references.bib

% Encoding: UTF-8

@article{Safanelli2025,
  title    = "Open Soil Spectral Library ({OSSL)}: Building reproducible soil
              calibration models through open development and community
              engagement",
  author   = "Safanelli, Jos{\'e} L and Hengl, Tomislav and Parente, Leandro L
              and Minarik, Robert and Bloom, Dellena E and Todd-Brown,
              Katherine and Gholizadeh, Asa and Mendes, Wanderson de Sousa and
              Sanderman, Jonathan",
  abstract = "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.",
  journal  = "PLoS One",
  volume   =  20,
  number   =  1,
  pages    = "e0296545",
  month    =  jan,
  year     =  2025,
  language = "en",
  doi = {10.1371/journal.pone.0296545}
}

@ARTICLE{Safanelli2023,
  title     = "An interlaboratory comparison of mid-infrared spectra
               acquisition: Instruments and procedures matter",
  author    = "Safanelli, Jos{\'e} L and Sanderman, Jonathan and Bloom, Dellena
               and Todd-Brown, Katherine and Parente, Leandro L and Hengl,
               Tomislav and Adam, Sean and Albinet, Franck and Ben-Dor, Eyal
               and Boot, Claudia M and Bridson, James H and Chabrillat, Sabine
               and Deiss, Leonardo and Dematt{\^e}, Jos{\'e} A M and Scott
               Demyan, M and Dercon, Gerd and Doetterl, Sebastian and van
               Egmond, Fenny and Ferguson, Rich and Garrett, Loretta G and
               Haddix, Michelle L and Haefele, Stephan M and Heiling, Maria and
               Hernandez-Allica, Javier and Huang, Jingyi and Jastrow, Julie D
               and Karyotis, Konstantinos and Machmuller, Megan B and Khesuoe,
               Malefetsane and Margenot, Andrew and Matamala, Roser and Miesel,
               Jessica R and Mouazen, Abdul M and Nagel, Penelope and Patel,
               Sunita and Qaswar, Muhammad and Ramakhanna, Selebalo and Resch,
               Christian and Robertson, Jean and Roudier, Pierre and
               Sabetizade, Marmar and Shabtai, Itamar and Sherif, Faisal and
               Sinha, Nishant and Six, Johan and Summerauer, Laura and Thomas,
               Cathy L and Toloza, Arsenio and Tomczyk-W{\'o}jtowicz, Beata and
               Tsakiridis, Nikolaos L and van Wesemael, Bas and Woodings,
               Finnleigh and Zalidis, George C and {\.Z}elazny, Wiktor R",
  journal   = "Geoderma",
  publisher = "Elsevier BV",
  volume    =  440,
  number    =  116724,
  pages     = "116724",
  month     =  dec,
  year      =  2023,
  copyright = "http://creativecommons.org/licenses/by/4.0/",
  language  = "en",
  doi = {10.1016/j.geoderma.2023.116724}
}

@ARTICLE{Partida2025,
  title     = "Building a near-infrared ({NIR}) soil spectral dataset and
               predictive machine learning models using a handheld {NIR}
               spectrophotometer",
  author    = "Partida, Colleen and Safanelli, Jose Lucas and Mitu, Sadia
               Mannan and Murad, Mohammad Omar Faruk and Ge, Yufeng and
               Ferguson, Richard and Shepherd, Keith and Sanderman, Jonathan",
  abstract  = "This near-infrared spectral dataset consists of 2,106 diverse
               mineral soil samples scanned, on average, on six different units
               of the same low-cost commercially available handheld
               spectrophotometer. Most soil samples were selected from the USDA
               NRCS National Soil Survey Center-Kellogg Soil Survey Laboratory
               (NSSC-KSSL) soil archives to represent the diversity of mineral
               soils (0-30 cm) found in the United States, while 90 samples
               were selected from Ghana, Kenya, and Nigeria to represent
               available African soils in the same archive. All scanning was
               performed on dried and sieved (<2 mm) soil samples. Machine
               learning predictive models were developed for soil organic
               carbon (SOC), pH, bulk density (BD), carbonate (CaCO3),
               exchangeable potassium (Ex. K), sand, silt, and clay content
               from their spectra in the R programming language using most of
               this dataset (1,976 US soils) and are included in this data
               release. Two model types, Cubist and partial least squares
               regression (PLSR) were developed using two strategies: (1) using
               an average of the spectral scans across devices for each sample
               and, (2) using the replicate spectral scans across devices for
               each sample. We present the internal performance of these models
               here. The dry spectra and Cubist models for these soil
               properties are available for download from
               10.5281/zenodo.7586621. An example of detailed code used to
               produce these models is hosted at the Open Soil Spectral
               Library, a free service of the Soil Spectroscopy for the Global
               Good Network (soilspectroscopy.org), enabling broad use of these
               data for multiple soil monitoring applications.",
  journal   = "Data Brief",
  publisher = "Elsevier BV",
  volume    =  58,
  number    =  111229,
  pages     = "111229",
  month     =  feb,
  year      =  2025,
  keywords  = "Chemometrics; Pedometrics; Soil analysis; Soil organic carbon;
               Soil spectroscopy",
  copyright = "http://creativecommons.org/licenses/by/4.0/",
  language  = "en",
  doi = {10.1016/j.dib.2024.111229}
}

