In vegetated coastal deltas, direct optical retrieval of surface soil salt content (SSC, 0–10 cm) is often hindered by canopy masking, mixed pixels, and seasonal variability in surface conditions. To improve SSC mapping under vegetation cover, this study developed a thresholded normalized difference vegetation index area-under-the-curve (NDVI-AUC) metric that integrates only the portion of the seasonal NDVI trajectory exceeding an ecologically defined threshold. Taking Dongying in the Yellow River Delta (YRD), China, as the study area, daily NDVI time series were reconstructed in Google Earth Engine (GEE) from Sentinel-2, Landsat-8/9, MODIS, and a Sentinel–Landsat fusion stream. An empirical electrical conductivity (EC)–SSC calibration was used to harmonize multi-year observations and construct a unified dataset of 177 topsoil samples collected in 2022, 2024, and 2025, which was divided into calibration (n = 118) and validation (n = 59) sets. Threshold traversal and Savitzky–Golay (SG) sensitivity tests were performed, and the negative exponential model was retained as the primary model after comparison with alternative monotonic decreasing functions. Across sensors, SSC showed a consistent inverse nonlinear relationship with NDVI-AUC. Threshold selection influenced model performance more strongly than SG smoothing. The Sentinel–Landsat fusion stream performed best, with R² values of 0.731 and 0.725 for calibration and validation, respectively, followed closely by Sentinel-2 (R² = 0.718 and 0.713). Landsat-8/9 showed moderate performance, whereas MODIS mainly represented background-scale patterns. The optimal 10 m implementation was further used to reconstruct annual SSC maps for 2021–2025, revealing stable coastal hotspots, localized bidirectional changes, and a modest model-derived decline in regional SSC. Overall, thresholded NDVI-AUC provides a simple, interpretable, and process-based metric for SSC mapping in vegetated coastal soils and can support agricultural decision makers in annual salinity hotspot screening and land management planning.

Accurate identification of the spatial distribution of winter wheat is important for agricultural resource allocation and production management. However, the applicability, complementary effects, and optimal observation windows of different remote sensing data sources across key phenological stages of winter wheat remain unclear. Taking Haiyang City and Wenshang County in Shandong Province of China as the study areas, this study used multi-source remote sensing data from Sentinel-1, Sentinel-2, and Landsat-8/9 to extract spectral and backscattering features at different phenological stages. Winter wheat classification was conducted using the Random Forest algorithm, and the classification performance of different data combinations at each growth stage was compared. The results showed that winter wheat classification accuracy varied clearly with phenological development, with peak performance occurring at the heading stage in Haiyang and the grain-filling stage in Wenshang. Optical data played a dominant role in winter wheat classification. The tillering stage was identified as the earliest candidate observation window in both regions, although independent evaluation confirmed reliable early classification only in Wenshang. The incorporation of synthetic aperture radar (SAR) data did not consistently improve accuracy at all stages but provided complementary information under specific phenological and regional conditions. Overall, Sentinel-2 showed the most stable performance, and the optimal observation stage and data combination depended on regional landscape conditions.

Residue-soil mixing and reduced spectral sensitivity complicate summer maize leaf area index (LAI) estimation from unmanned aerial vehicle multispectral imagery. We compared full-plot and canopy domains, paired differences, neighborhood statistics, and approximate days after sowing (DAS), calculated from a common reference date. Five input schemes (B₀–B₄) and four algorithms were evaluated using 345 samples collected on five dates in one growing season. Spatial validation comprised ten repetitions of five-fold grouped cross-validation, with preprocessing and feature selection restricted to training data. For gradient boosting decision trees, R² increased from 0.515 for B₀ to 0.686 for B₃ and 0.846 for B₄, whereas RMSE decreased from 0.834 to 0.671 and 0.470. Low-LAI Bias decreased from 0.580 to 0.343 and 0.160. Fixed-capacity controls supported conditional improvements after adding paired differences and neighborhood statistics. Approximate DAS produced the largest sequential RMSE reduction, mainly through between-date adjustment. Time-baseline and training-fold residual analyses distinguish calendar-time adjustment from within-date predictive information. High-LAI underestimation and unseen-date errors delimit application beyond the sampled dates and production-field conditions. Within this defined scope, the framework provides a reproducible comparison of image-domain representations under heterogeneous production conditions.