This dataset includes one scene acquired on (yy-mm-dd) 2012-07-25, covering the natural oasis eco-hydrology experimental area in the lower reaches of the Heihe River Basin. This datum contains panchromatic and multi-spectral bands, with spatial resolution of 0.6 m and 2.4 m, respectively. The data product level of this image is Level 2A. QuickBird dataset was acquired through purchase.
LI Xin
On 19 August 2012, a Leica ALS70 airborne laser scanner boarded by the Y-12 aircraft was used to obtain the point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 2900 m with the point cloud density 1 point per square meter. Aerial LiDAR-DSM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
Data content: precipitation data of the Aral Sea basin from 2015 to 2018. Data sources and processing methods: from the new generation of global precipitation measurement (GPM) of NASA (version 06, global precipitation observation program), the daily rainfall can be obtained by adding the three-hour rainfall data, and then the eight day rainfall can be obtained. Data quality: the spatial resolution is 0.1 ° x 0.1 ° and the temporal resolution is 8 days. The value of each pixel is the sum of rainfall in 8 days. Data application results: under the background of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics.
XIAO Qing, Wen Jianguang
This dataset includes three scenes, covering the artificial oasis eco-hydrology experimental area of the Heihe River Basin, which were acquired on (yy-mm-dd hh:mm, BJT) 2012-07-25 07:12, 2012-07-28 19:55, 2012-08-02 07:12. The data were all acquired at PingPong mode with product level of SLC, and these three images are of VV/VH, HH/HV and VV/VH polarization, respectively. COSMO-SkyMed dataset was acquired from Italian Space Agency (ASI) “COSMO-SkyMed project 1720: HYDROCOSMO” (Courtesy: Prof. Shi Jiancheng from the State Key Laboratory of Remote Sensing Science of China).
Agenzia Spaziale Italiana (ASI)
This dataset includes one scene acquired on (yy-mm-dd) 2012-09-06, covering the natural oasis eco-hydrology experimental area in the lower reaches of the Heihe River Basin. This datum contains panchromatic and multi-spectral bands, with spatial resolution of 2.5 m and 10 m, respectively. The data product level of this image is Level 1. QuickBird dataset was acquired through purchase.
China Centre for Resources Satellite Data and Application
This dataset includes one scene acquired on (yy-mm-dd) 2012-05-12, covering the Pailugou catchment. This datum is of panchromatic bands, with spatial resolution of 0.5 m. The data product level of this image is L2. WorldView dataset was acquired through purchase.
China Centre for Resources Satellite Data and Application
This dataset includes 44 scenes, covering the whole Heihe River Basin, which were acquired on (yy-mm-dd) 2012-08-25, 2012-09-03, 2012-09-08, 2012-09-13, 2012-09-18, 2012-09-23, 2012-09-28, 2012-10-03, 2012-10-13, 2012-10-18, 2012-10-22, 2012-11-01, 2012-11-11, 2012-11-21. The data are of multi-spectral bands with data product of Level 1. The spatial resolution is 1 m. ZY-3 dataset was acquired from purchase.
China Centre for Resources Satellite Data and Application
This dataset includes eight scenes, covering the artificial oasis eco-hydrology experimental area of the Heihe River Basin, which were acquired on (yy-mm-dd hh:mm) 2012-05-24, 2012-06-04, 2012-06-26, 2012-07-07, 2012-07-29, 2012-08-09, 2012-08-14, 2012-08-25. The data were all acquired around 19:00 (BJT) at StripMap mode with product level of MGD. Within them, the former six images are of HH/VV polarization with low incidence angle (22-24°), while the later two images acquired on 2012-08-14 and 2012-08-25 are of VV/VH polarization with higher incidence angle (39-40°). TerraSAR-X dataset was acquired from German Space Agency (DLR) through the general proposal of “Estimation of eco-hydrological variables using TerraSAR-X data in the Heihe River Basin, China” (project ID: HYD2096).
German Space Agency (DLR)
This is the MODIS data with 499 scenes covering the whole Heihe River basin in 2008 and 2009. The acquisition time is from 2008-04-23 to 2008-09-30 (295 scenes), and from 2009-05-01 to 2009-10-01 (204 scenes). MODIS data products have 36 channels with resolutions of 250m, 500m and 1000m respectively. The data format is pds, unprocessed, and the MODIS processing software is filed together with the original data. MODIS remote sensing data of Heihe Integrated Remote Sensing Joint Test are provided by Gansu Meteorological Bureau.
Gansu meteorological bureau
In 2007 and 2008, Landsat data set 49 scenes, covering the entire black river basin. The acquisition time is:2007-08-12, 2007-09-23, 2008-01-05, 2008-02-06, 2008-03-17, 2008-03-25, 2008-05-10, 2008-05-19, 2008-05-28, 2008-06-04, 2008-07-07, 2008-07-15, 2008-07-22, 2008-07-23, 2008-08-16, 2008-08-30,2008-09-08, 2008-09-15, 2008-09-17, 2008-10-01, 2008-10-10, 2008-10-19, 2008-10-26, 2008-11-02, 2008-11-04, 2008-11-18, 2008-11-20, 2008-11-27, 2008-12-06, 2008-12-13, 2008-12-14. The product is class L1 and has been geometrically corrected.It includes 4 scenes of TM image and 45 scenes of ETM+ image. The Landsat satellite remote sensing data set of heihe integrated remote sensing joint experiment was obtained through free download.
HU Ningke
ASAR (Advanced Synthetic Aperture Radar) is a Synthetic Aperture Radar sensor mounted on ENVISAT satellite. It operates in c-band with a wavelength of 5.6 cm and features multi-polarization, variable observation Angle and wide-range imaging. Heihe river basin of ENVISAT ASAR remote sensing data sets mainly through central Europe "dragon plan" project, the data to the Image mode, cross polarization (Alternating Polarisation) model with wide is given priority to, the spatial resolution of 30 meters. ENVISAT ASAR data 404 scenes are currently available in heihe river basin, including 82 scenes in APP mode, 7 scenes in IMP mode and 315 scenes in WSM mode. The acquisition time is: APP can choose the polarization mode, the time range is from 2007-08-15 to 2007-12-23, 2008-01-02 to 2008-12-20, 2009-02-15 to 2009-09-06; IMP imaging mode, time range from 2009-06-19 to 2009-07-12; WSM wide format, time range from 2005-12-05 to 2005-12-31,2006-01-06 to 2006-12-31, 2007-01-01 to 2007-12-30, 2008-01-01 to 2008-12-28, 2009-03-13 to 2009-05-22. Product level is L1B, without geometric correction, is amplitude data.
