The data set includes the sample survey data of alpine grassland and alpine meadow in Maduo County in September 2016. The sample size is 50cm × 50cm. The investigation contents include coverage, species name, vegetation height, biomass (dry weight and fresh weight), longitude and latitude coordinates, slope, aspect, slope position, soil type, vegetation type, surface characteristics (litter, gravel, wind erosion, water erosion, saline alkali spot, etc.), utilization mode, utilization intensity, etc.
LI Fei, Fei Li, Zhijun Zhang, Fei Li, Zhijun Zhang
The MODIS land cover type product is a data classification product (MOD12Q1) with different classification schemes for land cover features extracted from Terra data each year. These data are generated by reprojecting the standard MODIS land cover product MOD12Q1 to geographic coordinates with a spatial resolution of one-half degree. The basic land cover classification comprises the 17 types defined by the International Geosphere Biosphere Programme (IGBP): 11 types of natural vegetation classification, 3 types of land use and land inlays, and 3 types of nonvegetation land classification. It covers a longitude range of -180-180 degrees and a latitude range of -64-84 degrees. The data are in GeoTIFF format. This data are free to use, and the copyright belongs to the University of Maryland Department of Geography and NASA.
Channan, S, Channan, XU Xiyan
The NDVI data set is the sixth version of the MODIS Normalized Difference Vegetation Index product (2001-2016) jointly released by NASA EOSDIS LP DAAC and the US Geological Survey (USGS EROS). The product has a temporal resolution of 16 days and a spatial resolution of 0.05 degrees. This version is a Climate Modeling Grid (CMG) data product generated from the original NDVI product (MYD13A2) with a resolution of 1 kilometer. Please indicate the source of these data as follows in acknowledgments: The MOD13C NDVI product was retrieved online courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, The [PRODUCT] was (were) retrieved from the online [TOOL], courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota.
NASA
This dataset is the Fractional Vegetation Cover observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observations lasted for a vegetation growth cycle from May 2012 to September 2012 (UTC+8). Instruments and measurement method: Digital photography measurement is implemented to measure the FVC. Plot positions, photographic method and data processing method are dedicatedly designed. Details are described in the following: 0. In field measurements, a long stick with the camera mounted on one end is beneficial to conveniently measure various species of vegetation, enabling a larger area to be photographed with a smaller field of view. The stick can be used to change the camera height; a fixed-focus camera can be placed at the end of the instrument platform at the front end of the support bar, and the camera can be operated by remote control. 1. For row crop like corn, the plot is set to be 10×10 m2, and for the orchard, plot scale is 30×30 m2. Shoot 9 times along two perpendicularly crossed rectangular-belt transects. The picture generated of each time is used to calculate a FVC value. “True FVC” of the plot is then acquired as the average of these 9 FVC values. 2. The photographic method used depends on the species of vegetation and planting pattern: Low crops (<2 m) in rows in a situation with a small field of view (<30 ), rows of more than two cycles should be included in the field of view, and the side length of the image should be parallel to the row. If there are no more than two complete cycles, then information regarding row spacing and plant spacing are required. The FVC of the entire cycle, that is, the FVC of the quadrat, can be obtained from the number of rows included in the field of view. 3. High vegetation in rows (>2 m) Through the top-down photography of the low vegetation underneath the crown and the bottom-up photography beneath the tree crown, the FVC within the crown projection area can be obtained by weighting the FVC obtained from the two images. Next, the low vegetation between the trees is photographed, and the FVC that does not lie within the crown projection area is calculated. Finally, the average area of the tree crown is obtained using the tree crown projection method. The ratio of the crown projection area to the area outside the projection is calculated based on row spacing, and the FVC of the quadrat is obtained by weighting. 4. FVC extraction from the classification of digital images. Many methods are available to extract the FVC from digital images, and the degree of automation and the precision of identification are important factors that affect the efficiency of field measurements. This method, which is proposed by the authors, has the advantages of a simple algorithm, a high degree of automation and high precision, as well as ease of operation.
MU Xihan, HUANG Shuai, MA Mingguo
The NDVI data set is the latest release of the long sequence (1981-2015) normalized difference vegetation index product of NOAA Global Inventory Monitoring and Modeling System (GIMMS), version number 3g.v1. The temporal resolution of the product is twice a month, while the spatial resolution is 1/12 of a degree. The temporal coverage is from July 1981 to December 2015. This product is a shared data product and can be downloaded directly from ecocast.arc.nasa.gov. For details, please refer to https://nex.nasa.gov/nex/projects/1349/.
