סיווג הטקסטורה של משרד החקלאות האמריקאי בעומקי קרקע של 0-20 ס"מ ו-20-50 ס"מ.
באזורים של ג'ונגל צפוף (בדרך כלל מעל מרכז אפריקה), רמת הדיוק של המודל נמוכה ולכן יכול להיות שיוצגו ארטיפקטים כמו פסים.
Hengl, T., Miller, M.A.E., קריז'אן, ג'., ואחרים. מיפוי של תכונות ורכיבי תזונה בקרקע באפריקה ברזולוציה מרחבית של 30 מ' באמצעות למידת מכונה בהרכב דו-קנה מידה.
Sci Rep 11, 6130 (2021).
doi:10.1038/s41598-021-85639-y
סיווג הטקסטורה של משרד החקלאות האמריקאי בעומקי קרקע של 0-20 ס"מ ו-20-50 ס"מ. באזורים של ג'ונגל צפוף (בדרך כלל מעל מרכז אפריקה), רמת הדיוק של המודל נמוכה ולכן יכול להיות שיוצגו ארטיפקטים כמו פסים. חיזויים של תכונות הקרקע בוצעו על ידי Innovative Solutions for Decision Agriculture Ltd. (iSDA) ברזולוציה של 30 …
[null,null,[],[[["\u003cp\u003eThe ISDASOIL/Africa/v1/texture_class dataset provides USDA soil texture classifications for Africa at 30m resolution for 0-20cm and 20-50cm depths.\u003c/p\u003e\n"],["\u003cp\u003eThis dataset, spanning from 2001 to 2017, was created by iSDA using machine learning and remote sensing, with potential inaccuracies in dense jungle regions.\u003c/p\u003e\n"],["\u003cp\u003eSoil texture is classified into 12 categories (e.g., clay, sand, loam) with corresponding color codes for visualization.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is available under a CC-BY-4.0 license and users can access it through Google Earth Engine.\u003c/p\u003e\n"],["\u003cp\u003eFor further information, users are directed to iSDA's FAQ and technical documentation.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil USDA Texture Class\n\nDataset Availability\n: 2001-01-01T00:00:00Z--2017-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [iSDA](https://isda-africa.com/)\n\nTags\n:\n[africa](/earth-engine/datasets/tags/africa) [aluminium](/earth-engine/datasets/tags/aluminium) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) \n\n#### Description\n\nUSDA Texture Class at soil depths of 0-20 cm and 20-50 cm.\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n\n### Bands\n\n\n**Pixel Size**\n\n30 meters\n\n**Bands**\n\n| Name | Pixel Size | Description |\n|-----------------|------------|--------------------------------------|\n| `texture_0_20` | meters | USDA Texture Class at 0-20 cm depth |\n| `texture_20_50` | meters | USDA Texture Class at 20-50 cm depth |\n\n**texture_0_20 Class Table**\n\n| Value | Color | Description |\n|-------|---------|-----------------|\n| 1 | #d5c36b | Clay |\n| 2 | #b96947 | Silty Clay |\n| 3 | #9d3706 | Sandy Clay |\n| 4 | #ae868f | Clay Loam |\n| 5 | #f86714 | Silty Clay Loam |\n| 6 | #46d143 | Sandy Clay Loam |\n| 7 | #368f20 | Loam |\n| 8 | #3e5a14 | Silt Loam |\n| 9 | #ffd557 | Sandy Loam |\n| 10 | #fff72e | Silt |\n| 11 | #ff5a9d | Loamy Sand |\n| 12 | #ff005b | Sand |\n\n**texture_20_50 Class Table**\n\n| Value | Color | Description |\n|-------|---------|-----------------|\n| 1 | #d5c36b | Clay |\n| 2 | #b96947 | Silty Clay |\n| 3 | #9d3706 | Sandy Clay |\n| 4 | #ae868f | Clay Loam |\n| 5 | #f86714 | Silty Clay Loam |\n| 6 | #46d143 | Sandy Clay Loam |\n| 7 | #368f20 | Loam |\n| 8 | #3e5a14 | Silt Loam |\n| 9 | #ffd557 | Sandy Loam |\n| 10 | #fff72e | Silt |\n| 11 | #ff5a9d | Loamy Sand |\n| 12 | #ff005b | Sand |\n\n### Terms of Use\n\n**Terms of Use**\n\n[CC-BY-4.0](https://spdx.org/licenses/CC-BY-4.0.html)\n\n### Citations\n\nCitations:\n\n- Hengl, T., Miller, M.A.E., Križan, J., et al. African soil properties and nutrients\n mapped at 30 m spatial resolution using two-scale ensemble machine learning.\n Sci Rep 11, 6130 (2021).\n [doi:10.1038/s41598-021-85639-y](https://doi.org/10.1038/s41598-021-85639-y)\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 raw = ee.Image(\"ISDASOIL/Africa/v1/texture_class\");\nMap.addLayer(\n raw.select(0), {}, \"Texture class, 0-20 cm\");\nMap.addLayer(\n raw.select(1), {}, \"Texture class, 20-50 cm\");\n\nMap.setCenter(25, -3, 2);\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_texture_class) \n[iSDAsoil USDA Texture Class](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_texture_class) \nUSDA Texture Class at soil depths of 0-20 cm and 20-50 cm. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen. Soil property predictions were made by Innovative Solutions for Decision Agriculture Ltd. (iSDA) at 30 ... \nISDASOIL/Africa/v1/texture_class, africa,aluminium,isda,soil \n2001-01-01T00:00:00Z/2017-01-01T00:00:00Z \n-35.22 -31.46 37.98 57.08 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://isda-africa.com/)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_texture_class)"]]
