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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.
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]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: A SMAP-Anchored Sentinel-1 Change Detection Method for 100 m Surface Soil Moisture Mapping with Vegetation-Conditioned Constraints.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yunjia%22">Wang, Yunjia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Hao%22">Sun, Hao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sunhao@cumtb.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Pei%2C+Haoyu%22">Pei, Haoyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Jinhua%22">Gao, Jinhua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Zhenheng%22">Xu, Zhenheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yuxin%22">Wang, Yuxin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Dan%22">Wu, Dan</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p2045. 39p.
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  Data: <searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22Arid+regions%22">Arid regions</searchLink><br /><searchLink fieldCode="DE" term="%22Soil+mapping%22">Soil mapping</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18122045
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        Text: English
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        PageCount: 39
        StartPage: 2045
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      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Normalized difference vegetation index
        Type: general
      – SubjectFull: Arid regions
        Type: general
      – SubjectFull: Soil mapping
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      – TitleFull: A SMAP-Anchored Sentinel-1 Change Detection Method for 100 m Surface Soil Moisture Mapping with Vegetation-Conditioned Constraints.
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              Text: Jun2026
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