Optimal time-window for assessing soil salinity via Sentinel-2 multitemporal synthetic data in the arid agricultural regions of China

• Summer is optimal time-window for soil salinity assessment in arid farmlands. • Median-synthesized imagery improves soil salinity prediction accuracy. • Spectral indices outperform single bands in soil salinity prediction. Soil salinity is a critical issue affecting agricultural productivity in arid regions. Remote sensing is an effective tool for assessing and monitoring soil salinity to enable precision soil care. However, obtaining bare-soil information from agriculturally active regions remains challenging. Therefore, this study aimd to identify the optimal temporal window for assessing soil salinity. We developed three different time-synthesis strategies based on Sentinel-2 time-series images (1-month synthetic, 2-month synthetic, and seasonal synthetic image) through median and mean syntheses. We constructed estimation models (including random forest (RF) and gradient tree boosting (GTB)) using band and spectral indices information from synthetic images in the Google Earth Engine (GEE) platform. Additionally, we compared the results of different modeling strategies and assessed the uncertainty in soil salinity mapping. The results showed the optimal time-window for assessing soil salinization was the images synthesized in summer (June-August) (R 2 : 0.41–0.45), which was approximately 36.51% higher than that during the bare soil period (March-April). Assessment models constructed from summer synthetic imagery had a low uncertainty in soil salinity mapping. The median-based synthesis approach was the most effective, compared to the mean-based synthesis approach with an R 2 of 0.45 (RF validation mean). The six spectral indices including EVI, GYEX, TBI, GARI, NDSI, and NDVI proved more important in the estimation model than the original Sentinel-2 bands. Moreover, the red band (band 4) and short-wave infrared band (band 12) in the summer synthetic spectra exhibited the strongest correlation with soil salinity, with Pearson correlation coefficient of 0.56 for both. Our findings indicate that summer was the optimal period for assessing soil salinization in the arid agricultural regions of China. This study employs temporal synthesis techniques to accurately identify the specific “period” most closely correlated with ground-measured soil salinity (optimal time-window), offering a practical and efficient alternative strategy for precise salinization inversion in regions where remote-sensing data are scarce.