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Smart Agriculture ›› 2020, Vol. 2 ›› Issue (3): 129-138.doi: 10.12133/j.smartag.2020.2.3.201912-SA004

• Information Processing and Decision Making • Previous Articles     Next Articles

Hyperspectral Estimation Model Construction and Accuracy Comparison of Soil Organic Matter Content

LIU Tianlin1(), ZHU Xicun1,2(), BAI Xueyuan1, PENG Yufeng1, LI Meixuan1, TIAN Zhongyu1, JIANG Yuanmao3, YANG Guijun4   

  1. 1.College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China
    2.National Key Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Tai'an 271018, China
    3.College of Horticulture Science and Engineering, Shandong Agricultural University, Tai'an 271018, China
    4.National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
  • Received:2019-12-23 Revised:2020-06-26 Online:2020-09-30 Published:2020-09-29
  • corresponding author: Xicun ZHU E-mail:ltlstudy@163.com;zxc@sdau.edu.cn


Soil organic matter (SOM) is an important source of crop growth, its content can reflect soil fertility status. In order to realize the fast and real-time estimation of the SOM, based on hyperspectral data, a rapid estimation model of SOM content in orchards was established. A total of 100 brown soil samples were collected from the apple orchard of Qixia county, Yantai city, Shandong province. After drying and grinding, the hyper-spectrum of the soil was measured in the laboratory using ASD FieldSpec. The spectral data was preprocessed by the method of moving average, and the spectral reflectance features of orchard soil were analyzed to study the correlation between spectral reflectance and its soil organic matter content. In order to enhance the correlation between relevant spectral parameters and soil indexes, the original data were processed by using the multivariate scattering correction, the first derivative and the first derivative of MSC. After the sensitive wavelengths of soil organic matter content were selected and the spectral indexes were constructed. Multiple linear regression models (MLR), support vector machines (SVM) and random forest (RF) models were respectively established. The estimation accuracy of the orchard soil organic matter estimation model was measured by the determination coefficient (R2), root mean square error (RMSE) and relative analysis error (RPD). The sensitive wavelengths of soil organic matter content selected were 678, 709, 1931, 1939, 1996 and 2201 nm. The spectral parameters were constructed using the selected wavelengths, which were NDSI(678, 709), NDSI(678, 1931), NDSI(678, 2201), NDSI(709, 1939), and NDSI(1939, 2201). These models established include MLR, SVM and RF model. The RF model had the best precision. The calibration sample R2 was 0.8804, the RMSE was 0.1423 and RPD reached 2.25; the R2 of the verification model was 0.7466, the RMSE was 0.1266, and the RPD was 1.79. The results showed that the fitting effect of the hyperspectral inversion model based on RF regression analysis was better than that based on MLR analysis and SVM regression analysis. As a promising and effective method, RF can play a vital role in predicting soil organic matter. The results can help understanding the distribution of soil nutrients, guiding farmers to apply fertilizer reasonably and improving the efficiency of orchard production and management.

Key words: hyperspectral, soil organic matter, multiple linear regression, support vector machine, random forest, model

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