Abstract
Real-time soil moisture content (SMC) monitoring is an important parameter in precision agriculture that can be utilized to enhance soil and water management. Visible and near-infrared (vis-NIR) has been proposed as a promising method for SMC monitoring. However, vis-NIR reflectance response to soil moisture is strongly influenced by soil properties such as texture and organic matter content. Thus it is difficult to develop a general prediction model of vis-NIR that can estimate SMC of different soil types. To solve this problem, this study utilizes the External Parameter Orthogonalization (EPO) method to reduce the effect of soil type. Seven soils (CZ, GA, JN, PDS, PY, WF, and XZ) were collected from the North China Plain. A fiber-optic spectrometer (300–1000 nm) was used to obtain spectral response of seven soils adjusted to several SMC levels. The partial least square regression (PLSR) was used to model the pretreated vis-NIR spectra of the seven soils. The modeling results confirmed the feasibility of using vis-NIR spectroscopy to predict SMC on individual soils. However, the prediction models vary significantly between soil types. To obtain a generalized prediction model of SMC that can be applied to different soils, three soils were selected from the seven soils for calibration, while the rest were used for validation. The results showed that direct PLSR modeling failed to achieve a unified prediction model. To solve this problem, EPO was applied to minimize the influence of soil types. The results demonstrated that EPO-PLSR can realize a generalized SMC prediction model. It is suggested that the soils for building EPO-PLSR models should cover a wide span of soil organic matter (SOM) content. Soil with the least SOM content appeared to be best for developing an EPO transfer matrix.
Published Version
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