The dataset is a nearly 36-year (1983.7-2018.12) high-resolution (3 h, 10 km) global SSR (surface solar radiation) dataset, which can be used for hydrological modeling, land surface modeling and engineering application. The dataset was produced based on ISCCP-HXG cloud products, ERA5 reanalysis data, and MODIS aerosol and albedo products with an improved physical parameterization scheme. Validation and comparisons with other global satellite radiation products indicate that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). This SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The unit is W/㎡, instantaneous value.
TANG Wenjun
This dataset contains the monthly/yearly surface shortwave band albedo, fraction of absorbed photosynthetically active radiation (fPAR), leaf area index (LAI), vegetation continuous fields (tree cover and non-tree vegetation cover, VCF), land surface temperature (LST), net radiation (RN), evapotranspiration (ET), aboveground autotrophic respiration (RA-ag), belowground autotrophic respiration (RA-bg), gross primary production (GPP) and net primary production (NPP) in China from 2001 to 2018. The spatial resolution are 0.1 degree. Moreover, the dataset also includes these 11 ecosystem variables under climate-driven scenario (i.e., under no human disturbance). So, it can show the relative influences of climate change and human activities on land ecosystem in China during the 21st century.
CHEN Yongzhe, FENG Xiaoming, TIAN Hanqin, WU Xutong, GAO Zhen, FENG Yu, PIAO Shilong, LV Nan, PAN Naiqing, FU Bojie
The global monthly all-sky land surface temperature (2000-2020) is produced by the method from Chen et al. 2017 JHM.
CHEN Xuelong, BOB Su, MA Yaoming
Kilometer-level spatially complete (seamless) land surface temperature products have a wide range of applications needs in climate change and other fields. Satellite retrieved LST has high reliability. Integrating the LST retrieved from thermal infrared and microwave remote sensing observation is an effective way to obtain the SLT with certain accuracy and spatial integrity. Based on this guiding ideology, the author developed a framework for retrieving 1km and seamless LST over China landmass, and generated the LST data set accordingly (2002-2020) Firstly, a look-up table based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled by using geographic weighted regression to obtain 1km LST. Finally, the multi-scale Kalman filter is used to fuse AMSR-E/AMSR2 LST and MODIS LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST.
CHENG Jie, DONG Shengyue, SHI Jiancheng
This dataset includes passive microwave remote sensing brightness temperatures data for longitude and latitude projections and 0.25 degree resolution from 2002 to 2008 in China. 1. Data processing process: NSIDC produces AMSR-E gridded brightness temperature data by interpolating AMSR-E data (6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz) to the output grids from swath space using an Inverse Distance Squared (ID2) method. 2. Data format: Brightness temperature files: two-byte unsigned integers, little-endian byte order Time files: two-byte signed integers, little-endian byte order 3. Data naming: ID2rx-AMSRE-aayyyydddp.vnn.ccc (China-ID2r1-AMSRE-D.252002170A.v03.06V) ID2 Inverse Distance Squared r1 Resolution 1 swath input data AMSRE Identifies this an AMSR-E file D.25 Identifies this as a quarter degree file yyyy Four-digit year ddd Three-digit day of year p Pass direction (A = ascending, D = descending) vnn Gridded data version number (for example, v01, v02, v03) ccc AMSR-E channel indicator: numeric frequency (06, 10, 18, 23, 36, or 89) followed by polarization (H or V) 4. Cutting range: Corner Coordinates: Upper Left (60.0000000, 55.0000000) (60d 0'0.00 "E, 55d 0'0.00" N) Lower Left (60.0000000, 15.0000000) (60d 0'0.00 "E, 15d 0'0.00" N) Upper Right (140.0000000, 55.0000000) (140d 0'0.00 "E, 55d 0'0.00" N) Lower Right (140.0000000, 15.0000000) (140d 0'0.00 "E, 15d 0'0.00" N) Center (100.0000000, 35.0000000) (100d 0'0.00 "E, 35d 0'0.00" N) Origin = (60.000000000000000, 55.000000000000000) 5. Data projection: GEOGCS ["WGS 84", DATUM ["WGS_1984", SPHEROID ["WGS 84", 6378137,298.257223563, AUTHORITY ["EPSG", "7030"]], TOWGS84 [0,0,0,0,0,0,0], AUTHORITY ["EPSG", "6326"]], PRIMEM ["Greenwich", 0, AUTHORITY ["EPSG", "8901"]], UNIT ["degree", 0.0174532925199433, AUTHORITY ["EPSG", "9108"]], AUTHORITY ["EPSG", "4326"]]