The National Center for Atmospheric Research
This dataset is the FPAR observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observation period is from 24 May to 19 July, 2012 (UTC+8). Measurement instruments: AccuPAR (Beijing Normal University) Measurement positions: Core Experimental Area of Flux Observation Matrix 18 corn samples, 1 orchard sample, 1 artificial white poplar sample Measurement methods: For corn, to measure the incoming PAR on the canopy, transmission PAR under the canopy, reflected PAR on the canopy, reflected PAR under the canopy. For orchard and white poplar forest, to measure the incoming PAR outside of the canopy, transmission PAR under the canopy. Corresponding data: Land cover, plant height, crop rows identification
MA Mingguo
The dataset includes the fractional vegetation cover data generated from the stations of crop land, wetland, Gebi desert and desert steppe in Yingke Oasis and biomass data generated from the stations of crop land (corn) and wetland. The observations lasted for a vegetation growth cycle from 19 May, 2012 to 15 September, 2012. 1. Fractional vegetation cover observation 1.1 Observation time 1.1.1 Station of the crop land: The observations lasted from 20 May, 2012 to 15 September, 2012, and in five-day periods for each observation before 31 July and in ten-day periods for each observation after 31 July. The observation time for the station of crop land (corn) are 2013-5-20, 2013-5-25, 2013-5-30, 2013-6-5, 2013-6-10, 2013-6-16, 2013-6-22, 2013-6-27, 2013-7-2, 2013-7-7, 2013-7-12, 2013-7-17, 2013-7-27, 2013-8-3, 2013-8-13, 2013-8-25, 2013-9-5 and 2013-9-15. 1.1.2 The other four stations: The observations lasted from 20 May, 2012 to 15 September, 2012 and in ten-day periods for each observation. The observation time for the crop land are 2013-5-20, 2013-6-5, 2013-6-16, 2013-6-27, 2013-7-7, 2013-7-17, 2013-7-27, 2013-8-3, 2013-8-13, 2013-8-25, 2013-9-5 and 2013-9-15. 1.2 method 1.2.1 Instruments and measurement method Digital photography measurement is implemented to measure the FVC. Plot positions, photographic method and data processing method are dedicatedly designed. In field measurements, a long stick with the camera mounted on one end is beneficial to conveniently measure various species of vegetation, enabling a larger area to be photographed with a smaller field of view. The stick can be used to change the camera height; a fixed-focus camera can be placed at the end of the instrument platform at the front end of the support bar, and the camera can be operated by remote control. 1.2.2 Design of the samples Three and two plots with the area of 10×10 m^2 were measured for the station of the crop land and wetland, respectively. One plot with the area of 10×10 m^2 was measured for the other three stations. Shoot 9 times along two perpendicularly crossed rectangular-belt transects. The picture generated of each time is used to calculate a FVC value. “True FVC” of the plot is then acquired as the average of these 9 FVC values. 1.2.3 Photographic method The photographic method used depends on the species of vegetation and planting pattern. A long stick with the camera mounted on one end is used for the stations of crop land and wetland. For the station of the crop land, rows of more than two cycles should be included in the field of view (<30), and the side length of the image should be parallel to the row. If there are no more than two complete cycles, then information regarding row spacing and plant spacing are required. The FVC of the entire cycle, that is, the FVC of the quadrat, can be obtained from the number of rows included in the field of view. For other three stations, the photos of FVC were obtained by directly photographing for the lower heights of the vegetation. 1.2.4 Method for calculating the FVC The FVC calculation was implemented by the Beijing Normal University. The detail method can be found in the reference below. Many methods are available to extract the FVC from digital images, and the degree of automation and the precision of identification are important factors that affect the efficiency of field measurements. This method, which is proposed by the authors, has the advantages of a simple algorithm, a high degree of automation and high precision, as well as ease of operation (see the reference). 2. Biomass observation 2.1. Observation time 2.1.1 Station of the crop land: The observations lasted from 20 May 2012 to 15 September 2012, and in five-day periods for each observation before 31 July and in ten-day periods for each observation after 31 July. The observation time for the crop land are 2013-5-25, 2013-5-30, 2013-6-5, 2013-6-10, 2013-6-16, 2013-6-22, 2013-6-27, 2013-7-2, 2013-7-7, 2013-7-12, 2013-7-17, 2013-7-27, 2013-8-3, 2013-8-13, 2013-8-25, 2013-9-5 and 2013-9-15. 2.1.2 The station of wetland: The observations lasted from 20 May 2012 to 15 September 2012, and in ten-day periods for each observation. The observation time for the crop land are 2013-6-5, 2013-6-16, 2013-6-27, 2013-7-7, 2013-7-17, 2013-7-27, 2013-8-3, 2013-8-13, 2013-8-25, 2013-9-5 and 2013-9-15. 2.2. Method Station of the crop land: Three plots were selected and three strains of corn for each observation were random selected for each plot to measure the fresh weight (the aboveground biomass and underground biomass) and dry weight. Per unit biomass can be obtained according to the planting structure. Station of the wetland: Two plots of reed with the area of 0.5 m × 0.5 m were random selected for each observation. The reed of the two plots was cut to measure the fresh weight (the aboveground biomass) and dry weight. 2.3. Instruments Balance (accuracy 0.01 g); drying oven 3. Data storage All observation data were stored in excel. Other data including plant spacing, row spacing, seeding time, irrigation time, the time of cutting male parent and the harvest time of the corn for the station of cropland were also stored in the excel.
