The NASA-USDA Global soil moisture and the NASA-USDA SMAP Global soil
moisture dataset 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.
This dataset was developed by the Hydrological Science Laboratory (HSL) at
NASA's Goddard Space Flight Center in cooperation with USDA Foreign
Agricultural Services and USDA Hydrology and Remote Sensing Lab.
Bolten, J., W.T. Crow, X. Zhan, T.J. Jackson, and C.A. Reynolds (2010).
Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for
Operational Agricultural Drought Monitoring, IEEE Transactions on
Geoscience and Remote Sensing, 3(1): 57-66.
doi:10.1109/JSTARS.2009.2037163Google Scholar
Bolten, J., and W. T. Crow (2012). Improved prediction of quasi-global
vegetation conditions using remotely-sensed surface soil moisture,
Geophysical Research Letters, 39: (L19406).
doi:10.1029/2012GL053470Google Scholar
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, 10(4): 1328-1343,
doi:10.1109/JSTARS.2016.2639338
Sazib, N., I. E. Mladenova, J.D. Bolten (2018). Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data. Remote Sensing, 10(8), p.1265.
doi:10.3390/rs10081265Google Scholar
Kerr, Y. H., and D. Levine (2008). Forward to the special issue on
the Soil Moisture and Ocean Salinity (SMOS) mission, IEEE Transactions
on Geoscience and Remote Sensing, 46(3): 583-585.
doi:10.1109/TGRS.2008.917807Google Scholar
The NASA-USDA Global soil moisture and the NASA-USDA SMAP Global soil moisture dataset 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 …
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],[],[[["\u003cp\u003eThis dataset provides global soil moisture information at 0.25°x0.25° spatial resolution, including surface and subsurface moisture, profile, and anomalies.\u003c/p\u003e\n"],["\u003cp\u003eIt has been superseded by a newer dataset, NASA/SMAP/SPL4SMGP/007, and covers the period from 2010-01-13 to 2020-12-31.\u003c/p\u003e\n"],["\u003cp\u003eData is derived from integrating SMOS satellite observations into the Palmer model using an Ensemble Kalman Filter.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset was developed by NASA's Hydrological Science Laboratory in cooperation with USDA Foreign Agricultural Services and USDA Hydrology and Remote Sensing Lab.\u003c/p\u003e\n"],["\u003cp\u003eThis dataset is in the public domain and is available without restriction on use and distribution.\u003c/p\u003e\n"]]],[],null,["# NASA-USDA Global Soil Moisture Data [deprecated]\n\n**Caution:** This dataset has been superseded by [NASA/SMAP/SPL4SMGP/007](/earth-engine/datasets/catalog/NASA_SMAP_SPL4SMGP_007). \n\nDataset Availability\n: 2010-01-13T12:00:00Z--2020-12-31T12:00:00Z\n\nDataset Provider\n:\n\n\n [NASA GSFC](https://gimms.gsfc.nasa.gov/SMOS/jbolten/FAS/)\n\nCadence\n: 3 Days\n\nTags\n:\n geophysical \n hsl \n nasa \n smos \n soil \n soil-moisture \nusda \n\n#### Description\n\nThe NASA-USDA Global soil moisture and the NASA-USDA SMAP Global soil\nmoisture dataset provide soil moisture information across the globe at\n0.25°x0.25° spatial resolution. These datasets include\n[surface](https://gimms.gsfc.nasa.gov/SMOS/SMAP/Surface_Soil_Moisture_SMAP.pdf)\nand [subsurface](https://gimms.gsfc.nasa.gov/SMOS/SMAP/Sub_SurfaceSoil_Moisture_SMAP.pdf)\nsoil moisture (mm),\n[soil moisture profile](https://gimms.gsfc.nasa.gov/SMOS/SMAP/SoilMoisture_Profile_SMAP.pdf) (%),\nand surface and subsurface soil moisture anomalies. Soil moisture anomalies\nare unitless and represent standardized\nanomalies computed using a 31-days moving window. Values around 0\nindicate typical moisture conditions, while very positive and very\nnegative values indicate extreme wetting (soil moisture conditions are\nabove average) and drying (soil moisture conditions are below average),\nrespectively.\n\nThis dataset is generated by integrating\nsatellite-derived Soil Moisture Ocean Salinity (SMOS) Level 2 soil moisture\nobservations into the modified two-layer Palmer model using a 1-D Ensemble\nKalman Filter (EnKF) data assimilation approach. The assimilation of the\nSMOS soil moisture observations helped improve the model-based soil\nmoisture predictions particularly over poorly instrumented areas\n(e.g., Southern African, Middle East) of the world that lack good quality\nprecipitation data.\n\nThis dataset was developed by the Hydrological Science Laboratory (HSL) at\nNASA's Goddard Space Flight Center in cooperation with USDA Foreign\nAgricultural Services and USDA Hydrology and Remote Sensing Lab.