Data Paper Zone II Versions EN2 Vol 4 (2) 2019
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A Dataset of Permafrost Distribution along the China-Pakistan Economic Corridor in 2017
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Abstract & Keywords
Abstract: Dataset nameA Dataset of Permafrost Distribution along the China-Pakistan Economic Corridor in 2017Data authorsAi Minghao, Zhang Yaonan, Kang Jianfang, Feng Keting, Tian DeyuAuthor contact:Zhang Yaonan (yaonan@lzb.ac.cn)Data time range2017Geographic scope23°47′ N–40°55′ N, 60°20′ E–80°16′ ESpatial resolution1 kmData volume8.42 MBData formatTIF, SHPData service system URLhttp://www.crensed.ac.cn/portal/metadata/95e66f86-f868-429f-b62e-a526df42c566Fund projectNational Science & Technology Infrastructure Center Platform (Y719H71006), Chinese Academy of Sciences Informationization Special (XXH13506).Dataset components1. The Raster Graphic of Permafrost Distribution along the China-Pakistan Economic Corridor; 2. The Vector Graphic of Permafrost Distribution along the China-Pakistan Economic Corridor.
Keywords: China-Pakistan Economic Corridor; permafrost distribution; TTOP Model; MODIS land surface temperature
Dataset Profile
Dataset nameA Dataset of Permafrost Distribution along the China-Pakistan Economic Corridor in 2017
Data authorsAi Minghao, Zhang Yaonan, Kang Jianfang, Feng Keting, Tian Deyu
Author contact:Zhang Yaonan (yaonan@lzb.ac.cn)
Data time range2017
Geographic scope23°47′ N–40°55′ N, 60°20′ E–80°16′ E
Spatial resolution1 km
Data volume8.42 MB
Data formatTIF, SHP
Data service system URLhttp://www.crensed.ac.cn/portal/metadata/95e66f86-f868-429f-b62e-a526df42c566
Fund projectNational Science & Technology Infrastructure Center Platform (Y719H71006), Chinese Academy of Sciences Informationization Special (XXH13506).
Dataset components1. The Raster Graphic of Permafrost Distribution along the China-Pakistan Economic Corridor; 2. The Vector Graphic of Permafrost Distribution along the China-Pakistan Economic Corridor.
1.   Introduction
As the most widely distributed factor in the northern hemisphere, permafrost accounts for 56% of the land area, of which permafrost accounts for about 24%[1]. Permafrost is characteristics of poor thermal stability, strong hydrothermal activity, thick underground ice and the large proportion of ice-rich frozen soil. Under the background of global warming, permafrost is extremely sensitive to thermal disturbance of engineering activities. In the construction of permafrost regions, the activities during the construction period will inevitably change the local factors, such as surface cover, topographic factors and shallow surface energy balance. In addition, after the opening operation, the dynamic load changes hydrothermal environment of the freezing and thawing cycle, which directly leads to the increase of the frozen soil temperature and the upper limit. This will further increase the risk of frozen soil hazards, such as frost heaving, thaw settlement and lifting by frost, which could pose a serious threat to the construction and safe operation of permafrost regions and have a huge impact on the regional hydrogeological conditions and the evolution of ecological environment.[2][3][4]
As an important part of the Belt and Road Initiative, the China-Pakistan Economic Corridor is the main transportation route between China and Central Asia, West Asia and South Asia. It is of great significance to the economic and cultural exchanges between China and Pakistan and the neighboring countries. The Corridor passes through the Karakorum Mountains. In the alpine regions above 4300–4750 meters above the sea level, the frozen features are widely distributed such as frozen turf grass hills, hot melt ponds, stone rings and stone chains. The proper evaluation of the permafrost distribution in the China-Pakistan Economic Corridor is the basis for planning and solving the "four-in-one" practical engineering problems of the highway, railway, communication and oil pipelines along the China-Pakistan Economic Corridor. It is poses great significance for water resources utilization and ecological environmental protection in the Corridor. Especially, it is also of great importance to the border defense construction.
The traditional permafrost survey is usually carried out by field engineering surveys, but the areas along the Karakoram Highway are vast and there are few measured data. The existing permafrost surveys are mainly along the Karakoram Highway, and there is almost nothing about permafrost data. Due to the close cause and effect between permafrost and climate system, especially temperature conditions, researchers from China and other countries have developed a series of frozen soil distribution models, the basis of the interaction characteristics between permafrost and temperature. Based on analyzing the climate-frozen relationship, and in consideration of local topography and soil conditions, Smith et al. proposed a semi-empirical and semi-physical temperature at the top of permafrost (TTOP) mode[5]. The TTOP model has been applied to Canadian national scale and regional scales, such as the permafrost distribution simulation mapping of the Machensize River Basin, the Brado Plateau and the Angawa Bay, and has achieved good results[6][7][8] .
Thanks to the rapid development of remote sensing technology, with the help of this, researchers can obtain spatial information related to the distribution of permafrost over a large area and multi-temporal time. Combined with the existing spatial distribution model of permafrost, the macro, dynamic and rapid simulation of permafrost distribution could be realized on a vast scale. The study focuses on the areas of the China–Pakistan Economic Corridor, including Kashgar in Xinjiang, Kizilsu Kirghiz Autonomous Prefecture and the whole Pakistan (Fig. 1). The study uses the 2013–2017 MODIS surface temperature product as a data source in place of the measured data and inputs it into the TTOP model to prepare a permafrost distribution dataset for the China-Pakistan Economic Corridor.
2.   Data collection and processing methods
2.   1 Data source and preprocessing
The preparation date of permafrost distribution in the China-Pakistan Economic Corridor are based on the following data: the MODIS surface temperature data from 2013 to 2017, glacial cataloging data for the Pamirs of China in 2009, glacier cataloging for Pakistan in 2003 – 2004 and Harmonized World Soil Database version 1.2 for 2008 (HWSD v1.2), as shown in Table 1.
Table 1   Data list for research
Serial numberNameDateSourceType
1MOD11A2 LST2013–2017LPDAACRaster
2Glacial cataloging data for the Pamirs of China in 20092009WestDCVector
3Glacier cataloging for Pakistan2003-2004WestDCVector
4Harmonized World Soil Database (v1.2)2008FAORaster
As the main parameter in the surface energy balance and the main factor of the climate system, the surface temperature can characterize the degree of energy and water exchange between the ground and air. It is one of the key factors affecting the development, distribution and evolution of permafrost as well as the upper boundary condition for modeling permafrost. Both MOD11A1 and MYD11A1 use the split-window algorithm to invert the surface temperature from the MOIDS data, which are the daily L3 grade product with the spatial resolution of 1 km, including day and night data. They are derived from Land Process Distributed Data Archives Center (LPDAAC) of the National Aeronautics and Space Administration (NASA). The data are stored in HDF-EOS format with a sinusoidal projected coordinate system. 6 scene data are required to cover the entire CMB corridor.


Fig. 1   Schematic diagram of the geographical location of the study area
This study accessed 2013-2017 MOD11A1 day surface temperature data, MOD11A1 night surface temperature data, MYD11A1 day surface temperature data and MYD11A1 night surface temperature data, containing a total of 21,900 scenes. It adopted the MRT tool to mosaic and resample the data, convert all data coordinate systems to the WGS 84 coordinate system, and then use the tool vector boundary of the China-Pakistan Economic Corridor and the warp tool in the open source raster processing library GDAL for cropping.
Both the glacial cataloging data for the Pamirs of China and the glacier cataloging for Pakistan were accessed from Landsat TM/ETM+ data that are corrected by automatic extraction and expert intervention. The data originated from the Cold and Arid Region Scientific Data Center (WestDC)[9][10] . The two data vectors are combined and then cropped according to the scope of the study area.
The TTOP model requires the freeze-thaw thermal conductivity of different types of soil as its input parameters. The soil data are derived from the HWSDv1.2 constructed by the Vienna International Institute for Applied Systems Analysis (IIASA) and the Food and Agriculture Organization of the United Nations (FAO) in 2008.2. HWSDv1.2 includes the global distribution of different types of soils, with the soil classification standard of FAO 90, the data resolution of 1 km, and the data projection of WGS 84.
It cropped the glacial catalogue data and soil type data using the vector boundary of the China–Pakistan Economic Corridor to form glacial distribution data and soil type distribution data in the study area.
