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Comprehensive Evaluation of the GF-3 Series SAR Satellites for Soil Moisture and Surface Roughness Retrieval over Bare Soils.

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Title: Comprehensive Evaluation of the GF-3 Series SAR Satellites for Soil Moisture and Surface Roughness Retrieval over Bare Soils.
Authors: Li, Xiangdong1,2 (AUTHOR), Chen, Hongbing1,2 (AUTHOR), Ma, Jingwen2,3 (AUTHOR), Qiu, Xinxin4 (AUTHOR), Wang, Chunmei5 (AUTHOR), Ren, Jianhua6 (AUTHOR), Li, Xinbiao2,7 (AUTHOR), Li, Bingze1,2 (AUTHOR), Li, Lei2 (AUTHOR), Wang, Xigang3,7 (AUTHOR), Zheng, Xingming1,2,4 (AUTHOR) zhengxingming@iga.ac.cn
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1453. 24p.
Subjects: Soil moisture, Surface roughness, Random forest algorithms, Artificial satellites, Soil geography, Remote sensing, Calibration
Geographic Terms: China
Abstract: Highlights: What are the main findings? A systematic evaluation of mv and s retrieval using the GF-3 series SAR satellites was conducted across 11 experimental areas. The calibrated Oh94 model with prior constraints effectively mitigates the domain shift problem in unseen regions. What are the implications of the main findings? The proposed framework eliminates platform radiometric offsets, ensuring stable mv retrieval bias within 0.021 cm3·cm−3. This study verifies the feasibility of synergistic mv mapping, supporting large-area operational applications of the GF-3 constellation. Accurate quantification of soil moisture (mv) is of great scientific significance for regional hydrological modeling, meteorological forecasting, and drought and flood disaster monitoring. Although C-band SAR aboard the GF-3 satellite constellation supports large-scale retrieval, existing studies are mostly confined to local validation under simple surface conditions. Its retrieval performance across varied surface roughness (s), mv, soil texture, and topography, as well as the synergistic retrieval ability of the satellite constellation, has not been fully investigated. Therefore, this study systematically evaluated four mv retrieval strategies using quality-controlled satellite-ground synchronous observation data from 11 arid-to-humid experimental areas (378 plots) in China: Oh94 model inversion (Strategy I), calibrated Oh94 model inversion (Strategy II), calibrated Oh94 model inversion with prior constraints on mv and s (Strategy III), and random forest inversion (Strategy IV). Subsequently, the measured satellite backscattering coefficients ( σ obs 0 ) were compared with model simulations ( σ sim 0 ), yielding initial biases of 2.08 dB, 0.78 dB, and −0.29 dB for VV, HH, and HV polarizations, respectively, and these biases were significantly reduced to −0.01 dB, 0.00 dB, and −0.06 dB after systematic deviation correction (SDC). Overall, the root-mean-square errors (RMSE) of mv retrieval for Strategies I–IV were 0.092, 0.078, 0.058, and 0.046 cm3·cm−3, respectively, while those for s retrieval were 0.620, 0.578, 0.610, and 0.403 cm. Strategy IV achieved the highest mv retrieval accuracy owing to the robust nonlinear predictive capacity of machine learning. Nevertheless, Strategy III exhibited superior transferability in spatially independent validation, with an RMSE of 0.054 cm3·cm−3, outperforming Strategy IV (0.065 cm3·cm−3). This demonstrates that Strategy III possesses a stronger generalization ability than purely data-driven models under domain shifts. By incorporating prior constraints, Strategy III effectively mitigated radiometric inconsistencies within the satellite constellation, and mv retrieval biases among GF-3, GF-3B, and GF-3C converged stably within 0.021 cm3·cm−3, with RMSE ranging from 0.046 to 0.079 cm3·cm−3. This study validates the feasibility of synergistic mv retrieval over bare surfaces using the GF-3 SAR constellation, providing critical technical support for large-area operational mapping. [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
  Group: Ti
  Data: Comprehensive Evaluation of the GF-3 Series SAR Satellites for Soil Moisture and Surface Roughness Retrieval over Bare Soils.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Xiangdong%22">Li, Xiangdong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Hongbing%22">Chen, Hongbing</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Jingwen%22">Ma, Jingwen</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qiu%2C+Xinxin%22">Qiu, Xinxin</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Chunmei%22">Wang, Chunmei</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Jianhua%22">Ren, Jianhua</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xinbiao%22">Li, Xinbiao</searchLink><relatesTo>2,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Bingze%22">Li, Bingze</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Lei%22">Li, Lei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Xigang%22">Wang, Xigang</searchLink><relatesTo>3,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Xingming%22">Zheng, Xingming</searchLink><relatesTo>1,2,4</relatesTo> (AUTHOR)<i> zhengxingming@iga.ac.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1453. 24p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Soil+moisture%22">Soil moisture</searchLink><br /><searchLink fieldCode="DE" term="%22Surface+roughness%22">Surface roughness</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+satellites%22">Artificial satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Soil+geography%22">Soil geography</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? A systematic evaluation of mv and s retrieval using the GF-3 series SAR satellites was conducted across 11 experimental areas. The calibrated Oh94 model with prior constraints effectively mitigates the domain shift problem in unseen regions. What are the implications of the main findings? The proposed framework eliminates platform radiometric offsets, ensuring stable mv retrieval bias within 0.021 cm3·cm−3. This study verifies the feasibility of synergistic mv mapping, supporting large-area operational applications of the GF-3 constellation. Accurate quantification of soil moisture (mv) is of great scientific significance for regional hydrological modeling, meteorological forecasting, and drought and flood disaster monitoring. Although C-band SAR aboard the GF-3 satellite constellation supports large-scale retrieval, existing studies are mostly confined to local validation under simple surface conditions. Its retrieval performance across varied surface roughness (s), mv, soil texture, and topography, as well as the synergistic retrieval ability of the satellite constellation, has not been fully investigated. Therefore, this study systematically evaluated four mv retrieval strategies using quality-controlled satellite-ground synchronous observation data from 11 arid-to-humid experimental areas (378 plots) in China: Oh94 model inversion (Strategy I), calibrated Oh94 model inversion (Strategy II), calibrated Oh94 model inversion with prior constraints on mv and s (Strategy III), and random forest inversion (Strategy IV). Subsequently, the measured satellite backscattering coefficients ( σ obs 0 ) were compared with model simulations ( σ sim 0 ), yielding initial biases of 2.08 dB, 0.78 dB, and −0.29 dB for VV, HH, and HV polarizations, respectively, and these biases were significantly reduced to −0.01 dB, 0.00 dB, and −0.06 dB after systematic deviation correction (SDC). Overall, the root-mean-square errors (RMSE) of mv retrieval for Strategies I–IV were 0.092, 0.078, 0.058, and 0.046 cm3·cm−3, respectively, while those for s retrieval were 0.620, 0.578, 0.610, and 0.403 cm. Strategy IV achieved the highest mv retrieval accuracy owing to the robust nonlinear predictive capacity of machine learning. Nevertheless, Strategy III exhibited superior transferability in spatially independent validation, with an RMSE of 0.054 cm3·cm−3, outperforming Strategy IV (0.065 cm3·cm−3). This demonstrates that Strategy III possesses a stronger generalization ability than purely data-driven models under domain shifts. By incorporating prior constraints, Strategy III effectively mitigated radiometric inconsistencies within the satellite constellation, and mv retrieval biases among GF-3, GF-3B, and GF-3C converged stably within 0.021 cm3·cm−3, with RMSE ranging from 0.046 to 0.079 cm3·cm−3. This study validates the feasibility of synergistic mv retrieval over bare surfaces using the GF-3 SAR constellation, providing critical technical support for large-area operational mapping. [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/rs18101453
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      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 1453
    Subjects:
      – SubjectFull: Soil moisture
        Type: general
      – SubjectFull: Surface roughness
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Artificial satellites
        Type: general
      – SubjectFull: Soil geography
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      – SubjectFull: Remote sensing
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      – SubjectFull: Calibration
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      – SubjectFull: China
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              Text: May2026
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