In this paper, we present a conceptual approach that allows predicting the parameters of the Convective Lognormal Transfer model from knowledge of soil moisture and the Soil Moisture Characteristic (SMC), parameterized by means of the closed-form model of Kosugi (1996).
Managing water use, by controlling irrigation in response to soil or substrate moisture changes, to meet crop water demands is a popular approach but requires substrate specific moisture sensor calibrations and knowledge of the moisture levels that result in water deficit or over-watering.
Knowledge of soil moisture in the root zone is critical for crop growth estimation and irrigation scheduling.
Yet a profound knowledge of their moisture dynamics and fungal susceptibility is a prerequisite for proper use.
Knowledge of soil moisture is critical to understanding many of the hydrological processes that are of interest in soil hydrology, meteorology, and ecology research.
Adequate knowledge of soil moisture storage as well as evaporation and transpiration at the land surface is essential to the understanding and prediction of the reciprocal influences between land surface processes and weather and climate.
In recent years, however, research in this field has progressed and focused on a more fundamental approach through testing and modeling, in order to obtain more comprehensive knowledge of the moisture damage mechanisms.
The knowledge of soil moisture is important in studying climatology, earth science and most importantly irrigation decision support systems, but is often hard to determine since it is not possible to use critical measurements including moisture sensors all over the entire agricultural grid sector.
Spatial and temporal knowledge of soil moisture is important to effectively model the surface runoff from a river catchment.
Knowledge of soil moisture content is critical for drought and irrigation management, so as to increase crop yields and to gain a better understanding of natural processes linked to the water, energy and carbon cycles.