GENG Liying, Jia Shuzhen, Li Yimeng, MA Mingguo
The monthly average vegetation index data of Heihe River Basin is based on MODIS 1 km and 250 m NDVI products. From 250 m products, the grid value of Heihe River Basin is proposed as precision control, and the 1 km product is modified by HASM method. The monthly average vegetation index of Heihe River Basin from 2001 to 2011 was obtained by fusing multi-source NDVI data using HASM method. Resolution: 1km * 1km The average precipitation data set of Heihe River Basin adopts the data information of 21 meteorological conventional observation stations in Heihe River Basin and its surrounding areas and 13 national reference stations around Heihe River basin provided by Heihe planning data management center. The daily precipitation data of each station from 1961 to 2010 is calculated. If the coefficient of variation is greater than 100%, the daily precipitation distribution trend can be obtained by using the geographic weighted regression to calculate the relationship between the station and the geographical terrain factors; if the coefficient of variation is less than or equal to 100%, the relationship between the station precipitation value and the geographical terrain factors (longitude, latitude, elevation) is calculated by ordinary least square regression, and the daily precipitation score is obtained HASM (high accuracy surface modeling method) was used to fit and modify the residual error after removing the trend. Finally, the trend surface results and residual correction results are added to get the annual average precipitation distribution of Heihe River Basin from 1961 to 2010. Time resolution: annual average precipitation from 1961 to 2010. Spatial resolution: 500M.
YUE Tianxiang, ZHAO Na
This data set contains observation data of vegetation ecological properties in the middle and lower reaches of heihe river from January 1, 2015 to July 31, 2017. It contains 355 data, among which 208 are populus eupoplar and 147 are tamarisk.Ecological attributes include 4 groups of ecological parameters and a total of 15 categories of 74 indicators, as follows: Vegetation structure parameters (25 indicators in 5 categories) : Coverage: total coverage, three-layer coverage, average diameter of canopy; Height: three-layer height, canopy thickness, litter thickness, moss thickness, maximum root depth; Density: layer density and average diameter of trees; Leaf area index: maximum leaf area index and minimum leaf area index of three layers of trees and grass; Phenological stage: leaf spreading stage, leaf filling stage, leaf deciduous stage, complete deciduous stage. Vegetation productivity parameters (16 indicators in 3 categories) : Aboveground biomass: total biomass, three-layer stem biomass, leaf biomass; Root biomass: root biomass, 0-5, 5-15, 15-30, 30-50, 50-100, 100-250cm fine root biomass; Other biomass: litter layer, moss layer biomass and carbon storage. Physiological and ecological parameters (24 indicators in 4 categories) : Biomass distribution: proportion of rhizome and leaf distribution; Element content: carbon content of roots and leaves, carbon - nitrogen ratio, carbon content of litters, carbon content of moss; Blade shape: specific leaf area, blade length and width, leaf inclination; Characteristics of gas exchange: leaf water potential, net photosynthetic rate, stomatal conductance, transpiration rate, air temperature, intercellular CO2 concentration, photosynthetic effective radiation, etc. Hydrological parameters of vegetation (3 categories and 9 indicators) : Redistribution of rainfall: maximum interception, canopy interception, rain penetration, trunk flow Yield flow: yield flow, yield coefficient; Evaporation: plant transpiration, soil evaporation, soil evaporation depth.
