Ⅰ. Overview Landsat5 was launched in April 1999. As a supplement and enhancement to the Landsat series, it carries an EMT+ sensor. The parameters of each band are close to that of Landsat5, but the panchromatic band with a resolution of 15 m is added, and the resolution of thermal infrared band is increased to 60 m.This dataset was collected in 1999-2010. There were 97 scenes of TM data in the upper reaches of the Yellow River. Due to sensor damage, there were bands in the images. Ⅱ. Data processing description Product level is L1 and has been geometrically corrected. Ⅲ. Data content description The naming method is L5 and row number and column number _ column number and date (yyyymmdd), such as L75129032_03220040816. Ⅳ. Data usage description The main applications are soil use/cover and desertification monitoring.
0 2020-06-08
River and lake resources are important components for studying the Earth ecological environment, affecting global ecosystems, heat, material exchange and balance and serving as an important basis for studying changes in the global environmental mechanism. At present, the lack of global lake vector data with large-scale, high-precision, and large-range has hindered hydrological research on rivers and lakes. Taking the data collection of global rivers and lakes of Jun Chen as the source data and combining the domestic high-resolution image GF data of 2 to 3 years before and after 2010, a data set of global rivers and lakes was generated. This data set makes up for the shortcomings of low precision in some areas and is an editable lake and river vector data set with high accuracy.
0 2020-12-22
The dataset of LST (land surface temperature) observed by the thermal camera (ThermaCAM SC2000 and ThermaCAM S60) at 24°×18° was obtained in the Yingke oasis, Huazhaizi desert steppe and Linze grassland foci experimental areas on May 20, 24,28 and 30, Jun. 1, 4, 16 and 29, Jul. 7, 8 and 11, 2008. Meanwhile, the optical photos were acquired in Yingke oasis maize field, Huazhaizi desert No. 1 and 2 plots, Huazhaizi desert maize field and Linze grassland. The dataset of ground truth measurement was synchronizing with WiDAS (Wide-angle Infrared Dual-mode line/area Array Scanner), OMIS-II, Landsat TM and ASTER.
0 2019-09-13
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).
0 2020-10-09
The 2008 national remote sensing annual average surface temperature and freezing index is a 5 km instantaneous surface temperature data product based on MODIS Aqua/Terra four times a day by Ran Youhua et al. (2015). A new method for estimating the annual average surface temperature and freezing index has been developed. The method uses the average daily mean surface temperature observed by LST in morning and afternoon to obtain the daily mean surface temperature. The core of the method is how to recover the missing data of LST products. The method has two characteristics: (1) Spatial interpolation is carried out on the daily surface temperature variation observed by remote sensing, and the spatial continuous daily surface temperature variation obtained by interpolation is utilized, so that satellite observation data which is only once a day is applied; (2) A new time series filtering method for missing data is used, that is, the penalty least squares regression method based on discrete cosine transform. Verification shows that the accuracy of annual mean surface temperature and freezing index is only related to the accuracy of original MODIS LST, i.e. the accuracy of MODIS LST products is maintained. It can be used for frozen soil mapping and related resources and environment applications.
0 2020-06-03
This data set contains the element content data of a deep drilled formation near the open sea in the middle reaches of Heihe River. The borehole is located at 99.432 E and 39.463 n with a depth of 550m. The element scanning analysis was carried out at 1-3cm intervals for the drilled strata. The scanning was completed in the Key Laboratory of Western Ministry of environmental education, Lanzhou University, and 38705 effective element data were obtained.
0 2020-07-30
The dataset of ground truth measurement synchronizing with MODIS was obtained in the Linze grassland foci experimental area on Jun. 22, 2008. Simultaneous east-west ground measurements on the canopy temperature, the half-height temperature and the land surface radiative temperature were carried out by the hand-held infrared thermometer at intervals of 125m in 8 quadrates (2km×2km), No.1 quadrate (H01-H08) on Jun. 22, No.2 quadrate (H09-H16) on Jun. 23,No.3 quadrate (H17-H24) on Jun. 22, No.4 quadrat (H25-H32) on Jun. 23, No.5 quadrate (H33-H40) on Jun. 22, No.6 quadrate (H41-H48) on Jun. 23, No,7 quadrate (H49-H56) and No.8 quadrate (H57-H64) on Jun. 23. Data were archived in Excel format. See WATER: Dataset of setting of the sampling plots and stripes in the foci experimental area of Linze station for more information.
0 2019-09-11
1、 Data overview: use solinst leveloger automatic water level gauge to observe river water level, calculate flow data through water level flow curve, and manually observe the flow through self-made flow weir (see thumbnail). Due to the limited amount of manual observation data, further supplementary observation is needed to improve the water level discharge curve. 2、 Data content: we manually observe the water level and flow data of the two sections. The first section: the exit of area III divided by Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, the boundary point between cold desert zone and cold meadow zone, where the valley is deep and the bedrock is exposed. Coordinates of observation points (99 ° 53 ′ 37 ″ e, 38 ° 13 ′ 34 ″ n). The observation period is from July 21, 2012 to May 6, 2013. The observation frequency of automatic observation data is 1 time / 30 minutes from July 21 to July 25, 2012. 1 time / 15 minutes from July 25, 2012 to May 6, 2013. After September 15, 2012, there was an error in the automatic monitoring data of the observation point. The reason may be that the flow of the river channel became smaller, the probe was exposed to the air, and the water level gauge could not correctly reflect the change of the flow of the river channel. At the same time, the temperature decreased after September, and the river channel froze in winter. There was no automatic monitoring flow data during this period. The second section: the exit of No.2 area divided by Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, with flat terrain, is located at the catchment of the outlet of the alluvial delta Valley, and the south side is the shrub area. A small flow weir is built. The observation point coordinates (99 ° 52 ′ 58 ″ e, 38 ° 14 ′ 36 ″ n), and the observation frequency of automatic observation data is 1 time / 15 minutes. The observation period is from July 21, 2012 to May 6, 2013. After the observation point entered September, the river flow gradually decreased and there was no water in the river. At this time, the reading of water level gauge can not correctly reflect the change of river discharge. At the same time, our field experience shows that from September to May of the next year, the observation point is basically in a state of no water.
0 2020-03-12
The data set includes soil organic carbon concentrations data of representative soil samples collected from July 2012 to August 2013 in the Heihe River Basin. The first soil survey was conducted in 2012. After the representativeness evaluation of collected samples, we conducted an additional sampling in 2013. These samples are representative enough to represent the soil variation in the Heihe River Basin, of which the soil variation in each landscape could be accounted for. The sampling depths in field refer to the sampling specification of Chinese Soil Taxonomy, in which soil samples were taken from genetic soil horizons.
0 2020-05-25
This data set is collected according to the output results of tesim ecological process model, including biomass, plant N and P content, evapotranspiration, NPP and other model output results. Some of the results are obtained by field measurement, some by laboratory analysis of field samples, some by literature.
0 2020-03-02
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