Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement Article Swipe
José Lucas Safanelli
,
Tomislav Hengl
,
Leandro Parente
,
Robert Minařík
,
Dellena E. Bloom
,
Katherine EO Todd-Brown
,
Asa Gholizadeh
,
Wanderson de Sousa Mendes
,
Jonathan Sanderman
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0296545
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0296545
Soil spectroscopy is a widely used method for estimating soil properties that are important to environmental and agricultural monitoring. However, a bottleneck to its more widespread adoption is the need for establishing large reference datasets for training machine learning (ML) models, which are called soil spectral libraries (SSLs). Similarly, the prediction capacity of new samples is also subject to the number and diversity of soil types and conditions represented in the SSLs. To help bridge this gap and enable hundreds of stakeholders to collect more affordable soil data by leveraging a centralized open resource, the Soil Spectroscopy for Global Good initiative has created the Open Soil Spectral Library (OSSL). In this paper, we describe the procedures for collecting and harmonizing several SSLs that are incorporated into the OSSL, followed by exploratory analysis and predictive modeling. The results of 10-fold cross-validation with refitting show that, in general, mid-infrared (MIR)-based models are significantly more accurate than visible and near-infrared (VisNIR) or near-infrared (NIR) models. From independent model evaluation, we found that Cubist comes out as the best-performing ML algorithm for the calibration and delivery of reliable outputs (prediction uncertainty and representation flag). Although many soil properties are well predicted, total sulfur, extractable sodium, and electrical conductivity performed poorly in all spectral regions, with some other extractable nutrients and physical soil properties also performing poorly in one or two spectral regions (VisNIR or NIR). Hence, the use of predictive models based solely on spectral variations has limitations. This study also presents and discusses several other open resources that were developed from the OSSL, aspects of opening data, current limitations, and future development. With this genuinely open science project, we hope that OSSL becomes a driver of the soil spectroscopy community to accelerate the pace of scientific discovery and innovation.
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- article
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- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0296545
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0296545&type=printable
- OA Status
- gold
- Cited By
- 19
- References
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- OpenAlex ID
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https://openalex.org/W4406326961Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0296545Digital Object Identifier
- Title
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Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagementWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-13Full publication date if available
- Authors
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José Lucas Safanelli, Tomislav Hengl, Leandro Parente, Robert Minařík, Dellena E. Bloom, Katherine EO Todd-Brown, Asa Gholizadeh, Wanderson de Sousa Mendes, Jonathan SandermanList of authors in order
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https://doi.org/10.1371/journal.pone.0296545Publisher landing page
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0296545&type=printableDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0296545&type=printableDirect OA link when available
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Calibration, Environmental science, Computer science, Bottleneck, Remote sensing, Near-infrared spectroscopy, Soil science, Mathematics, Statistics, Physics, Geology, Embedded system, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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19Total citation count in OpenAlex
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2025: 19Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.calibration | 179 |
| abstract_inverted_index.centralized | 91 |
| abstract_inverted_index.evaluation, | 165 |
| abstract_inverted_index.exploratory | 130 |
| abstract_inverted_index.extractable | 199, 213 |
| abstract_inverted_index.harmonizing | 119 |
| abstract_inverted_index.independent | 163 |
| abstract_inverted_index.innovation. | 295 |
| abstract_inverted_index.monitoring. | 18 |
| abstract_inverted_index.represented | 68 |
| abstract_inverted_index.uncertainty | 186 |
| abstract_inverted_index.Spectroscopy | 96 |
| abstract_inverted_index.agricultural | 17 |
| abstract_inverted_index.conductivity | 203 |
| abstract_inverted_index.development. | 268 |
| abstract_inverted_index.establishing | 31 |
| abstract_inverted_index.incorporated | 124 |
| abstract_inverted_index.limitations, | 265 |
| abstract_inverted_index.limitations. | 243 |
| abstract_inverted_index.mid-infrared | 146 |
| abstract_inverted_index.spectroscopy | 1, 285 |
| abstract_inverted_index.stakeholders | 81 |
| abstract_inverted_index.environmental | 15 |
| abstract_inverted_index.near-infrared | 156, 159 |
| abstract_inverted_index.significantly | 150 |
| abstract_inverted_index.representation | 188 |
| abstract_inverted_index.best-performing | 174 |
| abstract_inverted_index.cross-validation | 139 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5011992634, https://openalex.org/A5022678523, https://openalex.org/A5059286326, https://openalex.org/A5036217930, https://openalex.org/A5089626916, https://openalex.org/A5023282235, https://openalex.org/A5022853382, https://openalex.org/A5061651690, https://openalex.org/A5008884945 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I1313003366, https://openalex.org/I1320745970, https://openalex.org/I205984670, https://openalex.org/I33213144 |
| citation_normalized_percentile.value | 0.9974086 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |