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Open Soil Spectral Library contains training MIR (91,631) and VisNIR (65,063) spectral scans + soil calibration data (>60,000 unique locations) and calibration models. Key data set: ossl_all_L1_v1.2.qs: soil laboratory, site and spectra information; Important note: The data set spatially over-represents USA and European Union, with little training data in Asia, South America and Australia, hence calibration models reflect primarily soils of USA and Europe. To use the models and data please install R and required packages. Read more about the QS data format and how to convert it to CSV or similar. Modeling steps are explained in detail in: https://github.com/soilspectroscopy/ossl-models. To visualize database please use: https://explorer.soilspectroscopy.org/ Complete OSSL documentation can be found at: https://soilspectroscopy.github.io/ossl-manual/ Soil Spectroscopy for the Global Good is a Coordinated Innovation Network funded by USDA NIFA Food and Agriculture Cyberinformatics Tools Program (Award #2020-67021-32467). Input datasets are property of the USDA NRCS National Soil Survey Center – Kellogg Soil Survey Laboratory, ICRAF-World Agroforestry, ISRIC-World Soil Information, the Africa Soil Information Service funded by the Bill and Melinda Gates Foundation, the European Soil Data Centre, the National Ecological Observatory Network, and ETH Zurich. For more advanced uses of the soil spectral libraries we advise to contact the original data producers especially to get help with using, extending and improving the original SSL data.
{"references": ["Orgiazzi, Alberto, Cristiano Ballabio, Panagiotis Panagos, Arwyn Jones, and Oihane Fern\u00e1ndez-Ugalde. 2018. \"LUCAS Soil, the largest expandable soil dataset for Europe: a review.\" European Journal of Soil Science 69 (1): 140\u201353. https://doi.org/10.1111/ejss.12499.", "Sanderman, Jonathan, Kathleen Savage, and Shree RS Dangal. 2020. \"Mid-infrared spectroscopy for prediction of soil health indicators in the United States.\" Soil Science Society of America Journal 84 (1): 251\u201361. https://doi.org/10.1002/saj2.20009.", "Summerauer, L., P. Baumann, L. Ramirez-Lopez, M. Barthel, M. Bauters, B. Bukombe, M. Reichenbach, et al. 2021. \"The Central African Soil Spectral Library: A New Soil Infrared Repository and a Geographical Prediction Analysis.\" SOIL 7 (2): 693\u2013715. https://doi.org/10.5194/soil-7-693-2021.", "Vagen, Tor-Gunnar, Leigh Ann Winowiecki, Luseged Desta, Ebagnerin Jerome Tondoh, Elvis Weullow, Keith Shepherd, and Andrew Sila. 2020. Mid-Infrared Spectra (MIRS) from ICRAF Soil and Plant Spectroscopy Laboratory: Africa Soil Information Service (AfSIS) Phase I 2009-2013. World Agroforestry - Research Data Repository. https://doi.org/10.34725/DVN/QXCWP1."]}
spectroscopy, machine learning, pH, carbon, soil, mlr
spectroscopy, machine learning, pH, carbon, soil, mlr
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