## 1 Introduction
Despite the availability of various RS options due to advancements in sensor technologies, data management, and analytics, the agricultural industry has yet to fully embrace RS technologies due to uncertainties regarding their adequacy, suitability, and techno-economic viability [15; 16]. RS technologies can aid in making site-specific management decisions at different crop production stages, thereby optimizing crop yield while addressing concerns related to environmental impact, profitability, and sustainability [15; 16]. Machine learning (ML) has become a specialized field within Artificial Intelligence (AI) that uses algorithms to extract insights from large datasets and apply this knowledgefor self-improvement in making accurate calculations or predictions [17]. The application of machine learning techniques is widely seen in predicting evaporation from water surfaces, evapotranspiration, and various factors related to water resources, hydrology, water quality, and reservoir operations [18; 19]. Computational intelligence and machine learning methodologies have evolved for the assessment, quantification, monitoring, and prediction of crops. The reliability of machine learning approaches and computational tools has facilitated the generation of accurate, timely future forecasts. By analyzing and processing historical data, future predictions can be derived. This research underscores the assessment and utilization of machine learning for predicting crop yields [20; 21]. ML has exhibited remarkable performance in diverse challenging tasks such as image categorization, facial recognition, parameter estimation, and natural language processing through learning intricate characteristics and connections from extensive training datasets. Recent investigations have delved into its application in yield prediction [22; 23]. Machine learning models expedite swift and optimal decision-making. The ML framework entails training and evaluation to forecast result accuracy. Recent methodologies have highlighted the merits and demerits of approaches proposed in the last five years, also comparing different machine learning algorithms utilized in contemporary agriculture [24]. [PERSON], [PERSON] [25] highlighted the effectiveness of the random forest (RF) model in predicting sugarcane yields early in the season. The purpose of estimating or predicting the water footprint is to estimate the amount of water a plant needs to produce a unit weight of the crop and is calculated as (m\({}^{3}\) kg\({}^{-1}\)). Accordingly, following sustainable water management systems for irrigation water reduces the water footprint of a single crop, which allows the use of the water that has been saved to increase the cultivated area and achieve food security. This is in general. As for countries that rely on rainwater for irrigation, estimating the green water footprint contributes significantly to determining the amount of water consumed for each crop. Accordingly, in the event of drought or lack of rainfall, water footprint calculations allow focusing on crops with a high economic return and a low water footprint while importing crops with a high water footprint from countries that do not suffer from drought at a low price, which achieves sustainable water and economic management for the country and integration between different countries. Limited research has addressed the sustainable management of current water resources by integrating the concept of water footprint with the use of remote sensing indicators to monitor plant health, soil salinity, and plant water stress over a period of about 20 years using machine learning algorithms to analyze large datasets and provide recommendations to combat climate change and promote sustainable water management.