Abstract
Crop identification is vital to make an inventory of the crops grown in a given area and their cultivation period. The Remote sensing (RS) techniques can provide information on the distribution of cultivated land, crop types, and areas for the agricultural sector's effective management.
 In remote sensing, various vegetation indices (VI) can analyze and evaluate multiple phenomena and themes. The Normalized Difference Vegetation Index (NDVI) is an essential and highly significant remote measurement widely used in agriculture for phenological monitoring and crop health (Ray and Dadhwal, 2001).
 In this work, we present a methodology for the contribution of NDVI from Landsat 7 (TM) and (ETM+) images to crop mapping in the Gharb region using a classification based on the pixel approach and estimating rice crop coefficient from NDVI.
 The classification results concern six main types of crops planted in this region (beet, maize, sugar cane, market gardening, cereals, and rice). The classification map showed differences in agricultural practices adopted by farmers in crop spatial distribution. The classification results showed the ability of this methodology to discriminate between crops.
 Crop coefficients were deduced from the NDVI extracted from the images. Due to meteorological data collected from the meteorological station TCSC of SK Tlet, the estimation of the reference evapotranspiration was made and subsequently the potential evapotranspiration of each crop during the agricultural season 2019-2020.
 The highest values for ETC were obtained when the crop was in its full development when water was mainly lost through transpiration after a slight decrease in the ratio values observed during the phase of the vegetative cycle (maturity).
 The water requirements (daily, monthly and annual) for the crops were determined and their electrical energy consumption.
 Renewable energy can be an effective solution to meet the energy needs of plots , greenhouses and large farms.
 A technical-economic study of different combinations of autonomous hybrid renewable energy systems (HRES) in order to meet the power supply needs of the above mentioned crops in the Gharb region. The renewable energy sources considered are solar, wind and biomass. The results show that for an average energy requirement of 92 kWh/day and a peak load of 6.5 kW, the unit energy cost of the optimal configuration scenario A (PV-wind-biomass-battery) is 0.19 $/kWh. Therefore, the design, development and implementation of the proposed system is a promising solution for the security of energy supply. For a 100% integration of renewable energy, the HRES produces electricity according to the following distribution: 11% from wind, 41% from solar and 48% from biomass.
Highlights
Agriculture is a vital and crucial element in the economy of the Gharb region
[15] Several researchers (Allen and al.. (2011)), Hunsaker and al.. (2005), Gonzalez-Dugo and al.. (2009) have studied and defined the correlation and the best possible relationship between the crop coefficients of multispectral Normalized Difference Vegetation Index (NDVI) images based on the vegetation surface's reflectance
[11] Kc is estimated from NDVI due to the strong relationship between the NDVI and the Kc (Ray and Dadhwal, 2001) [12].Due to the relationship between NDVI and KC, NDVI has always been considered as a parameter for monitoring and control of vegetation during its growth cycle (Justice and Townshend, 2002) [13]
Summary
Agriculture is a vital and crucial element in the economy of the Gharb region. To ensure efficient management of agriculture, geospatial data and statistics are indispensable. Traditional data collection techniques are expensive and unsuitable for monitoring seasonal crop development. The RS allows the mapping of crop types by monitoring their seasonal development using multidate images covering the crop growing cycle. [9] Research has shown a good simulation of crop coefficients derived from VI from multispectral images (Hunsaker et al, 2003). (2009) have studied and defined the correlation and the best possible relationship between the crop coefficients of multispectral NDVI images based on the vegetation surface's reflectance. [20] Different formulae were developed to estimate evapotranspiration; there are formulae based on energy balance (Penman, 1948; Allen and al.., 1998b; Nouri and al.., 2013) [18], formulas referring to the air temperature (Thornthwaite, 1948; Blaney, 1952) [19]. Researchers have revealed that VI extracted from satellite images can be used to estimate crop coefficients. The rice crop evapotranspiration was estimated by in situ lysimetric measurements per decade for the growing season 2019-2020
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