Soil Texture Classes (USDA) by Depth, 250m Resolution

A Refined Global Mapping for 1950-2017

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About this dataset

This open-source dataset offers researchers a granular and comprehensive view of the world's soils, providing soil texture classification from 0 to 200 cm depths with a 250m resolution and utilizing the soiltexture package in R developed by OpenLandMap.org. Using columns such as code, name, value, and color, this dataset brings precision to our understanding of global soils allowing a new level of research accuracy. Internally compressed using COMPRESS=DEFLATE creation option in GDAL for improved accessibly for external users - don't miss out on an unprecedented opportunity to explore the underlying characterstics and properties that make every landscape unique! Explore this valuable open source resource today!

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How to use the dataset

Steps on How To Use This Dataset:

• Understand the data columns - As discussed earlier, you will find four columns in this dataset namely – code (numeric), name (string), value (integer) and color (string). As mentioned before each row contains information regarding a certain type of soil texture associated with their respective codes, names, values and colors which can later be represented in global mapping solutions.
• Clean up data if required - Before you start your analysis it is best practice to clean up your data if required - this includes all irregularities like missing values due to any reasons/circumstances or incorrect labels assigned accidentally against particular entries in columns etcetera.
• Generate customized maps - After making sure that your dataset is complete without any issues now it’s time for visualizing using geographical mapping applications like R or QGIS etcetera based upon your own necessity(say Soil colourful maps depicting occurrences of any particular soil class family all over the world). Future use|interpretations concerning the content within this database are vast depending upon one’s initiative towards exemplifying correlations amongst other variables along with soils accumulation at different depths across vast tracts globally spanning from 1950-2017 eras through highly reliable 250 meters spatiotemporal resolutions as provided herewith!

Research Ideas

• Developing a soil health indicator to track changes in soil texture, fertility, and other physical characteristics over time on a global scale.
• Designing site-specific crop management plans to optimize water uptake and soil nutrient retention.
• Creating predictive models that forecast land suitability for different crops based on specific soil texture requirements

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: sol_texture.class_usda.tt_m_250m_b_1950..2017_v0.1.tif.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------------------------------------------------------------------------------| | Code | A numerical code that represents the soil texture class. (Integer) | | Name | The name of the soil texture class. (String) | | Value | The numerical value corresponding to each code indicating a specific type of soil texture within its corresponding category or range. (Integer) | | Color | The color associated with each individual class for easier visualization on maps or charts. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .