Daily cloud-free snow products of MODIS in the northern hemisphere (2000-2016)

This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.

0 2020-01-19

River lake ice phenology data in QPT V1.0 (2002-2018)

River lake ice phenology is sensitive to climate change and is an important indicator of climate change. 308 excel file names correspond to Lake numbers. Each excel file contains six columns, including daily ice coverage information of corresponding lakes from July 2002 to June 2018. The attributes of each column are: date, lake water coverage, lake water ice coverage, cloud coverage, lake water coverage and lake ice coverage after cloud treatment. Generally, the ice cover area ratio of 0.1 and 0.9 is used as the basis to distinguish the lake ice phenology. The excel file contained in the data set can further obtain four lake ice phenological parameters: Fus, fue, bus, bue, and 92 lakes. Two parameters, Fus and bue, can be obtained.

0 2020-01-19

Radiometer and scatterometer data for Antarctica and the Arctic ice sheet (1978-2015)

The microwave radiometer data set comprises brightness temperature data from SMMR (1978-1987), SSM/I (1987-2009) and SSMIS (2009-2015), with temporal coverage from 1978 to 2015 and a spatial resolution of 25 km. Each Antarctic data file consists of 316*332 grids, and each Arctic freeze-thaw data file consists of 304*448 grids. The microwave scatterometer data set comprises backscattering data from QScat (2000-2009) and ASCAT (2009-2015), with a temporal coverage from 2000 to 2015 and a spatial resolution of 4.45 km. Each Antarctic data file consists of 1940*1940 grids, and each Arctic data file consists of 810*680 grids. The temporal resolution of the data set is one day, and the data cover both Antarctica and Arctic ice sheets.

0 2020-01-19

Long-term serial data of snow area on the Tibetan Plateau (2007-2015)

The variation in the duration of snow on the Tibetan Plateau is relatively great, and the high mountainous areas around the plateau are rich in snow and ice resources. Taking full account of the terrain of the Tibetan Plateau and the snow characteristics in the mountains, the data set adopted AVHRR data to gradually realize generating data products for daily, ten-day, and monthly snow cover areas while maintaining the snow classification accuracy. These data included the daily/10-day/monthly snow cover area data for the Tibetan Plateau from 2007 to 2015, the average accuracy of which is 0.92. It can provide reliable data for snow changes during the historical periods of the Tibetan Plateau.

0 2020-01-18

Long-term C- and L-band SAR backscatter data for monitoring post-fire vegetation recovery in the tundra environment of the Anaktuvuk River, Alaska (Version 1.0) (2002-2017)

Wildfires can strongly affect the frozen soil environment by burning surface vegetation and soil organic matter. Vegetation affected by fire can take many years to return to mature pre-fire levels. In this data set, the effects of fires on vegetation regrowth in a frozen-ground tundra environment in the Anaktuvuk River Basin on the North Slope of Alaska were studied by quantifying changes in C-band and L-band SAR backscatter data over 15 years (2002-2017). After the fire, the C- and L-band backscattering coefficients increased by 5.5 and 4.4 dB, respectively, in the severe fire area compared to the unburned area. Five years after the fire, the difference in C-band backscattering between the fire zone and the unburned zone decreased, indicating that the post-fire vegetation level had recovered to the level of the unburned zone. This long recovery time is longer than the 3-year recovery estimated from visible wavelength-based NDVI observations. In addition, after 10 years of vegetation recovery, the backscattering of the L-band in the severe fire zone remains approximately 2 dB higher than that of the unburned zone. This continued difference may be caused by an increase in surface roughness. Our analysis shows that long-term SAR backscattering data sets can quantify vegetation recovery after fire in an Arctic tundra environment and can also be used to supplement visible-wavelength observations. The temporal coverage of the backscattering data is from 2002 to 2017, with a time resolution of one month, and the data cover the Anaktuvuk River area on the North Slope of Alaska. The spatial resolution is 30~100 m, the C- and L-band data are separated, and a GeoTIFF file is stored every month. For details on the data, see SAR Backscattering Data of the Anaktuvuk River Basin on the North Slope of Alaska - Data Description.