Li Xiaoyan, ZHAO Wenwu
The sample plot survey data are as follows: in August 2013, 30 forest sample plots were set up in tianlaochi basin, with the sample plot specification of 10 m×20 m, and the long side of the sample plot was parallel to the slope direction, including 26 Qinghai spruce forests, 2 Qilian yuanberlin forests and 2 spruce-cypress mixed forests. within the sample plot, the diameter at breast height (diameter at trunk height of 1.3 m) of each tree was measured by using a ruler. Using hand-held ultrasonic altimeter to measure the tree height and the height under branches (the height of the first living branch at the lower end of the crown) of each tree, measuring the crown width in the north-south direction and the east-west direction by using a tape scale, and positioning the sample plot by using differential GPS. Taking the carbon storage data of the sample plot as the optimal control condition, using Kriging interpolation to obtain the biomass spatial distribution map driving field, using HASM algorithm to simulate the forest biomass spatial distribution map of the waterlogging pool, the simulation results conform to the vegetation distribution law of the study area, and obtain better effects. Resolution 1m
YUE Tianxiang, ZHAO Na
The sample plot survey data are as follows: in August 2013, 30 forest sample plots were set up in tianlaochi basin, with the sample plot specification of 10 m×20 m, and the long side of the sample plot was parallel to the slope direction, including 26 Qinghai spruce forests, 2 Qilian yuanberlin forests and 2 spruce-cypress mixed forests. within the sample plot, the diameter at breast height (diameter at trunk height of 1.3 m) of each tree was measured by using a ruler. Using hand-held ultrasonic altimeter to measure the tree height and the height under branches (the height of the first living branch at the lower end of the crown) of each tree, measuring the crown width in the north-south direction and the east-west direction by using a tape scale, and positioning the sample plot by using differential GPS. Taking the carbon storage data of the sample plot as the optimal control condition, using Kriging interpolation to obtain the biomass spatial distribution map driving field, using HASM algorithm to simulate the forest biomass spatial distribution map of the waterlogging pool, the simulation results conform to the vegetation distribution law of the study area, and obtain better effects.
YUE Tianxiang, ZHAO Na
The data set include crop leaf stomatal conductance observed at four sample regions, that is the soil moisture control experimental field at Daman county, and the super station, and Shiqiao sample plots at Wuxing village in Zhangye city. 1) Objective Crop leaf stomatal conductance, a key biophysical parameter, was observed as model parameter or a priori knowledge for crop growth model, or evapotranspiration estimation. 2) Measuring instruments Leaf porometer. 3) Measuring site a. the soil moisture control experimental field at Daman county, Twelve soil water treatments are set. The crop leaf stomatal conductance for each treatment is measured on 17, 23 and 29 May, and 3, 9, 14 and 24 June, and 5 and 12 July. b. the Super Station The crop leaf stomatal conductance at the super station is measured on 22 and 28 May, 5, 11, 18, and 25 June, and 1, 8, 15, 22 and 31 July, 9, 15 and 22 August, and 3 and 11 September. c. the Shiqiao sample site The crop leaf stomatal conductance at the Shiqiao village is measured on 17, 22 and 28 May, 4, 11, 17 and 25 June, 1, 8, 15, 22, and 30 July, 8, 16 and 27 August, and 9 September. 4) Data processing The observational data was recorded in the sheets and reorganized in the EXCEL sheets. The time used in this dataset is in UTC+8 Time.
Xu Fengying, Wang Jing, Huang Yongsheng, LI Xin, MA Mingguo
The dataset combined with crop phrenology data and field management data which were investigated near the 13 eddy covariance (EC) stations. 1.1 Objective of investigation Objectives of investigation is to supply assistant information for experiment on EC, meteorology, and biophysics parameter. 1.2 Investigation spots and items Investigation spots include Jiu She of Shiqiao village (EC3), Xiaoman southern road (EC16), Wu She of Five stars village (EC13), Wu She of Xiaoman village (EC14), Er She of Shiqiao village (EC5), Liu She of Zhonghua village (EC11), Liu She of Shiqiao village (EC2), Wu She of JinCheng village (EC7), EC6, Liu She of Jincheng village (EC8), Yi She of Kangning village (EC9), Er She of Kangning village (EC10), and Si She of Jingcheng village (EC12). Investigation items comprise crop type, crop name, seed time, seed type, plant span, row span, field area, germination time, three leaves period, seven leaves period, farming way, farming time, irrigation time, irrigation water volume, fertilization time, fertilization type, and fertilization rate. The time used in this dataset is in UTC+8 Time. 1.3 Data collection Data was collected by using ask-reply approach according to investigation tables.