European Space Agency
This data set is the multispectral data used to retrieve 30 meter Lai and fAPAR products in 2012. It is obtained by the environmental satellite CCD sensor with a resolution of 30 m and four bands. This data set has been geometric corrected, radiometric corrected and converted into reflectivity image.
FAN Wenjie
This dataset includes five scenes, covering the artificial oasis eco-hydrology experimental area of the Heihe River Basin, which were acquired on (yy-mm-dd) 2012-04-05, 2012-04-21, 2012-05-07, 2012-06-24, 2012-07-10. The data were all acquired around 11:50 (BJT) with data product of Level 2. Landsat ETM+ dataset was downloaded from http://glovis.usgs.gov/.
United States Geological Survey (USGS) UitedStateGeologicalSurvey UitedStateGeologicalSurvey
This dataset includes one scene acquired on (yy-mm-dd hh:mm, BJT) 2012-07-06 06:30, covering the artificial oasis eco-hydrology experimental area of the Heihe River Basin. This datum was acquired at Stripmap-Quad mode with product level of SLC, and this image includes VV, VH, HH and HV polarization with a spatial resolution of 8 m. Radarsat-2 dataset was acquired from the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences (Courtesy: Dr. Chen Quan).
the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences
The data set contains all single glacial reserves (in KM3) in the Tibetan Plateau of 1970s and 2000s. This data set comes from the result data of the paper entitled "consolidating the Randolph glacier inventory and the glacier inventory of China over the Qinghai titanium plate and investigating glacier changes since the mid-20th century". The first draft of this paper has been completed and is planned to be submitted to earth system science data. The 1970s basic glacier catalog data in the dataset is extracted from Randolph glacier Inventory data set, 2000s basic glacial catalogue is from China's second glacial catalogue data set. Based on the glacial boundary extracted from the two data sets and combined with the grid based bedrock elevation data set (https://www.ngdc.noaa.gov/mgg/global/global.html, DOI: 10.7289/v5c8276m) and the glacial table obtained by a slope dependent method Based on the surface elevation data set, the single glacier reserves in the two catalogues are calculated. In addition, the calculation results of single glacier reserves obtained in this study have been compared and verified with the calculation results of partial glacier reserves, relevant remote sensing data sets, and the global glacier thickness data set based on the average of multiple glacier model sets in multiple directions, and the errors in the calculation results have also been quantified. The establishment of the data set is expected to provide the data basis for the future regional water resources estimation and glacier ablation research, and the acquisition of the data also provides a new idea for the future glacier reserves research.
WANG Zhongjing
QuickBird satellite was launched by Digital Globe corporation on October 18, 2001. It has 4 multi-spectral bands and 1 panchromatic band, with a spatial resolution of 0.61m for panchromatic bands and a spatial resolution of 2.5m for multi-spectral bands and a width of 16.5 * 16.5 km. There are two QuickBird remote sensing images in heihe river basin.The acquisition time and coverage were: 2004-03-23, covering zhangye area;2004-08-08, covering danokou and drainage ditch drainage basin. The product level is level L2 and has been geometrically corrected by the system.
LI Xin, GUO Jianwen
During lidar and widas flight in summer 2012, the ground synchronously carried out the continuous observation of differential GPS of ground base station, and obtained the synchronous GPS static observation data, which is used to support the synchronous solution of aviation flight data. Measuring instrument: Two sets of triple R8 GNSS system. Zgp8001 sets Time and place of measurement: On July 19, 2012, EC matrix lidar flew and observed at mjwxb (northwest of Maojiawan) and sbmz (shibamin) two base stations at the same time On July 25, 2012, lidar of hulugou small watershed and tianmuchi small watershed in the upper reaches flew, observed in XT Xiatang, lidar of Zhangye City calibration field in the middle reaches, and observed in mjwxb (northwest of Maojiawan) On July 26, 2012, lidar flight of hulugou small watershed and tianmuchi small watershed in the upper reaches was observed in XT Xiatang, lidar flight of Zhangye City calibration field in the middle reaches was observed in HCZ (railway station) On August 1, 2012, the upper east and West branches of widas flew and observed in yng (yeniugou) On August 2, 2012, the midstream EC matrix test area widas flew and observed in HCZ (railway station) On August 3, 2012, the midstream EC matrix test area widas flew and observed in mjwxb (northwest Maojiawan) Data format: Original data format before differential preprocessing.
LIU Xiangfeng, MA Mingguo
On June 26, 2012, the satellite transit ground synchronous observation was carried out in the TerraSAR-X sample near the super station in the dense observation area of Daman. TerraSAR-X satellite carries X-band synthetic aperture radar (SAR). The daily transit image is HH / VV polarized, with a nominal resolution of 3 m, an incidence angle of 22-24 ° and a transit time of 19:03 (Beijing time), which mainly covers the ecological and hydrological experimental area of the middle reaches artificial oasis. The local synchronous data set can provide the basic ground data set for the development and verification of active microwave remote sensing soil moisture retrieval algorithm. Quadrat and sampling strategy: Six natural blocks are selected in the southeast of the super station, with an area of about 100 m × 100 m. One plot in the northwest corner of the sample plot is watermelon field, others are corn. The basis of sample selection is: (1) considering different vegetation types, i.e. watermelon and corn; (2) considering the visible light pixel, the sample size of 100m square can guarantee at least 4 30 M-pixel is located in the sample; (3) the location of the sample is near the super station, with convenient transportation. The observation of the super station is in the north, and there is a water net node on both sides of the East and the west, which makes it possible to integrate these observations in the future; (4) in addition, there are some obvious points around the sample, which can ensure that the geometric correction of the SAR image is more accurate in the future. Considering the resolution of the image, 21 splines (distributed from east to West) are collected at 5m intervals. Each line has 21 points (north-south direction) at 5m intervals. Three hydroprobe data acquisition systems (HDAS, reference 2) are used to measure at the same time. The sampling interval is controlled by the scale and moving splines on the measuring line to make up for the lack of using hand-held GPS. Measurement content: About 440 points on the quadrat were obtained, and each point was observed twice, i.e. two times in each sampling point, one time inside the film (marked as a in the data record) and one time outside the film (marked as B in the data record); although the watermelon land was also covered with film, considering that it was not laid horizontally, only the soil moisture at the non covered position was measured (marked as B in the two data records). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and imaginary part of soil complex dielectric are observed. Because the vegetation in this area has been sampled and observed once every five days, no special vegetation synchronous sampling has been carried out on that day. Data: The data format of this data set is vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file.
