A SMAP-Anchored Sentinel-1 Change Detection Method for 100 m Surface Soil Moisture Mapping with Vegetation-Conditioned Constraints.
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| Title: | A SMAP-Anchored Sentinel-1 Change Detection Method for 100 m Surface Soil Moisture Mapping with Vegetation-Conditioned Constraints. |
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| Authors: | Wang, Yunjia1 (AUTHOR), Sun, Hao1 (AUTHOR) sunhao@cumtb.edu.cn, Pei, Haoyu1 (AUTHOR), Gao, Jinhua1 (AUTHOR), Xu, Zhenheng1 (AUTHOR), Wang, Yuxin1 (AUTHOR), Wu, Dan1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p2045. 39p. |
| Subjects: | Remote sensing, Normalized difference vegetation index, Arid regions, Soil mapping |
| Abstract: | Highlights: What are the main findings? Sentinel-1 backscatter-change dynamics exhibit an NDVI-dependent upper envelope related to vegetation-induced dynamic-range compression. SMAP dry/wet quantiles provide stable moisture anchors for scaling Sentinel-1-derived relative wetness to 100 m volumetric surface soil moisture. What are the implications of the main findings? Vegetation-conditioned normalization improves the interpretability of Sentinel-1 change detection under sparse-to-moderate vegetation. The method provides a lightweight SMAP–Sentinel-1 retrieval route for 100 m soil moisture mapping in comparable semi-arid cropland–grassland regions. High-resolution surface soil moisture (SM) is needed for local hydrological and agricultural applications, but reliable retrieval at 100 m remains challenging. Within this broader methodological context, radiometer-constrained SAR change detection remains a practical and interpretable option for high-resolution soil moisture retrieval. It uses SAR-derived temporal changes to describe fine-scale wetting and drying processes, while passive microwave observations provide volumetric moisture references. This study proposes an improved SMAP-anchored Sentinel-1 change-detection framework (ISSF) for 100 m SM mapping. ISSF addresses these limitations by fitting NDVI-binned upper-envelope samples with a nonlinear quadratic function to normalize the vegetation-dependent backscatter-change range and by using multi-year SMAP dry/wet quantiles to scale the normalized relative wetness into volumetric SM. ISSF was evaluated using in situ measurements, a near-concurrent airborne reference, SMAP-based products, and direct transfer to OzNet. In the Shandian River Basin, ISSF achieved R = 0.549 and ubRMSE = 0.062 m3 m−3 at the point scale. Relative to three benchmark change-detection methods, ISSF increased R by 11–53% and reduced ubRMSE by 7–15%. For the airborne-referenced event, ISSF showed R = 0.635 and ubRMSE = 0.027 m3 m−3. Under direct transfer to OzNet, ISSF achieved mean R = 0.55 and mean ubRMSE = 0.05 m3 m−3. These results indicate that ISSF provides a practical and interpretable approach for 100 m soil moisture mapping in semi-arid regions with sparse to moderate vegetation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Sentinel-1 backscatter-change dynamics exhibit an NDVI-dependent upper envelope related to vegetation-induced dynamic-range compression. SMAP dry/wet quantiles provide stable moisture anchors for scaling Sentinel-1-derived relative wetness to 100 m volumetric surface soil moisture. What are the implications of the main findings? Vegetation-conditioned normalization improves the interpretability of Sentinel-1 change detection under sparse-to-moderate vegetation. The method provides a lightweight SMAP–Sentinel-1 retrieval route for 100 m soil moisture mapping in comparable semi-arid cropland–grassland regions. High-resolution surface soil moisture (SM) is needed for local hydrological and agricultural applications, but reliable retrieval at 100 m remains challenging. Within this broader methodological context, radiometer-constrained SAR change detection remains a practical and interpretable option for high-resolution soil moisture retrieval. It uses SAR-derived temporal changes to describe fine-scale wetting and drying processes, while passive microwave observations provide volumetric moisture references. This study proposes an improved SMAP-anchored Sentinel-1 change-detection framework (ISSF) for 100 m SM mapping. ISSF addresses these limitations by fitting NDVI-binned upper-envelope samples with a nonlinear quadratic function to normalize the vegetation-dependent backscatter-change range and by using multi-year SMAP dry/wet quantiles to scale the normalized relative wetness into volumetric SM. ISSF was evaluated using in situ measurements, a near-concurrent airborne reference, SMAP-based products, and direct transfer to OzNet. In the Shandian River Basin, ISSF achieved R = 0.549 and ubRMSE = 0.062 m3 m−3 at the point scale. Relative to three benchmark change-detection methods, ISSF increased R by 11–53% and reduced ubRMSE by 7–15%. For the airborne-referenced event, ISSF showed R = 0.635 and ubRMSE = 0.027 m3 m−3. Under direct transfer to OzNet, ISSF achieved mean R = 0.55 and mean ubRMSE = 0.05 m3 m−3. These results indicate that ISSF provides a practical and interpretable approach for 100 m soil moisture mapping in semi-arid regions with sparse to moderate vegetation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18122045 |
