Study on the Calibration Transfer of Soil Nutrient Concentration from the Hyperspectral Camera to the Normal Spectrometer

The calibration transfer between instruments is mainly aimed at the calibration transfer between normal spectrometers. There are few studies on the calibration transfer of soil nutrient concentration from a hyperspectral camera to a normal spectrometer. In this paper, 164 soil samples from three regions in Qingdao, China, were collected. The spectral data of normal spectrometer and hyperspectral camera and the concentration of total carbon and nitrogen were obtained. And then, the models of soil total carbon and nitrogen content were established by using the spectral data of a normal spectrometer. The hyperspectral data were transferred by a variety of methods, such as single conventional calibration transfer algorithm, combination of multiple calibration transfer algorithms, and calibration transfer algorithm after spectral pretreatment. The transferred hyperspectral data were predicted by the total carbon and total nitrogen concentration model established by using a normal spectrometer. The absolute coefficients and root mean square error of prediction (RMSEP) were used to evaluate the prediction performance after calibration transfer. After trying many calibration transfer methods, the prediction performance of calibration transfer by the Repfile-PDS and Repfile-SNV methods was the best. In the calibration transfer of the Repfile-PDS method, when the number of PDS windows was 27 and the number of standard data was 40, the and the RMSEP of TC concentration were 0.627 and 2.351. When the number of PDS windows was 25 and the number of standard data was 100, the and the RMSEP of TN concentration were 0.666 and 0.297. In the calibration transfer of the Repfile-SNV method, when the number of TC and TN standard data was 120, the was the largest, 0.701 and 0.722, respectively, and the RMSEP was 2.880 and 0.399, respectively. After the hyperspectral data were calibration transferred by the above algorithms, they could be predicted by the soil TC and TN concentration model established by using a normal spectrometer, and better prediction results can be obtained. The solution of the calibration transfer of soil nutrient concentration from the hyperspectral camera to the normal spectrometer provides a powerful basis for rapid prediction of a large number of image information data collected by using a hyperspectral camera. It greatly reduces the workload and promotes the application of hyperspectral camera in quantitative analysis and rapid measurement technology.

[1]  Budiman Minasny,et al.  Mid-infrared spectroscopy and partial least-squares regression to estimate soil arsenic at a highly variable arsenic-contaminated site , 2015, International Journal of Environmental Science and Technology.

[2]  Xiang Yu,et al.  Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula , 2016 .

[3]  Marcelo Nascimento Martins,et al.  A comparative study of calibration transfer methods for determination of gasoline quality parameters in three different near infrared spectrometers. , 2008, Analytica chimica acta.

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

[5]  Rebecca L. Whetton,et al.  Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy , 2016 .

[6]  Jürgen Popp,et al.  Towards an improvement of model transferability for Raman spectroscopy in biological applications , 2017 .

[7]  Abdul Mounem Mouazen,et al.  On-line visible and near infrared spectroscopy for in-field phosphorous management , 2016 .

[8]  Daniel Cozzolino,et al.  Near infrared spectroscopy as a tool to monitor contaminants in soil, sediments and water—State of the art, advantages and pitfalls , 2016 .

[9]  Maomao Zhang,et al.  Rapid Detection of Knot Defects on Wood Surface by Near Infrared Spectroscopy Coupled with Partial Least Squares Discriminant Analysis , 2016 .

[10]  Andrew Rawson,et al.  The prediction of soil chemical and physical properties from mid-infrared spectroscopy and combined partial least-squares regression and neural networks (PLS-NN) analysis , 2009 .

[11]  Markus Steffens,et al.  Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging , 2018, Scientific Reports.

[12]  Meiyan Wang,et al.  Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy , 2018 .

[13]  Jon Atli Benediktsson,et al.  A Novel Technique for Optimal Feature Selection in Attribute Profiles Based on Genetic Algorithms , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Nathalie Gorretta,et al.  Clay content mapping from airborne hyperspectral Vis-NIR data by transferring a laboratory regression model , 2017 .

[15]  T. Fearn,et al.  On the geometry of SNV and MSC , 2009 .

[16]  Antonio J. Plaza,et al.  Cloud implementation of the K-means algorithm for hyperspectral image analysis , 2016, The Journal of Supercomputing.

[17]  M. Blanco,et al.  Control production of polyester resins by NIR spectroscopy , 2008 .

[18]  J. Cooper,et al.  Virtual Standard Slope and Bias Calibration Transfer of Partial Least Squares Jet Fuel Property Models to Multiple near Infrared Spectroscopy Instruments , 2011 .

[19]  D. Diner,et al.  Linearization of a scalar matrix operator method radiative transfer model with respect to aerosol and surface properties , 2013 .

[20]  Yizeng Liang,et al.  Calibration model transfer for near-infrared spectra based on canonical correlation analysis. , 2008, Analytica chimica acta.

[21]  Jiemei Chen,et al.  Optimal partner wavelength combination method with application to near-infrared spectroscopic analysis☆ , 2016 .

[22]  Xueying Li,et al.  Calibration Transfer of Soil Total Carbon and Total Nitrogen between Two Different Types of Soils Based on Visible-Near-Infrared Reflectance Spectroscopy , 2018, Journal of Spectroscopy.

[23]  Zhou Shi,et al.  Prediction of soil attributes using the Chinese soil spectral library and standardized spectra recorded at field conditions , 2016 .

[24]  Rick L. Lawrence,et al.  Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  D. Abookasis,et al.  Direct measurements of blood glucose concentration in the presence of saccharide interferences using slope and bias orthogonal signal correction and Fourier transform near-infrared spectroscopy. , 2011, Journal of biomedical optics.

[26]  Quansheng Chen,et al.  Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms. , 2008, Journal of pharmaceutical and biomedical analysis.

[27]  M. Greve,et al.  Field-Scale Predictions of Soil Contaminant Sorption Using Visible–Near Infrared Spectroscopy , 2016 .

[28]  J. Cooper,et al.  Calibration transfer of near‐IR partial least squares property models of fuels using virtual standards , 2011 .

[29]  Steven D. Brown,et al.  Transfer of multivariate calibration models: a review , 2002 .

[30]  Tao Dong,et al.  Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor , 2017, Sensors.

[31]  S. Priori,et al.  Using Visible-Near Infrared Spectroscopy to Predict Soil Properties of Mugan Plain, Azerbaijan , 2016 .

[32]  Qing Li,et al.  Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen , 2017, Sensors.

[33]  Xin Li,et al.  Calibration transfer of near-infrared spectroscopy by canonical correlation analysis coupled with wavelet transform. , 2017, The Analyst.

[35]  Hidetoshi Asai,et al.  Vis-NIR Spectroscopy and PLS Regression with Waveband Selection for Estimating the Total C and N of Paddy Soils in Madagascar , 2017, Remote. Sens..

[36]  M Gishen,et al.  Analysis of elements in wine using near infrared spectroscopy and partial least squares regression. , 2008, Talanta.

[37]  Swarnajyoti Patra,et al.  An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images , 2018 .