Modelling and Predicting of Soil Electrical Conductivity and PH from Semi-arid Grassland Using VIS-NIR Spectroscopy Technology

Abstract

The electrical conductivity (EC) and pH value are key indicators for soil physical and chemical properties, which can reflect the level of soil acid and alkali, furthermore, influence the vegetation growth. The spectroscopy technique can estimate and evaluate electrical conductivity and pH value rapidly and efficiently, which can provide useful information on the real-time soil management in the semi-arid rangeland or grassland. We picked the semi-arid grassland of northern China covering an area about 200 km² as the target research area, given that it is highly sensitive to grazing and mining affect. Soil samples were collected from 72 sampling sites in this area, which covered grazing exclusion, over grazing and grassland restoration area. The SVC HR-1024 spectroradiometer was used to acquire soil spectrum. This study aims to indicate the spectral characteristic for soil EC and pH, and propose a predicting modeling method with optimal input spectral region and transformation by comparing the support vector machine (SVM) regression method and partial least squares (PLSR) regression modeling method. Our results showed that: (1) once EC value is larger than 0.10 μs/m, the soil spectral reflectance decreases with increasing of EC value. The absorption depth, width and area at 1900 nm reduce with increasing of EC value as well; (2) There are positive correlation between EC, pH value and soil spectral reflectance. The highest correlation coefficient value of 0.7 between pH and reflectance is recorded at visible region around 500 nm; (3) The SVM modeling method produce the higher prediction accuracy (RPD = 2.18, RMSE = 0.035, R2 = 0.78 for EC, RPD > 3, RMSE = 0.349, R2 = 0.91 for pH) rather than PLSR methods in soil EC and pH prediction. This study indicated that it was possible to use the spectroradiometer technology to predict EC and pH value for the soil from semi-arid grassland, which would provide the basis for soil acid and alkali detecting using hyper-spectral remote sensing technology.