2018--数据产品-A Big Earth Data Platform for Three Poles

Current Browsing: 2018

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (leaf area index of Sidaoqiao, 2018)

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.

2020-07-25

Qilian mountains integrated observatory network: Dataset of Heihe integrated observatory network (Phenology camera observation dataset of Arou superstation, 2018)

The dataset contains phenological camera observation data collected at the Arou Superstation in the midstream of the Heihe integrated observatory network from June 13 to November 16, 2018. The instrument was developed with data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures high-quality data with a resolution of 1280×720 by looking-downward. The calculation of the greenness index and phenology are following 3 steps: (1) calculate the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) according to the region of interest, (2) perform gap-filling for the invalid values, filtering and smoothing, and (3) determine the key phenological parameters according to the growth curve fitting (such as the growth season start date, Peak, growth season end, etc.) There are also 3 steps for coverage data processing: (1) select images with less intense illumination, (2) divide the image into vegetation and soil, and (3) calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.

2020-07-25