The use of Sentinel 2 to quantify N, Ca, and K in walnut orchards

Abstract

’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. This study aims to develop and validate predictive models for quantifying N, Ca, and K levels in ’Persian walnut’ orchards using Setinel-2 satellite data (9 different spectral bands and 2 vegetation indices (NDVI and NDWI)), addressing the challenge of large-scale nutrient management. The predictive models, using multivariate regression method, to predict N, Ca and K in walnut leaves, were satisfactory, with R2 values of 0.70, 0.60 and 0.74, with RPD values of 2,2; 1,64 and 1,96 for respectively. Therefore, the results obtained indicate that remote sensing is a potential technology to assess the nutrient status in crops in a faster and simpler way than traditional plant leaf analysis procedures.