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Estimation of Soil Organic Matter Content Based on Regional Feature Bands

  • Lihua XuEmail author
  • Deti Xie
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

To estimate soil organic matter (SOM) content using hyper-spectral data, regional feature bands and principle component regression (PCR) were built a model of SOM. The results showed that the coefficients of determination (R2) of PCR model based on the regional feature bands were 0.650 for calibration set and 0.628 for validation set, respectively. The Root Mean Square Error (RMSE) values were 2.641 g/kg and 2.852 g/kg, respectively. The PCR models based on the significant bands had better estimation accuracy, but its total correlation coefficient(R = 0.770) between predicted SOM and measured SOM was lower than of model based on the regional feature bands (R = 0.803). Therefore, the PCR model based on regional feature bands provides a better estimation result than model based on significant bands.

Keywords

Regional feature bands Significant bands Soil organic matter Principle component regression 

Notes

Acknowledgement

This work was supported by Fundamental Research Funds for the Central Universities (No.XDJK2016C083), Chongqing Research Program of Basic Research and Frontier Technology (No. cstc2016jcyjA0184) and National Natural Science Foundation of China (No. 41671291).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Resources and EnvironmentSouthwest UniversityChongqingChina

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