WANG Shuguo, MA Mingguo, LI Xin
On June 15, 2012, the satellite transit ground synchronous observation was carried out in the TerraSAR-X sample near the super station in the dense observation area of Daman. TerraSAR-X satellite carries X-band synthetic aperture radar (SAR). The daily transit image is HH / VV polarized, with a nominal resolution of 3 m, an incidence angle of 22-24 ° and a transit time of 19:03 (Beijing time), which mainly covers the ecological and hydrological experimental area of the middle reaches artificial oasis. The local synchronous data set can provide the basic ground data set for the development and verification of active microwave remote sensing soil moisture retrieval algorithm. Quadrat and sampling strategy: Six natural blocks are selected in the southeast of the super station, with an area of about 100 m × 100 m. One plot in the northwest corner of the sample plot is watermelon field, others are corn. The basis of sample selection is: (1) considering different vegetation types, i.e. watermelon and corn; (2) considering the visible light pixel, the sample size of 100m square can guarantee at least 4 30 M-pixel is located in the sample; (3) the location of the sample is near the super station, with convenient transportation. The observation of the super station is in the north, and there is a water net node on both sides of the East and the west, which makes it possible to integrate these observations in the future; (4) in addition, there are some obvious points around the sample, which can ensure that the geometric correction of the SAR image is more accurate in the future. Considering the resolution of the image, 21 splines (distributed from east to West) are collected at 5 m intervals. Each line has 23 points (north-south direction) at 5 m intervals. Four hydroprobe data acquisition systems (HDAS, reference 2) are used to measure at the same time. The sampling interval is controlled by the scale and moving splines on the measuring line to make up for the lack of using hand-held GPS. Measurement content: About 500 points on the quadrat were obtained, and each point was observed twice, i.e. in each sampling point, once in the film (marked a in the data record) and once out of the film (marked b in the data record); although the watermelon land was also covered with film, considering that it was not laid horizontally, only the soil moisture at the non covered position was measured (marked b in both data records). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and imaginary part of soil complex dielectric are observed. The vegetation team completed the measurement of biomass, Lai, vegetation water content, plant height, row ridge distance, chlorophyll, etc. Data: This data set includes two parts: soil moisture observation and vegetation observation. The former saves the data format as a vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file; the vegetation sampling information is recorded in the excel table.
WANG Shuguo, MA Mingguo, LI Xin
The purpose of differential GPS positioning survey is to unify multiple survey areas into the same coordinate system and realize accurate absolute positioning through joint survey with national high-level control point coordinates. Under the national geodetic coordinate system of 2000, the accurate positioning of flux observation matrix, hulugou small watershed, tianmuchi small watershed and dayokou watershed and target is completed. In order to realize the geometric correction and absolute positioning of optical image, SAR image and airborne lidar data, the layout of ground control points and high-precision measurement are completed. In the middle reaches of the area, one national high-level control point is jointly surveyed in the five directions of East, South, West, North and middle. Measuring instrument: There are 3 sets of triple R8 GNSS system. Measurement principle: For the control network encryption point, it is connected with the high-level known points in four quadrants around the survey area and distributed evenly in the survey area. For the ground control point (GCP), the obvious characteristic points (such as house corner, road intersection, inflection point, etc.) of the ground layout target and the independent ground objects are adopted and evenly distributed in the survey area. For the ground points with high accuracy requirements, the principle of average value of multiple (at least three) measurements is adopted. Measurement method: In the test area, the control network is encrypted, and GPS static measurement and national high-level control network are used for joint measurement and calculation. During measurement, multiple GPS receivers conduct static synchronous observation at different stations, and the observation time is strictly in accordance with the control network measurement specifications. The ground points in the test area are accurately located. GPS-RTK positioning technology is used and the national high-level control points are used to calibrate to the local coordinate system. When the mobile station obtains the fixed solution during the coordinate acquisition, the measurement is carried out again and the single measurement lasts for 5S. Measuring position: (1) Flux observation matrix 17 stations, Las tower, waternet, soilnet and bnunet nodes in the core area of flux observation matrix; ground control points in CASI flight area; ground corner reflector positions in radar coverage area; ground target positions in lidar flight area. (2) Hulugou small watershed Ground target location of lidar flight area. (3) Tianmuchi small watershed Ground target location of lidar flight area. (4) Dayokou Basin Satellite image geometric correction ground control point. Data format: GPS static survey, the original data format is ". Dat" and ". T01" (or ". T02") files (or converted renix data) and "field record". GPS-RTK survey, the original project is ". Job" file (or converted ". DC" file). The test results are submitted in the format of exported ". CSV" data, which can be viewed and edited by Excel software. Measurement time: June 19, 2012 to July 30, 2012
LIU Xiangfeng, MA Mingguo
The vegetation phenology data set of Heihe River basin provides remote sensing phenology products from 2012 to 2015. The spatial resolution is 1km and the projection type is sinusoidal. MODIS Lai product mod15a2 is used as the phenological remote sensing monitoring data source, and MODIS land cover classification product mcd12q1 is used as the auxiliary data set for extraction. The product algorithm first uses the time series data reconstruction method (bise method) to control the data quality of the input time series; then uses the main algorithm (logistic function fitting method) and the backup algorithm (piecewise linear fitting method) to extract the vegetation phenological parameters, realizes the complementary calculation method, guarantees the accuracy and improves the inversion rate. The algorithm can extract up to three growth cycles in a year, each growth cycle contains six data sets, including the start point of vegetation growth, the start point of growth peak, the end point of growth peak, the end point of growth, the fastest growth and the fastest decline. At the same time, it records the growth cycle type, growth season length, quality identification, etc., a total of 25 data sets. The phenology product reduces the missing rate of inversion, improves the stability of the product, and the data set is relatively reliable with rich information.
LI Jing
Biological productivity refers to the material production capacity of organisms and their groups or even larger scale (including ecosystem and biosphere). It changes with the environment. Therefore, it becomes an indicator of environmental change and the health of the earth system. Net primary productivity (NPP) of vegetation refers to the remaining part of total organic matter (GPP) produced by photosynthesis of green plants in unit time unit area after deducting autotrophic respiration (RA). The NPP products in Heihe River Basin mainly focus on the important parameters par and FPAR of the model of light energy utilization, and improve the algorithm and product production. The FPAR inversion model that distinguishes the direct radiation from the scattered radiation and the par inversion method based on the combination of static and polar orbit satellites are proposed. Finally, the net primary productivity data set of Heihe River Basin is produced by using the light utilization model. The algorithm improves the temporal and spatial resolution of data products, and the accuracy of products is also significantly improved.
LI Li, ZHONG Bo, WU Junjun, WU Shanlong, XIN Xiaozhou
The 30 m / month vegetation index (NDVI / EVI) data set of Heihe River basin provides the monthly NDVI / EVI composite products from 2011 to 2014. This data uses the characteristics of HJ / CCD data of China's domestic satellite, which has both high time resolution (2 days after Networking) and spatial resolution (30 m), to construct multi angle observation data set. The average composite MC method is used as the main algorithm for synthesis, and the backup algorithm uses VI method. At the same time, the main observation angles of the multi-source data set are used as part of the quality descriptor to help analyze the angle effect of the composite vegetation index residue. The remote sensing data acquired every month can provide more angles and more observations than the single day sensor data, but the quality of multi-phase and multi angle observation data is uneven due to the difference of on orbit operation time and performance of the sensor. Therefore, in order to effectively use the multi-temporal and multi angle observation data, before using the multi-source data set to synthesize the vegetation index, the algorithm designs the data quality inspection of the multi-source data set, removing the observation with large error and inconsistent observation. The verification results in the middle reaches of Heihe River show that the NDVI / EVI composite results of the combined multi temporal and multi angle observation data are in good agreement with the ground measured data (R2 = 0.89, RMSE = 0.092). In a word, the 30 m / month NDVI / EVI data set of Heihe River Basin comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products, so as to realize the stable standardized products from scratch and better serve the application of remote sensing data products.
LI Jing, LIU Qinhuo, ZHONG Bo, WU Junjun, WU Shanlong
The 30 m / month synthetic leaf area index (LAI) data set of Heihe River basin provides the monthly Lai synthetic products from 2011 to 2014. This data uses the domestic satellite HJ / CCD data with high time resolution (2 days after Networking) and spatial resolution (30 m) to construct the multi angle observation data set. Considering the impact of surface classification and terrain fluctuation, the algorithm is selected according to the characteristics of different vegetation types Choosing a suitable parameterization scheme of integrated model, inversion Lai based on look-up table method. The remote sensing data acquired every month can provide more angles and more observations than the single day sensor data, but the quality of multi-phase and multi angle observation data is uneven due to the difference of on orbit operation time and performance of the sensor. Therefore, in order to effectively use multi temporal and multi angle observation data, a data quality inspection scheme is designed. Using the Lai ground observation data of 9 forest quadrats, 20 farmland quadrats and 14 savanna quadrats from dayokou area in the upper reaches of Heihe River and Yingke and Linze areas in the middle reaches to verify the Lai in July, the inversion results are in good agreement with the measurement results, and the average error is less than 1; in addition, the Lai inversion results of the combined multi temporal and multi angle observation data are in good agreement with the ground measurement data （R2=0.9，RMSE=0.42）。 In a word, the 30 m / month synthetic leaf area index (LAI) data set of Heihe River Basin comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products, so as to better serve the application of remote sensing data products.
LIU Qinhuo, FAN Wenjie, ZHONG Bo
The 30 m / month synthetic photosynthetic effective radiation absorption ratio (fAPAR) data set of Heihe River basin provides the monthly Lai synthetic products from 2011 to 2014. This data uses the characteristics of HJ / CCD data of China's domestic satellite, which has both high time resolution (2 days after Networking) and spatial resolution (30 m), to construct multi angle observation data set, considering different vegetation types, based on land cover classification map, combined with 30 m /Monthly synthetic leaf area index (LAI) products were produced by fapar-p model based on energy conservation. Based on the principle of energy conservation, the algorithm considers the multiple bounces between vegetation, soil and vegetation, as well as the influence of various factors such as sky scattered light. By analyzing the process of the interaction between photons and canopy, from the point of view that the movement of photons in the canopy is equal to the probability of re collision when multiple scattering occurs, a uniform and continuous vegetation fAPAR model is established. In addition, the effects of various factors on the fAPAR model were analyzed, including soil and leaf reflectance, aggregation index, and G function. The algorithm is highly dynamic, and can get better results for different soil background, vegetation type, radiation conditions, light and observation geometry, weather conditions. Compared with the data of corn canopy par measurement in Yingke irrigation area of Zhangye City, Gansu Province on July 8, 2012, the 30 m / month fAPAR product has a high consistency with the ground observation data, and the error with the observation value is less than 5%. In a word, the 30 m / month synthetic photosynthetic effective radiation absorption ratio (fAPAR) data set of Heihe River Basin comprehensively uses the multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products, and better serves the application of remote sensing data products.
FAN Wenjie, LIU Qinhuo, ZHONG Bo, WU Junjun, WU Shanlong
The 1km / 5day vegetation index (NDVI / EVI) data set of Heihe River basin provides a 5-day resolution NDVI / EVI composite product in 2015. The data uses the characteristics of China's domestic FY-3 satellite data with high time resolution (1 day) and spatial resolution (1km) to construct a multi angle observation data set. Based on the analysis of the multi-source data set and the existing composite vegetation index products and algorithms A global synthetic vegetation index product algorithm system based on multi-source data set is proposed. The vegetation index synthesis algorithm of MODIS is basically adopted, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on the semi empirical walthal model. Using the algorithm system, the composite vegetation index is calculated for the first level data and the second level data, and the quality is identified. Multi-source data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm system first classifies the quality of multi-source data sets, which can be divided into primary data, secondary data and tertiary data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. In the middle reaches of Heihe River, the verification results of farmland and forest areas show that the NDVI / EVI composite results of combined multi temporal and multi angle observation data are in good agreement with the ground measured data (RMSE = 0.105). Compared with the time series of MODIS mod13a2 product, it fully shows that when the time resolution is increased from 16 days to 5 days, a stable and high-precision vegetation index can describe the details of vegetation growth in detail. In a word, the NDVI / EVI data set of Heihe River Basin, which is 1km / 5day, comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products and better serves the application of remote sensing data products.
