Applications of agro-hydrological sensors and models for sustainable irrigation

Abstract

In the last two decades, research on water resource monitoring and management has mainly been aimed at reducing irrigation water volume and energy consumption. At the same time, the effects of climate change and agricultural policies have also been major research interests. Therefore, there is an interest in focusing on the assessment of irrigation performance to improve water management and to increase the sustainability of irrigated agricultural territories. Recent advances in optoelectronics, mechatronics, communication, and information technologies have allowed for the implementation of low cost, easy to operate, and virtual free maintenance of data acquisition systems to be used in soil-crop water status monitoring, as well as in smart irrigation systems. Agro-hydrological models have been recognized as an economic and simple tool to quantify crop water requirements in the decisionmaking processes for both farm and basin scales. They can simulate the mass and/or energy exchange processes in the soil-plant-atmosphere continuum under different spatial and temporal scales. In combination with new technologies such as sensors and remote sensing, these models are promising techniques that have accelerated spatial data collection substantially. Remote sensing and wireless sensor networks can cover scales from a single leaf to complete irrigation systems and can create data sets for large numbers of agricultural families and farming conditions. Hence, the agro-hydrological sensor-model based approach must be properly chosen and calibrated to increase its use on larger commercial farms, even using remote sensing data. This Special Issue consists of a collection of seven papers that cover a broad range of advances on modelling and partitioning evapotranspiration, modelling soil water and salt content, and modelling irrigation systems, with the aim to increase the sustainability of irrigation derived from the adoption of monitoring and optimum management practices