This data set is hyperspectral observation data of typical vegetation along Sichuan Tibet Railway in September 2019, using the airborne spectrometer of Dajiang M600 resonon imaging system. Including the hyperspectral data observed in the grassland area of Lhasa in 2019, with its own latitude and longitude. The hyperspectral survey was mainly sunny. Before flight, whiteboard calibration was carried out; when data were collected, there was a target (that is, the standard reflective cloth suitable for the grass), which was used for spectral calibration; there were ground mark points (that is, letters with foam plates), and the longitude and latitude coordinates of each mark were recorded for geometric precise calibration. The DN value recorded by Hyperspectral camera of UAV can be converted into reflectivity by using Spectron Pro software. Hyperspectral data is used to extract spectral characteristics of different vegetation types, vegetation classification, inversion of vegetation coverage and so on.
ZHOU Guangsheng, JI Yuhe, LV Xiaomin, SONG Xingyang
The data set was obtained from UAV aerial photography during the field investigation of the Qinghai Tibet Plateau in August 2020. The data size is 10.1 GB, including more than 11600 aerial photos. The shooting sites mainly include Lhasa, Shannan, Shigatse and other areas along the road, residential areas and surrounding areas. The aerial photos mainly reflect the local land use / cover type, facility agriculture distribution, grassland coverage and other information. The aerial photos have longitude, latitude and altitude information, which can provide better verification information for land use / cover remote sensing interpretation, and can also be used for vegetation coverage estimation, and provide better reference information for land use research in the study area.
LV Changhe, LIU Yaqun
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
This dataset contains measurements of L-band brightness temperature by an ELBARA-III microwave radiometer in horizontal and vertical polarization, profile soil moisture and soil temperature, turbulent heat fluxes, and meteorological data from the beginning of 2016 till August 2019, while the experiment is still continuing. Auxiliary vegetation and soil texture information collected in dedicated campaigns are also reported. This dataset can be used to validate the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite based observations and retrievals, verify radiative transfer model assumptions and validate land surface model and reanalysis outputs, retrieve soil properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations. ELBARA-III horizontal and vertical brightness temperature are computed from measured radiometer voltages and calibrated internal noise temperatures. The data is reliable, and its quality is evaluated by 1) Perform ‘histogram test’ on the voltage samples (raw-data) of the detector output at sampling frequency of 800 Hz. Statistics of the histogram test showed no non-Gaussian Radio Frequency Interference (RFI) were found when ELBAR-III was operated. 2) Check the voltages at the antenna ports measured during sky measurements. Results showed close values. 3) Check the instrument internal temperature, active cold source temperature and ambient temperature. 3) Analysis the angular behaviour of the processed brightness temperatures. -Temporal resolution: 30 minutes -Spatial resolution: incident angle of observation ranges from 40° to 70° in step of 5°. The area of footprint ranges between 3.31 m^2 and 43.64 m^2 -Accuracy of Measurement: Brightness temperature, 1 K; Soil moisture, 0.001 m^3 m^-3; Soil temperature, 0.1 °C -Unit: Brightness temperature, K; Soil moisture, m^3 m^-3; Soil temperature, °C/K
Bob Su, WEN Jun
Snow pits were observed daily at Altay base station（lon：88.07、lat: 44.73） from November 27, 2015 to March 26, 2016. Parameters include: snow stratification, stratification thickness, density, particle size, temperature. The frequency of observation was daily. The particle size was observed by a microscope with camera, the density was observed by snowfork, snow shovel and Snow Cone, and the temperature was automatically observed by temperature sensor. The observation time was 8:00-10:100 am local time. The snow particle size is observed according to the natural stratification of snow. The snow particles of each layer are collected, and at least 2 photos are taken. The long axis and short axis of at least 10 groups of particles are measured by corresponding software. Unit: mm. The density was observed at equal intervals, snowfork every 5 cm, snow shovel every 10 cm, snow cone to observe the density of the whole snow layer, and the density of each layer was observed three times. The unit is g / cm3. The height of temperature observation is 0cm, 5cm, 10cm, 15cm, 25cm, 35cm, 45cm, 55cm. The recording frequency was once every 1 minute. The unit is OC.
The data set is obtained by UAV aerial photography during five field visits to the Qinghai Tibet Plateau in 2018-2019. The data size is 77.6 GB, including more than 11600 aerial photos. The aerial film was shot in five times, from July 19, 2018 to July 26, 2018, September 9, 2018 to September 16, 2018, April 24, 2019 to May 10, 2019, July 6, 2019 to July 20, 2019, September 1, 2019 to September 7, 2019. The shooting location mainly includes the roads and surrounding areas between major cities in Lhasa, shigaze, Naqu, Shannan, Linzhi, Changdu, Diqing, Ganzi, ABA, Gannan and Golog. The aerial photos clearly reflect the local land use / cover type, vegetation distribution, grassland degradation, vegetation coverage, river and lake distribution and other information. The aerial photos have longitude and latitude and altitude information, which can provide better verification information for the remote sensing interpretation of land use / cover, and also can be used for the estimation of vegetation coverage, and for the study of land use in the study area Good reference information is provided.
LV Changhe, LIU Yaqun
This dataset includes component temperatures measured by the thermal imager at the Mixed Forest and Sidaoqiao stations between 23 July and 18 August, 2014. The Mixed Forest (101.1335 °E, 41.9903 °N, 874 m.a.s.l.) and Sidaoqiao (101.1374 °E, 42.0012 °N, 873 m.a.s.l.) stations were located in the downstream of the Heihe River basin, Dalaihubu Town, Ejin Banner, Inner Mongolia. At the Mixed Forest station, a Testo 890-2 thermal imager (Testo Inc., Germany) with a resolution of 640 × 480 pixels was employed to acquire brightness temperature images. The imager was manually operated from a 10-m height platform of the tower between 10:00-16:00 (China Standard Time, CST) with an observation interval of 1-h on cloudless days. On August 4th observations were acquired between 11:00 and 17:00 at an interval of 10-min to match observations acquired with an airborne TIR imager. The ground based imager was pointed to five viewing directions (southeast-SE, east-E, northeast-NE, northwest-NW, and southwest-SW) and was inclined 25°–45° below the horizon depending on viewing direction. At Sidaoqiao station, a Testo 875-2i imager (Testo Inc., Germany) with a resolution of 160 × 120 pixels was manually operated from a 10-m high platform to acquire brightness temperature images in directions SW, SE, NE, and NW. Depending on the targets in each viewing direction, the imager was inclined to 30°–45° below the horizon. Observations at Sidaoqiao and Mixed Forest stations were almost synchronous. Furthermore, visible images were taken simultaneously with the aforementioned two TIR imagers (2048 × 1536 pixels for Testo 890-2 and 640 × 480 pixels for Testo 875-2i).
This dataset includes component temperatures measured by the thermal infrared (TIR) radiometers at the Mixed Forest and Sidaoqiao stations between 22 July, 2014 and 19 July, 2016. The Mixed Forest (101.1335 °E, 41.9903 °N, 874 m.a.s.l.) and Sidaoqiao (101.1374 °E, 42.0012 °N, 873 m.a.s.l.) stations were located in the downstream of the Heihe River basin, Dalaihubu Town, Ejin Banner, Inner Mongolia. At the Mixed Forest station, two TIR radiometers (SI-111, Apogee Instruments Inc., USA) connected to a data logger (CR800, Campbell Scientific Inc., USA) measured component temperatures of the sunlit canopy and shaded canopy. TIR radiometers were mounted horizontally at 5 m height on iron rods just south and north of a tree and pointed to its canopy. The distance from the sensor to the canopy was ~1 m. At the Sidaoqiao station, two SI-111 TIR radiometers connected to a CR800 data logger measured component temperatures of the soil and shrub. The first sensor pointed from 2 m height under a viewing zenith angle of 45° to bare soil; the second sensor was mounted at 1-m height and pointed horizontally into the shrub canopy.
This dataset is the data of human activities in the key areas of Qilian Mountain in 2018, spatial resolution 2m. This dataset focuses on mine mining, urban expansion, cultivated land development, hydropower construction, and tourism development in the key areas of Qilian Mountain.Through high-resolution remote sensing images, compare the changes before and after the statistics. For the maps of the landforms in the Qilian Mountains, check and verify them one by one; re-interpret the plots that are suspicious of the map; collect the relevant data in the field that cannot be reflected by the images, check and correct the location. At the same time, unified input and editing of map attribute information. Generating a data set of human activities in the key areas of the Qilian Mountains in 2018.
QI Yuan，ZHANG Jinlong，JIA Yongjuan，ZHOU Shengming，WANG Hongwei
This data set is the land use data of the key areas of Qilian mountain in 2018, spatial resolution 2m. This data set is based on the data of climate, altitude, topography, and land cover type of the Qilian mountain. Through the high-resolution remote sensing images to interprets the surface cover types. For the land types that cannot be reflected by the images, collect relevant data in the field, check and correct the land use types. At the same time, the maps and attribute information are uniformly entered and edited to form land use data in the Qilian Mountain area in 2018.
WANG Hongwei, QI Yuan, ZHANG Jinlong, YAN Changzhen, DUAN Hanchen, JIA Yongjuan
The remote sensing image interpretation mark is also called the interpretation factor, which can directly reflect the image features of the ground object information. The interpreter uses these marks to identify the nature, type or condition of the feature or phenomenon on the image, so it is for the remote sensing image data. Human-computer interactive interpretation is of great significance. The image used in the data to establish the interpretation mark avoids the summer with high vegetation coverage, and avoids the data with more snow cover, cloud cover or smog influence.According to the basic geographic information data extraction requirements, the combination of the remote sensing image band combination order and the full color band are selected.Avoid data loss when enhancing data. The requirement for selecting a typical marker-building area on an image is that the range is moderate to reflect the typical features of the type of landform, including as many basic geographic information elements as possible in the type of landform and the image quality is good. After the selection of the marking area is completed, look for all the basic geographic information element categories contained in the marking area, and then select various typical maps as the collection marks, then go to the field for field verification,including 3429 sampling reference points and 1,870 photos, and the translation of the library was established, and the unreasonable parts were modified until they were consistent with the field. At the same time, the ground photo of the map is taken to make the image and the actual ground elements relate to each other, expressing the authenticity and intuitiveness of the remote sensing image interpretation mark, and to deepen the user's understanding of the interpretation mark.
