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.
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
Entekhabi, D, Njoku, EG, O'Neill, PE, Kellogg, KH, Crow, WT, Edelstein,
WN, Entin, JK, Goodman, SD, Jackson, TJ, Johnson, J, Kimball, J, Piepmeier,
JR, Koster, RD, Martin, N, McDonald, KC, Moghaddam, M, Moran, S, Reichle,
R, Shi, JC, Spencer, MW, Thurman, SW, Tsang, L & Van Zyl, J (2010).
The soil moisture active passive (SMAP) mission, Proceedings of the IEEE,
98(5): 704-716.
doi:10.1109/JPROC.2010.2043918Article
O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, and R. Bindlish
(2016).
SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 4.
Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed
Active Archive Center.doi:10.5067/ZX7YX2Y2LHEB
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 …
[[["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 data at 0.25°x0.25° spatial resolution, including surface and subsurface moisture, moisture profile, and anomalies.\u003c/p\u003e\n"],["\u003cp\u003eIt has been superseded by a newer dataset, NASA/SMAP/SPL4SMGP/007, and is no longer recommended for use.\u003c/p\u003e\n"],["\u003cp\u003eThe data is derived from integrating SMAP satellite observations into the Palmer model using an Ensemble Kalman Filter.\u003c/p\u003e\n"],["\u003cp\u003eIt covers the period from April 2, 2015, to December 31, 2020, and is freely available for use and distribution.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset was developed by NASA's Hydrological Science Laboratory in collaboration with USDA agencies.\u003c/p\u003e\n"]]],["This dataset offers global soil moisture data from 2015 to 2020 at a 0.25°x0.25° resolution, derived from NASA's SMAP satellite observations. Key data points include surface and subsurface soil moisture (mm), soil moisture profile (%), and unitless surface/subsurface anomalies, indicating wetting or drying trends. The data, generated by NASA's Hydrological Science Laboratory, integrates SMAP data into the Palmer model via Ensemble Kalman Filter for improved global moisture predictions. This dataset is deprecated, use NASA/SMAP/SPL4SMGP/007 instead.\n"],null,["# NASA-USDA SMAP 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: 2015-04-02T12:00:00Z--2020-12-31T12:00:00Z\n\nDataset Provider\n:\n\n\n [NASA GSFC](https://gimms.gsfc.nasa.gov/SMOS/SMAP/)\n\nCadence\n: 3 Days\n\nTags\n:\n geophysical \n hsl \n nasa \n smap \n soil \n soil-moisture \nusda \n\n#### Description\n\nThe NASA-USDA Global soil moisture and the NASA-USDA SMAP Global soil\nmoisture datates 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 satellite-derived Soil Moisture\nActive Passive (SMAP) Level 3 soil moisture observations into the modified\ntwo-layer Palmer model using a 1-D Ensemble Kalman Filter (EnKF) data\nassimilation approach. The assimilation of the SMAP soil moisture\nobservations helped improve the model-based soil moisture predictions\nparticularly over poorly instrumented areas of the world that lack good\nquality precipitation 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- **Entekhabi, D, Njoku, EG, O'Neill, PE, Kellogg, KH, Crow, WT, Edelstein,\n WN, Entin, JK, Goodman, SD, Jackson, TJ, Johnson, J, Kimball, J, Piepmeier,\n JR, Koster, RD, Martin, N, McDonald, KC, Moghaddam, M, Moran, S, Reichle,\n R, Shi, JC, Spencer, MW, Thurman, SW, Tsang, L \\& Van Zyl, J (2010).**\n The soil moisture active passive (SMAP) mission, *Proceedings of the IEEE* ,\n 98(5): 704-716.\n [doi:10.1109/JPROC.2010.2043918](https://doi.org/10.1109/JPROC.2010.2043918)\n [Article](https://ieeexplore.ieee.org/document/5460980)\n- **O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, and R. Bindlish\n (2016).**\n SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 4.\n Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed\n Active Archive Center.[doi:10.5067/ZX7YX2Y2LHEB](https://doi.org/10.5067/ZX7YX2Y2LHEB)\n\n### DOIs\n\n- \u003chttps://doi.org/10.1029/2012GL053470\u003e\n- \u003chttps://doi.org/10.1109/JPROC.2010.2043918\u003e\n- \u003chttps://doi.org/10.1109/JSTARS.2016.2639338\u003e\n- \u003chttps://doi.org/10.3390/rs10081265\u003e\n- \u003chttps://doi.org/10.5067/ZX7YX2Y2LHEB\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/SMAP_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, 46.529, 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_SMAP_soil_moisture) \n[NASA-USDA SMAP Global Soil Moisture Data \\[deprecated\\]](/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP_soil_moisture) \nThe 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 ... \nNASA_USDA/HSL/SMAP_soil_moisture, geophysical,hsl,nasa,smap,soil,soil-moisture,usda \n2015-04-02T12:00:00Z/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/10.5067/ZX7YX2Y2LHEB](https://doi.org/https://gimms.gsfc.nasa.gov/SMOS/SMAP/)\n- [https://doi.org/10.5067/ZX7YX2Y2LHEB](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP_soil_moisture)"]]
