The proportion data set of daily cloudless MODIS snow cover area in babaohe river basin (2008.1.1-2014.6.1) was obtained after cloud removal processing using a cloud removal algorithm based on cubic spline function interpolation on the basis of daily cloudless MODIS snow cover product-mod10a1 (tang zhiguang, 2013). This data set adopts the projection method of UTM (horizontal axis isometric cutting cylinder), with a spatial resolution of 500m, and provides Daily Snow Albedo daily-sad results for the babao river basin.The data set is a daily file from January 1, 2008 to June 1, 2014.Each file is the snow albedo result of the day, with a value of 0-100 (%), is the ENVI standard file, and the naming rule is: mod10a1.ayyyyddd_h25v05_snow_sad_grid_2d_reproj_babaohe_nocloud.img, where YYYY represents the year, DDD stands for Julian day (001-365/366).The file can be opened directly with ENVI or ARCMAP software. The original MODIS snow cover data products processed by declouding are derived from MOD10A1 products processed by the us national snow and ice data center (NSIDC). This data set is in HDF format and USES sinusoidal projection. The attributes of the cloud-free MODIS albedo data set (2008.1.1-2014.1.1) in babaohe river basin are composed of the spatial and temporal resolution, projection information and data format of the dataset.
2020-03-29
This data comes from "China's 1:100000 land use data". China's 1:100000 land use data is constructed in three years based on LANDSAT MSS, TM and ETM Remote sensing data by means of satellite remote sensing, organized by 19 research institutes affiliated to the Chinese Academy of Sciences under the national macro survey and dynamic research on remote sensing of resources and environment, a major application project of the eighth five year plan of the Chinese Academy of Sciences. Using a hierarchical land cover classification system, this data divides the whole country into six first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class categories. This is the most accurate land use data product in China, which has played an important role in the national land resource survey, hydrological and ecological research.
2020-03-26
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.
2020-03-10
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
2020-03-10
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.
2020-03-08
Evapotranspiration monitoring is very important for agricultural water resource management, regional water resource utilization planning and sustainable development of social economy. The limitation of traditional monitoring et method is that it can't be observed in large area at the same time, so it can only be limited to the observation point. Therefore, the cost of personnel and equipment is relatively high. It can't provide the ET data of different land use types and crop types. Remote sensing can be used for quantitative monitoring of ET. the feature of remote sensing information is that it can reflect not only the macro structural characteristics of the earth's surface, but also the micro local differences. This data uses MODIS data and m-sebal model from June to September 2012 and time scale expansion scheme based on reference evaporation ratio to estimate the spatial and temporal distribution of evapotranspiration in the whole growth season of the middle reaches of Heihe River, and uses ground observation data to evaluate m-sebal model and time scale expansion scheme in detail. Its time resolution is day by day, spatial resolution is 250m, and data coverage is in the middle reaches of Heihe River, unit: mm. The projection information of the data is as follows: UTM projection, 47N.
2020-03-08
This data set is collected according to the output results of tesim ecological process model, including biomass, plant N and P content, evapotranspiration, NPP and other model output results. Some of the results are obtained by field measurement, some by laboratory analysis of field samples, some by literature.
2020-03-02
On 4 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 Linze region and Heihe riverway. The relative flight altitude is 2500 meters. Land surface temperature product was obtained at a resolution of 3 m using a modified temperature/emissivity separation algorithm based on TASI surface radiance data. The product were validated with in situ ground measurements. The validation results indicated that the Land surface temperature product agreed with the ground LSTs well with RMSE lower than 1.5 K.
2019-09-15
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.
2019-09-15
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.
2019-09-15
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