Abstract. A substantial number of models, predicting the Soil Moisture Characteristic Curve (SMC) from Particle Size Distribution (PSD) data, underestimate the dry range of the SMC especially in soils with high clay and organic matter contents. In this study, we applied a continuous form of the PSD model to predict the SMC and subsequently, we developed a physically based scaling approach to reduce the model's bias at the dry range of the SMC. The soil particles packing parameter, obtained from the porosity was considered as a characteristic length. The model was tested by using eighty-two soil samples, selected from the UNSODA database. The result showed that the scaling approach properly estimate the SMC for all soil samples. In comparison to the formerly used physically based SMC model, the proposed approach improved the model estimations by an average of 30% for all soil samples. However, the advantage of this new approach was larger for the fine and medium textured soils than that for the coarse textured soil. In view that in this approach there is no further need for empirical parameters, we conclude that this approach could become applicable for estimating SMC at the larger field scale.
Prediction of soil moisture characteristic curve based on Internet of Things and enterprise e-commerce operation