2.   2 Data processing steps
2.2.1   TTOP model
The essence of the TTOP model is to estimate the thermal condition of permafrost, by using the roof temperature of the active layer. The model assumes that the soil matrix is homogeneous, and on the annual scale, the soil-atmosphere thermal exchange interface is in thermal equilibrium. The TTPO model is defined as follows:
\(TTOP=\mathrm{ }\frac{\frac{{K}_{\mathrm{t}}}{{K}_{\mathrm{f}}}({n}_{\mathrm{t}}\bullet DDT-{n}_{\mathrm{f}}\bullet DDF)}{P}\) (1)
In the formula, TTOP is the index for distinguishing permafrost; TTOP<0 means that there is permafrost, TTOP>0 means there is seasonal frozen soil or no frozen soil; P is the cycle as 365 days; \({\mathrm{K}}_{\mathrm{t}}\) and \({\mathrm{K}}_{\mathrm{f}}\) are the melting thermal conductivity and the freezing thermal conductivity respectively; DDT is the melting index, which is the sum of the duration when the temperature is continuously above 0 °C in a year and its numerical product, expressed as \(\mathrm{℃}\bullet \mathrm{d}\mathrm{a}\mathrm{y}\); DDF is the freezing index, which is sum of the duration when the temperature is below 0 °C in a year and its numerical product, expressed as \(\mathrm{℃}\bullet \mathrm{d}\mathrm{a}\mathrm{y}\).
\({\mathrm{n}}_{\mathrm{t}}\) and \({\mathrm{n}}_{\mathrm{f}}\) are melting and freezing factors, respectively. The results of the melting and freezing factor correction model are introduced due to the influence of surface vegetation and snow cover on the earth-atmosphere thermal exchange. The permafrost development area of the China-Pakistan Economic Corridor is in a cold highland area, which is also an extremely arid area with sparse vegetation. This study ignores the effect of snow cover and vegetation, and assigns both \({\mathrm{n}}_{\mathrm{t}}\) and \({\mathrm{n}}_{\mathrm{f}}\) to 1.
2.2.2   Surface temperature interpolation
Each scene of MODIS LST data has many vacancy values in the study area, and the vacancy values need to be interpolated in time series. The surface temperature usually shows obvious periodicity. Li Shuxun et al. used the cosine function to simulate the surface temperature of the Qinghai-Tibet Plateau time series[11], and established the relationship between surface temperature and time. The expression is as follows:
\({T}_{s}\left(t\right)=\stackrel{-}{T}+Acos(\omega t+\phi )\) (2)
Wherein, \({T}_{s}\) is the surface temperature; \(t\) is the time; \(\stackrel{-}{T}\) is the average temperature in the time series. A is the temperature amplitude in the time series. In \(\omega =2\mathrm{\pi }/T\), T is the time series period, and \(\phi \) is the phase angle.
Even though the simulation method above can reflect the surface temperature cycle change, the parameter calculation is not accurate enough. Taking the average temperature and temperature amplitude as the cosine function coefficient would result in a large deviation overall after the function fitting. In this study, \(\mathrm{T}\), A and φ are used as the undetermined coefficients. The corresponding pixels of each scene datum of 365 scene data are fitted by least squares method, and the vacancy value of surface temperature data is interpolated by the fitting function.
2.2.3   Thermal Conductivity
The thermal conductivity of soil melting and freezing is a parameter related to soil properties in the TTPO model, and its value depends on factors such as soil particle gradation, dry bulk density, mineral composition and water content. \(\mathrm{T}\mathrm{T}\mathrm{O}\mathrm{P}\) Due to the lack of detailed parameters of the soil properties of the China-Pakistan Economic Corridor, it is difficult to calculate the thermal conductivity of the soil on each grid. Based on the Quaternary map of the Qinghai-Tibet Plateau, Wang Zhixia et al. classified the soil according to geological genesis, and then calculated the thermal conductivity of the frozen-thawed state for each type using the Johansen method when they studied the permafrost distribution of the Plateau[12][13][14] . Since this method is based on the nature of the soil itself and the regional differences are relatively small, this study suggests that it can be used in the China-Pakistan Economic Corridor. The HWSD data contain soil classification information, and the soil classification names on the grid can be accessed. But the FAO soil classification system is based on the soil diagnosis layer and diagnostic characteristics. After comparison and screening, the freeze-thaw thermal conductivity based on HWSD data is obtained.
