Les données mondiales améliorées sur l'humidité du sol SMAP de la NASA et de l'USDA fournissent des informations sur l'humidité du sol à l'échelle mondiale, avec une résolution spatiale de 10 km. Cet ensemble de données inclut les anomalies d'humidité du sol en surface et en sous-surface (-), ainsi que l'humidité du sol en surface, en sous-surface, le profil d'humidité du sol (%) et l'humidité du sol (mm).
L'ensemble de données est généré en intégrant les observations de l'humidité du sol de niveau 3 Active Passive (SMAP) dérivées par satellite dans le modèle Palmer modifié à deux couches à l'aide d'une approche d'assimilation de données de filtre Kalman d'ensemble (EnKF) en 1D.
Les anomalies d'humidité du sol ont été calculées à partir de la climatologie du jour concerné.
La climatologie a été estimée sur la base de l'ensemble des données d'observation du satellite SMAP et de l'approche de la fenêtre mobile centrée sur 31 jours. L'assimilation des observations de l'humidité du sol SMAP permet d'améliorer les prévisions de l'humidité du sol basées sur des modèles, en particulier dans les régions du monde mal instrumentées qui manquent de données de précipitations de bonne qualité.
Cet ensemble de données a été développé par le laboratoire des sciences hydrologiques du centre de vol spatial Goddard de la NASA, en coopération avec les services agricoles étrangers de l'USDA et le laboratoire d'hydrologie et de télédétection de l'USDA.
Sazib, N., J. D. Bolten et I. E. Mladenova. 2021.
Utilisation de NASA Soil Moisture Active Passive pour évaluer la sensibilité au feu et les impacts potentiels en Australie et en Californie.
IEEE Journal of Selected Topics in Applied Earth Observations and
Remote Sensing, 15: 779-787.
doi:10.1109/jstars.2021.3136756
Mladenova, I.E., Bolten, J.D., Crow, W., Sazib, N. et Reynolds, C., 2020.
Surveillance de la sécheresse agricole grâce à l'assimilation des données de récupération de l'humidité du sol SMAP dans un modèle global de bilan hydrique du sol. Avant. Big Data,
3(10).
doi:10.3389/fdata.2020.00010
Sazib, N., J. D. Bolten et I. E. Mladenova. 2021.
Utilisation de NASA Soil Moisture Active Passive pour évaluer la sensibilité au feu et les impacts potentiels en Australie et en Californie.
IEEE Journal of Selected Topics in Applied Earth Observations and
Remote Sensing, 15: 779-787.
doi:10.1109/jstars.2021.3136756
Mladenova, I.E., Bolten, J.D., Crow, W.T., Sazib, N., Cosh, M.H., Tucker, C.J. et Reynolds,
C., 2019.
Évaluation de l'application opérationnelle de SMAP pour la surveillance mondiale de la sécheresse agricole.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
12(9): 3387-3397.
doi:10.1109/JSTARS.2019.2923555
Sazib, N., Mladenova, I., & Bolten, J. (2020).
Évaluation de l'impact d'ENSO sur l'agriculture en Afrique à l'aide de données d'observation de la Terre.
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Sazib, N., Mladenova, I. et Bolten, J., 2018.
Utiliser Google Earth Engine pour évaluer la sécheresse à l'aide de données mondiales sur l'humidité du sol.
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Bolten, J., W.T. Crow, X. Zhan, T.J. Jackson et C.A. Reynolds (2010).
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Amélioration de la prédiction des conditions de végétation quasi mondiales à l'aide de l'humidité du sol en surface détectée à distance, Geophysical Research Letters, 39 : (L19406).
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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).
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I. E. Mladenova, J.D. Bolten, W.T. Crow, M.C. Anderson, C.R. Hain, D.M. Johnson, R. Mueller (2017). Comparaison des indices d'humidité du sol, de stress évaporatif et de végétation pour estimer les rendements de maïs et de soja aux États-Unis,
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
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O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson et R. Bindlish (2016).
SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 4.
