Abstract

Soil provides ecosystemservices, supports human health and habitation, stores carbon and regulatesemissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agroecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Usingwavelets to treat the spectra, whichwere recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess andmonitor soil at scales ranging fromregional to global.New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of.

Keywords

Soil spectral library; Global soil dataset; Soil vis–NIR spectra; Vis–NIR spectroscopy; Multivariate statistics; Machine learning Wavelets

Published in

Earth-Science Reviews
2016, Volume: 155, pages: 198-230

SLU Authors

• Stenberg, Bo

  • Department of Soil and Environment, Swedish University of Agricultural Sciences
    

Sustainable Development Goals

Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss


UKÄ Subject classification

Soil Science
Agricultural Science
Environmental Sciences related to Agriculture and Land-use


Publication identifier

DOI: https://doi.org/10.1016/j.earscirev.2016.01.012

Permanent link to this page (URI)

https://res.slu.se/id/publ/75308