DOM data of lakes on the Qinghai Tibet Plateau (2017)

The data source of this data set is the European Space Agency (ESA) multispectral satellite Sentinel-2. It includes the annual mean data of CDOM and DOC of Qinghai Tibet Plateau lakes in 2017. Method of use: Based on the CDOM data of the measured sample points, the image reflectance information is extracted, the best prediction variable is selected through Pearson correlation analysis, and a multiple stepwise regression CDOM prediction model is constructed to obtain the CDOM results of the Qinghai Tibet Plateau water body. Because CDOM has a good correlation with DOC, DOC prediction results are calculated by CDOM. Adjustment R of the CDOM model of the final Qinghai Tibet Plateau ² Up to 0.81.

0 2022-11-18

Domestic high-resolution 2-50m fusion orthophoto validation data set in key rivers and lakes research area of Qinghai Tibet Plateau (2015-2020)

Data content: this data set is the historical archived satellite data of the domestic high score series (GF1 / 2 / 3 / 4) in the key river and lake research areas of the Qinghai Tibet Plateau from 2015 to 2020, which can cover the typical river and lake areas for effective monitoring. The time range of the data is from 2015 to 2020. Data source and processing method: the data are level 1 products. After equalizing radiation correction, the changes affecting the sensors are corrected by the equalizing functions of different detectors. Some data are based on the Landsat 8 images in the same period as the base map, and control points are selected for geometric correction of the images. Then, orthophoto correction is carried out based on DEM data, and band fusion processing is carried out for the corresponding data. Data quality description: the Gaofen series satellites are processed by the China Resources Satellite Application Center. There are raw data received by the satellite ground receiving station of the Chinese Academy of Sciences and processed products at all levels. Among them, level 1a (pre-processing level radiometric correction image product): image data processed by data analysis, uniform radiometric correction, noise removal, MTFC, CCD splicing, band registration, etc; And provide RPC files for satellite direct attitude orbit data production. Refer to the data website of China Resources Satellite Application Center for details. Data application achievements and prospects: the data are domestic high-resolution data with high resolution, which can be used to monitor the changes of the Qinghai Tibet Plateau as a water tower in Asia and the generated images, and test the accuracy of other data in the region

0 2022-08-29

Surface information of Qinghai-Tibet engineering corridor (2014-2020)

The dataset is the remote sensing image data ofGF-1 satellite in the Qinghai-Tibet engineering corridor obtained by China High Resolution Earth Observation Center. After the fusion processing of multispectral and panchromatic bands, the image data with a spatial resolution of 2 m is obtained. In the process of obtaining ground vegetation information, the classification technology of combining object-oriented computer automatic interpretation and manual interpretation is adopted, The object-oriented classification technology is to collect adjacent pixels as objects to identify the spectral elements of interest, make full use of high-resolution panchromatic and multispectral data space, texture and spectral information to segment and classify, and output high-precision classification results or vectors. In actual operation, the image is automatically extracted by eCognition software. The main processes are image segmentation, information extraction and accuracy evaluation. After verification with the field survey, the overall extraction accuracy is more than 90%.

0 2022-08-29

Spatial distribution of multi-year means (2000-2018) and temporal trends (1982-2020) of the start and end of the vegetation growing season (SOS and EOS) across the Tibetan Plateau

This datasets include the spatial distribution multi-year means of the SOS and EOS from 2000 to 2018, and the temporal trends of the SOS and EOS from 1982 to 1999 and 2000 to 2020 across the Tibetan Plateau. Based on AVHRR NDVI, MODIS NDVI, and EVI, four steps were used to minimize bias and noise in SOS and EOS extracted from time series of vegetation indexes. First, pixels with multiple-year average vegetation indexes lower than a threshold are regarded as areas of low or no vegetation coverage and are excluded. The pixels with weak seasonality of greenness are also excluded. Second, values of vegetation indexes contaminated by snow cover, ice, or both in winter (December–early March) are substituted with the mean of non-contaminated, high-quality vegetation indexes values during winter. Third, remaining negative vegetation indexes bias caused by clouds and aerosols in other seasons is calibrated by a Savitzky–Golay filtering technique. Finally the improved annual time series of vegetation indexes is fitted to double logistic or modified double logistic functions. Based on thresholds and inflection-point, the SOS and EOS across the Tibetan Plateau were extracted. The spatial resolution of the datasets were 250m and 1/12°. The data quality is reliable.