Table 2   Table of freeze-thaw thermal conductivity of different types of soil
Serial numberSoil typesFAO classification abbreviation\({K}_{\mathrm{t} }(W/m\bullet ℃)\)\({K}_{\mathrm{f}} (W/m\bullet ℃)\)
1LeptosolLP1. 342. 32
2FluvisolFL1. 552. 17
3HistoslHS0. 521. 70
4Glacial moraineGG1. 652. 78
5OthersCL, RK etc.1. 502. 20
2.2.4   Glacier effect
This study ruled out the permafrost under the glacier. Some glaciers would be mistakenly classified as permafrost if the permafrost distribution is calculated by surface temperature and TTOP model. Hence, the permafrost under the glacier should be removed according to the glacial distribution data in the study area.
2.2.5   The Simulation Process of Permafrost Distribution
Firstly, the day surface temperature data LST_Day_1km of MOD11A1 and MYD11A1 in 2013–2017 are arithmetically averaged, and the night surface temperature data LST_Night_1km is arithmetically averaged; then the average daily surface temperature of the day is fused with the average value of the night surface temperature. In this way, we can get the average daily temperature. The way to fuse is: we can get the average temperature value of the day by means of arithmetic average, if both the day mean value and the night mean value exist. If there is a value vacancy, the average temperature value of the day will be vacant; then the time series of the surface temperature data will be interpolated, and the 5 data of the corresponding date in 5a will be averaged; and then DDT and DDF can be calculated. Then we can make the soil melting thermal conductivity map and the soil freezing thermal conductivity map of the study area. Finally, input the data above into the TTOP model, and remove the glacial coverage area. We can get the permafrost distribution map of the China-Pakistan Economic Corridor by binary classification. \(\mathrm{D}\mathrm{D}\mathrm{T}\mathrm{D}\mathrm{D}\mathrm{F},\mathrm{T}\mathrm{T}\mathrm{O}\mathrm{P}\) The data processing flow is shown in Fig. 2:


Figure 2   Flowchart of frozen soil distribution data processing
3.   Data Sample Description
The dataset is divided into two folders for storage: permafrost distribution raster data and permafrost distribution vector data. The former folder includes the permafrost distribution raster map CPEC_Permafrost Distribution with 1 km spatial resolution and saved in tif format. The latter folder is the permafrost distribution vector image CPEC_Permafrost Distribution saved in SHP format. All data geographic coordinate systems are WGS 84. The data results are shown in Fig. 3.
4.   Data Quality Control and Evaluation
4.   1 Data Quality Control and Evaluation
The MODIS surface temperature data fitted by the cosine function and the least squares method can be evaluated by a determination coefficient. The determination coefficient represents a numerical feature of a random variable and a plurality of random variables, and is used to reflect the approximation degree of the model to the sample data, which is often represented by R2. \(\left(0,\mathrm{ }1\right)\)The value range of R2 is (0, 1), and the closer to 1, the higher the goodness of the fit. The expression is as follows:
\({R}^{2}=\frac{\sum _{i=1}^{n}{(\stackrel{^}{{y}_{i}}-\stackrel{-}{y})}^{2}}{\sum _{i=1}^{n}{({y}_{i}-\stackrel{-}{y})}^{2}}\) (3)
Wherein, \(\stackrel{-}{\mathrm{y}}\) is the sample data mean, \(\stackrel{^}{{\mathrm{y}}_{\mathrm{i}}}\) is the model prediction value, and \({\mathrm{y}}_{\mathrm{i}}\) is the sample value.


Fig. 3   The Permafrost Distribution Map of China-Pakistan Economic Corridor
There are a total of 1,238,512 pixels in the boundary area of the China-Pakistan Economic Corridor. The daily average surface temperature of 2013-2017 is time-sequence fitted. The maximum determination coefficient R2 is 0.95185 and the minimum value is 0.0014. The spatial distribution and frequency distribution histogram is shown in Fig. 4. It can be seen from the figure that the overall goodness of fit in most areas of the China-Pamirs Corridor is higher (R2 ≥ 0.6), and the area with poor fitting (R2 < 0.4) is mainly found in the glaciers of the Pamirs and the coast of Pakistan. However, this study leaves out the distribution of permafrost under glacial cover. And the coastal climate and altitude of Pakistan provide no condition for the formation of permafrost. Hence, the above-mentioned poorly fitted area has little effect on the simulated distribution of permafrost. Therefore, the way of time series fitting is practicable in the simulation study of the permafrost distribution of the China-Pakistan Economic Corridor.




