Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is a key physiological variable in the study of carbon cycling and is one of the basic variables to describe vegetation ecosystems. The classification results of surface vegetation types in Qinghai-Tibet Plateau region are obtained based on the Landsat reflectance data(30m spatial resolution). According to NDVI of different vegetation types, the remote sensing inversion model is constructed to produce the growing season FPAR products for each vegetation type. This product can be used as one of the parameters to calculate vegetation carbon sequestration and evaluate vegetation ecosystem status.
This data set is mainly the SRTM terrain data obtained by International Center for Tropical Agriculture （CIAT）with the new interpolation algorithm, which better fills the data void of SRTM 90. The interpolation algorithm was adpoted from Reuter et al. (2007). SRTM's data organization method is as follows: divide a file into 24 rows (-60 to 60 degrees) and 72 columns (-180 to 180 degrees) in every 5 degrees of latitude and longitude grid, and the data resolution is 90 meters. Data usage: SRTM data are expressed as elevation values with 16-bit values (-/+/32767 m), maximum positive elevation of 9000m, and negative elevation (12000m below sea level). For null data use the -32767 standard.
This dataset includes one scene acquired on (yy-mm-dd) 2012-07-25, covering the natural oasis eco-hydrology experimental area in the lower reaches of the Heihe River Basin. This datum contains panchromatic and multi-spectral bands, with spatial resolution of 0.6 m and 2.4 m, respectively. The data product level of this image is Level 2A. QuickBird dataset was acquired through purchase.
Proba (project for on board autonomy) is the smallest earth observation satellite launched by ESA in 2001. Chris (compact high resolution imaging Spectrometer) is the most important imaging spectrophotometer on the platform of proba. It has five imaging modes. With its excellent spectral spatial resolution and multi angle advantages, it can image land, ocean and inland water respectively for different research purposes. It is the only on-board sensor in the world that can obtain hyperspectral and multi angle data at the same time. It has high spatial resolution, wide spectral range, and can collect rich information in biophysics, biochemistry, etc. At present, there are 23 scenes of proba Chris data in Heihe River Basin. The coverage and acquisition time are as follows: 4 scenes in Arjun dense observation area, 2008-11-18, 2008-12-05, 2009-03-29, 2009-05-22; 1 scene in pingdukou dense observation area, 2009-07-13; 7 scenes in Binggou basin dense observation area, 2008-11-19, 2008-11-26, 2008-12-06, 2009-01-10, 2009-03-04, 2009-03-30, 2009-03-31; dayokou basin dense observation area, 2009-07-13 There are two views in the observation area, 2008-10-23, 2009-06-08; one in Linze area, 2008-06-23; one in Minle area, 2008-10-22; seven in Yingke oasis dense observation area, 2008-04-30, 2008-05-09, 2008-06-04, 2008-07-01, 2008-07-19, 2009-05-31, 2009-08-10. The product level is L1 without geometric correction. Except that there are only four angles for the images of 2009-03-29 and 2009-05-24 in the Arjun encrypted observation area, each image has five different angles. The remote sensing data set of the comprehensive remote sensing joint experiment of Heihe River, proba Chris, was obtained through the "dragon plan" project (Project No.: 5322) (see the data use statement for details).
The data includes ten typical hydropower stations in Datong River Basin of Qinghai-Tibet Plateau in July 2020, including Duolong Hydropower Station, Gousikou Hydropower Station, Jinxing Hydropower Station, Kasuoxia Hydropower Station, Liancheng Hydropower Station, Nazixia Hydropower Station, Stone Gorge Hydropower Station, Tianwanggou Hydropower Station, Tiemai Hydropower Station and Xueyitan Hydropower Station. Data are helpful to study the distribution and use of hydropower stations in Datong River Basin. The data were taken by the expedition team through aerial photography using DJI UAV RTK and Royal Series, and spliced by DJI mapping software. The aerial image data has high definition, which can obviously observe the water level difference between upstream and downstream of the hydropower station and the topographic distribution around the hydropower station. The data can be applied to the research field of hydropower stations in Qinghai-Tibet Plateau, providing relevant analysis data.
Surface albedo is a critical parameter in land surface energy balance. This dataset provides the monthly land surface albedo of UAV remote sensing for typical ground stations in the middle reaches of Heihe river basin during the vegetation growth stage (June to October) in 2020 (The data of Huazhaizi station in August is not available because of technical problem). The algorithm for calculating albedo is an empirical method, which was developed based on a comprehensive forward simulation dataset based on 6S model and typical spectrums. This method can effectively transform the surface reflectance to the broadband surface albedo. The method was then applied to the surface reflectance acquired by UAV multi-spectral sensor and the broadband surface albedo with a 0.2-m spatial resolution was eventually obtained.
Data content: the data set product contains the 30-meter resolution product of suspended solids concentration in the water body of the Qinghai-Tibet Plateau, which can be used as the key parameters for ecosystem-related research in Qinghai-Tibet Plateau. Data sources and processing methods: Product inversion is mainly based on the Landsat series data, by extracting the effective aquatic reflectance, to obtain the water composition information. This product is the preliminary result of extracting the concentration information of suspended solids in water using the empirical / semi-empirical method. Data quality: the overall accuracy is high, and the product will be further optimized in combination with the measured data of scientific research. Results and prospects of data application: the data set will be continuously updated and can be used for the study and analysis of ecosystem change in the Qinghai-Tibet Plateau.
The data set is based on NDVI 3G calculated by GIMMS AVHRR sensor data, which represents the greenness of vegetation. The source data range is global, and the Qinghai Tibet plateau region is selected in this data set. This data integrates the original semi monthly scale data into the monthly data. The processing method is to take the maximum value of two NDVI of a month to achieve the effect of noise removal as far as possible. This data set is one of the most widely used NDVI data, and is often used to evaluate the temporal and spatial patterns of vegetation greenness, which has practical significance and theoretical value.
The dataset is the land surface temperature (LST) product from 1980s to 2019 over the Tibetan Plateau. The dataset is retrieved based on Landsat images and a practical single-channel (PSC) algorithm. When validated with the simulation data set, the root-mean-square error (RMSE) of the PSC algorithm was 1.23 K. The corresponding quality assessment (QA) product is also generated to identify cloud, cloud shadow, ice and snow. LST is a commonly used land surface parameter, which can provide data product support for the research and applications in resources survey, ecological environment monitoring, global change research and other fields.
This dataset includes the Antarctica ice sheet mass balance estimated from satellite gravimetry data, April 2002 to December 2019. The satellite measured gravity data mainly come from the joint NASA/DLR mission, Gravity Recovery And Climate Exepriment (GRACE, April 2002 to June 2017), and its successor, GRACE-FO (June 2018 till present). Considering the ~1-year data gap between GRACE and GRACE-FO, we extra include gravity data estimated from GPS tracking data of ESA's Swarm 3-satellite constellation. The GRACE data used in this study are weighted mean of CSR, GFZ, JPL and OSU produced solutions. The post-processing includes: replacing GRACE degree-1, C20 and C30 spherical harmonic coefficients with SLR estimates, destriping filtering, 300-km Gaussian smoothing, GIA correction using ICE6-G_D (VM5a) model, leakage reduction using forward modeling method and ellipsoidal correction.