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

Reflectance spectroscopy detects management and landscape differences in soil carbon and nitrogen.

Not AvailableMany studies have calibrated visible and near-infrared (VNIR) diffuse reflectance spectroscopy (DRS) to various soil properties; however, few studies have used VNIR DRS to detect treatment differences in controlled experiments. Therefore, our objective was to investigate the ability of VNIR DRS to detect treatment differences in topsoil organic C (SOC) and total N (TN) compared with standard dry combustion analysis. A long-term (since 1991) experiment in central Missouri, where cropping systems were replicated across a typical claypan soil landscape was studied. Soil samples from two depths (0–5 and 5–15 cm) were obtained in 2008 at summit, backslope, and footslope positions for three grain cropping systems. Estimates of SOC by VNIR DRS using oven-dried soil samples and an independent calibration set were very good, with R2 = 0.87 and RMSE = 2.4 g kg−1. Estimates of TN were somewhat less accurate (R2 = 0.79, RMSE = 0.24 g kg−1). Field-moist VNIR DRS results were also good, but with 13 to 17% higher RMSE. Trends in differences among treatment means were very similar for dry combustion, oven-dry soil VNIR, and field-moist VNIR. Dry combustion was best at separating treatment means, followed by dry soil VNIR and fi eld-moist VNIR. Differences among methods were relatively minor for 0- to 5-cm depth samples but more pronounced for 5- to 15-cm samples. Efficiency of the VNIR method, particularly when applied ton field-moist soil, suggests that it deserves consideration as a tool for determining near-surface SOC and TN differences in field experiments.Not Availabl

Near-Infrared Spectroscopy Technology for Soil Nutrients Detection Based on LS-SVM

Part 1: Decision Support Systems, Intelligent Systems and Artificial Intelligence ApplicationsInternational audienceThe detection method of the soil nutrients (organic matter and available N, P, K) were analyzed based on the near infrared spectroscopy technology in order to decision-making for precision fertilization. 54 samples with 7m×7m was collected using DGPS receiver positioning in a soybean field. The soil organic matter, available nitrogen (N), available phosphorus (P), available potassium (K) content was determined, the near-infrared diffuse reflectance spectrum of the soil samples were obtained by FieldSpec3 spectrometer. 54 samples were randomly divided into 40 prediction sets and 14 validation sets. After smoothing, the eight principal components of original spectra were extracted by principal component analysis (PCA). Prediction model of soil organic matter, available nitrogen (N), available phosphorus (P), potassium (K) were respectively established with the eight principal component as input and soil nutrients by measured as the output, and the 14 validation samples were predicted. The results showed that the soil organic matter, available nitrogen (N), available phosphorus (P), potassium (K) prediction model were set up with principal component analysis and LS-SVM, which the correlation coefficients between the prediction value and measurement value were 0.8708, 0.7206, 0.8421 and 0.6858, the relative errors of the LS-SVM prediction was smaller and those mean values were 1.09%, 1.06%, 4.08% and 0.69%. The method of soil organic matter content prediction is feasible