Mary Jo Brodzik, Matthew Savoie, Richard Armstrong, Ken Knowles
Land surface temperature (LST) is a key parameter in the study of surface energy balance. It is widely used in the fields of meteorology, climate, hydrology, agriculture and ecology. As an important means to obtain global and regional scale LST information, satellite (thermal infrared) remote sensing is vulnerable to the influence of cloud cover and other atmospheric conditions, resulting in temporal and spatial discontinuity of LST remote sensing products, which greatly limits the application of LST remote sensing products in related research fields. The preparation of this data set is based on the empirical orthogonal function interpolation method, using Terra / Aqua MODIS surface temperature products to reconstruct the lst under ideal clear sky conditions, and then using the cumulative distribution function matching method to fuse era5 land reanalysis data to obtain the lst under all-weather conditions. This method makes full use of the spatio-temporal information of the original MODIS remote sensing products and the cloud impact information in the reanalysis data, alleviates the impact of cloud cover on LST estimation, and finally reconstructs the high-quality global 0.05 ° spatio-temporal continuous ideal clear sky and all-weather LST data set. This data set not only realizes the seamless coverage of space-time, but also has good verification accuracy. The reconstructed ideal clear sky LST data in the experimental areas of 17 land cover types in the world, the average correlation coefficient (R) is 0.971, the bias (bias) is -0.001 K to 0.049 K, and the root mean square error (RMSE) is 1.436 K to 2.688 K. The verification results of the reconstructed all-weather LST data and the measured data of ground stations: the average R is 0.895, the bias is 0.025 K to 2.599 K, and the RMSE is 4.503 K to 7.299 K. The time resolution of this data set is 4 times a day, the spatial resolution is 0.05 °, the time span is 2002-2020, and the spatial range covers the world.
ZHAO Tianjie, YU Pei
Evapotranspiration monitoring is very important for agricultural water resource management, regional water resource utilization planning and sustainable development of social economy. The limitation of traditional monitoring et method is that it can't be observed in large area at the same time, so it can only be limited to the observation point. Therefore, the cost of personnel and equipment is relatively high. It can't provide the ET data of different land use types and crop types. Remote sensing can be used for quantitative monitoring of ET. the feature of remote sensing information is that it can reflect not only the macro structural characteristics of the earth's surface, but also the micro local differences. This data uses MODIS data and m-sebal model from June to September 2012 and time scale expansion scheme based on reference evaporation ratio to estimate the spatial and temporal distribution of evapotranspiration in the whole growth season of the middle reaches of Heihe River, and uses ground observation data to evaluate m-sebal model and time scale expansion scheme in detail. Its time resolution is day by day, spatial resolution is 250m, and data coverage is in the middle reaches of Heihe River, unit: mm. The projection information of the data is as follows: UTM projection, 47N.
ZHOU Yanzhao, ZHOU Jian
This dataset is land surface phenology estimated from 16 days composite MODIS NDVI product (MOD13Q1 collection6) in the Three-River-Source National Park from 2001 to 2020. The spatial resolution is 250m. The variables include Start of Season (SOS) and End of Season (EOS). Two phenology estimating methods were used to MOD13Q1, polynomial fitting based threshold method and double logistic function based inflection method. There are 4 folders in the dataset. CJYYQ_phen is data folder for source region of the Yangtze River in the national park. HHYYQ_phen is data folder for source region of Yellow River in the national park. LCJYYQ_phen is data folder for source region of Lancang River in the national park. SJY_phen is data folder for the whole Three-River-Source region. Data format is geotif. Arcmap or Python+GDAL are recommended to open and process the data.