\n\n### Bands\n\n\n**Pixel Size**\n\n27830 meters\n\n**Bands**\n\n| Name | Units | Min | Max | Pixel Size | Description |\n|---------|---------------|------|---------|------------|----------------------------------|\n| `ssm` | mm | 0\\* | 25.39\\* | meters | Surface soil moisture |\n| `susm` | mm | 0\\* | 274.6\\* | meters | Subsurface soil moisture |\n| `smp` | Fraction | 0\\* | 1\\* | meters | Soil moisture profile |\n| `ssma` | Dimensionless | -4\\* | 4\\* | meters | Surface soil moisture anomaly |\n| `susma` | Dimensionless | -4\\* | 4\\* | meters | Subsurface soil moisture anomaly |\n\n\\* estimated min or max value\n\n### Terms of Use\n\n**Terms of Use**\n\nThis dataset is in the public domain and is available\nwithout restriction on use and distribution. See [NASA's\nEarth Science Data \\& Information Policy](https://www.earthdata.nasa.gov/engage/open-data-services-and-software/data-and-information-policy)\nfor additional information.\n\n### Citations\n\nCitations:\n\n- **Bolten, J., W.T. Crow, X. Zhan, T.J. Jackson, and C.A. Reynolds (2010).**\n Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for\n Operational Agricultural Drought Monitoring, *IEEE Transactions on\n Geoscience and Remote Sensing* , 3(1): 57-66.\n [doi:10.1109/JSTARS.2009.2037163](https://doi.org/10.1109/JSTARS.2009.2037163)\n [Google Scholar](https://scholar.google.com/scholar?as_sdt=0%2C21&q=Improved+prediction+of+quasi-global+vegetation+conditions+using+remotely-sensed+surface+soil+moisture%2C+&btnG=)\n- **Bolten, J., and W. T. Crow (2012).** Improved prediction of quasi-global\n vegetation conditions using remotely-sensed surface soil moisture,\n *Geophysical Research Letters* , 39: (L19406).\n [doi:10.1029/2012GL053470](https://doi.org/10.1029/2012GL053470)\n [Google Scholar](https://scholar.google.com/scholar?as_sdt=0%2C21&q=Improved+prediction+of+quasi-global+vegetation+conditions+using+remotely-sensed+surface+soil+moisture%2C+&btnG=)\n- **I. E. Mladenova, J.D. Bolten, W.T. Crow, M.C. Anderson, C.R. Hain,\n D.M. Johnson, R. Mueller (2017).** Intercomparison of Soil Moisture,\n Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean\n Yields Over the U.S., *IEEE Journal of Selected Topics in Applied Earth\n Observations and Remote Sensing* , 10(4): 1328-1343,\n [doi:10.1109/JSTARS.2016.2639338](https://doi.org/10.1109/JSTARS.2016.2639338)\n- **Sazib, N., I. E. Mladenova, J.D. Bolten (2018).** Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data. *Remote Sensing* , 10(8), p.1265.\n [doi:10.3390/rs10081265](https://doi.org/10.3390/rs10081265)\n [Google Scholar](https://scholar.google.com/scholar_lookup?title=Leveraging%20the%20Google%20Earth%20Engine%20for%20Drought%20Assessment%20Using%20Global%20Soil%20Moisture%20Data&author=N.%20Sazib&author=I.%20Mladenova&author=J.%20Bolten&journal=Remote%20Sens&volume=10&issue=8&pages=1265&publication_year=2018)\n- **Kerr, Y. H., and D. Levine (2008).** Forward to the special issue on\n the Soil Moisture and Ocean Salinity (SMOS) mission, *IEEE Transactions\n on Geoscience and Remote Sensing* , 46(3): 583-585.\n [doi:10.1109/TGRS.2008.917807](https://doi.org/10.1109/TGRS.2008.917807)\n [Google Scholar](https://scholar.google.com/scholar?as_sdt=0%2C21&q=Forward+to+the+special+issue+on+the+Soil+Moisture+and+Ocean+Salinity+%28SMOS%29+mission&btnG=)\n\n### DOIs\n\n- \u003chttps://doi.org/10.1029/2012GL053470\u003e\n- \u003chttps://doi.org/10.1109/JSTARS.2016.2639338\u003e\n- \u003chttps://doi.org/10.1109/TGRS.2008.917807\u003e\n- \u003chttps://doi.org/10.3390/rs10081265\u003e\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar dataset = ee.ImageCollection('NASA_USDA/HSL/soil_moisture')\n .filter(ee.Filter.date('2017-04-01', '2017-04-30'));\nvar soilMoisture = dataset.select('ssm');\nvar soilMoistureVis = {\n min: 0.0,\n max: 28.0,\n palette: ['0300ff', '418504', 'efff07', 'efff07', 'ff0303'],\n};\nMap.setCenter(-6.746, 15.529, 2);\nMap.addLayer(soilMoisture, soilMoistureVis, 'Soil Moisture');\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/NASA_USDA/NASA_USDA_HSL_soil_moisture) \n[NASA-USDA Global Soil Moisture Data \\[deprecated\\]](/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture) \nThe NASA-USDA Global soil moisture and the NASA-USDA SMAP Global soil moisture dataset 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 ... \nNASA_USDA/HSL/soil_moisture, geophysical,hsl,nasa,smos,soil,soil-moisture,usda \n2010-01-13T12:00:00Z/2020-12-31T12:00:00Z \n-60 -180 90 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [https://doi.org/10.3390/rs10081265](https://doi.org/https://gimms.gsfc.nasa.gov/SMOS/jbolten/FAS/)\n- [https://doi.org/10.3390/rs10081265](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture)"]]
