Exploring bare soil digital mapping: identifying alternative variables to replace ECa via remote sensing, a case study on two Italian fields at different latitude

• Multi-temporal images of bare soil as an effective proxy to assess soil variability (SYSI). • Alternative approaches that through SYSI can accurately map field variability. • Replacing ECa by means of SYSI covariates. • A step-by-step methodology brings up the relationships between ECa and SYSI covariates. Site-specific management in agriculture, which accounts for variability within a field, is a cornerstone of sustainable agronomic practices. However, despite the availability of numerous methods to measure spatial variability, their limitations hinder large-scale adoption, posing challenges to the broader implementation of precision agriculture. This study aims to identify spectral indices derived from bare-soil analysis as potential substitutes for electrical conductivity (ECa) in mapping spatial variability. The approach aligns with the need for cost-effective, scalable, and less labor-intensive solutions to manage field variability. Using multi-temporal bare-soil imagery spanning five years across two fields under intermittent cultivation in Italy, we applied principal component analysis to evaluate correlations between spectral indices and ECa. Both fields demonstrated strong correlations between ECa and the first principal component (PC1). Key variables identified as highly correlated with ECa included the Brightness Index (0.66), Near-Infrared (0.53), and Red reflectance (0.58). The percentage variance explained by PC1 was 75.4 % for Field 1 and 79.0 % for Field 2. Finally, PC1 is correlated with ECa in the two areas in the measure of 0.73 and 0.53, respectively. This work addresses the problem of substituting ECa with covariates derived from bare-soil analysis from a purely statistical perspective as a first necessary step aiming at identifying the most promising covariates. A subsequent study will address this issue from a pedological standpoint. These findings highlight the potential of remote sensing data and spectral indices from multi-temporal imagery to replace direct ECa measurements, enabling rapid and accurate mapping of spatial variability in annual croplands.