## 1 Introduction
They obtained an accurate estimate of the PAWC (which is usually time-consuming and expensive) using the Pedo-Transfer Function (PTF) approach, suggesting that it is possible to obtain estimates of the PAWC at the regional scale from easily measured data. [PERSON] and [PERSON] [40] investigated the variability of soil water and its impact on the amount of soil water available to grapevines under various rainfall patterns in the Penedes wine region, Spain. The Soil Water Assessment Tool model (SWAT) was used to simulate soil water during the period of 2000-2012. The FTSW was used as an indicator of water availability. The model enabled determining the effects of soil water content in early and later maturing grape varieties, highlighting the importance of considering water availability in varietal selection. [PERSON] et al. [41] assessed how well the Simulateur mulTIdisciplinaire pour les Cultures Standard (STICS) model captures grapevine phenology, biomass production, yield, and soil water content in two scenarios with different rainfall distributions and water management approaches. A good estimate of the main stages of grapevine phenology and soil water content was obtained. A comparison of the FTSW with grapevine leaf water potential measurements showed that the vine water stress period was correctly estimated. The FTSW can be calculated using soil moisture content data recorded by Internet of Things (IoT) sensors. However, IoT sensors have some drawbacks, including the need for maintenance and the possibility of data pauses. When IoT sensors fail or no sensor is available in the field, a model that can predict the FTSW with minimal inputs is a good tool for estimating the missing data. To the best of our knowledge, there is no study on the estimation of the FTSW using DL models. This literature review highlights the various machine learning and deep learning algorithms that have been applied to irrigation management, with a focus on predicting the soil moisture, irrigation amount and timing, and soil water holding capacity. These studies demonstrated the potential of these algorithms to improve irrigation management and decision-making. The hypothesis behind this research was that LSTM can detect the relationship between historical, weather data (temperature, relative humidity, rainfall, wind speed, vapor pressure deficit, solar radiation), reference Evapotranspiration (ETo), and the dependent variable, FTSW, in a vineyard and that these relationships can be utilized to effectively forecast the FTSW for the future or for other vineyards with comparable climate, soil type, and management. The objectives of this study were (1): to assess the ability of the LSTM model to predict the FTSW in conjunction with observed data from IoT sensors deployed in a commercial vineyard in Portugal with minimal inputs, (2) to evaluate the prediction result for another vineyard in a completely different situation with an independent dataset. The remainder of this paper is organized as follows. An explanation of the models and methods used in this work, the dataset used, and the training configuration is provided in the Materials and Methods Section 2. The results of the proposed model when applied to the validation datasets are presented in Section 3. The summary of the work can be found in Section 4.