'Persian walnut' (Juglans regia L.) is one of the most consumed nut species in the world, and N, K, and Ca nutrition are critical for its growth and quality. Mineral nutrition management in fruit crops over large areas is a challenging task only possible with a remote sensing data approach and using rapid analytical methods to correlate remotely sensed data with ground data. In the present study, predictive models to quantify N, Ca, and K were developed based on remote sensing data from the Sentinel-2 satellite (9 different spectral bands and 2 vegetation indices (NDVI and NDWI)) using a multiple linear regression approach. The predictive models for N, Ca and K were satisfactory, with R2 values of 0.72, 0.61 and 0.79, respectively. Therefore, the results obtained indicate that remote sensing is a potential technology to assess the nutrient status in crops in a faster and more reliable way than traditional plant leaf analysis procedures.