Boulder, Colorado, États-Unis. NASA National Snow and Ice Data Center Distributed Active Archive Center.doi:10.5067/ZX7YX2Y2LHEB
Les données mondiales améliorées sur l'humidité du sol SMAP de la NASA et de l'USDA fournissent des informations sur l'humidité du sol dans le monde entier, avec une résolution spatiale de 10 km. Cet ensemble de données comprend : la surface, le sous-sol, l'humidité du sol (mm), le profil d'humidité du sol (%), les anomalies d'humidité du sol en surface et en sous-sol (-). L'ensemble de données est généré en intégrant les données Soil Moisture Active Passive (SMAP) issues des satellites…
[null,null,[],[[["\u003cp\u003eThis dataset has been superseded by a newer version, NASA/SMAP/SPL4SMGP/007.\u003c/p\u003e\n"],["\u003cp\u003eThe NASA-USDA Enhanced SMAP dataset provides global soil moisture information at 10-km resolution, including surface and subsurface measurements, anomalies, and soil moisture profiles.\u003c/p\u003e\n"],["\u003cp\u003eIt covers the period from April 2, 2015, to August 2, 2022, and is generated by integrating SMAP satellite observations into a hydrological model.\u003c/p\u003e\n"],["\u003cp\u003eThis dataset is in the public domain and available without restriction.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset was developed by NASA's Goddard Space Flight Center in cooperation with USDA Foreign Agricultural Services and USDA Hydrology and Remote Sensing Lab.\u003c/p\u003e\n"]]],["This dataset provides global soil moisture data at a 10-km resolution from 2015-04-02 to 2022-08-02, derived from NASA's SMAP satellite. It offers surface and subsurface soil moisture in mm, soil moisture profiles in percentage, and soil moisture anomalies, generated using a data assimilation approach. The information is accessible through Earth Engine, using the `ee.ImageCollection(\"NASA_USDA/HSL/SMAP10KM_soil_moisture\")` code, and it is publicly available without usage restrictions. However, it has been superseded by a new dataset: NASA/SMAP/SPL4SMGP/007.\n"],null,["# NASA-USDA Enhanced 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--2022-08-02T12:00:00Z\n\nDataset Provider\n:\n\n\n [NASA GSFC](https://doi.org/10.1109/jstars.2021.3136756)\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 Enhanced SMAP Global soil moisture data provides soil moisture information across\nthe globe at 10-km spatial resolution. This dataset includes: surface,\nsubsurface, soil moisture (mm), soil moisture profile (%),\nsurface and subsurface soil moisture anomalies (-).\n\nThe dataset is generated by integrating satellite-derived Soil Moisture Active Passive (SMAP)\nLevel 3 soil moisture observations into the modified two-layer Palmer model using a 1-D\nEnsemble Kalman Filter (EnKF) data assimilation approach.\nSoil moisture anomalies were computed from the climatology of the day of interest.\nThe climatology was estimated based on the full data record of the SMAP satellite observation\nand the 31-day-centered moving-window approach. The assimilation of the SMAP soil moisture\nobservations help improve the model-based soil moisture predictions particularly over poorly\ninstrumented areas of the world that lack good quality precipitation data.\n\nThis dataset was developed by the Hydrological Science Laboratory at NASA's Goddard Space\nFlight Center in cooperation with USDA Foreign Agricultural Services and USDA Hydrology\nand Remote Sensing Lab.\n\n### Bands\n\n\n**Pixel Size**\n\n10000 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- **Sazib, N., J. D. Bolten, and I. E. Mladenova. 2021.**\n Leveraging NASA Soil Moisture Active Passive for Assessing Fire\n Susceptibility and Potential Impacts Over Australia and California.\n *IEEE Journal of Selected Topics in Applied Earth Observations and\n Remote Sensing* , 15: 779-787.