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
Active Passive (SMAP) Level 3 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 SMAP soil moisture
observations helped improve the model-based soil moisture predictions
particularly over poorly instrumented areas 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
Active Passive (SMAP) Level 3 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 SMAP soil moisture
observations helped improve the model-based soil moisture predictions
particularly over poorly instrumented areas 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, smap, soil, usda |
License | proprietary |
Temporal Extent | 3/31/2015, 5:00:00 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 |