GE Yingchun, Ma Chunfeng, LI Xin
The dataset of photosynthesis was observed by LI-6400XT Portable Photosynthesis System in the artificial oasis eco-hydrology experimental area of the Heihe River Basin. Observation items included two main crops in the middle reaches of Heihe river: wheat and maize, which located in the town of Pingchuan in Linze and the Super Station of Wuxing, respectively. Observation periods lasted from mid-May to September. This dataset included the raw observation data and the pretreatment data of wheat and maize observed by LI-6400 during the observation periods. Objectives of observation: The photosynthetic datasets can be used in the study of plant physiological ecology characteristic and the simulation and validation for the eco-hydrological models. Instrument and theory of the observation: (1) Measuring instrument: LI-6400XT Portable Photosynthesis System; (2) Measuring theory: Using the infrared gas analyzer to measure the change of CO2 concentration, and then measuring the differences of CO2 concentration between the sample chamber and the referenced chamber so as to acquire the net productivity of the leaf. Time and site of observation: (1) Observation site of the wheat: in the town of Pingchuan in Linze; Observation time: 2012-05-17，2012-06-08 to 2012-6-13; (2) Observation site of the maize: in the Super Station of Wuxing; Observation time: from 2012-05-19 to 2012-08-15. The time used in this dataset is in UTC+8 Time. Data processing: The raw data of LI-6400 were archived in text format and can be opened by text editor or excel, the preprocessed data were in Excel format. Every time period of observation was archived in a single document, named as “date + type + time”, every leaf was recorded 3 times, and then added a remark.
WANG Haibo
This dataset is the LAI observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observation period is from 24 May to 20 September 2012 (UTC+8). Measurement instruments: LAI-2000 (Beijing Normal University) Measurement positions: Core Experimental Area of Flux Observation Matrix 18 corn samples, 1 orchard sample, 1 artificial white poplar sample Measurement methods: To measure the incoming sky radiation on the canopy firstly. Then the transmission sky radiation are mearued under the canopy for serveral times. The canopy LAI is retrieved by using the gap probability model.
Li Yun, Wang Yan, MA Mingguo
The data set include crop height observed at four sample regions, that is the soil moisture control experimental field at Daman county, and the EC plots, the super station, and Shiqiao sample plots at Wuxing village in Zhangye city. 1) Objective Crop height, a key biophysical parameter, was observed for evapotranspiration estimation in regional scale and the retrieval of other biophysical parameters as well as the application in eco-hydrological models. 2) Measurement instrument: Steel tape. 3) Measurement site a. the soil moisture control experimental field at Daman county, Twelve soil water treatments are set. The wheat height are measured on 17, 23 and 29 May, and 3, 9, 14 and 24 June, and 5 and 12 July. b. the EC site Maize height at 14 EC site (EC-2,EC-3,EC-5,EC-6,EC-7,EC-8,EC-9, EC-10, EC-11, EC-12, EC-13, EC-14, EC-15, EC-16) are measured on 14, 21, 25 and 31 May, 7, 13, 23 and 28 June, 3, 13, 18 and 23 July, 3, 12 and 28 August. c. the super station Maize height at the super station is measured on 22 and 28 May, 5, 11, 18, and 25 June, and 1, 8, 15, 22 and 31 July, 9, 15 and 22 August, and 3 and 11 September. d. the Shiqiao sample site Maize height at the Shiqiao village is measured on 17, 22 and 28 May, 4, 11, 17 and 25 June, 1, 8, 15, 22, and 30 July, 8, 16 and 27 August, and 9 September. 4) Data processing The observational data was recorded in the sheets and reorganized in the EXCEL sheets. The time used in this dataset is in UTC+8 Time.
Wang Jing, Xu Fengying, Huang Yongsheng, LI Xin, MA Mingguo
The dataset contains vegetation type and plant structure 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 and SASI the middle reaches of the Heihe River Basin between 25 June and 6 August in 2012. Instruments: High-precision handheld GPS (2-3 m) and digital camera were used as main tools in the survey. Measurement method: Vegetation range in the middle reaches of the Heihe River Basin and survey route could be decided with the help of Google Earth. Wuxing village in Xiaoman town was selected to survey detailed and other places were investigated as far to reach as possible. Main methods were to write down the longitude and latitude, phenology of the plant structure, take photos for the vegetation. Dataset contains: longitude and latitude, vegetation type, area and phenology. Observation Place: CASI flight area in artificial oasis in the middle reaches, CASI stripe flight area in the middle reaches and Zhangye district. Date: From 25 June and 6 August in 2012.
