Global Land Data Assimilation System (GLDAS) ingests satellite and ground-based observational data products. Using advanced land surface modeling and data assimilation techniques, it generates optimal fields of land surface states and fluxes.
GLDAS-2.0 is one of two components of the GLDAS Version 2 (GLDAS-2) dataset, the second being GLDAS-2.1. GLDAS-2.0 is reprocessed with the updated Princeton Global Meteorological Forcing Dataset (Sheffield et al., 2006) and upgraded Land Information System Version 7 (LIS-7). It covers the period 1948-2010, and will be extended to more recent years as corresponding forcing data become available.
The model simulation was initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulation used the common GLDAS datasets for land cover (MCD12Q1: Friedl et al., 2010), land water mask (MOD44W: Carroll et al., 2009), soil texture (Reynolds, 1999), and elevation (GTOPO30). The MODIS based land surface parameters are used in the current GLDAS-2.x products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012).
Documentation:
- Readme
- How-to
- 全球陆地数据同化系统（GLDAS）摄取了卫星和地面观测数据产品。它使用先进的陆地表面建模和数据同化技术，生成陆地表面状态和通量的最佳领域。 GLDAS-2.0是GLDAS第二版（GLDAS-2）数据集的两个组成部分之一，第二个是GLDAS-2.1。GLDAS-2.0是用更新的普林斯顿全球气象强迫数据集（Sheffield等人，2006）和升级的土地信息系统第7版（LIS-7）重新处理的。它涵盖了1948-2010年，并将随着相应的强迫数据的获得而扩展到更近的年份。 模型模拟在1948年1月1日初始化，使用当年LSM气候学中的土壤水分和其他状态场。模拟使用了通用的GLDAS数据集，用于土地覆盖（MCD12Q1：Friedl等人，2010）、土地水分掩蔽（MOD44W：Carroll等人，2009）、土壤纹理（Reynolds，1999）和海拔（GTOPO30）。目前的GLDAS-2.x产品使用的是基于MODIS的地表参数，而GLDAS-1和之前的GLDAS-2产品（2012年10月之前）使用的是AVHRR基础参数。 提供者注：扩展名为_tavg的是过去3小时的平均变量，扩展名为'_acc'的是过去3小时的累积变量，扩展名为'_inst'的是瞬时变量，扩展名为_f的是强制变量。 GES DISC Hydrology Documentation
Provider's Note: the names with extension _tavg are variables averaged over the past 3-hours, the names with extension '_acc' are variables accumulated over the past 3-hours, the names with extension '_inst' are instantaneous variables, and the names with '_f' are forcing variables.
Dataset Availability
1948-01-01T00:00:00 - 2010-12-31T00:00:00
Dataset Provider
NASA GES DISC at NASA Goddard Space Flight Center
Collection Snippet
ee.ImageCollection("NASA/GLDAS/V20/NOAH/G025/T3H")
Resolution
27830 meters
Bands Table
Name | Description | Min* | Max* | Units |
|---|---|---|---|---|
Albedo_inst | Albedo | 4.99 | 82.25 | % |
AvgSurfT_inst | Average surface skin temperature | 194.55 | 351.63 | K |
CanopInt_inst | Plant canopy surface water | 0 | 0.5 | kg/m^2 |
ECanop_tavg | Canopy water evaporation | 0 | 671.88 | W/m^2 |
ESoil_tavg | Direct evaporation from bare soil | 0 | 592.64 | W/m^2 |
Evap_tavg | Evapotranspiration | 0 | 0.0002 | kg/m^2/s |
LWdown_f_tavg | Downward long-wave radiation flux | 44.62 | 561.46 | W/m^2 |
Lwnet_tavg | Net long-wave radiation flux | -359.07 | 130.59 | W/m^2 |
PotEvap_tavg | Potential evaporation rate | -241.88 | 1513.78 | W/m^2 |
Psurf_f_inst | Pressure | 47824.13 | 109036.41 | Pa |
Qair_f_inst | Specific humidity | 0 | 0.06 | kg/kg |
Qg_tavg | Heat flux | -517.58 | 485.13 | W/m^2 |
Qh_tavg | Sensible heat net flux | -872.46 | 797.71 | W/m^2 |
Qle_tavg | Latent heat net flux | -243.71 | 716.69 | W/m^2 |
Qs_acc | Storm surface runoff | 0 | 131.39 | kg/m^2 |
Qsb_acc | Baseflow-groundwater runoff | 0 | 42.3 | kg/m^2 |
Qsm_acc | Snow melt | 0 | 27.58 | kg/m^2 |
Rainf_f_tavg | Total precipitation rate | 0 | 0.01 | kg/m^2/s |
Rainf_tavg | Rain precipitation rate | 0 | 0.01 | kg/m^2/s |
RootMoist_inst | Root zone soil moisture | 2 | 943.52 | kg/m^2 |
SWE_inst | Snow depth water equivalent | 0 | 117283.5 | kg/m^2 |
SWdown_f_tavg | Downward short-wave radiation flux | 0 | 1329.22 | W/m^2 |
SnowDepth_inst | Snow depth | 0 | 293.2 | m |
Snowf_tavg | Snow precipitation rate | 0 | 0.004 | kg/m^2/s |
SoilMoi0_10cm_inst | Soil moisture | 1.99 | 47.59 | kg/m^2 |
SoilMoi10_40cm_inst | Soil moisture | 5.99 | 142.8 | kg/m^2 |
SoilMoi40_100cm_inst | Soil moisture | 11.99 | 285.6 | kg/m^2 |
SoilMoi100_200cm_inst | Soil moisture | 20 | 476 | kg/m^2 |
SoilTMP0_10cm_inst | Soil temperature | 218.75 | 329.55 | K |
SoilTMP10_40cm_inst | Soil temperature | 227.3 | 317.08 | K |
SoilTMP40_100cm_inst | Soil temperature | 232.59 | 313.47 | K |
SoilTMP100_200cm_inst | Soil temperature | 234.5 | 311.86 | K |
Swnet_tavg | Net short wave radiation flux | 0 | 1128.86 | W/m^2 |
Tair_f_inst | Air temperature | 197.03 | 326.2 | K |
Tveg_tavg | Transpiration | 0 | 611.89 | W/m^2 |
Wind_f_inst | Wind speed | 0.06 | 30.31 | m/s |
* = Values are estimated数据引用：
影像属性
Name | Type | Description |
|---|---|---|
end_hour | Double | End hour |
start_hour | Double | Start hour |
引用：
Rodell, M., P.R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J.K. Entin, J.P. Walker, D. Lohmann, and D. Toll, The Global Land Data Assimilation System, Bull. Amer. Meteor. Soc., 85(3), 381-394, 2004.
代码：
代码语言：javascript复制var dataset = ee.ImageCollection('NASA/GLDAS/V20/NOAH/G025/T3H')
.filter(ee.Filter.date('2010-06-01', '2010-06-02'));
var averageSurfaceSkinTemperatureK = dataset.select('AvgSurfT_inst');
var averageSurfaceSkinTemperatureKVis = {
min: 250.0,
max: 300.0,
palette: ['1303ff', '42fff6', 'f3ff40', 'ff5d0f'],
};
Map.setCenter(71.72, 52.48, 3.0);
Map.addLayer(
averageSurfaceSkinTemperatureK, averageSurfaceSkinTemperatureKVis,
'Average Surface Skin Temperature [K]');
