Data kelembapan tanah Global SMAP yang ditingkatkan NASA-USDA memberikan informasi kelembapan tanah di seluruh dunia dengan resolusi spasial 10 km. Set data ini mencakup: permukaan,
subpermukaan, kelembapan tanah (mm), profil kelembapan tanah (%),
anomali kelembapan tanah permukaan dan subpermukaan (-).
Set data ini dibuat dengan mengintegrasikan pengamatan kelembapan tanah aktif pasif (SMAP) Level 3 yang berasal dari satelit ke dalam model Palmer dua lapisan yang dimodifikasi menggunakan pendekatan asimilasi data Ensemble Kalman Filter (EnKF) 1-D.
Anomali kelembapan tanah dihitung dari klimatologi hari yang diinginkan.
Klimatologi diperkirakan berdasarkan catatan data lengkap pengamatan satelit SMAP dan pendekatan jendela bergerak yang berpusat pada 31 hari. Asimilasi pengamatan kelembapan tanah SMAP membantu meningkatkan prediksi kelembapan tanah berbasis model, terutama di wilayah dunia yang memiliki sedikit instrumen dan tidak memiliki data presipitasi berkualitas baik.
Set data ini dikembangkan oleh Hydrological Science Laboratory di Goddard Space Flight Center NASA bekerja sama dengan USDA Foreign Agricultural Services dan USDA Hydrology and Remote Sensing Lab.
Band
Ukuran Piksel 10.000 meter
Band
Nama
Unit
Min
Maks
Ukuran Piksel
Deskripsi
ssm
mm
0*
25,39*
meter
Kelembapan tanah permukaan
susm
mm
0*
274,6*
meter
Kelembapan tanah di bawah permukaan
smp
Pecahan
0*
1*
meter
Profil kelembapan tanah
ssma
Tanpa dimensi
-4*
4*
meter
Anomali kelembapan tanah permukaan
susma
Tanpa dimensi
-4*
4*
meter
Anomali kelembapan tanah di bawah permukaan
* perkiraan nilai min atau maks
Persyaratan Penggunaan
Persyaratan Penggunaan
Set data ini berada di domain publik dan tersedia tanpa batasan penggunaan dan distribusi. Lihat Kebijakan Data & Informasi Ilmu Bumi NASA untuk mengetahui informasi tambahan.
Kutipan
Kutipan:
Sazib, N., J. D. Bolten, dan I. E. Mladenova. 2021.
Memanfaatkan NASA Soil Moisture Active Passive untuk Menilai Kerentanan dan Potensi Dampak Kebakaran di Australia dan California.
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. dan Reynolds, C., 2020.
Pemantauan kekeringan pertanian melalui asimilasi pengambilan kelembapan tanah SMAP ke dalam model neraca air tanah global. Depan. Big Data,
3(10).
doi:10.3389/fdata.2020.00010
Sazib, N., J. D. Bolten, dan I. E. Mladenova. 2021.
Memanfaatkan NASA Soil Moisture Active Passive untuk Menilai Kerentanan dan Potensi Dampak Kebakaran di Australia dan California.
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. dan Reynolds,
C., 2019.
Mengevaluasi penerapan operasional SMAP untuk pemantauan kekeringan pertanian global.
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).
Menilai Dampak ENSO terhadap Pertanian di Afrika menggunakan Data Pengamatan Bumi.
Frontiers in Sustainable Food Systems, 4, 188.
doi:10.3389/fsufs.2020.509914Google Scholar
Sazib, N., Mladenova, I. dan Bolten, J., 2018.
Memanfaatkan Google Earth Engine untuk penilaian kekeringan menggunakan data kelembapan tanah global.
Remote sensing,
10(8): 1265.
doi:10.3390/rs10081265
Bolten, J., W.T. Crow, X. Zhan, T.J. Jackson, dan C.A. Reynolds (2010).
Mengevaluasi Kegunaan Pengambilan Kelembapan Tanah yang Diindera dari Jarak Jauh untuk Pemantauan Kekeringan Pertanian Operasional, IEEE Transactions on Geoscience and Remote Sensing, 3(1): 57-66.
doi:10.1109/JSTARS.2009.2037163Google Scholar
Bolten, J., dan W. T. Crow (2012).
Peningkatan prediksi kondisi vegetasi kuasi-global menggunakan kelembapan tanah permukaan yang dideteksi dari jarak jauh, Geophysical Research Letters, 39: (L19406).
[doi:10.1029/2012GL053470][https://doi.org/10.1029/2012GL053470)
Google Cendekia
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).
Misi soil moisture active passive (SMAP), Proceedings of the IEEE,
98(5): 704-716.
doi:10.1109/JPROC.2010.2043918
I. E. Mladenova, J.D. Bolten, W.T. Crow, M.C. Anderson, C.R. Hain, D.M. Johnson, R. Mueller
(2017). Perbandingan Kelembapan Tanah, Tekanan Evaporasi, dan Indeks Vegetasi untuk Memperkirakan Hasil Panen Jagung dan Kedelai di AS,
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
10(4): 1328-1343.
doi:10.1109/JSTARS.2016.2639338
O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, dan R. Bindlish
(2016).
SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Versi 4.
Boulder, Colorado, Amerika Serikat. NASA National Snow and Ice Data Center Distributed
Active Archive Center.doi:10.5067/ZX7YX2Y2LHEB
Data kelembapan tanah Global SMAP yang ditingkatkan NASA-USDA memberikan informasi kelembapan tanah di seluruh dunia pada resolusi spasial 10 km. Set data ini mencakup: permukaan, bawah permukaan, kelembapan tanah (mm), profil kelembapan tanah (%), anomali kelembapan tanah permukaan dan bawah permukaan (-). Set data ini dibuat dengan mengintegrasikan Soil Moisture Active Passive (SMAP) yang berasal dari satelit …
[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)"]]