0 2020-01-18

Ice velocity of the Amery ice shelf in the Antarctic (Version 1.0) (2003-2013)

Using the Modis1B data of 11 scenes from 2003 to 2013 (the ice shelf Modis1B data published on the NSIDC website), the surface velocity of the Antarctic Amery Ice Shelf was extracted by the subpixel cross-correlation method, the ice velocity was extracted by the COSI-Corr software, and then the time sequence of annual average velocities for nearly ten years was obtained. Due to the lack of field observations in the study area, the accuracy of the ice flow results was estimated by using the offset value of the stable region, and the ice flow error was approximately ±50 m/year. The ice velocity data date from 2003 to 2013, the temporal resolution is one year, and the data cover the Amery area with a spatial resolution of 500 m. A GeoTIFF file of velocity data is stored every year. For details regarding the data, please refer to the Amery Ice Flow Field - Data Description.

0 2020-01-18

Ice elevation changes for typical glaciers on the QTP V1.0 (2000-2013)

The continuous advancement of SAR interferometry technology makes it possible to obtain multitemporal DEMs with high precision in the glacial area. In particular, in 2000, the Shuttle Radar Topography Mission (SRTM) led by NASA provided DEM data covering the area from 56ºS to 60ºN; the TanDEM-X bistatic SAR interferometry system of DLR could provide the global DEM data with high resolution and precision. These high-quality, large-coverage SAR interferometry data, as well as published DEM data products, provided valuable information for using the multitemporal DEMs to detect changes in ice thickness. The temporal coverage of the ice thickness variation data of typical glaciers on the Tibetan Plateau was from 2000 to 2013, covering Puruogangri and the west Qilian Mountains with a spatial resolution of 30 meters. Using TanDEM-X bistatic InSAR data and a C-band SRTM DEM, the differential radar interferometry method was first used to generate a TanDEM-X DEM with high precision. Then, based on the precise registration of DEM, the DEM data obtained in different periods were compared. Lastly, the ice thickness changes were estimated. The format of the data set was GeoTIFF, and each typical glacier ice thickness change was stored in a folder. For details of the data, please refer to the Ice elevation changes for typical glaciers on the Tibetan Plateau - Data Description.

0 2020-01-18

Dataset of thin cloud micro physical characteristics based on the infrared radiation spectrum of the ground in Arctic Alaska (2000-2014)

Thin cloud inversion data, a remote sensing inversion product, was collected for an Arctic site in Alaska based on observations of the infrared radiation spectrum of the ground in conjunction with an optimization method. The temporal coverage of the data is from 2000 to 2014, and the temporal resolution is one hour. The data represent the average characteristics of the different cloud layers. The spatial coverage is one site in Arctic Alaska, with latitude and longitude coordinates of 71°19′22.8′′N, 156°36′32.4′′ W. The characteristic variables include cloud water effective radius, cloud water content, cloud ice effective radius, cloud ice content, and cloud optical thickness; the corresponding observation inversion error ranges are approximately 10%, 20%, 10%, 20%, and 15%, respectively. The data files are in the .dat format.

0 2020-01-18

China lake dataset (1960s-2015)

The multi-decadal lake number and area changes in China during 1960s–2015 are derived from historical topographic maps and >3831 Landsat satellite images, including lakes as fine as ≥1 km2 in size. The total area of lakes in China has increased by 5858.06 km2 (9%) between 1960s and 2015, and with heterogeneous spatial variations. Lake area changes in the Tibetan Plateau, Xinjiang, and Northeast Plain and Mountain regions reveal significant increases of 5676.75, 1417.15, 1134.87 km2 (≥15%), respectively, but the Inner-Mongolian Plateau shows an obvious decrease of 1223.76 km2 (22%). We find that 141 new lakes have appeared predominantly in the arid western China; but 333 lakes, mainly located in the humid eastern China, have disappeared over the past five decades.

0 2020-01-16

The ecosystem productivity of Pan Third Pole

The Pan Third Pole is sensitive to global climate change, its warming rate is more than twice of the global rate, and it is affected by the synergy of westerlies and monsoons. How to respond to climate change will have a profound impact on regional ecological security. However, the estimation of NPP by current products is still uncertain. For this reason, this product combines multi-source remote sensing data, including AVHRR NDVI, MODIS reflectivity data, a variety of climate variables (temperature, precipitation, radiation, VPD) and a large number of field measured data, and uses machine learning algorithm to retrieve the net primary production capacity of Pan third polar ecosystem.

0 2020-01-15