In the context of global land degradation and increasing salinization of cultivated land, accurately and sustainably estimating soil salinization is essential for effective land management. This study explores a novel approach to improve soil salt content (SSC) monitoring by minimizing the influence of long-term environmental variability and incorporating relevant environmental factors. Focusing on exposed cultivated land in arid regions, we analyzed the sensitivity of multispectral indices and environmental factors to SSC during pre-seeding and post-harvest bare soil periods. Our findings indicate that soil mechanical composition and salinity indices exhibited strong correlations with SSC, which further enhanced when the periods were analyzed separately. Dividing the bare soil periods improved the model performance, increasing R² by 10.2%–55.7%, and the support vector regression model performed better, with an R² of 0.77, RMSE of 0.11%. This approach offers a robust framework for precision agriculture and sustainable land management in salinity-affected regions.

Spectral remote sensing enables efficient acquisition of large-scale land surface information and is a key approach for monitoring soil salinity content (SSC). However, surface mulching significantly alters the spectral reflectance responses of croplands, increasing the uncertainty of SSC retrieval using remote sensing. This study aimed to systematically identify SSC-sensitive spectral features under different mulching conditions and to evaluate the effects of spatial resolution on SSC–spectral relationships. Multi-resolution datasets were constructed based on plastic mulch geometric parameters, and SSC–spectral relationships were analyzed using correlation methods and recursive feature elimination (RFE). Results indicate that under near-ground ultra-high-resolution conditions, the correlation between inter-mulch bare soil spectral features and SSC was weakly influenced by mulch type, and distinguishing mulch types provides limited improvement in inter-variable relationships. Pearson’s r exceeded 0.40 for both white- and black-mulched samples, and distinguishing mulch types provided only marginal gains in model accuracy (RFR–RFE R² = 0.9524 for white-mulched and 0.9252 without distinguishing; R² = 0.9387 for black-mulched). In contrast, under multi-resolution settings at the field scale, separating black-mulched, white-mulched, and non-mulched fields significantly enhanced the correlation between spectral indices (SIs) and SSC, with the coefficient of determination (R²) based on the recursive feature elimination (RFE) algorithm increasing by up to 0.28. The highly sensitive SIs of non-mulched farmland are generally consistent with those of white-mulched farmland but differ markedly from those of black-mulched farmland. Scale optimization analysis further indicated that the optimal spatial resolution was 1.35 m for white-mulched and non-mulched farmland. Black-mulched farmland performed best at 5.4 m, likely because stronger spectral masking by black mulch increases mixed-pixel dominance and benefits from spatial aggregation. These findings provide methodological guidance and practical approaches to accurately retrieve SSC in plastic-mulched croplands and to determine the optimal image spatial resolution.