LI Jing, LIU Qinhuo, ZHONG Bo, YANG Aixia
The 1km / 5day vegetation index (NDVI / EVI) data set of Heihe River basin provides a 5-day resolution NDVI / EVI composite product from 2011 to 2014. The data uses the characteristics of FY-3 data, a domestic satellite, with high time resolution (1 day) and spatial resolution (1km), to construct a multi angle observation data set, which is the basis for analyzing multi-source data sets and existing composite vegetation index products and algorithms On the basis of this, an algorithm system of global composite vegetation index production based on multi-source data set is proposed. The vegetation index synthesis algorithm of MODIS is basically adopted, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on the semi empirical walthal model. Using the algorithm system, the composite vegetation index is calculated for the first level data and the second level data, and the quality is identified. Multi-source data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm system first classifies the quality of multi-source data sets, which can be divided into primary data, secondary data and tertiary data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. In the middle reaches of Heihe River, the verification results of farmland and forest areas show that the NDVI / EVI composite results of combined multi temporal and multi angle observation data are in good agreement with the ground measured data (RMSE = 0.105). Compared with the time series of MODIS mod13a2 product, it fully shows that when the time resolution is increased from 16 days to 5 days, a stable and high-precision vegetation index can describe the details of vegetation growth in detail. In a word, the NDVI / EVI data set of Heihe River Basin, which is 1km / 5day, comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products and better serves the application of remote sensing data products.
LI Jing, LIU Qinhuo, ZHONG Bo, YANG Aixia
The 1 km / 5-day FVC data set of Heihe River basin provides the 5-day FVC synthesis results in 2015. The data uses the data of Terra / MODIS, Aqua / MODIS, and domestic satellites fy3a / MERSI and fy3b / MERSI to build a multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. The whole country is divided into different vegetation divisions and land types, and the conversion coefficient of NDVI and FVC is calculated respectively. The conversion coefficient look-up table and 1km / 5-day synthetic NDVI product production area 1km / 5-day synthetic FVC product are used. In the Heihe River Basin, 1 km / 5-day synthetic FVC products can directly obtain vegetation coverage ratio through high-resolution data to reduce the impact of low-resolution data heterogeneity; in addition, select the typical period of vegetation growth and change, obtain the corresponding growth curve parameters of each pixel by fitting the vegetation index of each pixel time series; and then cooperate with land use map and vegetation classification map, To find the representative uniform pixel of the region to train the conversion coefficient of vegetation index. Compared with the results of high-resolution aster reference FVC in Heihe River Basin, the first step is to aggregate the aster products in Heihe River basin to 1km scale by combining the measured ground data and using the scale up method, and to obtain the aster aggregate FVC data, which is based on spot vegetation remote sensing data released by geoland 2 project (geov1 for short) The results show that the results of geov1 are higher than those of ASTER image combined with ground measurement, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin are between the two, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin in the experimental area are better than those of geov1 products. In a word, the comprehensive utilization of multi-source remote sensing data to improve the estimation accuracy and time resolution of FVC parameter products can better serve the application of remote sensing data products.
MU Xihan, RUAN Gaiyan, ZHONG Bo, LIU Qinhuo
The 1 km / 5-day FVC data set of Heihe River basin provides the 5-day FVC synthesis results from 2011 to 2014. The data uses the data of Terra / MODIS, Aqua / MODIS, and domestic satellites fy3a / MERSI and fy3b / MERSI to build a multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. The whole country is divided into different vegetation divisions and land types, and the conversion coefficient of NDVI and FVC is calculated respectively. The conversion coefficient look-up table and 1km / 5-day synthetic NDVI product production area 1km / 5-day synthetic FVC product are used. In the Heihe River Basin, 1 km / 5-day synthetic FVC products can directly obtain vegetation coverage ratio through high-resolution data to reduce the impact of low-resolution data heterogeneity; in addition, select the typical period of vegetation growth and change, obtain the corresponding growth curve parameters of each pixel by fitting the vegetation index of each pixel time series; and then cooperate with land use map and vegetation classification map, To find the representative uniform pixel of the region to train the conversion coefficient of vegetation index. Compared with the results of high-resolution aster reference FVC in Heihe River Basin, the first step is to aggregate the aster products in Heihe River basin to 1km scale by combining the measured ground data and using the scale up method, and to obtain the aster aggregate FVC data, which is based on spot vegetation remote sensing data released by geoland 2 project (geov1 for short) The results show that the results of geov1 are higher than those of ASTER image combined with ground measurement, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin are between the two, and the results of 1 km / 5-day synthetic FVC products in Heihe River Basin in the experimental area are better than those of geov1 products. In a word, the comprehensive utilization of multi-source remote sensing data to improve the estimation accuracy and time resolution of FVC parameter products can better serve the application of remote sensing data products.
MU Xihan, RUAN Gaiyan, ZHONG Bo, LIU Qinhuo
The 5-day Lai synthesis results in 2015 are provided by the 1 km / 5-day Lai data set of Heihe River Basin. The data set is constructed by using the data of Terra / MODIS, Aqua / MODIS, as well as the domestic satellites fy3a / MERSI and fy3b / MERSI to construct the multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. Multi-source remote sensing data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm first classifies the quality of multi-source data sets, which can be divided into first level data, second level data and third level data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. The purpose of quality evaluation and classification is to provide the basis for the selection of the optimal data set and the design of inversion algorithm flow. Leaf area index product inversion algorithm is designed to distinguish mountain land and vegetation type, using different neural network inversion model. Based on global DEM map and surface classification map, PROSAIL model is used for continuous vegetation such as grassland and crops, and gost model is used for forest and mountain vegetation. Using the reference map generated by the measured ground data of the forests in the upper reaches of Heihe River and the oasis in the middle reaches, and scaling up the corresponding high-resolution reference map to 1km resolution, compared with the Lai product, the product has a good correlation between the farmland and the forest area and the reference value, and the overall accuracy basically meets the accuracy threshold of 0.5%, 20% specified by GCOS. By cross comparing this product with Lais products such as MODIS, geov1 and glass, the accuracy of this Lai product is better than that of similar products compared with reference value. In a word, the synthetic Lai data set of 1km / 5 days in Heihe River Basin comprehensively uses multi-source remote sensing data to improve the estimation accuracy and time resolution of Lai parameter products, so as to better serve the application of remote sensing data products.