WANG Xufeng
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
MAO Kebiao
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
MAO Kebiao
The meteorological elements distribution map of the plateau, which is based on the data from the Tibetan Plateau National Weather Station, was generated by PRISM model interpolation. It includes temperature and precipitation. Monthly average temperature distribution map of the Tibetan Plateau from 1961 to 1990 (30-year average values): t1960-90_1.e00，t1960-90_2.e00，t1960-90_3.e00，t1960-90_4.e00，t1960-90_5.e00， t1960-90_6.e00，t1960-90_7.e00，t1960-90_8.e00，t1960-90_9.e00，t1960-90_10.e00， t1960-90_11.e00，t1960-90_12.e00 Monthly average temperature distribution map of the Tibetan Plateau from 1991 to 2020 (30-year average values): t1991-20_1.e00，t1991-20_2.e00，t1991-20_3.e00，t1991-20_4.e00，t1991-20_5.e00， t1991-20_6.e00，t1991-20_7.e00，t1991-20_8.e00，t1991-20_9.e00，t1991-20_10.e00， t1991-20_11.e00，t1991-20_12.e00， Precipitation distribution map of the Tibetan Plateau from 1961 to 1990 (30-year average values): p1960-90_1.e00，p1960-90_2.e00，p1960-90_3.e00，p1960-90_4.e00，p1960-90_5.e00， p1960-90_6.e00，p1960-90_7.e00，p1960-90_8.e00，p1960-90_9.e00，p1960-90_10.e00， p1960-90_11.e00，p1960-90_12.e00 Precipitation distribution map of the Tibetan Plateau from 1991 to 2020 (30-year average values): p1991-20_1.e00，p1991-20_2.e00，p1991-20_3.e00，p1991-20_4.e00，p1991-20_5.e00， p1991-20_6.e00，p1991-20_7.e00，p1991-20_8.e00，p1991-20_9.e00，p1991-20_10.e00， p1991-20_11.e00，p1991-20_12.e00， The temporal coverage of the data is from 1961 to 1990 and from 1991 to 2020. The spatial coverage of the data is 73°~104.95° east longitude, 26.5°~44.95° north latitude, and the spatial resolution is 0.05 degrees×0.05 degrees (longitude×latitude), and it uses the geodetic coordinate projection. Name interpretation: Monthly average temperature: The average value of daily average temperature in a month. Monthly precipitation: The total precipitation in a month. Dimensions: The file format of the data is E00, and the DN value is the average value of monthly average temperature (×0.01°C) and the average monthly precipitation (×0.01 mm) from January to December. Data type: integer Data accuracy: 0.05 degrees × 0.05 degrees (longitude × latitude). The original sources of these data are two data sets of 1) monthly mean temperature and monthly precipitation observation data from 128 stations on the Tibetan Plateau and the surrounding areas from the establishing times of the stations to 2000 and 2) HadRM3 regional climate scenario simulation data of 50×50 km grids on the Tibetan Plateau, that is, the monthly average temperature and monthly precipitation simulation values from 1991 to 2020. From 1961 to 1990, the PRISM (Parameter elevation Regressions on Independent Slopes Model) interpolation method was used to generate grid data, and the interpolation model was adjusted and verified based on the site data. From 1991 to 2020, the regional climate scenario simulation data were downscaled to generate grid data by the terrain trend surface interpolation method. Part of the source data came from the results of the GCM model simulation; the GCM model used the Hadley Centre climate model HadCM2-SUL. a) Mitchell JFB, Johns TC, Gregory JM, Tett SFB (1995) Climate response to increasing levels of greenhouse gases and sulphate aerosols. Nature, 376, 501-504. b) Johns TC, Carnell RE, Crossley JF et al. (1997) The second Hadley Centre coupled ocean-atmosphere GCM: model description, spinup and validation. Climate Dynamics, 13, 103-134. The spatial interpolation of meteorological data adopted the PRISM (Parameter-elevation Regressions on Independent Slopes Model) method: Daly, C., R.P. Neilson, and D.L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140~158. Due to the difficult observational conditions in the plateau area and the lack of basic research data, there were deletions of meteorological data in some areas. After adjustment and verification, the accuracy of the data was only good enough to be used as a reference for macroscale climate research. The average relative error rate of the monthly average temperature distribution of the Tibetan Plateau from 1961 to 1990 was 8.9%, and that from 1991 to 2020 was 9.7%. The average relative error rate of precipitation data on the Tibetan Plateau from 1961 to 1990 was 20.9%, and that from 1991 to 2020 was 22.7%. The area of missing data was interpolated, and the values of obvious errors were corrected.
ZHOU Caiping
This data set contains the surface temperature and surface emissivity products retrieved from 12 ASTER data in the middle reaches of Heihe River Basin in 2012. The 12 scenes ASTER data all cover the ecological and hydrological experimental area of the middle reaches artificial oasis. The acquisition time (Beijing time) is: 2012-05-302012-06-152012-06-242012-07-102012-08-02, 2012-08-112012-08-182012-08-272012-09-03, 2012-09-122012-09-192012-09-28. The transit time of the above data is around 12:15 (Beijing time). Firstly, the L1B data is corrected by aster L3 data, and then the L1B data is corrected by MODIS mod07 atmospheric profile product with the same transit time and the atmospheric radiation transfer model MODTRAN. In order to improve the accuracy of atmospheric correction, the water vapor scaling (WVS) atmospheric correction method is used. Finally, the aster temperature emissivity separation (TES) algorithm is used to retrieve the surface temperature and the surface emissivity of five bands. The results show that the average deviation of surface temperature products is less than 0.5K and RMSE is less than 2K. This data set can provide reliable input data for remote sensing estimation of key water and heat variables of heterogeneous surface.
LI Hua, WANG Heshun
The data set analyzes the spatial and temporal distribution, impact and loss of typical global flood disasters from 2018 to 2019. In 2018, there were 109 flood disasters in the world, with a death toll of 1995. The total number of people affected was 12.62 million. The direct economic loss was about 4.5 billion US dollars, which was at a low level in the past 30 years. The number of global flood incidents in 2018 was higher in the first half of the year than in the second half of the year, and the frequency of occurrence was higher from May to July. Therefore, based on three typical disaster events such as the hurricane flood in Florence in the United States in 2018, the flooding of the Niger River in Nigeria in 2018, and the Shouguang flood in Shandong Province in 2018, the disaster background, hazard factors, and disaster situation were analyzed. .
JIANG Zijie, JIANG Weiguo, WU Jianjun, ZHOU Hongmin
On 4 July 2012 (UTC+8), TASI sensor carried by the Harbin Y-12 aircraft was used in a visible near Infrared hyperspectral airborne remote sensing experiment, which is located in Linze region and Heihe riverway. The relative flight altitude is 2500 meters. Land surface temperature product was obtained at a resolution of 3 m using a modified temperature/emissivity separation algorithm based on TASI surface radiance data. The product were validated with in situ ground measurements. The validation results indicated that the Land surface temperature product agreed with the ground LSTs well with RMSE lower than 1.5 K.
XIAO Qing, Wen Jianguang
The research project on land surface data assimilation system in western China belongs to the major research plan of "environmental and ecological science in western China" of the national natural science foundation. the person in charge is researcher Li Xin of the institute of environment and engineering in cold and arid regions of the Chinese academy of sciences. the project runs from January 2003 to December 2005. The output data set of the Land Surface Assimilation System in Western China is one of the data achievements of the project. It is a Chinese Land Surface Data Assimilation System constructed by Dr. Huang Chun Lin and researcher Li Xin of the Institute of Cold and Arid Region Environment and Engineering, Chinese Academy of Sciences. CoLM model is used as a model operator to couple microwave radiation transmission models for different surface states such as soil (including melting and freezing), snow cover, etc. and to assimilate passive microwave observations (SSM/I and AMSR-E), so that the system can finally output assimilation data of soil moisture, soil temperature, snow cover, frozen soil, sensible heat, latent heat, evaporation, etc. with higher accuracy. Data format and naming: It is stored in a monthly folder and contains 24 hours of data every day. The naming rules are as follows: YYYMMDDHH.grid, where YY is the year (2002), MM is the month, DD is the day, HH is the hour,. grid and. flux are file extensions, the former is the state variable output result and the latter is the flux output result. The file format is a binary FLOAT value, that is, every 4 bytes represents a value.
