A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological 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. Using wavelets to treat the spectra, which were 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 and monitor soil at scales ranging from regional 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

Prediction of Soil Fertility Properties from a Globally Distributed Soil Mid-Infrared Spectral Library

Globally applicable calibrations to predict standard soil properties based on infrared spectra may increase the use of this reliable technique. The objective of this study was to evaluate the ability of mid-infrared diffuse reflectance spectroscopy (4000-602 cm(-1)) to predict chemical and textural properties for a globally distributed soil spectral library. We scanned 971 soil samples selected from the International Soil Reference and Information Centre database. A high-throughput diffuse reflectance accessory was used with optics that exclude specular reflectance as a potential source of error. Archived data on soil chemical and physical properties were calibrated to first derivative spectra using partial least-squares regression. Good predictions for the spatially independent validation set were achieved for pH value, organic C content, and cation exchange capacity (CEC) (n = 291, r(2) of linear regression of predicted against measured values >= 0.75 and ratio of standard deviation of measured values to root mean square error of prediction (RPD) >= 2.0). The root mean square errors of prediction (RMSEP) were 0.75 pH units, 9.1 g organic C kg(-1) and 5.5 cmol(c) CEC kg(-1). Satisfactory predictions (r(2) = 0.65-0.75, RPD = 1.4-2.0) were obtained for exchangeable Mg concentration and clay content. The respective RMSEPs were 4.3 cmol(c) kg(-1) and 126 g kg(-1). Poorer predictions (r2 = 0.61 and 0.64) were achieved for sand and exchangeable Ca contents. Although RMSEP values are large relative to laboratory analytical errors, our results suggest a marked potential for the global spectral library as a tool for advice on land management, such as the classification of new samples into basic soil fertility classes based on organic C and clay contents, CEC, and pH. Further research is needed to test the stability of this global calibration on new data sets

Fast and Inexpensive Detection of Total and Extractable Element Concentrations in Aquatic Sediments Using Near-Infrared Reflectance Spectroscopy (NIRS)

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