Rapid Soil Analyses Using Modern Sensing Technology: Toward a More Sustainable Agriculture

  • El-Sayed Ewis OmranEmail author
Part of the The Handbook of Environmental Chemistry book series (HEC, volume 77)


Modern sensing technology must be utilized to provide farmers with rapid soil analysis in making farming more sustainable. Modern technologies in agriculture have been given an important role for the improvement of agricultural productions, e.g., sustainable agriculture, in order to maintain food security. It has been known that modern agricultural technology can sustainably improve agricultural production. Up-to-date information on soil properties is imperative for sustainable agriculture. Conventional soil analyses cannot efficiently give this information since they are slow and expensive and sometimes incorporate environmentally damaging chemicals. Soil spectroscopy is a well-known technique to assess soil properties quickly and quantitatively.

To assess the utility of spectroscopy for soil characteristic (clay content, salinity, and OM) prediction, 35 soil samples collected from Bahr El Baqar, Egypt were scanned in the 350–2,500 nm region (FieldSpec Spectroradiometer). Reflectance spectroscopy gives an alternate method to nondestructively characterize key soil properties. Chemometrics techniques have been utilizing to estimate soil properties from visible and near-infrared (VNIR, 350–1,200 nm) and shortwave-infrared (SWIR, 1,200–2,500 nm) reflectance domains. Partial least squares regression (PLSR) was put in place to develop calibration models, which were independently tested for the predictions of soil organic carbon, salinity, and clay content from the soil spectra. Some spectral data pre-processing techniques were carried out to diminish noise, to offset effects, and to improve the linearity between measured absorbance and soil properties. These models were developed by the correlation between spectral characteristics and physicochemical soil properties separately for each soil property, using PLSR analysis. The continuum removal (CR) spectra yielded the best calibration models with respect to estimates of the soil salinity, which generated R2 values of 0.62. In the case of the clay content, the prediction capacity of the method proved to be high (R2 = 0.57) using CR. These results can be explained by the strong spectral activity of organic carbon and clay in the VNIR-SWIR region. The model accuracy (RMSE OM = 0.425) is low, indicating the need for improving the measurement protocol to achieve more reliable data and to test other pre-processing and modeling methods as well. The deviation of the arch (DOA) at 600 nm is indicative of the convex and concave features of the spectral curves generated by OM. The DOA contains the majority of information regarding OM and can be utilized to estimate OM.


Bahr El Baqar Chemometrics Egypt Partial least squares regression Sensing technology Soil analyses Spectroscopy Sustainable agriculture 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Soil and Water Department, Faculty of AgricultureSuez Canal UniversityIsmailiaEgypt

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