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Newsletter #1: https://tinyurl.com/1wx2o36t
Newsletter #2: http://tinyurl.com/2v797qqw
Newsletter #3: https://tinyurl.com/3tjda7d2
Newsletter #4: https://tinyurl.com/6v6d8bza
Newsletter #5: https://tinyurl.com/ycysb9xr
Wadoux, A. 2021: “Estimating soil properties with spectral data”, https://doi.org/10.5446/52954
Ramirez-Lopez, L. 2021: “Getting accurate predictions from large and diverse spectral libraries”, https://doi.org/10.5446/52955
Soreneson, P. 2022: “Mapping Soil Organic Carbon In Soil Profiles using Imaging Spectroscopy”, https://doi.org/10.5446/57308
Shepherd, K. D., Ferguson, R., Hoover, D., van Egmond, F., Sanderman, J., & Ge, Y. (2022). A Global Soil Spectral Calibration Library and Estimation Service. Soil Security, 100061. 10.1016/j.soisec.2022.100061
Sanderman, J., Savage, K., Dangal, S.R.S., Duran, G., et al. (2021) Can agricultural management induced changes in soil organic carbon be detected using mid-infrared Spectroscopy? Remote Sensing, 13, 2265. 10.3390/rs13122265
Gholizadeh, A., Neumann, C., Chabrillat, S., van Wesemael, B., et al. (2021). Soil organic carbon estimation using VNIR-SWIR spectroscopy: The effect of multiple sensors and scanning condition. Soil and Tillage Research, 211, 105017. 10.1016/j.still.2021.105017
van Egmond, F. M., Ferguson, R., Shepherd, K. D., Peng, Y., et al. (2021) A Global Soil Spectral Calibration Library and Estimation Service. ASA, CSSA, SSSA International Annual Meeting, Salt Lake City, UT. https://scisoc.confex.com/scisoc/2021am/meetingapp.cgi/Paper/138364
Pittaki-Chrysodonta, Z., Hartemink, A., Sanderman, J., Ge, Y., Huang, J. (2021). Evaluation three calibration transfer methods for predictions of soil properties using mid-infrared spectroscopy. Soil Science Society of America Journal. 10.1002/saj2.20225
Dangal, S.R.S., Sanderman, J. (2020). Is standardization necessary for sharing a large mid-infrared soil spectral library? Sensors, 20, 6729. 10.3390/s20236729
Sanderman, J., Todd-Brown, K.E., Hengl, T., Dangal, S.R.S., et al. (2020). Spectroscopy to fill the soil data gap. ASA-CSSA-SSSA International Annual Meeting. November 2020. https://scisoc.confex.com/scisoc/2020am/prelim.cgi.Paper.131585
Sanderman, J., Dangal, S.R.S., Todd-Brown, K.E., Hengl, T., et al. (2020). Filling the soil data gap. American Geophysical Union Fall Meeting. December 2020. http://agu.confex.com/agu/fm20/meetingapp.cgi/Paper/713137
Sanderman, J. (2021) Which emerging approaches can enable widespread soil carbon measurement and monitoring? ISCN | AGU | USDA Climate Hubs series. July 13 2021. https://www.youtube.com/watch?v=vrtru5wzwgQ&t=1958s
Luce, M. S., Ziadi, N., & Viscarra Rossel, R. A. (2022). GLOBAL-LOCAL: A new approach for local predictions of soil organic carbon content using large soil spectral libraries. Geoderma, 425, 116048. 10.1016/j.geoderma.2022.116048
Viscarra Rossel, R. A., Behrens, T., Ben‐Dor, E., Chabrillat, S., et al. (2022). Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century. European Journal of Soil Science, 73(4), e13271. 10.1111/ejss.13271
Shen, Z., Ramirez-Lopez, L., Behrens, T., Cui, L., et al. (2022). Deep transfer learning of global spectra for local soil carbon monitoring. ISPRS Journal of Photogrammetry and Remote Sensing, 188, 190-200. 10.1016/j.isprsjprs.2022.04.009
