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Abstract: The Tibetan Plateau is known as the ridge of the world , the water tower of Asia, and the third pole of the earth. The Pan-Third Pole region, which extends from the third pole westwards to cover an area of about 20 million km2, constitutes the core area of the “Belt and Road”. The ongoing second comprehensive scientific investigation of the Tibetan Plateau intends to reveal the mechanism of environmental changes and optimize the ecological security barrier system in the Tibetan Plateau. It plays an important role in promoting the sustainable development of the Tibetan Plateau, the construction of national ecological civilization and the protection of global ecological environment. The launch of the Tiangong-2 wide-band imager in September 2016 has enabled a continuous observation of the Tibetan Plateau, which generated a large number of multi-temporal and multi-spectral remote sensing data. On this basis, we built the thematic datasets for the Tibetan Plateau based on a series of data processing and quality control measures, including radiation correction, geometric correction, and quantitative inversion. The datasets were then distributed and shared through the space application data promoting service platform for China Manned Space Engineering (http://www.msadc.cn). The datasets include high-quality image data, surface reflectance products, surface temperature products, surface brightness temperature products, normalized vegetation index products, normalized snow index products and normalized water index products for the Tibetan Plateau acquired by the Tiangong-2 Wide-band Imaging Spectrometer since its launch. It provides rich data for studies of land resources, lake hydrology, ecological environment and biodiversity, climate change, and glacial changes of this region.
Keywords: Tiangong-2; Tibetan Plateau; Wide-band Imaging Spectrometer; multi-spectral data; thematic dataset
|Title||Thematic datasets for the Tibetan Plateau from Tiangong-2 Wide-band Imaging Spectrometer|
|Data authors||Li Shengyang, Zhang Wanfeng, Liu Zhiwen, Qin Bangyong, Yu Haijun, Wang Bo, Shao Yuyang, Liu Kang, Li Leijuan, Hei Baoqin, Li Xuan, Ren Haigen|
|Data corresponding author||Qin Bangyong (email@example.com)|
|Time range||From September 2016 to November 2018|
|Geographical scope||Geographical position of the Tibetan Plateau: 26°00′N–39°47′ N ,73°19′E–104°47′ E. It mainly consists of mountains, lakes and rivers, including Kunlun Mountains, Tanggula Mountains, Himalayas Mountains, Qinghai Lake, Selin Lake, Nam Co Lake, Yangtze River, Yellow River, Lancang River and Nu River.|
|Spatial resolution||(1) Visible-near infrared spectrum (100 m); (2) Short-wave infrared spectrum (200 m); (3) Thermal infrared spectrum (400 m).|
|Data volume||2.0 TB|
|Data format||The image data is Geotiff format (*.tif), the image thumbnail is png format, the thumb map is jpg format, and the auxiliary file is xml format.|
|Data service system||http://www.msadc.cn/t2/t2web/data/data_list.jsp|
|Sources of funding||National Science and Technology Infrastructure Program of China, "Fundamental Science Data Sharing Platform" (DKA2018-12-02-23)|
|Dataset composition||The datasets consist of four parts of data: (1) Visible-near infrared spectrum data; (2) Short-wave infrared spectrum data; (3) Thermal infrared spectrum data; (4) Thematic products: surface reflectance products, surface temperature products, surface brightness temperature products, normalized vegetation index products, normalized snow index products and normalized water index products.|
The Tibetan Plateau is known as the ridge of the world, the water tower of Asia, and the third pole of the earth. It lies between 26°00′–39°47′ north latitude and 73°19′–104°47′ east longitude. It is about 2800 km long from east to west and 300–1500 km wide from north to south. It has a total area of about 2.5 million square kilometers. The Tibetan Plateau is the largest plateau in China and the highest altitude plateau in the world. On Tibetan Plateau, the mountains and rivers are densely covered, and the terrain is steep and changeable. Its average elevation is over 4000 m. The average annual temperature in the hinterland of the plateau is below 0 °C, and the average temperature of the warmest month is less than 10 °C. It contains 10 climate zones which spans sub-tropical humid climates to the cold and arid plateau climate from the south to north, and is the opener and regulator of climate change in the northern hemisphere. Its unique geological structure, topography and plateau climate have created a unique natural resource and ecological environment system, which has become an important strategic resource reserve base and ecological security barrier of China .
The Wide-band Imaging Spectrometer of the Tiangong-2 Space Laboratory (hereinafter referred to as the Wide-band Imaging Spectrometer) is a wide-width (about 300 km)optical remote sensor, which first realized multi-spectral and large field of scanning imaging on a single instrument in China, including 3 spectral segments: visible-near infrared spectrum(0.4 μm–1.0 μm, 14 channels), short-wave infrared spectrum (1.0 μm–1.7 μm, 2 channels) and thermal infrared spectrum (8.0 μm–10.0 μm, 2 channels). The spatial resolution of the three spectral segments is 100 m, 200 m and 400 m respectively . The Wide-band Imaging Spectrometer has obtained a large number of data and generated corresponding thematic products of the Tibetan Plateau since launch, including surface reflectance products, surface temperature products, surface brightness temperature products, normalized vegetation index products, normalized snow index products, and normalized water index products, the total amount of these data products exceeds 2.0 TB. The coverage is shown in Figure 1.
The thematic dataset of the Tibetan Plateau contains data products and thematic products, of which data products have visible-near infrared spectrum data products, short-wave infrared spectrum data products and thermal infrared spectrum data products, thematic products have surface reflectance products, surface temperature products, normalized vegetation index products and normalized snow index products. This dataset can be used as reference data for scientific research and comprehensive investigation of the Tibetan Plateau. It can be applied to the fields of climate change, biodiversity, land cover types and lake hydrological monitoring changes.
2.1 Data products of the Wide-band Imaging Spectrometer
With a push-broom imaging mode, the Wide-band Imaging Spectrometer has three fields of view along the visible-near infrared spectrum range, two fields of view along the short-wave infrared spectrum range, and two fields of view along the thermal infrared spectrum range. Its data products processing flow are as follow: firstly, the downlink data undergo format conversion to produce the raw data of each spectrum band. Then, the raw data of multiple fields of view are combined for uniformity correction to produce a wide-swath image. After that, the wide swath image is divided into a plurality of standard-size images. Finally, the standard-size images undergo relative and absolute radiometric calibration and geometric correction respectively to produce the radiance image products with geographical coordinates . The processing flow is shown in Figure 2.
2.2 The thematic products
The thematic products of the Wide-band Imaging Spectrometer including surface reflectance products, surface temperature products, surface brightness temperature products, normalized vegetation index products, normalized snow index products, and normalized water index products. On the basis of the standard data product generated, the surface reflectance products and surface brightness temperature products are obtained by atmospheric correction with the radiation transfer model or the dark pixel method, and the surface temperature products, normalized vegetation index products, normalized snow index products and normalized water index products are further generated .The vegetation index formula is:
Here, is the normalized vegetation index, is the reflectivity obtained by near infrared channel, and is the reflectivity of the red channel, and are the channal 5 and channal 9 of visible-near infrared spectrum of the Wide-band Imaging Spectrometer respectively.
The normalized snow index is constructed by taking advantage of the unique variation characteristics of snow cover's high reflectivity in green light band and low reflectivity in short-wave infrared band which can effectively identify snow cover.The formula is:
Here, is the normalized snow index, is the reflectivity of the green light channel, and is the reflectivity of the short-wave infrared channel, and are the channal 11 of visible-near infrared spectrum and the channal 2 of short-wave infrared spectrum of the Wide-band Imaging Spectrometer respectively. According to experience, the area of can be defined as snow covered area generally.
The normalized water index is based on the normalized ratio index of the reflectance of the green band and the near infrared band . The formula is:
Here, is the normalized water index, and are the channal 11 and channal 5 of visible-near infrared spectrum of the Wide-band Imaging Spectrometer respectively.
The data products of Wide-band Imaging Spectrometer contains ground spatial information and spectral characteristics. It adopts the standard data formats in earth observation filed, and contains complete metadata information to facilitate data reading and using.
The file name is marked with “aircraft_load name_product identification_data type_the start time of data collection_the end time of data collection_product level_segment number_scene number_product generation time_version number.file suffix”.
The detailed descriptions of each fieldare shown in Table 1.
|The field name||Meaning||Abbreviation|
|Aircraft||Tiangong-2 Space Laboratory||T2|
|Load name||Wide-band Imaging Spectrometer||MWI|
|Product identification||Visible-near infrared spectrum||VNI|
|Short-wave infrared spectrum||SWI|
|Thermal infrared spectrum||INF|
|Data type||Image data||IMG|
|Surface reflectance products||LSR-RTM|
|Surface temperature products||LST|
|The start time of data collection||Using Beijing time as the standard, expressed as "year, month, day, hour, minute and second"||20180121122946|
|The end time of data collection|
|Product level||Level-two data products||L2|
|Level-five data products||L5|
|Segment number||Not segmented||000|
|The first segment, The second segment……||001、002……|
|Scene number||The first scene in current image segment……||1、2……|
|Product generation time||Using Beijing time as the standard, expressed as "year, month, day, hour, minute and second"||20181111143510|
|Version number||Identify different versions of data products||V111、V211|
|File suffix||Image data product||tif|
|The metadata of data products||xml|
Figure 3 Visible-near infrared image and xml file
Figure 3 is an approximate true color image synthesized using three channels of the visible-near infrared spectrum data product, which are channel 8, channel 10, and channel 12, the filename is T2_MWI_VNI_IMG_20180121122946_20180121124748_L2_000_8_20181111143510_V211.tif.
