## 1 Introduction  
According to World Bank data, the global rural population declined from 66.39% of the total population in 1960 to 44.72% in 2016. Meanwhile, agricultural added value, as a percentage of GDP (gross domestic product), decreased from 7.59 to 3.43% during the period 1994-2017. Despite these changes, the continued growth of the global human population has resulted in phases of deforestation together with innovations, increasing the efficiency of agricultural systems, and subsequent crises of unprecedented demographic, economic and urban expansion [1; 2]. In semi-arid regions like Morocco, agricultural practices are facing a series of challenges not limited to climate change, which is reflected in an increasingly warmer and drier climate [3], coupled with the increasingly random spatiotemporal variability of rainfall, and associated droughs and floods [4; 5; 6; 7; 8]. These issues are exacerbated by increasingly complex land use and land cover practices that in turn adversely affect socio-economic development [9]. Agriculture, as an important economic sector, is therefore deeply impacted, given crop production dependence on the annual rainfall distribution [4; 5; 6; 7; 8]. It is clear that humans have created and are now witnessing a great agricultural ecosystem disturbance [8]. Recently, geospatial technologies have been used extensively for spatiotemporal monitoring of environmental phenomena, including land use/land cover changes [6; 9; 10; 11; 12; 13; 14; 15], understanding the ecosystem functions [16; 17], identifying agricultural systems and crop mapping [3; 8; 18; 19], estimating fractional crop cover and crop residue [20], estimating the impacts of urbanization on agricultural dynamics [3], identifying the karst cavities in agricultural areas [21; 22; 23], and water balance assessments at regional and local scales [6; 24]. Many investigations have shown a great deal of potential in terms of different machine learning approaches in imagery classification, such as vector machine support [3; 25; 26; 27; 28; 29] and random forest [30; 31; 32]. These innovative data interrogation and modeling approaches are critically important for estimating agricultural crop water use. This is important in order to implement effective strategies for advanced water resource management for agriculture in response to contemporary water budget challenges. Several works have been published supporting these needs in recent years [33; 34; 35; 36; 37; 38; 39; 40; 41], particularly in semi-arid zones [42]. [PERSON] et al. [42] used the CROPWAT model to determine the irrigation needs of the Jatropha crop in Botswana. They used the CROPWAT model to estimate baseline evapotranspiration (ETo), evapotranspiration (ETc), irrigation water requirements (IWR) and yield response to irrigation scheduling in Botswana. The results showed that the annual ETo from 2014 to 2016 at the station was 1456 mm. The lowest monthly ETo (50.10 mm) was observed in June and the highest (182.59 mm) in January. This model is widely used, especially in understanding the changes in crop water requirements [38], which are defined as the depth of water required to meet the evapotranspiration water loss (ETc) of a disease-free crop growing in large fields; this parameter is important for promoting sustainable development. In particular, the model is used in the determination of crop water and of the effects of irrigation programming on the crop [40; 41]. This model allows calculation of the water requirements of the different crops using soil data, climatic data, and data on the crops themselves. Therefore, to determine the crop's water requirement, several parameters were calculated. _Calculation of Potential Evaporation of Crop ETc:_ Before calculating the ETc, specific studies on the water requirements of crops in the area should be examined; the meteorological and research stations and the environment should also be visited. The calculation of this parameter is done by the following two main steps:  
1.