Strategies for the efficient estimation of soil organic carbon at the field scale with vis-NIR spectroscopy: Spectral libraries and spiking vs. local calibrations
暂无分享,去创建一个
[1] S. Grunwald,et al. Transferability and Scalability of Soil Total Carbon Prediction Models in Florida, USA , 2018, Pedosphere.
[2] F. Castaldi,et al. Estimation of soil organic carbon in arable soil in Belgium and Luxembourg with the LUCAS topsoil database , 2018 .
[3] Luboš Borůvka,et al. Building soil spectral library of the Czech soils for quantitative digital soil mapping. , 2018 .
[4] Michael Vohland,et al. Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms , 2017, Remote. Sens..
[5] Qinghu Jiang,et al. Estimation of soil organic carbon and total nitrogen in different soil layers using VNIR spectroscopy: Effects of spiking on model applicability , 2017 .
[6] Yi Peng,et al. Comparing predictive ability of laser-induced breakdown spectroscopy to visible near-infrared spectroscopy for soil property determination , 2017 .
[7] Abdul Mounem Mouazen,et al. Predictive performance of mobile vis-near infrared spectroscopy for key soil properties at different geographical scales by using spiking and data mining techniques , 2017 .
[8] Giorgio Matteucci,et al. Effect of calibration set size on prediction at local scale of soil carbon by Vis-NIR spectroscopy , 2017 .
[9] Christoph Emmerling,et al. Using Variable Selection and Wavelets to Exploit the Full Potential of Visible–Near Infrared Spectra for Predicting Soil Properties , 2016 .
[10] Hans-Peter Piepho,et al. Pitfalls in the use of middle-infrared spectroscopy: representativeness and ranking criteria for the estimation of soil properties , 2016 .
[11] Viacheslav I. Adamchuk,et al. A global spectral library to characterize the world’s soil , 2016 .
[12] Meiyan Wang,et al. Effects of Subsetting by Parent Materials on Prediction of Soil Organic Matter Content in a Hilly Area Using Vis–NIR Spectroscopy , 2016, PloS one.
[13] De-Cheng Li,et al. Selection of “Local” Models for Prediction of Soil Organic Matter Using a Regional Soil Vis-NIR Spectral Library , 2016 .
[14] Abdul Mounem Mouazen,et al. Do we really need large spectral libraries for local scale SOC assessment with NIR spectroscopy , 2016 .
[15] S. Siciliano,et al. Spiking regional vis-NIR calibration models with local samples to predict soil organic carbon in two High Arctic polar deserts using a vis-NIR probe , 2015, Canadian Journal of Soil Science.
[16] Zhou Shi,et al. Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library , 2015 .
[17] G. Pan,et al. Soil carbon, multiple benefits , 2015 .
[18] Keith D. Shepherd,et al. Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring , 2015 .
[19] Rattan Lal,et al. Societal value of soil carbon , 2014, Journal of Soil and Water Conservation.
[20] 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 .
[21] Abdul Mounem Mouazen,et al. Assessment of soil organic carbon at local scale with spiked NIR calibrations: effects of selection and extra-weighting on the spiking subset , 2013 .
[22] 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.
[23] J. M. Soriano-Disla,et al. The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties , 2014 .
[24] J. Niedźwiecki,et al. Effect of the number of calibration samples on the prediction of several soil properties at the farm-scale , 2014 .
[25] N. Batjes,et al. Measuring and monitoring soil carbon , 2014 .
[26] Claudy Jolivet,et al. Which strategy is best to predict soil properties of a local site from a national Vis–NIR database? , 2014 .
[27] Panos Panagos,et al. Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach , 2014 .
[28] H. S. Mahmood,et al. Evaluation and implementation of vis-NIR spectroscopy models to determine workability , 2013 .
[29] Michael Vohland,et al. Usefulness of near-infrared spectroscopy for the prediction of chemical and biological soil properties in different long-term experiments , 2013 .
[30] Luca Montanarella,et al. Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy , 2013, PloS one.
[31] Mogens Humlekrog Greve,et al. Predicting Soil Organic Carbon at Field Scale Using a National Soil Spectral Library , 2013 .
[32] 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.
[33] Montanarella Luca,et al. LUCAS Topoil Survey - methodology, data and results , 2013 .
[34] Richard Webster,et al. Predicting soil properties from the Australian soil visible–near infrared spectroscopic database , 2012 .
[35] Claudy Jolivet,et al. Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database , 2012 .
[36] Mogens Humlekrog Greve,et al. Development of a Danish national vis—NIR soil spectral library for SOC determination , 2012 .
[37] Abdul Mounem Mouazen,et al. Influence of the number of samples on prediction error of visible and near infrared spectroscopy of selected soil properties at the farm scale , 2011 .
[38] B. Stenberg,et al. Near‐infrared spectroscopy for within‐field soil characterization: small local calibrations compared with national libraries spiked with local samples , 2010 .
[39] César Guerrero,et al. Spiking of NIR regional models using samples from target sites: effect of model size on prediction accuracy. , 2010 .
[40] Michael Vohland,et al. The Use of Laboratory Spectroscopy and Optical Remote Sensing for Estimating Soil Properties , 2010 .
[41] R. V. Rossel,et al. Visible and near infrared spectroscopy in soil science , 2010 .
[42] Frans van den Berg,et al. Review of the most common pre-processing techniques for near-infrared spectra , 2009 .
[43] Antoine Stevens,et al. Assessment and monitoring of soil quality using near‐infrared reflectance spectroscopy (NIRS) , 2009 .
[44] Rick L. Lawrence,et al. Comparing local vs. global visible and near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) calibrations for the prediction of soil clay, organic C and inorganic C , 2008 .
[45] David J. Brown. Using a global VNIR soil-spectral library for local soil characterization and landscape modeling in a 2nd-order Uganda watershed , 2007 .
[46] Ron Wehrens,et al. The pls Package: Principal Component and Partial Least Squares Regression in R , 2007 .
[47] P. Miller,et al. Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana , 2005 .
[48] Wouter Saeys,et al. Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy , 2005 .
[49] S. Wold,et al. PLS-regression: a basic tool of chemometrics , 2001 .
[50] James B. Reeves,et al. Near Infrared Reflectance Spectroscopy for the Analysis of Agricultural Soils , 1999 .
[51] R. Barnes,et al. Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra , 1989 .
[52] L. A. Stone,et al. Computer Aided Design of Experiments , 1969 .