The quality of data products is controlled during each step of data processing, to ensure the quality and precision of data products to meet the application requirement. In order to monitor the radiation performance changes of the instrument in-orbit in August 2018, the field radiation calibration of Wide-band Imaging Spectrometer test was carried out in dunhuang and qinghai lake. Based on the parameters of synchronous measurement in the field, the absolute radiation calibration coefficients of each channel of Wide-band Imaging Spectrometer are calculated, which effectively improved the radiation accuracy of data products. The absolute radiation calibration accuracy in visible-near infrared spectrum and short-wave infrared spectrum are better than 10% and the absolute radiation calibration accuracy in thermal infrared spectrum is better than 2K.
In the aspect of data quality assessment, the indexes of data format, radiation quality, geometric accuracy, cloud cover rate, etc. were adopted to evaluate the data products quality. According to the quality evaluation results, data products which meet the quality requirements were released and sharing.
Data format check include data format correctness, data integrity, and data validity checks. Data format correctness mainly contains data file naming, data content and data structure correctness and so on. The data integrity mainly contains data file completeness, data content integrity and so on. Data validity mainly checks whether the data and parameter values are valid.
The radiation quality of data products should satisfies the following accuracy requirement. The average cross-validation error of the surface reflectance and related index products between Wide-band Imaging Spectrometer and MODIS data products is less than 10%.The average cross-validation error of the surface brightness temperature and surface brightness temperature products is less than 2K.The average cross-validation error of surface temperature products is within 2.5K.
The average geometric positioning accuracy of data products is within 10 pixels.
In addition, in order to ensure the availability of data products, cloud cover rate of images is detected, the images with a lot of cloud have been eliminated in the released.
The medium-resolution multispectral data products from the Wide-band Imaging Spectrometer are widely used in monitoring the large scale targets, climate change, biodiversity change, land cover type and lake hydrological change monitoring in the region of Tibetan Plateau. The data products of visible-near infrared spectrum and surface reflectance products can be applied to climate change monitoring , land cover type survey, glaciers and snow distribution , extraction of plateau lakes, ecological environment assessment and sudden disaster monitoring; The data products of short-wave infrared spectrum can be used for soil moisture , crop growth monitoring, cloud monitoring and other application. The data products of thermal infrared spectrum and surface temperature products can be used for surface temperature inversion and analysis, fire monitoring, urban heat island effect and other applications.
Thank Manned Space Engineering for providing data products of Wide-band Imaging Spectrometer.
Authors and contributions
Li Shengyang, PhD, Professor. His research interests include technologies concerning aerospace ground-based data system, intelligent processing of space science data and remote-sensing data. His main contribution includes the architecture design, implementation of the ground processing and service system and data operation.
Zhang Wanfeng, PhD, Associate Professor. His research interests include parallel computing and concurrent scheduling technology based on high-performance computing. His main contribution includes the architecture design of the ground data processing system, the CPU+GPU parallelization of the load data processing algorithm, and the operation and maintenance of the system.
Liu Zhiwen, PhD, Assistant Professor. His research interests include geometric correction of remote-sensing images. His main contribution includes the realization of the data processing algorithm of the Wide-band Imager.
Qin Bangyong, PhD, Assistant Professor. His research interests include aerospace data processing, data quality analysis and control. His main contribution includes the design and implementation of the data quality analysis and evaluation system.
Yu Haijun, MSc, Engineer. His research interests include data visualization technology and its application. His main contribution includes data product format generation of the data processing system, the design and implementation of three-dimensional earth data visualization management system.
Wang Bo, MSc, Engineer. Her research interests include fault tolerance technology in data storage. Her main contribution include operation maintenance of the data archiving system, and the operation maintenance of space application data promoting service platform for China Manned Space Engineering.
Shao Yuyang, MSc, Assistant Professor. His research interests include data utilization technology. His main contribution includes the design, implementation and maintenance of the quick-look systems of Wide-band Imager.
Liu Kang, MSc, Engineer. Her research interests include remote sensingapplication in ecology. Her main contribution includes data distribution service anddata application research.
Li Leijuan, MSc,Assistant engineer. Her research interests include remote sensing application in ocean. Her main contribution includes data distribution service and data application research.
Hei Baoqin, MSc, Senior Engineer. Her research interests include datapreprocessing technology. Her main contribution includes data preprocessing andnormalization.
