Auxiliary data, such as a digital elevation model or hydrological maps can be incorporated to further discriminate land cover classes applying additional rules. This abundance information of the reference spectra is in a second step used to set up decision rules for land cover class discrimination which depends on terrain knowledge. Reference spectra of certain surface components serve to describe the spectral surface variability and to calculate abundance channels. The concept of Linear Spectral Unmixing (LSU) involves the multispectral image information to derive abundance channels which can be more physically interpreted. Sub-pixel classifiers based on statistical rules use more of the embodied information in satellite data than pure pixel values. Commonly used supervised classification methods as the Maximum Likelihood classifier are not appropriate for a detailed differentiation of certain land cover classes such as vegetation associations, The optimisation of land cover class identification by the use of a sub-pixel classifier is presented in this paper. Best classification results are usually achieved with supervised classification methods. Land cover mapping is one of the earliest tasks of satellite data interpretation. Results can help target efforts to improve the implementation of conservation strategies to ensure the sustainability of water use and food production in this region of Morocco. As a consequence of the continuous overexploitation and unsustainable management, a lot of wells have run dry and there have been drinking water shortages in the city of Zagora, the closest city nearby. Additionally, the decline was accompanied by a significant increase in electrical conductivity (salinity) values over the same time interval from 1077.55 to 1211.9 ВµS/cm. The groundwater level declined rapidly by about 10 m below ground level during the 5 years of the study period. Results show a rapid expansion of agricultural areas from just 185.11 ha in 2007 to 2560.1 ha in 2018. Remote sensing data was also used to monitor the watermelon cultivation expansion. Field measurements, statistics, Kriging interpolation, and regression methods were used to measure the temporal variations in the groundwater level (GL) and salinity between 20 to determine the correlation between different parameters. This study aims to measure the impact of watermelon expansion on groundwater resources in the Feija Basin, which is one of the largest watermelon cultivation areas in Southern Morocco. The cultivation of watermelons has been a fast growing agriculture industry in the arid, desert regions of Morocco, relying on groundwater pumping and transformation of rangelands to farms due to growing demand for the fruit in national and international markets.

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