Estimation of soil organic carbon in arable soil in Belgium and Luxembourg with the LUCAS topsoil database

F . C a s t a l d i a , S . C h a b r i l l a t b, C . C h a r t i n a, V . G e n o t c, A . R . J o n e s d & B . v a n W e s e m a e l a aGeorges Lemaı̂tre Centre for Earth and Climate Research, Earth and Life Institute, Universite Catholique de Louvain, Croix du Sud 2, L7.05.16, 1348 Louvain la neuve, Belgium, bHelmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum GFZ, Telegrafenberg, 14473 Potsdam, Germany, cStation Provinciale d’Analyses Agricoles, Rue de Dinant 110, 4557, Tinlot, Belgium, and dEuropean Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi, 2749, 21027, Ispra, Italy

[1]  S. Ogle,et al.  Climate-smart soils , 2016, Nature.

[2]  Ronald D. Snee,et al.  Validation of Regression Models: Methods and Examples , 1977 .

[3]  Bo Stenberg,et al.  Improving the prediction performance of a large tropical vis‐NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques , 2014 .

[4]  John S. Shenk,et al.  Population Definition, Sample Selection, and Calibration Procedures for Near Infrared Reflectance Spectroscopy , 1991 .

[5]  Martial Bernoux,et al.  National calibration of soil organic carbon concentration using diffuse infrared reflectance spectroscopy , 2016 .

[6]  Abdul Mounem Mouazen,et al.  Do we really need large spectral libraries for local scale SOC assessment with NIR spectroscopy , 2016 .

[7]  J. Tukey Some thoughts on clinical trials, especially problems of multiplicity. , 1977, Science.

[8]  Mogens Humlekrog Greve,et al.  Development of a Danish national vis—NIR soil spectral library for SOC determination , 2012 .

[9]  R. V. Rossel,et al.  Using data mining to model and interpret soil diffuse reflectance spectra. , 2010 .

[10]  B. Minasny,et al.  Digital Soil Map of the World , 2009, Science.

[11]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[12]  Mogens Humlekrog Greve,et al.  Predicting Soil Organic Carbon at Field Scale Using a National Soil Spectral Library , 2013 .

[13]  Budiman Minasny,et al.  Soil carbon 4 per mille , 2017 .

[14]  T. Fearn Standardisation and Calibration Transfer for near Infrared Instruments: A Review , 2001 .

[15]  J. Shenk,et al.  New Standardization and Calibration Procedures for Nirs Analytical Systems , 1991 .

[16]  Claudy Jolivet,et al.  Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database , 2012 .

[17]  Zhou Shi,et al.  Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations , 2014, Science China Earth Sciences.

[18]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[19]  Roger,et al.  Spectroscopy of Rocks and Minerals , and Principles of Spectroscopy , 2002 .

[20]  E. Ben-Dor The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400-2500 nm) during a controlled decomposition process , 1997 .

[21]  Pierre Dardenne,et al.  Near Infrared Reflectance Spectroscopy for Estimating Soil Characteristics Valuable in the Diagnosis of Soil Fertility , 2011 .

[22]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[23]  R. Casa,et al.  Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon , 2016 .

[24]  Stefano Pignatti,et al.  A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing , 2013 .

[25]  Bas van Wesemael,et al.  Regional assessment of soil organic carbon changes under agriculture in Southern Belgium (1955-2005) , 2007 .

[26]  Eyal Ben-Dor,et al.  Normalizing reflectance from different spectrometers and protocols with an internal soil standard , 2016 .

[27]  Viacheslav I. Adamchuk,et al.  A global spectral library to characterize the world’s soil , 2016 .

[28]  Arwyn Jones,et al.  The LUCAS topsoil database and derived information on the regional variability of cropland topsoil properties in the European Union , 2013, Environmental Monitoring and Assessment.

[29]  Panos Panagos,et al.  Estimating the soil organic carbon content for European NUTS2 regions based on LUCAS data collection. , 2013, The Science of the total environment.

[30]  Sabine Chabrillat,et al.  Use of hyperspectral images in the identification and mapping of expansive clay soils and the role of spatial resolution , 2002 .

[31]  Stefano Pignatti,et al.  Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data , 2015, Remote. Sens..

[32]  J. Ferrari,et al.  An Investigation of Hope and Context. , 2014, Journal of community psychology.

[33]  César Guerrero,et al.  Spiking of NIR regional models using samples from target sites: effect of model size on prediction accuracy. , 2010 .

[34]  P. Clark,et al.  Determinación de concentraciones séricas de 25(OH) D en niños con lupus eritematoso sistémico y artritis idiopática juvenil , 2015 .

[35]  Panos Panagos,et al.  Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach , 2014 .

[36]  Luca Montanarella,et al.  Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy , 2013, PloS one.

[37]  B. Stenberg,et al.  Near‐infrared spectroscopy for within‐field soil characterization: small local calibrations compared with national libraries spiked with local samples , 2010 .

[38]  Yufeng Ge,et al.  Prediction of Soil Carbon in the Conterminous United States: Visible and Near Infrared Reflectance Spectroscopy Analysis of the Rapid Carbon Assessment Project , 2016 .

[39]  Maurice K. Wong,et al.  Algorithm AS136: A k-means clustering algorithm. , 1979 .