Li Xuan, PhD, Engineer. His research interests include remote sensing imagetarget identification. His main contribution includes data processing and thematicmap making.
Ren Haigen, MSc, Senior Engineer. His research interests is big data technology and application. His main contribution includes data operation management.
Thank Manned Space Engineering for providing data products of Wide-band Imaging Spectrometer.
 Zhang Y, Li B, Zheng D, et al. On the scope and area of the Tibetan Plateau [J]. Geographical research, 2002, 21(1): 1-8.
 Zhang L, Wang C, Yang H, et al. Phenological dataset and map of land cover types of the Tibetan Plateau, and grassland biomass dataset of qinghai lake area [J/OL]. Science Data Bank, 2017, 2(2). DOI: 10.11922/csdata.170.2017.0132.
 Qin B, Li S, Hao Z et al. Earth observation data of the Tiangong-2 Space Laboratory (2016-2017) [J/OL]. Science Data Bank, 2017, 2(4). (2017-12-20). DOI:10.11922/csdata.2017.0003.zh.
 Wen H. The Tiangong-2 space laboratory has achieved multiple "first" validation [J]. China Space, 2016: 4-7.
 Wei J. The Tiangong-2 has been successfully launched which is China's first space laboratory[J]. China Space, 2016: 9.
 Nie X, Liu R, Nie A, et al. Research on new combined water index model based on TM image[J]. Jiangsu agricultural science, 2018, 46(24): 374-378.
 HALL D K, RIGGS G A, SALOMONSON V V. Development of methods for mapping glogal snow cover using moderate resolution imaging spectroradiometer data [J]. Remote Sensing of Environment, 1995, 54(2): 127-140.
 Li H, Zhao Y. Comparative study on water extraction methods based on TM image [J]. Frontiers of science and technology, 2018, 9: 195-196.
 Jiang M, Xu W, Zhang J, et al. Dataset of automatic observations of snow depth on the Tibetan Plateau (2015-2016) [J/OL]. Science Data Bank, 2017, 2(2). DOI: 10.11922/csdata.170.2017.0116.
 Guo X, Wang Y, Zhang G, et al. Atmospheric physical observation data from field stations on the Tibetan Plateau [J/OL]. Science Data Bank, 2016, 1(1). DOI: 10.11922/csdata.170.2015.0003.
 Zhou Y, Gao Q. Dataset of extreme climate index on the Tibetan Plateau (1960-2012)[J/OL]. Science Data Bank, 2017, 2(2). DOI: 10.11922/csdata.170.2017.0143.
 Wu G X, HE B, DUAN A M, et al. Formation and Variation of the Atmospheric Heat Source over the Tibetan Plateau and Its climate effects[J]. ADVANCES IN ATMOSPHERIC SCIENCES, 2017, 34: 169-1184.
 Wu K, Liu S, Guo W. Dataset of glacier distribution in gangrigabu region (1980-2015) [J/OL]. Science Data Bank, 2018, 3(4). (2018-06-05). DOI: 10.11922/csdata.2018.0013.zh.
 La B, Qiu Y, Chu D, et al. Dataset of karola glacier distribution in Tibet (1972-2017) [J/OL]. Science Data Bank, 2017, 2(2). DOI: 10.11922/csdata.170.2017.0121.
 Zhang Y, Liu S, Wang X. Dataset of spatial distribution of surface moraines of hailuogou glacier in gongga mountain in 2009 [J/OL]. Science Data Bank, 2018, 3(4). (2018-06-26). DOI: 10.11922/csdata.2018.0022.zh.
 Qiu Y, Guo H, Chu D, et al. Snow area dataset cloudlessly from MODIS of the Tibetan Plateau [J/OL]. Science Data Bank, 2016, 1(1). (2018-10-31). DOI: 10.11922/csdata.170.2016.0003.
 Liu K, Ren H, Li S, et al. Automatic extraction of frozen lake on the Tibetan Plateau based on Tiangong-2 multispectral data [J]. Infrared and Laser Engineering, 2019, 48(3).
 Rui J, An J, Niu H, Li X. Biological dataset of grassland soil in Inner Mongolia and the Tibetan Plateau [J/OL]. Science Data Bank, 2016, 1(3). DOI:10.11922/csdata.170.2015.0021.
Li S, Zhang W, Liu Z, et al. Thematic datasets for the Tibetan Plateau from Tiangong-2 Wide-band Imaging Spectrometer[DB/OL].Science Data Bank, DOI: 10.11922/sciencedb.701（2019）.
How to cite this article
Li L, Ren H, Li S, Qin B. Thematic datasets for the Tibetan Plateau from Tiangong-2 Wide-band Imaging Spectrometer[J/OL]. China Scientific Data 4(2019). DOI: 10.11922/csdata.2018.0086.zh.