LI Jing, Yin Gaofei, YIN Gaofei, ZHONG Bo, WU Junjun, WU Shanlong
The 1 km / 5-day Lai data set of Heihe River basin provides the 5-day Lai synthesis results of 2010-2014. The data uses Terra / MODIS, Aqua / MODIS, as well as domestic satellites fy3a / MERSI and fy3b / MERSI sensor data to build a multi-source remote sensing data set with a spatial resolution of 1 km and a time resolution of 5 days. Multi-source remote sensing data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm first classifies the quality of multi-source data sets, which can be divided into first level data, second level data and third level data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. The purpose of quality evaluation and classification is to provide the basis for the selection of the optimal data set and the design of inversion algorithm flow. Leaf area index product inversion algorithm is designed to distinguish mountain land and vegetation type, using different neural network inversion model. Based on global DEM map and surface classification map, PROSAIL model is used for continuous vegetation such as grassland and crops, and gost model is used for forest and mountain vegetation. Using the reference map generated by the measured ground data of the forests in the upper reaches of Heihe River and the oasis in the middle reaches, and scaling up the corresponding high-resolution reference map to 1km resolution, compared with the Lai product, the product has a good correlation between the farmland and the forest area and the reference value, and the overall accuracy basically meets the accuracy threshold of 0.5%, 20% specified by GCOS. By cross comparing this product with Lais products such as MODIS, geov1 and glass, the accuracy of this Lai product is better than that of similar products compared with reference value. In a word, the synthetic Lai data set of 1km / 5 days in Heihe River Basin comprehensively uses multi-source remote sensing data to improve the estimation accuracy and time resolution of Lai parameter products, so as to better serve the application of remote sensing data products.
LI Jing, Yin Gaofei, YIN Gaofei, ZHONG Bo, WU Junjun, WU Shanlong
Water scarcity，food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. Soil relative humidity index is one of the indicators to characterize soil drought and can directly reflect the status of crops' available water.
FAN Wenjie
Firstly, the canopy reflectance is expressed as a function of a series of parameters, such as Lai / fAPAR, wavelength, soil and leaf reflectance, aggregation index, incidence and observation angle. For several key parameters, the parameter table is established as the input of inversion. Then input the surface reflectance data and land cover data after preprocessing, and use the LUT method to retrieve the fAPAR products. See the reference for detailed algorithm. Image format: TIF Image size: about 1m per scene Time frame: 2012 Time resolution: month by month Spatial resolution: 1km
FAN Wenjie
This dataset contains three basic remote sensing data of digital topography (DEM), TM remote sensing image and NDVI vegetation index of badan jilin desert. 1. DEM, digital terrain data, from the SRTM1 data set released by NASA in the United States, was cropped in the desert area.The resolution is 30 m.The data is stored in the DEM folder, and the dm.ovr file can be opened by ArcGIS. 2. TM image data.The composite data of Landsat TM/ETM + 543 band released by NASA were cropped in the desert lake group distribution area.The resolution is 30 m.From 1990 to 2010, one scene was selected in summer and one scene in autumn every five years to analyze the long-term changes of the lake.In 2002, there was a scene for each quarter to analyze the changes of the lake during the year.The data is stored in TM folder, TIFF format, can be opened by ArcGIS or ENVI software.The file naming rule is yyyymm.tif, where yyyy refers to the year and mm to the month. For example, 199009 refers to the time corresponding to the impact data of September 1990. 3. NDVI, vegetation index.The modis-ndvi product MOD13Q1, released by NASA, was cropped in desert areas.The NDVI data of every ten days of the growing season (June, July, August and September) from 2000 to 2012 are included. The spatial resolution is 250 m and the temporal resolution is 16 days.Stored in NDVI folder, TIFF format, can be opened by ArcGIS or ENVI software.Mosaic_tmp_yyyyddd.hdfout.250m_16_days_ndvi_roi.tif, Where yyyy represents the year and DDD represents the day of DDD of the year.
JIN Xiaomei, HU Xiaonong
The medium resolution imaging spectrometer (MERIS) is a sensor mounted on the ENVISAT satellite of the European Space Agency. It has 15 spectral segments and scans the earth's surface by push sweep method. The incident angle of the point below the star is 68.5 degrees and the width is 1150km. At present, there are 56 ENVISAT MERIS data in Heihe River Basin. Acquisition time: 2008-05-01, 2008-05-02, 2008-05-03, 2008-05-05, 2008-05-07, 2008-05-08, 2008-05-11, 2008-05-14, 2008-05-17 (2 scenes), 2008-05-20 (2 scenes), 2008-05-21 (2 scenes), 2008-05-23 (2 scenes), 2008-05-24, 2008-05-30, 2008-05-31, 2008-06-01, 2008-06-02, 2008-06-05, 2008-06-06, 2008-06-09, 2008-06-12, 2008-06-15, 2008-06-18, 2008-06-21, 2008-06-22, 2008-06-24 (2 scenes), 2008-06-25, 2008-06-27, 2008-06-30, 2008-07-01, 2008-07-02, 2008-07-04, 2008-07-07, 2008-07-10, 2008-07-11, 2008-07-13 (2 scenes), 2008-07-13, 2008-07-16, 2008-07-17, 2008-07-20, 2008-07-23 (2 scenes), 2008-07-26 (2 scenes), 2008-07-27, 2008-07-29, 2008-07-30, 2008-08-01, 2008-08-02. The product level is L1B without geometric correction. The ENVISAT MERIS remote sensing data set of Heihe integrated remote sensing joint experiment was obtained through the China EU "dragon plan" project (Project No.: 5322) (see the data use statement for details).
HU Ningke
In 2007, 2008 and 2009, ENVISAT ASAR data 179 scenes, covering the whole Heihe River Basin. Among them, there were 63 in 2007, 71 in 2008 and 45 in 2009. Imaging mode and acquisition time are respectively: app can select polarization mode from August 15, 2007 to December 23, 2007, from January 02, 2008 to December 202009-02-15, 2008 to September 06, 2009; imp imaging mode from June 19, 2009 to July 12, 2009; WSM wide mode from January 1, 2007 to December 302008-01-01, 2007 to November 28, 2008, from March 13, 2009 to May 22, 2009. The product level is L1B, which is amplitude data without geometric correction. The ENVISAT ASAR remote sensing data set of Heihe comprehensive remote sensing joint experiment is mainly obtained through the China EU "dragon plan" project (Project No.: 5322 and 5344); the WSM wide model data in 2007 and January 2008 are obtained from Professor Bob Su of ITC; the 8-view app can be purchased from the earth observation and digital earth center of Chinese Academy of Sciences.
