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Quickly and correctly mapping soil nutrients significantly impact accurate fertilization, food security, soil productivity, and sustainable agricultural development. We evaluated the potential of the new PRISMA hyperspectral sensor for mapping soil organic matter (SOM), available soil phosphorus (P₂O₅), and potassium (K₂O) content over a cultivated area in Khouribga, northern Morocco. These soil nutrients were estimated using (i) the random forest (RF) algorithm based on feature selection methods, including feature subset evaluation and feature ranking methods belonging to three categories (i.e., filter, wrapper, and embedded techniques), and (ii) 107 soil samples taken from the study area. The results show that the RF-embedded method produced better predictive accuracy compared with the filter and wrapper methods. The model for SOM showed moderate accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>v</mi><mi>a</mi><mi>l</mi></mrow><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> = 0.5, RMSEP = 0.43%, and RPIQ = 2.02), whereas that for soil P₂O₅ and K₂O exhibited low efficiency (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>v</mi><mi>a</mi><mi>l</mi></mrow><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> = 0.26 and 0.36, RMSEP = 51.07 and 182.31 ppm, RPIQ = 0.65 and 1.16, respectively). The interpolation of RF-residuals by ordinary kriging (OK) methods reached the highest predictive results for SOM (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>v</mi><mi>a</mi><mi>l</mi></mrow><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> = 0.69, RMSEP = 0.34%, and RPIQ = 2.56), soil P₂O₅ (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>v</mi><mi>a</mi><mi>l</mi></mrow><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> = 0.44, RMSEP = 44.10 ppm, and RPIQ = 0.75), and soil K₂O (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mrow><mi>v</mi><mi>a</mi><mi>l</mi></mrow><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> = 0.51, RMSEP = 159.29 ppm, and RPIQ = 1.34), representing the best fitting ability between the hyperspectral data and soil nutrients. The result maps provide a spatially continuous surface mapping of the soil landscape, conforming to the pedological substratum. Finally, the hyperspectral remote sensing imagery can provide a new way for modeling and mapping soil fertility, as well as the ability to diagnose nutrient deficiencies.