Ng, W., Minasny, B., Jeon, S. H., McBratney, A. (2022). Mid-infrared spectroscopy for accurate measurement of an extensive set of soil properties for assessing soil functions. Soil Security, 6, 100043. 10.1016/j.soisec.2022.100043
Summerauer, L., Baumann, P., Ramirez-Lopez, L., Barthel, M., et al. (2021). The central African soil spectral library: a new soil infrared repository and a geographical prediction analysis. Soil, 7(2), 693-715. 10.5194/soil-7-693-2021
Sanderman, J., Savage, K., & Dangal, S. R. (2020). Mid‐infrared spectroscopy for prediction of soil health indicators in the United States. Soil Science Society of America Journal, 84(1), 251-261. 10.1002/saj2/20009
Dematte, J.A.M., Dotto, A.C., Paiva, A.F.S., Sato, M.V., et al. (2019). The Brazilian soil spectral library (BSSL): A general view, application and challenges. Geoderma, 354, 113793. 10.1016/j.geoderma.2019.05.043
Matamala, R., Jastrow, J.D., Calderon, F.J., Liang, C., et al. (2019). Predictiong the decomposability of arctic tundra soil organic matter with mid infrared spectroscopy. Soil Biology and Biochemistry, 129, 1–12. 10.1016/j.soilbio.2018.10.014
Dangal, S. R., Sanderman, J., Wills, S., & Ramirez-Lopez, L. (2019). Accurate and precise prediction of soil properties from a large mid-infrared spectral library. Soil Systems, 3(1), 11. 10.3390/soilsystems3010011
Wijewardane, N.K., Ge, Y., Wills, S., Libohova, Z. (2018). Predictiong physical and chemical properties of US soils with a mid-infrared reflectance spectral library. Soil Science Society of America Journal, 82, 722–731. 10.2136/sssaj2017.10.0361
Viscarra Rossel, R.A., Behrens, T., Ben-Dor, E., Brown, D.J., et al. (2016). A global spectral library to characterize the world’s soil. Earth-Science Reviews, 155, 198–230. 10.1016/j.earscirev.2016.01.012
Ben-Dor, E., Ong, C., Lau, I.C. (2015). Reflectance measurements of soils in the laboratory: Standards and protocols. Geoderma, 245–246, 112–124. 10.1016/j.geoderma.2015.01.002
Nocita, M., Stevens, A., van Wesemael, B., Aitkenhead, M., et al. (2015). Soil spectroscopy: An alternative to wet chemistry for soil monitoring. Advances in Agronomy, 132, 139–159. 0.1016/bs.agron.2015.02.002
Baldock, J.A., Hawke, B., Sanderman, J., Macdonald, L.M. (2013). Predicting contents of carbon and its component fractions in Australian soils from diffuse reflectance mid-infrared spectra. Soil Research, 51, 577–595. 10.1071/SR13077
Gholizadeh, A., Boruvka, L., Saberioon, M.M., Vasat, R. (2013). Visible, near-infrared and mid-infrared spectroscopy application for soil assessment with emphasis on soil organic matter content and quality: State-of-the-art and key issues. Applied Spectroscopy, 67, 1349–1362. 10.1366/13-07288
Toth, G., Jones, A., Montanarella, L. (2013). The LUCAS topsoil database and derived information on the regional variability of cropland topsoil properties in the European Union. Environmental Monitoring and Assessment, 185, 7409–7425. 10.1007/s10661-013-3109-3
Terhoeven-Urselmans, T., Vagen, T., Spaargaren, O., Shepherd, K.D. (2010). Prediction of soil fertility from a globally distributed soil mid-infrared spectral library. Soil Science Society of America Journal, 74, 1792–1799. 10.2136/sssaj2009.0218
Viscarra Rossel, R.A., Behrens, T. (2010). Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 158, 46–54. 10.1016/j.geoderma.2009.12.025
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