LI Xin, HUANG Chunlin
On 10 July 2012 (UTC+8), TASI sensor carried by the Harbin Y-12 aircraft was used in a visible near Infrared hyperspectral airborne remote sensing experiment, which is located in the observation experimental area (30×30 km). The relative flight altitude is 2500 meters. Land surface temperature product was obtained at a resolution of 3 m using a modified temperature/emissivity separation algorithm based on TASI surface radiance data. The product were validated with in situ ground measurements. The validation results indicated that the Land surface temperature product agreed with the ground LSTs well with RMSE lower than 1.5 K.
XIAO Qing, Wen Jianguang
This is China's high temporal and spatial resolution surface solar radiation data set from 2007-2014. The time resolution is hourly, and the spatial resolution is 5 km. Each hour corresponds to a file named RAD_yyyymmddhh.dat, where yyyy represents the year, mm represents the month, dd represents the day, and hh represents the hour (world time). Longitude (X-axis) grid: 70.025:0.05:140.025, latitude (Y-axis) grid: 59.975:-0.05:14.975. The file is a binary file, in the format of float (real*4), with no header file. There are three steps to acquire this data set: (1) integrating polar-orbiting satellite MODIS and Japanese geostationary meteorological satellite MTSAT data and developing a cloud detection algorithm suitable for MTSAT and an estimation method for MTSAT cloud attribute information (effective particle radius and path water content); (2) developing a broad-band radiation model with cloud attribute, aerosol, water vapor, ozone and other inputs to form an efficient and rapid surface solar radiation inversion technique; and (3) inputting the acquired high-resolution cloud parameter information and other elements such as aerosol, water vapor, and ozone into the broad band radiation transmission model, finally obtaining the high temporal and spatial resolution surface solar radiation data set of China. It has been verified that the instantaneous root mean square error (RMSE) is generally less than 100 W•m-2, and the daily mean root mean square error (RMSE) is generally less than 35 W•m-2.
TANG Wenjun
The Qinghai Tibet Plateau belongs to the plateau mountain climate. The precipitation, its seasonal distribution and the change of precipitation forms have been one of the hot spots in the global climate change research. The data includes precipitation data of Qinghai Tibet Plateau, with spatial resolution of 1km * 1km, temporal resolution of month and year, and time coverage of 2000, 2005, 2010 and 2015. The data are obtained by Kring interpolation of meteorological data of National Meteorological Science Information Center. The data can be used to analyze the temporal and spatial distribution of precipitation over the Qinghai Tibet Plateau. In addition, the data can also be used to analyze the temporal and spatial variation of precipitation over the Qinghai Tibet Plateau, which is of great significance to the study of the ecological environment of the Qinghai Tibet Plateau.
FANG Huajun
On 30 June 2012 (UTC+8), TASI sensor carried by the Harbin Y-12 aircraft was used in a visible near Infrared hyperspectral airborne remote sensing experiment, which is located in the observation experimental area (30×30 km). The relative flight altitude is 2500 meters. Land surface temperature product was obtained at a resolution of 3 m using a modified temperature/emissivity separation algorithm based on TASI surface radiance data. The product were validated with in situ ground measurements. The validation results indicated that the Land surface temperature product agreed with the ground LSTs well with RMSE lower than 1.5 K.
XIAO Qing, Wen Jianguang
This data set is a national high-resolution solar radiation data set covering 34 years (1983.7-2017.6), with a resolution of 10 km. The data unit is W / m2. The data set is developed by merging the global high-resolution (3 hours, 10 km) surface solar radiation data set (1983-2017) with isccp-hxg cloud products as the main input, with ground based sunshine duration derived surface solar raidation data from 2261 meteorological stations in China by using the geographic weighted regression method. The validation results show that this dataset can provide more accurate simulation of long-term variability of surface solar radiation than that of gewex-srb, cmsaf-clara-a2 and the isccp-hxg based surface solar radiation product. This data can provide favorable data support for the application and research of long-term change of hydrology in land surface process simulation.
FENG Fei, WANG Kaicun
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