Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework

Abstract

Time series modeling and forecasting play important roles in many practical fields. A good understanding of soil water content and salinity variability and the proper prediction of variations in these variables in response to changes in climate conditions are essential to properly plan water resources and appropriately manage irrigation and fertilization tasks. This paper provides a 48-h forecast of soil water content and salinity in the peculiar context of irrigation with reclaimed water in semi-arid environments. The forecasting was performed based on (i) soil water content and salinity data from 50 cm beneath the soil surface with a time resolution of 15 min, (ii) hourly atmospheric data and (iii) daily irrigation amounts. Exploratory data analysis and data pre-processing phases were performed and then statistical models were constructed for time series forecasting based on the set of available data. The obtained prediction models showed good forecasting accuracy and good interpretability of the results.

Evaluation of Three Soil Moisture Profile Sensors Using Laboratory and Field Experiments

“…Using Internet of things (IoT) technology, soil moisture measurements can be integrated into wireless sensor networks (WSNs) to provide near real-time information for water- and nutrient-efficient agro-technical measures [ 4 , 5 ], often referred to as smart farming or agriculture 4.0 [ 6 ]. These data can be integrated into stochastic or machine learning models to forecast soil water contents under differing agricultural and climatic scenarios [ 7 , 8 , 9 ]. Due to their (perceived) simplicity, low power consumption, and non-destructive nature, electromagnetic soil moisture measurements are particularly suitable for data-driven decision making in agriculture [ 10 , 11 , 12 ].…”