Evaluating calibration methods for predicting soil available nutrients using hyperspectral VNIR data

Haijun Qi, Tarin Paz-Kagan, Arnon Karnieli, Xiu Jin, Shaowen Li

Research output: Contribution to journalArticlepeer-review


Soil nutrients, including available nitrogen (N), phosphorous (P), and potassium (K), are critical properties for monitoring soil fertility and function. Spectroscopy analysis has proven to be a rapid and effective means for predicting soil properties, in general, and NPK, in particular. However, different calibration methods, including preprocessing transformations (PPTs) and regression algorithms (RAs), considerably affect the performance of prediction models. In this study, raw spectrum and 21 PPTs, combined with three RAs, for a total of 66 calibration methods, were investigated for modeling and predicting soil NPK using hyperspectral VNIR data (400–1000 nm). The ratio of performance to deviation (RPD) of validation set was selected to evaluate the prediction accuracy and the ratio between the interpretable sum squared deviation and the real sum squared deviation (SSR/SST) of the validation set was also used to evaluate the explanatory power of the models. It was found that there is a tradeoff between RPD and SSR/SST values; under this tradeoff, the multiplicative scatter correction, combined with the back-propagation neural network, was preferred for predicting P (RPD = 2.23, SSR/SST = 0.81). The Savitzky-Golay filtering + logarithmic transformation, combined with the partial least squares – regression, was preferred for predicting K (RPD = 1.47, SSR/SST = 0.95). However, with extremely low RPD and SSR/SST values, the prediction of N was unreliable in this study. The evaluation approach presented in this paper suggests a framework for choosing a calibration method for spectroscopy analysis for predicting soil NPK and perhaps some other properties.

Original languageEnglish
Pages (from-to)267-275
Number of pages9
JournalSoil and Tillage Research
StatePublished - 1 Jan 2018


  • Calibration
  • Prediction methods
  • Regression algorithms
  • Soil measurements
  • Spectral analysis

All Science Journal Classification (ASJC) codes

  • Agronomy and Crop Science
  • Soil Science
  • Earth-Surface Processes


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