editorial Committee. Zhouqu County Annals [J]. Shanghai: Sanlian Bookstore. 1996 [28] Xiahe County Annals Editorial Committee. Xiahe County Annals [J]. Gansu: Gansu Cultural Publishing House, 1999 [29] Zhuoni County Annals Editorial Committee. Zhuoni County Annals [J]. Gansu: Gansu Nationality Publishing House, 1994 [30] Diebu County Annals Editorial Committee. Diebu County Annals [J]. Gansu: Lanzhou University Press, 1998 [31] Pengxian County Annals Editorial Committee. Pengxian County Annals [J]. Sichuan: Sichuan People's Publishing House, 1989 [32] Guanxian County Annals Editorial Committee. Guanxian County Annals [J]. Sichuan: Sichuan People's Publishing House, 1991 [33] Wenjiang County Annals Editorial Committee. Wenjiang County Annals [J]. Sichuan: Sichuan People's Publishing House, 1990 [34] Shifang County Annals Editorial Committee. Shifang County Annals [J]. Sichuan: Sichuan University Press, 1988 [35] Tianquan County Annals Editorial Committee. Tianquan County Annals [J]. Sichuan: Sichuan Science and Technology Press, 1997 [36] Shimian County Annals Editorial Committee. Shimian County Annals [J]. Sichuan: Sichuan Cishu Publishing House, 1999 [37] Lushan County Annals Editorial Committee. Lushan County Annals [J]. Sichuan: Fangzhi Publishing House, 2000 [38] Hongyuan County Annals Editorial Committee. Hongyuan County Annals [J]. Sichuan: Sichuan People's Publishing House, 1996 [39] Wenchuan County Annals Editorial Committee. Wenchuan County Annals [J]. Sichuan: Bayu Shushe, 2007 [40] Derong County Annals Editorial Committee. Derong County Annals [J]. Sichuan: Sichuan University, 2000 [41] Baiyu County Annals Editorial Committee. Baiyu County Annals [J]. Sichuan: Sichuan University Press, 1996 [42] Batang County Annals Editorial Committee. Batang County Annals [J]. Sichuan: Sichuan Nationality Publishing House, 1993 [43] Jiulong County Annals Editorial Committee. Jiulong County Annals(Sequel) (1986-2000) [J]. Sichuan: Sichuan Science and Technology Press, 2007 [44] County Annals Editorial Committee of Derung-Nu Autonomous County Gongshan. County Annals of Derung-Nu Autonomous County Gongshan [J]. Beijing: Nationality Publishing House, 2006 [45] Lushui County Annals Editorial Committee. Lushui County Annals [J]. Yunnan: Yunnan People's Publishing House, 1995 [46] Deqin County Annals Editorial Committee. Deqin County Annals [J]. Yunnan: Yunnan Nationality Publishing House, 1997 [47] Yutian County Annals Editorial Committee. Yutian County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2006 [48] Cele County Annals Editorial Committee. Cele County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2005 [49] Hetian County Annals Editorial Committee. Hetian County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2006 [50] Qiemo County Local Chronicles Editorial Committee. Qiemo County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996 [51] Shache County Annals Editorial Committee. Shache County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996 [52] Yecheng County Annals Editorial Committee. Yecheng County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1999 [53] Akto County Local Chronicles Editorial Committee. Akto County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996 [54] Wuqia County Local Chronicles Editorial Committee. Wuqia County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1995
0 2022-10-10
NDVI reflects the background effects of plant canopy, such as soil, wet ground, snow, dead leaves, roughness, etc., and is related to vegetation cover. It is one of the important parameters to reflect the crop growth and nutrient information. According to this parameter, the N demand of crops in different seasons can be known, which is an important guide to the reasonable application of N fertilizer. Correct NDVI (C-NDVI) is the value of NDVI after excluding the influence of climate elements (temperature, precipitation, etc.) on NDVI. Taking precipitation as an example, studies on the lag effect of precipitation on vegetation growth show that the lag time of precipitation effects varies in different regions due to differences in vegetation composition and soil types. In this study, we post-processed the MODIS NDVI data and firstly correlated the NDVI value of the current month with the precipitation of the current month, the average value of the precipitation of the current month with that of the previous month, and the average value of the precipitation of the current month with that of the previous two months to determine the optimal lag time. The NDVI was regressed on precipitation and air temperature to obtain the correlation coefficients, and then the corrected NDVI values were calculated by the difference between the MODIS NDVI and the NDVI regressed on climate factors. We corrected NDVI using climate data to give reliable vegetation correction indices for the circum-Arctic Circle (range north of 66°N) and the Tibetan Plateau (range 26°N to 39.85°N and 73.45°E to 104.65°E) for 2013 and 2018. The spatial resolution of the data is 0.5 degrees and the temporal resolution is monthly values.
0 2022-09-03
This dataset is the monthly precipitation data of China, with a spatial resolution of 0.0083333 ° (about 1km) and a time range of 1901.1-2021.12. The data format is NETCDF, i.e.. Nc format. This dataset is generated in China through the Delta spatial downscaling scheme based on the global 0.5 ° climate dataset released by CRU and the global high-resolution climate dataset released by WorldClim. In addition, 496 independent meteorological observation point data are used for verification, and the verification results are reliable. This data set covers the main land areas in China (including Hong Kong, Macao and Taiwan), excluding islands and reefs in the South China Sea. In order to facilitate storage, the data are all int16 type and stored in nc files, with precipitation units of 0.1mm. NC data can be mapped using ArcMAP software; Matlab software can also be used for extraction processing. Matlab has released the function to read and store nc files. The read function is ncread, and switch to the nc file storage folder. The statement is expressed as: ncread ('XXX.nc ',' var ', [i j t], [leni lenj lent]), where XXX.nc is the file name, and is the string required' '; Var is from XXX The variable name read in NC. If it is a string, '' is required; i. J and t are the starting row, column and time of the read data respectively, and leni, lenj and lent i are the length of the read data in the row, column and time dimensions respectively. In this way, this function can be used to read in any region and any time period in the study area. There are many commands about NC data in the help of Matlab, which can be viewed. WGS84 is recommended for data coordinate system.
0 2022-10-08
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