moisture datates provide soil moisture information across the globe at
0.25°x0.25° spatial resolution. These datasets include
surface
and subsurface
soil moisture (mm),
soil moisture profile (%),
and surface and subsurface soil moisture anomalies. Soil moisture anomalies
are unitless and represent standardized
anomalies computed using a 31-days moving window. Values around 0
indicate typical moisture conditions, while very positive and very
negative values indicate extreme wetting (soil moisture conditions are
above average) and drying (soil moisture conditions are below average),
respectively.
This dataset is generated by integrating
satellite-derived Soil Moisture Ocean Salinity (SMOS) Level 2 soil moisture
observations into the modified two-layer Palmer model using a 1-D Ensemble
Kalman Filter (EnKF) data assimilation approach. The assimilation of the
SMOS soil moisture observations helped improve the model-based soil
moisture predictions particularly over poorly instrumented areas
(e.g., Southern African, Middle East) of the world that lack good quality
precipitation data.
The NASA-USDA Global soil moisture and the NASA-USDA SMAP Global soil
moisture datates provide soil moisture information across the globe at
0.25°x0.25° spatial resolution. These datasets include
surface
and subsurface
soil moisture (mm),
soil moisture profile (%),
and surface and subsurface soil moisture anomalies. Soil moisture anomalies
are unitless and represent standardized
anomalies computed using a 31-days moving window. Values around 0
indicate typical moisture conditions, while very positive and very
negative values indicate extreme wetting (soil moisture conditions are
above average) and drying (soil moisture conditions are below average),
respectively.
This dataset is generated by integrating
satellite-derived Soil Moisture Ocean Salinity (SMOS) Level 2 soil moisture
observations into the modified two-layer Palmer model using a 1-D Ensemble
Kalman Filter (EnKF) data assimilation approach. The assimilation of the
SMOS soil moisture observations helped improve the model-based soil
moisture predictions particularly over poorly instrumented areas
(e.g., Southern African, Middle East) of the world that lack good quality
precipitation data.
Name | Description | Gee Unit |
---|---|---|
ssm | Surface soil moisture | mm |
susm | Subsurface soil moisture | mm |
smp | Soil moisture profile | fraction |
ssma | Surface soil moisture anomaly | - |
susma | Subsurface soil moisture anomaly | - |
Providers | |
---|---|
NASA GSFC (producer, licensor) | |
Google Earth Engine (host) | |
STAC Version | 0.6.0 |
Keywords | geophysical, hsl, moisture, nasa, smos, soil, usda |
License | proprietary |
Temporal Extent | 12/31/2009, 4:00:00 PM - now |
Citation | I. E. Mladenova, J.D. Bolten, W.T. Crow, M.C. Anderson, C.R. Hain, D.M. Johnson, R. Mueller(2017). Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S., IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 4, pp. 1328-1343, [DOI 10.1109/JSTARS.2016.2639338](https://doi.org/10.1109/JSTARS.2016.2639338) |
Type | image_collection |
GSD | arc degreesm |
Cadence | days |