@ARTICLE{Mitu2024,
  title     = "Evaluating consistency across multiple {NeoSpectra} (compact
               Fourier transform near‐infrared) spectrometers for estimating
               common soil properties",
  author    = "Mitu, Sadia M and Smith, Colleen and Sanderman, Jonathan and
               Ferguson, Richard R and Shepherd, Keith and Ge, Yufeng",
  abstract  = "AbstractRapid and cost‐effective techniques for soil analysis
               are essential to guide sustainable land management and
               production agriculture. This study aimed at evaluating the
               performance and consistency of portable handheld
               Fourier‐transform near‐infrared spectrometers and the NeoSpectra
               scanners in estimating 12 common soil physical and chemical
               properties including pH; organic carbon (OC); inorganic carbon
               (IC); total nitrogen (TN); cation exchange capacity (CEC); clay,
               silt, and sand fractions; and exchangeable potassium (K),
               phosphorus (P), calcium (Ca), and magnesium (Mg). A diverse set
               of samples (n = 600) were retrieved from a national‐scale soil
               archive of the Kellogg Soil Survey Laboratory of USDA‐NRCS and
               scanned with five NeoSpectra scanners. Predictive models for the
               soil properties were developed using partial least squares
               regression (PLSR), Cubist, and memory‐based learning (MBL).
               Cubist outperformed PLSR and MBL, with the best prediction
               performance for clay, OC, and CEC (R2 > 0.7), followed by IC,
               sand, silt, and Mg (R2 > 0.6), and then pH, TN, and Ca (R2 >
               0.5). K and P were predicted somewhat poorly with R2 of 0.48 and
               0.22. All five NeoSpectra yielded comparable near‐infrared (NIR)
               spectral data and the PLSR models for the soil properties (in
               terms of model regression coefficients). However, the
               consistency assessment showed that the model performance was
               significantly decreased when the training and testing spectra
               were from different NeoSpectra scanners. It is concluded that
               NeoSpectra scanners could be rapid and cost effective for
               estimating certain soil properties, and calibration transfer
               should be considered for applications where multiple devices are
               involved and high estimation accuracy from NIR data is required.",
  journal   = "Soil Sci. Soc. Am. J.",
  publisher = "Wiley",
  volume    =  88,
  number    =  4,
  pages     = "1324--1339",
  month     =  jul,
  year      =  2024,
  copyright = "http://creativecommons.org/licenses/by/4.0/",
  language  = "en",
  doi = {10.1002/saj2.20678}
}

@Article{mlr3,
    title = {{mlr3}: A modern object-oriented machine learning
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    author = {Michel Lang and Martin Binder and Jakob Richter and
      Patrick Schratz and Florian Pfisterer and Stefan Coors and Quay
      Au and Giuseppe Casalicchio and Lars Kotthoff and Bernd Bischl},
    journal = {Journal of Open Source Software},
    year = {2019},
    month = {dec},
    doi = {10.21105/joss.01903},
    url = {https://joss.theoj.org/papers/10.21105/joss.01903},
  }

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  month = nov,
  publisher = {Elsevier {BV}},
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  pages = {295--316},
  author = {Li Yang and Abdallah Shami},
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}

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  year = {2019},
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  publisher = {{MDPI} {AG}},
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  number = {1},
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  author = {Shree Dangal and Jonathan Sanderman and Skye Wills and Leonardo Ramirez-Lopez},
  title = {Accurate and Precise Prediction of Soil Properties from a Large Mid-Infrared Spectral Library},
  journal = {Soil Systems}
}

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  author = {Loretta G. Garrett and Jonathan Sanderman and David J. Palmer and Fiona Dean and Sunita Patel and James H. Bridson and Thomas Carlin},
  title = {Mid-infrared spectroscopy for planted forest soil and foliage nutrition predictions,  New Zealand case study},
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}

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  author = {Marcus Schiedung and Severin-Luca Bell{\`{e}} and Avni Malhotra and Samuel Abiven},
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}

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}

@article{Wijewardane2016,
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  publisher = {Wiley},
  volume = {80},
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}