Institute of Remote Sensing and Digital earth, Chinese Academy of Sciences
The phased array type l-land synthetic aperture radar (PALSAR) is a phased array L-band SAR sensor mounted on alos satellite. The sensor has three observation modes: high resolution, scanning synthetic aperture radar and polarization, which make it possible to obtain a wider ground width than the general SAR. At present, there are 13 scenes of ALOS pallsar data in Heihe River Basin. The coverage and acquisition time are as follows: 1 scene in the northeast of Zhangye City, HH / HV polarization, 2008-04-25; 2 scenes in Binggou basin + Arjun encrypted observation area, HH / HV polarization, 2008-05-122008-06-27; 2 scenes in Dayekou basin + Yingke oasis intensified observation area, HH / HV polarization, 2008-05-122008-06-27; observation station encrypted observation area Survey area + Linze station densified observation area + Linze grassland densified observation area 2 scenes, HH / HV polarization, time 2008-05-122008-06-27; Linze station densified observation area 1 scene, HH / HV polarization, time 2008-05-12; Binggou basin densified observation area 1 scene, HH / HV polarization, time 2008-07-14; bindukou densified observation area 4 scenes, 2008-04-25 2 scenes, HH / HV polarization, 2008-06-10 2 scenes, HH pole Change. The product level is L1 without geometric correction. The alos PALSAR remote sensing data set of Heihe comprehensive remote sensing joint experiment was obtained from JAXA by Dr. Takeo tadono, researcher Ye Qinghua and Professor Shi Jiancheng (the cooperation project between Qinghai Tibet Institute of Chinese Academy of Sciences and JAXA). (Note: "+" means to overwrite at the same time)
Japan Aerospace Exploration Agency
Using ETWatch model with the system complete the heihe river basin scale 1 km resolution 2014 surface evaporation data with middle oasis 30 meters resolution on scale data set, the surface evaporation raster image data of the data sets, it is the time resolution of scale from month to month, the spatial resolution of 1 km scale (covering the whole basin) and 30 meters scale (middle oasis area), the unit is mm.Data types include monthly, quarterly, and annual data. The projection information of the data is as follows: Albers equal-area cone projection, Central longitude: 110 degrees, First secant: 25 degrees, Second secant: 47 degrees, Coordinates by west: 4000000 meter. File naming rules are as follows: 1) 1 km resolution remote sensing data set Monthly cumulative ET value file name: heihe-1km_2014m01_eta.tif Heihe refers to heihe river basin, 1km means the resolution is 1km, 2014 means the year of 2014, m01 means the month of January, eta means the actual evapotranspiration data, and tif means the data is tif format. Name of quarterly cumulative ET value file: heihe-1km_2014s01_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2014 represents the year of 2014, s01 represents the period from January to march, and the first quarter, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Annual cumulative value file name: heihe-1km_2014y_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2014 represents the year of 2014, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format. 2) remote sensing data set with a resolution of 30 meters Monthly cumulative ET value file name: heihe-midoasa-30m_2014m01_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents 2014, m01 represents January, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Name of quarterly cumulative ET value file: heihe-midoasa-30m_2014s01_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents 2014, s01 represents january-march, and the first quarter, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Annual cumulative value file name: heihe-midoasa-30m_2014y_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents the year of 2014, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format.
WU Bingfang
Near-surface atmospheric driving data prepared by ETMonitor and WRF models based on remote sensing surface evapotranspiration model were used to estimate the daily surface evapotranspiration of the heihe river basin at 1km from 2009 to 2011.The coordinate system is the longitude and latitude projection, and the spatial range is 96.5e -- 102.5e, 37.5n -- 43N.Using daily data storage, data format for GEOTIFF, naming: yyyyddd_EvapoTranspiration. tif, including yyyy for years, DDD for ordinal.The data type is single-precision floating point in mm/d and the invalid value is -9.
JIA Li
Near-surface atmospheric driving data prepared by ETMonitor and WRF models based on remote sensing surface evapotranspiration model were used to estimate the average surface evapotranspiration of the heihe river basin with a resolution of 250m in 8 days from may to September 2012.The coordinate system is the projection of equal latitude and longitude, and the spatial range is 96.5e -- 102.5e, 37.5n -- 43N.8 days data using synthetic way of storage, the data format for GEOTIFF, naming: 2012 ddd_evapotranspiration. Tif, including a DDD, ordinal number, for example 2012121 _evapotranspiration. Tif said 2012 day ordinal number is 121-128 days, the average surface evaporation unit is mm/d.The data type is single-precision floating point with an invalid value of -9.
JIA Li
On 10 July 2012 (UTC+8), TASI sensor carried by the Harbin Y-12 aircraft was used in a visible near Infrared hyperspectral airborne remote sensing experiment, which is located in the observation experimental area (30×30 km), Linze region and Heihe riverway. The relative flight altitude is 2500 meters. The wavelength of TASI is 8-11.5 μm with a spatial resolution of 3 meters. Through the ground sample points and atmospheric data, the data are recorded in surface radiance processed by geometric correction and atmospheric correction.
XIAO Qing, Wen Jianguang
This dataset includes seven scenes; two scenes cover the Dayekou catchment on (yy-mm-dd) 2012-08-19 and 2012-08-28, one scene covers the airport desert experimental site on 2012-06-29, three scenes cover the Daman foci experimental area on 2012-06-21, 2012-07-10 and 2012-08-27, and one scene covers the natural oasis eco-hydrology experimental area in the lower reaches of the Heihe River Basin. The data were all acquired around 9:00 (BJT) of full swath mode with data product of Level 1A. PROBA CHRIS dataset was acquired through the European Space Agency (ESA)-Ministry of Science and Technology of China (MOST) Cooperative Dragon 2 (project ID: 5322) and Dragon 3 (project ID: 10649) Programme.
