This data set includes six data files, which are: (1) soil temperature and moisture data of alpine meadow elevation gradient_ Dangxiong, Tibet (2019-2020). This data is the hourly observation data of temperature and water content at different soil depths (5cm and 20cm) of the alpine meadow at 4400m, 4500m, 4650m, 4800m, 4950m and 5100m above sea level in Dangxiong, Tibet during 2019-2020. (2) Meteorological environment data of Sejila Mountain Forest line_ Linzhi, Tibet (2019), the data is the hourly meteorological environment (including wind speed, air temperature 1 m away from the surface, relative humidity 1 m away from the surface, air temperature 3 m away from the surface, relative humidity 3 m away from the surface, atmospheric pressure, total radiation, net radiation, photosynthetically active radiation, 660 nm) of the forest line of Sejila Mountain in Linzhi, Tibet in 2019 Hourly observation data of red light radiation, 730nm infrared radiation, surface temperature, atmospheric long wave radiation, surface long wave radiation, underground 5cm-20cm-60cm heat flux, underground 5cm-20cm-60cm soil temperature and humidity, rainfall and snow thickness, among which some observation data are missing due to equipment power failure in plateau area, which has been explained in the data. (3) NDVI of vegetation at major meteorological stations_ In the Qinghai Tibet Plateau (2020), NDVI survey data and average values of vegetation near 25 meteorological stations are included. (4) Land use survey data set_ Along the Sichuan Tibet Railway (2019), including 35 survey points along the Sichuan Tibet railway land use survey data, including survey time, location, latitude and longitude, altitude, slope aspect, main vegetation types and dominant species. (5) Leaf area index survey data_ The leaf area index (LAI) of main vegetation types along Sichuan Tibet Railway (2019) was measured by SunScan canopy analyzer and lai-2200. (6) Survey data of soil temperature and humidity_ Along the Sichuan Tibet Railway (2019), including 34 survey points along the Sichuan Tibet Railway: location, longitude and latitude, altitude, soil surface temperature, soil moisture at 30cm, the data were recorded as 3 repeated measurements at each survey point. The data set can be used to analyze and study the change law of vegetation environment in Qinghai Tibet Plateau.
The ground sample data was collected by LAI-2000 canopy analyzer, and the collection area was located in Dayekou, Wuxing Village (2012) and other areas. The main measure of vegetation is corn. The LAI value of the corn was obtained using the LAI2000, and the observation was repeated twice in a pattern of “one up and four down”. The leaf area of each leaf of the corn plant was obtained using CD202, and a total of three corns were collected.
The forest hydrology experimental area of Heihe River integrated remote sensing experiment includes the dense observation area of Dayekou basin and the dense observation area of Pailugou basin. Due to the concentrated distribution of the fixed sample plots in the drainage ditch basin, these sample plots lack of representativeness to the forest of the whole dayokou basin, so in June 2008, 43 temporary forest sample plots were set up in the whole dayokou basin. The data set is the ground observation data of the 43 temporary plots. In addition to the measurement and recording of stand status and site factors, Lai was also observed. The instruments used to measure each wood in the sample plot are mainly tape, DBH, flower pole, tree measuring instrument and compass. The DBH, tree height, height under branch, crown width in cross slope direction, crown width along slope direction and single tree growth were measured for each tree. WGS84 latitude and longitude coordinates of the center point of the sample plot were measured with different hand-held GPS, and the positioning error was about 5-30m. Other observation factors include: Forest Farm, slope direction, slope position, slope, soil thickness, canopy density, etc. The implementation time of these temporary sample plots is from 2 to 30 June 2008. The data set can provide ground data for the development of remote sensing inversion algorithm of forest structure parameters.
The dataset is Lai data of ground sample points in Heihe River Basin, collected by LAI-2000 canopy analyzer. The collection area is located in Zhangye rural demonstration base, Ejina Banner, Jiuquan Satellite Center (2011) and other areas. The main measured vegetation is corn. The Lai value of maize was obtained by using lai2000, and the observation was repeated twice in the mode of one up four down. Cd202 was used to obtain the leaf area of each leaf of maize plant, and three maize plants were collected.
This dataset contains the LAI measurements from the Daman superstation in the middle reaches of the Heihe integrated observatory network from June 11 to September 18 in 2018. The site (100.372° E, 38.856°N) was located in the maize surface, near Zhangye city in Gansu Province. The elevation is 1556 m. There are 3 observation samples, each of which is about 30m×30m in size, and the latitude and longitude ranges are (100.373297°E~100.374205°E, 38.857871°N~38.858390°N), (100.373918°E~100.373897°E, 38.854025°). N~38.854941°N), (100.368007°E~100.369044°E, 38.850678°N~38.851580°N). Five sub-canopy nodes and one above-canopy node are arranged in each sample. The LAI data is obtained from LAINet measurements following four steps: (1) the raw data is light quantum (level 0); (2) the daily LAI can be obtained using the software LAInet (level 1); (3) the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); (4) for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.
This dataset contains the LAI measurements from the Sidaoqiao in the downstream of the Heihe integrated observatory network from June 16 to October 18 in 2018. The site was located in Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 870 m. There are 2 observation samples, around Sidaoqiao superstation (101.1374E, 42.0012N) and Mixed forest station (101.1335E, 41.9903N), each of which is about 30m×30m in size. Five sub-canopy nodes and one above-canopy node are arranged in each sample. The LAI data is obtained from LAINet measurements following four steps: (1) the raw data is light quantum (level 0); (2) the daily LAI can be obtained using the software LAInet (level 1); (3) the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); (4) for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.
Image format: tif Image size: about 925M per scene Time range: may-october 2012 Time resolution: month Spatial resolution: 30m The algorithm firstly adopts the canopy BRDF model and presents the canopy reflectivity as a function of a series of parameters such as FAPAR, wavelength, reflectance of soil and leaves, aggregation index, incidence and observation Angle.The parameter table is established for several key parameters as the input of inversion.Then input the pre-processed surface reflectance data and land cover data, and invert LAI/FAPAR products by look-up table (LUT) method. See references for detailed algorithm.
Leaf area index, also known as leaf area coefficient, refers to the multiple of the total area of plant leaves in the land area per unit land area. Leaf area index is an important structural parameter of ecosystem, which is used to reflect the number of plant leaves, the change of canopy structure, the life activity of plant community and its environmental effect, to provide structured quantitative information for the description of material and energy exchange on the canopy surface, and to balance the energy of carbon accumulation, vegetation productivity and the interaction between soil, plant and atmosphere, Vegetation remote sensing plays an important role. Plant canopy imager CI - 110 was used to measure the alpine shrub and spruce leaf area index in hulugou watershed. The measurement period is July 22, 2014. It includes the main shrub types and Picea crassifolia forest in hulugou watershed. The data set mainly includes the original data of CI-110 measurement, including image and leaf area analysis image.
The algorithm firstly adopts the canopy BRDF model and represents the canopy reflectivity as a function of a series of parameters such as LAI/FAPAR, wavelength, reflectivity of soil and leaves, aggregation index, incidence and observation Angle.The parameter table is established for several key parameters as the input of inversion.Then input the pre-processed surface reflectance data and land cover data, and invert LAI products by look-up table (LUT) method.See references for detailed algorithms. Image format: tif Image size: about 1M per scene Time range: 2000-2012 Temporal resolution: 8 days Spatial resolution: 1km