The data set was acquired by uav aerial photography during the field investigation on the Tibetan Plateau in 2018. The data size was 5.72 GB, including more than 800 photos.The photo was taken from July 19, 2008 to July 26, 2008. The shooting locations mainly include yambajing, keshi village, apaixin village, zhongguo village, mirin village, ri village, chongkang village, kesong village, semi village, yamzhuo yoncho and the surrounding areas.Aerial photos more clearly reflect the local land cover, land use type distribution density, rivers and lakes, vegetation, etc.), work for land use remote sensing provides better validation information, can also be used for the estimation of vegetation coverage, for the study of land use in the study area provided a good reference information.
LV Changhe, LIU Yaqun
On 1 August 2012, Wide-angle Infrared Dual-mode line/area Array Scanner (WIDAS) carried by the Harbin Y-12 aircraft was used in a visible near Infrared thermal Dual-mode airborne remote sensing experiment, which is located in the artificial oasis eco-hydrology experimental area. WIDAS includes a CCD camera with a spatial of resolution 0.08 m, a visible near Infrared multispectral camera with five bands scanner (an maximum observation angle 48° and spatial resolution 0.4 m), and a thermal image camera with a spatial resolution of 2 m. The CCD camera data are recorded in DN values processed by mosaic and orthorectification.
XIAO Qing, Wen Jianguang
On August 19, 2018, the wetland sample in Qumali County, located in the source area of the Yangtze River, was aerially photographed by DJI Elf 4 UAV. A total of 31 routes were set up, flying at a height of 100 m, and the overlap of adjacent photographs was not less than 70%. A total of 1551 aerial photographs were obtained and stored in two folders named "Drone Photoes Part1" and "Drone Photoes Part2".
WANG Xufeng, WEI Yanqiang, WANG Xufeng, WANG Xufeng
On 3 August 2012, Wide-angle Infrared Dual-mode line/area Array Scanner (WIDAS) carried by the Harbin Y-12 aircraft was used in a visible near Infrared thermal Dual-mode airborne remote sensing experiment, which is located in the artificial oasis eco-hydrology experimental area (5×5 km). WIDAS includes a CCD camera with a spatial resolution of 0.08 m, a visible near Infrared multispectral camera with five bands scanner (an maximum observation angle 48° and spatial resolution 0.4 m), and a thermal image camera with a spatial resolution of 2 m. The CCD camera data are recorded in DN values processed by mosaic and orthorectification.
XIAO Qing, Wen Jianguang
On 26 July 2012, Wide-angle Infrared Dual-mode line/area Array Scanner (WIDAS) carried by the Harbin Y-12 aircraft was used in a visible near Infrared thermal Dual-mode airborne remote sensing experiment, which is located in the artificial oasis eco-hydrology experimental area (5×5 km). WIDAS includes a CCD camera with a spatial resolution of 0.2 m, a visible near Infrared multispectral camera with five bands scanner (an maximum observation angle 48° and spatial resolution 1 m), and a thermal image camera with a spatial resolution of 4.8 m. The CCD camera data are recorded in DN values processed by mosaic and orthorectification.
XIAO Qing, Wen Jianguang
On August 22, 2018, in the Lancang River Source Park, a camera was carried on DJI Elf 4 UAV to take aerial photographs of the sample area. A total of 20 routes (5 missing routes) were set up, flying at a height of 100 m, and the overlap degree of adjacent photos was not less than 70%. A total of 1160 aerial photographs were obtained and stored in two folders of "100 MEDIA" and "101 MEDIA".
WANG Xufeng, WEI Yanqiang
On 25 August and 28 August, 2012, a RCD30 camera of Leica Company boarded on the Y-12 aircraft was used to obtain CCD image. RCD30 camera has a focal length of 80 mm and four bands including red, green, blue and near-infrared bands. The absolute flight altitude is 4800 and 5200 m, and ground sample distance is 6-19 cm. The product includes TIF images and exterior orientation elements.
XIAO Qing, Wen Jianguang
On 19 August 2012, a RCD30 camera of Leica Company boarded on the Y-12 aircraft was used to obtain the CCD image. RCD30 camera has a focal length of 80 mm and four bands including red, green, blue and near-infrared bands. The absolute flight altitude is 2900 m and ground sample distance is 10 cm. The data includes TIF images and exterior orientation elements.
XIAO Qing, Wen Jianguang