Fig. 4   The spatial distribution and frequency distribution histogram
4. 2   The Simulation Result Evaluation of Permafrost Distribution
Due to the lack of field survey data on the permafrost distribution, the permafrost data along the Karakoram Highway are used as a verification. Zhang Xuejin et al. proposed that the permafrost of China and Pakistan was distributed in the Khunjerab section from K807+000 to K811+343.165 above 4,500 meters above sea level, with a continuous distribution length of 4.3 km[15]. Zhu Yingyan et al. mentioned that the permafrost of the China-Pakistan Economic Corridor was distributed in the section near Khunjerab, about 4,300 meters above sea level[16]. The locations of permafrost mentioned in the two documents are close and can be mutually verified. In this study, we get the permafrost distribution map by means of remote sensing data and model simulation. The results obtained along the Karakoram Highway are shown in Fig.5, which are consistent with the above two documents.


Fig. 5   The Distribution Map of Khunjerab Permafrost
5.   Data Value
The traditional permafrost mapping is mainly based on field measured data, such as borehole temperature data and conventional meteorological data. In spite of a high accuracy of the results, it must be based on a sufficiently long time series and sufficient reliable observation data. The permafrost distribution area of the China-Pakistan Economic Corridor is characteristic of harsh natural environment and complex geopolitical environment. There are many restrictions on the investigation and in situ observation of permafrost in this area.
\(\mathrm{T}\mathrm{T}\mathrm{O}\mathrm{P}\) Based on the surface temperature data products of the remote sensing data MODIS, this paper simulates the permafrost distribution in the China-Pakistan Corridor by means of the TTOP model. In terms of the mismatch between the data and the model time scale, the study uses a fitting method to reconstruct the data in time series. It adopts the World Soil Database to estimate the freezing and thawing thermal conductivity of different types of soils as the TTOP model parameters, finally obtained the permafrost distribution map of the corridor, and uses the coefficient of determination to evaluate the goodness of fit in the data reconstruction process.\(\mathrm{T}\mathrm{T}\mathrm{O}\mathrm{P}\) Field survey is the best way to research the distribution of permafrost. This paper collects the description of the distribution of permafrost in the literature. Compared with the simulation results, the simulation results of permafrost distribution based on MODIS surface temperature data and TTOP model are highly consistent with the field survey results in the literature.
The permafrost distribution data of the China-Pakistan Economic Corridor is an important basic data for site selection, design and planning in the Corridor. It can also be used as the basic data for permafrost changes in the corridor under the background of global climate change. Based on the orthophoto data and combined with the comprehensive analysis of climate, hydrology and ecology, it is of great significance for the long-term safe operation of the project and the sustainable development of the ecological environment.
6.   Data Usage and Recommendations
The dataset of permafrost distribution along the China-Pakistan Economic Corridor in 2017 are saved as a raster TIF format and vector SHP format. Commonly-used GIS and remote sensing software such as ArcGIS, QGIS, ENVI and ERDAS can support the reading and manipulation of this data.
Acknowledgments
Thanks to LPDAAC for providing the MODIS data; thanks to the FAO for providing the World Soil Database (v1.2); thanks to the Cold and Arid Region Scientific Data Center (WestDC) for providing the second glacial cataloging data for the Pamirs of China and the glacier cataloging for Pakistan; thanks to the Supercomputing Center Lanzhou Branch of Chinese Academy of Sciences for providing computing and storage resources.
[1] ZHANG T, BARRY R G, KONWLES K, et al. Statistics and characteristics of permafrost and ground-ice distribution in the Northern Hemisphere [J]. Polar Geogr, 1999, 23: 132-154.
[2] SHENG Y, ZHANG J M, LIU Y Z, et al. Thermal regime in the embankment of Qinghai-Tibet Highway in permafrost regions [J]. Cold Regions Sci Technol, 2002, 35: 25-44.
[3] WU Q B, CHENG G D, MA W, et al. Technical approaches on permafrost thermal stability for Qinghai-Tibet Railway [J]. Geomech Geoeng: Int J, 2006, 1: 119-127.
[4] MA W, MU Y, LI G, et al. Responses of embankment thermal regime to engineering activities and climate change along the Qinghai-Tibet Railway(in Chinese) [J]. Scientia Sinica Terrae, 2013, 43: 478-489.
[5] SMITH M W, RISEBOROUGH D W. Permafrost monitoring and detection of climate change [J]. Permafrost and Periglacial Process, 1996, 7: 301-309.
[6] HENRY K, AND M W. Smith 2001. A Model-Based Map of Ground Temperatures for the Permafrost Regions of Canada [J]. Permafrost and Periglacial Processes, 2001, 12: 389-398.