Zhang Miao
The data set include crop biomass observed at four sample regions, that is the soil moisture control experimental field at Daman county, and the EC plots, the super station, and Shiqiao sample plots at Wuxing village in Zhangye city. 1) Objective Crop biomass, a key biophysical parameter, was observed for calibration and validation of crop growth model and the retrieval of other biophysical parameters as well as the application in eco-hydrological models. 2) Measurement instrument: Electronic balance (±0.1g) and oven. 3) Measurement site a. the soil moisture control experimental field at Daman county, Twelve soil water treatments are set. The wheat biomass for each treatment is measured on 17, 23 and 29 May, and 3, 9, 14 and 24 June, and 5 and 12 July. b. the EC site Maize biomass at 14 EC site (EC-2,EC-3,EC-5,EC-6,EC-7,EC-8,EC-9, EC-10, EC-11, EC-12, EC-13, EC-14, EC-15, EC-16) are measured on 14, 21, 25 and 31 May, 7, 13, 23 and 28 June, 3, 13, 18 and 23 July, 3, 12 and 28 August. c. the super station Maize biomass at the super station is measured on 22 and 28 May, 5, 11, 18, and 25 June, and 1, 8, 15, 22 and 31 July, 9, 15 and 22 August, and 3 and 11 September. d. the Shiqiao sample site Maize biomass at the Shiqiao village is measured on 17, 22 and 28 May, 4, 11, 17 and 25 June, 1, 8, 15, 22, and 30 July, 8, 16 and 27 August, and 9 September. 4) Data processing The observational data was recorded in the sheets and reorganized in the EXCEL sheets.
Xu Fengying, Wang Jing, Ma Chunfeng, Huang Yongsheng, LI Xin, MA Mingguo
A land surface temperature observation system was set up in apple orchard near by the No.17 eddy covariance system of the MUlti-Scale Observation experiment on Evapotranspiration over heterogeneous land surfaces 2012 (MUSOEXE-12). This observation site can offer in situ calibration data of apple trees for TASI, WiDAS and L band sensor used in aerospace experiment. Observation Site: This point is located in a large and homogeneous apple orchard in Zhangye Experiment Field, Gansu Academy of Agricultural Sciences. It’s 4 meters away from southwest of No.17 eddy covariance system, and observation height is 4.55 m. Crown size of observed apple tree is 4 m × 4 m. Underlying surface of observation site is mainly apple trees. The coordinates of this site: 38°50′41.70" N，100°22′11.40" E. Observation Instrument: The observation system consists of one SI-111 infrared radiometers (Campbell, USA) installed vertically downward to apple tree. Observation Time: This site operates from 3 August, 2012 to 27 September, 2012. Observation data laagered by every 1 minute uninterrupted. Output data contained sample data of every 1 minute. Accessory data: Land surface (apple tree) infrared temperature (by SI-111) can be obtained. Dataset is stored in *.dat file, which can be read by Microsoft excel or other text processing software (UltraEdit, et. al). Table heads meaning: Target_C_Avg, apple tree temperature @ 4.55 m (℃); SBT_C_Avg, body temperature of SI-111 sensor (℃). Dataset is stored day by day, named as: data format + site name + interval time + date + time. The detailed information about data item showed in data header introduction in dataset.
MA Mingguo
The dataset includes the chlorophyll content of vegetation in different site which has different types of vegetation, acquired on 8 July, 2012, in order to validate the Chlorophyll products. Observation instruments: Sampling, Acetone extraction method Measurement methods: To analyze the influence height on chlorophyll , we select 12 different corn samples based on the height of corn. To compare the chlorophyll content of different types of vegetation, we also select 3 types of vegetation sample on the first EC tower, 1 beans sample near the seventeenth EC tower and 3 reed samples on wetland. A total of selected 19 different samples are analyzed in the laboratory in the College of Life Science, Hexi. We extract chlorophyll a, chlorophyll b, the content of total chlorophyll of selected samples. Dataset contents: Chlorophyll a, chlorophyll b, the content of total chlorophyll Measurement time: 8 July, 2012
Jia Shuzhen
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