A Thresholded NDVI-AUC Metric from Multi-Source Optical Time Series for Mapping Surface Soil Salt Content in Vegetated Coastal Areas.
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| Title: | A Thresholded NDVI-AUC Metric from Multi-Source Optical Time Series for Mapping Surface Soil Salt Content in Vegetated Coastal Areas. |
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| Authors: | Cui, Zi'ang1 (AUTHOR), Liu, Yazhou1,2 (AUTHOR), Song, Rufei1,3 (AUTHOR), Wang, Jingzhe2,4 (AUTHOR), Zhang, Zipeng3,5 (AUTHOR), Ge, Xiangyu1,3 (AUTHOR), Liu, Fangbing1,2 (AUTHOR), Wang, Zhengdong1,3 (AUTHOR), Ding, Jianli4,5 (AUTHOR), Wang, Jinjie3,5 (AUTHOR), Han, Lijing1 (AUTHOR) hanlijing@sdust.edu.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1522. 25p. |
| Subjects: | Normalized difference vegetation index, Soil salinity, MODIS (Spectroradiometer), Remote sensing, Landsat satellites, Time series analysis, Coastal plants |
| Geographic Terms: | Yellow River Delta (China), China |
| Abstract: | Highlights: What are the main findings? We developed a thresholded NDVI-AUC metric to estimate surface soil salt content (SSC, 0–10 cm) under vegetation cover. Across Sentinel–Landsat fusion, Sentinel-2, Landsat-8/9, and MODIS, SSC showed a consistent inverse relationship with NDVI-AUC; threshold selection and sensor characteristics influenced model performance more strongly than smoothing. What are the implications of the main findings? NDVI-AUC provides an interpretable time-series alternative to single-date bare soil indices in vegetated coastal landscapes. The 10 m implementation supports annual SSC hotspot mapping and land-management decisions in the Yellow River Delta. 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We developed a thresholded NDVI-AUC metric to estimate surface soil salt content (SSC, 0–10 cm) under vegetation cover. Across Sentinel–Landsat fusion, Sentinel-2, Landsat-8/9, and MODIS, SSC showed a consistent inverse relationship with NDVI-AUC; threshold selection and sensor characteristics influenced model performance more strongly than smoothing. What are the implications of the main findings? NDVI-AUC provides an interpretable time-series alternative to single-date bare soil indices in vegetated coastal landscapes. The 10 m implementation supports annual SSC hotspot mapping and land-management decisions in the Yellow River Delta. 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101522 |