[7] WRIGHT JF, DUCHESNE C, COTE M M. Regional-scale permafrost mapping using the TTOP ground temperature model[C]. In Proceedings of the Eighth International Conference on permafrost. Zurich, Switzerland, Lisse, 2003.
[8] WAY R G, LEWKOWICZ A G. Modelling the spatial distribution of permafrost in Labrador–Ungava using the temperature at the top of permafrost [J/OL]. Canadian Journal of Earth Sciences, 2016, 53(10): 1010-1028. https://doi.org/10.1139/cjes-2016-0034.
[9] Liu Shiyin, Guo Wangqin, the Second Glacial Gataloging Data for the Pamirs of China (V1. 0)[J/OL]. Cold and Arid Region Scientific Data Center (WestDC), 2014. DOI: 10. 3972/glacier. 004. 2013. db.
[10] MOOL P K, BAJRACHARYA S R, SHRESTHA B, et al. Inventory of Glaciers and Glacial Lakes and the Identification of Potential Glacial Lake Outburst Floods (GLOFs) Affected by Global Warming in the Mountains of Himalayan Region(Indus Basin Pakistan Himalaya) [M]. Kathmandu, Nepal: ICIMOD, 2005.
[11] Li Shuxun, Cheng Guodong. Water and Heat Transport in Freezing and Thawing Soils, Lanzhou, Lanzhou University Press
[12] Wang Zhixia, Applications of Permafrost Distribution Models on the Qinghai-Tibet Plateau [D]. Lanzhou, Lanzhou Universuty, 2010.
[13] JOHANSEN O. Thermal Conductivity of Soil [D]. Trondheim: University of Trondheim, Norway, 1975.
[14] LI X, CHENG G D, WU Q B et al. Modeling Chinese cryospheric change by using GIS technology[J]. Cold Regions Science and Technology, 2003, 36(1-3): 1-9.
[15] Zhang Xuejin, Study on the Distribution Characteristics and Control for Geological Disasters along International Karakoram Highway (KKH), [J]. Journal of Chongqing Technology and Business University (Natural Science Edition). 2013, 30(2): 45-50.
[16] Zhu Yingyan, Yang Zhiquan, Liao Liping. Glacialized Geomorphological Geohazard along China-Pakistan International Karakoram Highway [J]. Journal of Catastrophology, 2014, 2014(3): 81-90.
Data citation
1. Ai M, Zhang Y, Kang J, et al. A Dataset of Permafrost Distribution along the China-Pakistan Economic Corridor in 2016 [DB/OL]. National Special Environment and Function of Observation and Research Stations Shared Service Platform, 2018. (2018-06-13). DOI: 10.12072/casnw.049.2018.db.
Article and author information
How to cite this article
Ai M, Zhang Y, Kang J, et al. A Dataset of Permafrost Distribution along the China-Pakistan Economic Corridor in 2016 [J/OL]. China Scientific Data, 2019, 4(3). (August 26 2019). DOI: 10.11922/csdata.2018.0053.zh.
Ai Minghao
Contribution: the reconstruction of time series surface temperature, model flow design and implementation.
(1986- ), male, from Jining City, Shandong Province, master, engineer, majoring in big data application in cold and arid regions.
Zhang Yaonan
Contribution: Data processing flow design.
(1966- ), male, from Tianshui City, Gansu Province, Doctor, researcher, majoring in geoscience big data.
Kang Jianfang
Contribution: data collection and analysis.
(1981- ), female, from Qin’an County, Gansu Province, master, engineer, majoring in the preparation methods of remote sensing data.
Feng Keting
Contribution: Data pre-processing scheme design.
(1980- ), male, Zhongwei City, Ningxia Hui Autonomous Autonomy, Doctor, engineer, majoring in remote sensing drought.
Tian Deyu
Contribution: Data batch processing.
(1993- ), male, from Siziwang Banner of Inner Mongolia Autonomous Region, postgraduate student, majoring in remote sensing application in cold and arid areas.
National Science & Technology Infrastructure Center Platform (Y719H71006), Chinese Academy of Sciences Informationization Special (XXH13506).
Publication records
Published: Sept. 4, 2019 ( VersionsEN2
Updated: Sept. 4, 2019 ( VersionsEN3
Released: Oct. 15, 2018 ( VersionsZH2
Published: Sept. 4, 2019 ( VersionsZH3
References
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