ESA-MOST cooperative Dragon programme(No. 5322，10649)
The dataset of spectral reflectance of canopy leafs observed by the integrating sphere was obtained by ASD Spectroradiometer (350～2 500 nm) and integrating spheres from BNU and the reference board (40% before Jun. 15 and 20% hereafter), in the Linze station foci experimental area. Maize quadrate, the desert green quadrate and withered scrub quadrate in Linze station (on May 28, 30, Jun. 19, 30 and Jul. 9), Wulidun farmland quadrates (on Jun. 24, 29 and Jul. 11) and the desert strips were measured. According to the fact that the ratio of the two DN values equals that of their reflectivity, the reflectivity and the tranmittivity can be calculated with the caliberation coefficient, reflection DN of the observed objects and reference plates. The reflectivity and the tranmittivity of interior vegetation leaves can be got by the integrating spheres. Raw spectral data were archived as binary files, which were recorded daily in detail, and pre-processed data on reflectance and transmittivity were archived as text files (.txt).
LI Jing, Li Xiangyun, Qu Yonghua, SUN Qingsong, XIAO Zhiqiang, YU Yingjie, LIU Sihan, BAI Yanfen, WANG Yang, CHEN Shaohui, JIANG Hao, LI Shihua
Trough the select tasking, we obtained the WorldView-2 stereo image data in Dayekou Watershed production in mid-May 2012. In the same year from July to August, 27 GPS ground control points (GCP) and checkpoints were measured based on the watershed differential GPS control network. Based on the full-field GCPs, the rational polynomial coefficients (RPC) files of WorldView-2 images were corrected in the digital photogrammetry software system. In the stereo model, 60 high-precision tie points evenly distributed were got through image matching technology, and the 1-m and 2-m resolution digital elevation model (DEM) were rapid extracted. Based on collinearity equations, images at nadir were corrected to adjust relief displacements and geometric errors, and the 0.5-m resolution digital orthorectified images DOM were obtained with the principle of digital differential rectification in Dayekou Basin.
Zhang Yanli, MA Mingguo
On 25 August 2012, a Leica ALS70 airborne laser scanner boarded on the Y-12 aircraft was used to obtain LiDAR DSM point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 5200 m with the point cloud density 1 point per square meter. Aerial LiDAR-DSM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
This dataset contains the spectra of white cloth and black cloth obtained in the simultaneous time during the airborn remote sensing which supports the airboren data preprocessing as CASI, SASI and TASI , and the spetra of the typical targets in the middle reaches of the Heihe River Basin. Instruments: SVC-HR1024 from IRSA, ASD Field Spec 3 from CEODE, Reference board Measurement method: the spectra radiance of the targets are vertically measured by the SVC or ASD; before and after the target, the spectra radiance of the reference board is measured as the reference. This dataset contains the spectra recorded by the SVC-HR1024 ( in the format of .sig which can be opened by the SVC-HR1024 software or by the notepad ) and the ASD (in the format of .asd), the observation log (in the format of word or excel), and the photos of the measured targets. Observation time: 15-6-2012, the spectra of typical targets in the EC matrix using SVC 16-6-2012, the spectra of typical targets in the wetland by SVC 29-6-2012, the spectra of typical vegetation and soil in Daman site and Gobi site by ASD 29-6-2012, the spectra of white cloth and black cloth by ASD which is simultaneous with the airborne CASI data 30-6-2012, the spectra of vegetation and soil in the desert by ASD 5-7-2012, the spectra of white cloth and black cloth by ASD which is simultaneous with the airborne CASI data 7-7-2012, the spectra of corn in the Daman site for the research of daily speral variation. 8-7-2012, the spectra of white cloth and black cloth by ASD which is simultaneous with the airborne CASI data 8-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation 9-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation 10-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation 11-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation. The time used in this dataset is in UTC+8 Time.
XIAO Qing, MA Mingguo
The dataset contains vegetation type in the middle reaches of the Heihe River Basin, which was used to validate products from remote sensing. It was generated from investigating the land cover strips of CASI during 2012. Instruments: High-precision handheld GPS (2-3 m) and digital camera were used as main tools in the survey. Measurement method: Hierarchical classification is applied based on CASI data. According to various land types, pixel classifications is used for forest, grassland, bare land and building lands; in-situ observations and investigations are used for different crops. Dataset contains: land types, including maize, leek, poplar trees, cauliflower, bell pepper, potatoes, endive sprout, orchard, watermelon, kidney bean, pear orchard, shadow, and non-vegetation, except for 14 others which are not classified. Observation site: core experimental areas with 5*5 matrix structure in the middle reaches of the Heihe river basin Date: From 25 June in 2012 (UTC+8) on.
Zhang Miao
The datasets of “Land Cover Map of Heihe River Basin” provide monthly land cover classification data in 2012-2013. The HJ-1/CCD data with both high spatial resolution (30 m) and high temporal (2 days) frequency was used to construct the time series data. The NDVI curves from the time series HJ-1/CCD data can depict the variation of typical land surface. Different land use type has different NDVI curve. Rules were set to extract every land use type information. The datasets of “Land Cover Map of Heihe River Basin” hold the traditional land use types including water bodies, urban and built-up, croplands, evergreen coniferous forests, deciduous broadleaf forests and so on. Crop type classification (including maize, spring wheat, highland barely, rape and so on), snow and ice and glaciers information updates, make the datasets more detailed. Compared with previous land cover map and other products, the classification result of the datasets is visually bette. Especially in middle stream, the accuracy of crop classification is quite high compared with the data from the ground campaign. The accuracy of land cover map of the datasets in 2012 was evaluated using very high spatial resolution remote sensing data within Google Earth and data from campaign, and the overall accuracy can be as high as 92.19%. In a word, the datasets of “Land Cover Map of Heihe River Basin” is not only high in overall accuracy, but also more detailed in crop fine classification. Furthermore, it updated some new classes like glaciers and snow. The datasets of “Land Cover Map of Heihe River Basin” are consequently the classification datasets with the highest accuracy and most detailed information up to now.
ZHONG Bo, YANG Aixia
On 30 June 2012 (UTC+8), TASI sensor carried by the Harbin Y-12 aircraft was used in a visible near Infrared hyperspectral airborne remote sensing experiment, which is located in the observation experimental area (30×30 km), Linze region and Heihe riverway. The relative flight altitude is 2500 meters. The wavelength of TASI is 8-11.5 μm with a spatial resolution of 3 meters. Through the ground sample points and atmospheric data, the data are recorded in surface radiance processed by geometric correction and atmospheric correction. Land surface temperature (LST) data was retrieved by temperature/emissivity separation algorithm.
XIAO Qing, Wen Jianguang
On 25 July 2012, a Leica ALS70 airborne laser scanner boarded on the Y-12 aircraft was used to obtain the point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 4800 m with the point cloud density 1 points per square meter. Aerial LiDAR-DEM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
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