0 2022-08-23

Network of soil temperature and moisture on the Pali (2015-2021)

The soil temperature and moisture observation network is located south of Tibetan Plateau, with an average elevation of 4,486 meters, providing soil moisture, soil temperature and freeze-thaw measured datasets. Data content (data file, table name, and observation indicators included) : (1) Number of sites: 25 observation sites (2) observation variables: (soil moisture and soil temperature) (3) Observation depths: (0-5, 10, 20 and 40 cm) (4) Geographic coverage: 27.7°-28.1°N; 89.1°-89.4°E (5) Spatial resolution: passive microwave satellite pixel (0.3°) (6) Temporal resolution: 30 min resolution (7) Soil moisture measurement accuracy and resolution: ± 2% VWC and 0.1% VWC. Data content field description: (1) Variable 1-6: Date (Integer: yyyy-mm-dd-hh-mm-ss; UTC+8) (2) Variable 7-34: Observational data values at each site (real, missing value: -99.00) (3) Soil moisture(SM): %vol(m³/m³) (4) Soil temperature(ST): ℃ Data correction and quality control: The 30 min resolution temperature data are the direct sampling data after quality control, and the soil moisture volume content is the correction value based on the soil moisture measurement by the drying method.

0 2022-08-07

Aboveground biomass and vegetation cover data of Qinghai-Tibet Plateau (1990-2020)

The data set product contains the aboveground biomass and vegetation coverage data products of the Qinghai-Tibet Plateau every five years from 1990 to 2020 (1990, 1995, 2000, 2005, 2010, 2015 and 2020).The aboveground biomass of the Qinghai-Tibet Plateau is the remote sensing inversion product of above-ground biomass inversion models based on different land cover types including grassland, forest, etc. Vegetation coverage data of the Qinghai-Tibet Plateau is inversed using remote sensing by the dimidiate pixel model. Among them, the aboveground biomass and vegetation coverage data from 2000 to 2020 were estimated based on MODIS data, the spatial resolution was 250 m; the aboveground biomass and vegetation coverage data of 1990 and 1995 were estimated based on NOAA AVHRR data, the spatial resolution after resampling process is 250 m. This dataset can provide basic data for revealing the temporal and spatial pattern of land cover areas and quality on the Qinghai-Tibet Plateau and supporting the assessment of ecosystems, ecological assets and ecological security.

0 2022-07-20

Landsat-based continuous monthly 30m NDVI Dataset in Qilian mountain area in 2021 (V1.0)

Normalized Difference Vegetation Index (NDVI) is the sum of the reflectance values of the NIR band and the red band by the Difference ratio of the reflectance values of the NIR band and the red band. Vegetation index synthesis refers to the selection of the best representative of vegetation index within the appropriate synthesis cycle, and the synthesis of a vegetation index grid image with minimal influence on spatial resolution, atmospheric conditions, cloud conditions, observation geometry, and geometric accuracy and so on. This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly NDVI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.

0 2022-06-21

Landsat-based continuous monthly 30m NPP Dataset in Qilian mountain area in 2021 (V1.0)

Net Primary Productivity (NPP) refers to the total amount of organic matter produced by photosynthesis in green plants per unit time and area. As the basis of water cycle, nutrient cycle and biodiversity change in terrestrial ecosystems, NPP is an important ecological indicator for estimating earth support capacity and evaluating sustainable development of terrestrial ecosystems. This data set includes the monthly synthesis of 30m*30m surface LAI products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly NPP products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.

0 2022-06-21

Landsat-based continuous monthly 30m FVC Dataset in Qilian mountain area in 2021 (V1.0)

Fractional Vegetation Coverage (FVC) is defined as the proportion of the vertical projection area of Vegetation canopy or leaf surface to the total Vegetation area, which is an important indicator to measure the status of Vegetation on the surface. In this dataset, vegetation coverage is an evaluation index reflecting vegetation coverage. 0% means that there is no vegetation in the surface pixel, that is, bare land. The higher the value, the greater the vegetation coverage in the region. This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly FVC products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.

0 2022-06-21

Landsat-based continuous monthly 30m LAI Dataset in Qilian mountain area in 2021 (V1.0)

Leaf Area Index (LAI) is defined as half of the total Leaf Area within the unit projected surface Area, and is one of the core parameters used to describe vegetation. LAI controls many biological and physical processes of vegetation, such as photosynthesis, respiration, transpiration, carbon cycle and precipitation interception, and meanwhile provides quantitative information for the initial energy exchange on the surface of vegetation canopy. LAI is a very important parameter to study the structure and function of vegetation ecosystem. This data set includes the monthly synthesis of 30m LAI products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly LAI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.

0 2022-06-21