\n [doi:10.1109/jstars.2021.3136756](https://doi.org/10.1109/jstars.2021.3136756)\n\n **Mladenova, I.E., Bolten, J.D., Crow, W., Sazib, N. and Reynolds, C., 2020.**\n Agricultural drought monitoring via the assimilation of SMAP soil moisture retrievals into a\n global soil water balance model. *Front. Big Data* ,\n 3(10).\n [doi:10.3389/fdata.2020.00010](https://doi.org/10.3389/fdata.2020.00010)\n- **Sazib, N., J. D. Bolten, and I. E. Mladenova. 2021.**\n Leveraging NASA Soil Moisture Active Passive for Assessing Fire\n Susceptibility and Potential Impacts Over Australia and California.\n *IEEE Journal of Selected Topics in Applied Earth Observations and\n Remote Sensing* , 15: 779-787.\n [doi:10.1109/jstars.2021.3136756](https://doi.org/10.1109/jstars.2021.3136756)\n- **Mladenova, I.E., Bolten, J.D., Crow, W.T., Sazib, N., Cosh, M.H., Tucker, C.J. and Reynolds,\n C., 2019.**\n Evaluating the operational application of SMAP for global agricultural drought monitoring.\n *IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing* ,\n 12(9): 3387-3397.\n [doi:10.1109/JSTARS.2019.2923555](https://doi.org/10.1109/JSTARS.2019.2923555)\n- **Sazib, N., Mladenova, I., \\& Bolten, J. (2020).**\n Assessing the Impact of ENSO on Agriculture over Africa using Earth Observation Data.\n *Frontiers in Sustainable Food Systems* , 4, 188.\n [doi:10.3389/fsufs.2020.509914](https://doi.org/10.3389/fsufs.2020.509914)\n [Google Scholar](https://scholar.google.com/scholar?cluster=10102210156681705582&oi=scholarr)\n- **Sazib, N., Mladenova, I. and Bolten, J., 2018.**\n Leveraging the google earth engine for drought assessment using global soil moisture data.\n *Remote sensing* ,\n 10(8): 1265.\n [doi:10.3390/rs10081265](https://doi.org/10.3390/rs10081265)\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 Operational Agricultural\n Drought Monitoring, *IEEE Transactions on Geoscience and Remote Sensing* ,\n 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).**\n Improved prediction of quasi-global vegetation conditions using remotely sensed surface soil\n moisture, *Geophysical Research Letters* ,\n 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- **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- **I. E. Mladenova, J.D. Bolten, W.T. Crow, M.C. Anderson, C.R. Hain, D.M. Johnson, R. Mueller\n (2017).** Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for\n Estimating Corn and Soybean Yields Over the U.S.,\n *IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing* ,\n 10(4): 1328-1343.\n [doi:10.1109/JSTARS.2016.2639338](https://doi.org/10.1109/JSTARS.2016.2639338)\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.2009.2037163\u003e\n- \u003chttps://doi.org/10.1109/JSTARS.2016.2639338\u003e\n- \u003chttps://doi.org/10.1109/JSTARS.2019.2923555\u003e\n- \u003chttps://doi.org/10.1109/jstars.2021.3136756\u003e\n- \u003chttps://doi.org/10.3389/fsufs.2020.509914\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/SMAP10KM_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_SMAP10KM_soil_moisture) \n[NASA-USDA Enhanced SMAP Global Soil Moisture Data \\[deprecated\\]](/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP10KM_soil_moisture) \nThe NASA-USDA Enhanced SMAP Global soil moisture data provides soil moisture information across the globe at 10-km spatial resolution. This dataset includes: surface, subsurface, soil moisture (mm), soil moisture profile (%), surface and subsurface soil moisture anomalies (-). The dataset is generated by integrating satellite-derived Soil Moisture Active Passive (SMAP) ... \nNASA_USDA/HSL/SMAP10KM_soil_moisture, geophysical,hsl,nasa,smap,soil,soil-moisture,usda \n2015-04-02T12:00:00Z/2022-08-02T12:00:00Z \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://doi.org/10.1109/jstars.2021.3136756)\n- [https://doi.org/10.5067/ZX7YX2Y2LHEB](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP10KM_soil_moisture)"]]