@article{angelopoulou2020laboratory,
  title={From laboratory to proximal sensing spectroscopy for soil organic carbon estimation—a review},
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@article{brocca2019sm2rain,
  title={{SM2RAIN--ASCAT (2007--2018): global daily satellite rainfall data from ASCAT soil moisture observations}},
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@article{dematte2020bare,
  title={Bare earth's surface spectra as a proxy for soil resource monitoring},
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@Article{Summerauer2021,
AUTHOR = {Summerauer, L. and Baumann, P. and Ramirez-Lopez, L. and Barthel, M. and Bauters, M. and Bukombe, B. and Reichenbach, M. and Boeckx, P. and Kearsley, E. and Van Oost, K. and Vanlauwe, B. and Chiragaga, D. and Heri-Kazi, A. B. and Moonen, P. and Sila, A. and Shepherd, K. and Bazirake Mujinya, B. and Van Ranst, E. and Baert, G. and Doetterl, S. and Six, J.},
TITLE = {The central African soil spectral library: a new soil infrared repository and a geographical prediction analysis},
JOURNAL = {SOIL},
VOLUME = {7},
YEAR = {2021},
NUMBER = {2},
PAGES = {693--715},
URL = {https://soil.copernicus.org/articles/7/693/2021/},
DOI = {10.5194/soil-7-693-2021}
}

@article{sanderman2020mid,
  title={{Mid-infrared spectroscopy for prediction of soil health indicators in the United States}},
  author={Sanderman, Jonathan and Savage, Kathleen and Dangal, Shree RS},
  journal={Soil Science Society of America Journal},
  volume={84},
  number={1},
  doi={10.1002/saj2.20009},
  pages={251--261},
  year={2020},
  publisher={Wiley Online Library}
}

@article{jensen2018converting,
  title={Converting loss-on-ignition to organic carbon content in arable topsoil: pitfalls and proposed procedure},
  author={Jensen, JL and Christensen, BT and Schj{\o}nning, P and Watts, CW and Munkholm, LJ},
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  year={2018},
  doi={10.1111/ejss.12558},
  publisher={Wiley Online Library}
}

@article{sanderman2021can,
  title={Can Agricultural Management Induced Changes in Soil Organic Carbon Be Detected Using Mid-Infrared Spectroscopy?},
  author={Sanderman, Jonathan and Savage, Kathleen and Dangal, Shree RS and Duran, Gabriel and Rivard, Charlotte and Cavigelli, Michel A and Gollany, Hero T and Jin, Virginia L and Liebig, Mark A and Omondi, Emmanuel Chiwo and others},
  journal={Remote Sensing},
  volume={13},
  number={12},
  pages={2265},
  year={2021},
  doi={10.3390/rs13122265},
  publisher={Multidisciplinary Digital Publishing Institute}
}

@article{dudek2021mid,
  title={Mid-Infrared Spectroscopy Supports Identification of the Origin of Organic Matter in Soils},
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  year={2021},
  doi={10.3390/land10020215},
  publisher={Multidisciplinary Digital Publishing Institute}
}

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@article{vohland2022quantification,
  title={{Quantification of soil organic carbon at regional scale: Benefits of fusing vis-NIR and MIR diffuse reflectance data are greater for in situ than for laboratory-based modelling approaches}},
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}

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@article{deSantana2023,
  doi = {10.1016/j.geoderma.2023.116491},
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  month = jun,
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}

@article{wijewardane2018predicting,
  title={{Predicting physical and chemical properties of US soils with a mid-infrared reflectance spectral library}},
  author={Wijewardane, Nuwan K and Ge, Yufeng and Wills, Skye and Libohova, Zamir},
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}

@article{ayres2019quantitative,
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  author={Ayres, Edward},
  journal={Soil Science Society of America Journal},
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  pages={973--981},
  year={2019},
  publisher={Wiley Online Library}
}

@article{orgiazzi2018lucas,
  title={{LUCAS Soil, the largest expandable soil dataset for Europe: a review}},
  author={Orgiazzi, Alberto and Ballabio, Cristiano and Panagos, Panagiotis and Jones, Arwyn and Fern{\'a}ndez-Ugalde, Oihane},
  journal={European Journal of Soil Science},
  volume={69},
  number={1},
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  doi={10.1111/ejss.12499},
  year={2018},
  publisher={Wiley Online Library}
}

@book{Vagen_2020,
author = {Vagen, Tor-Gunnar and Winowiecki, Leigh Ann and Desta, Luseged and Tondoh, Ebagnerin Jerome and Weullow, Elvis and Shepherd, Keith and Sila, Andrew},
publisher = {World Agroforestry - Research Data Repository},
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@article{hong2022fusion,
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@book{malone2021soil,
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@book{BenedettiEgmond2021,
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@book{hengl2019predictive,
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