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<p>Surface soil moisture (SSM) plays a vital role in scientific research related to crop management. It is commonly used to monitor crop growth, estimate yields, predict drought conditions, and support agricultural water resource management. This study integrates Synthetic Aperture Radar (SAR) data from Sentinel-1A and multispectral optical imagery from Sentinel-2B. Sentinel-1A SAR data were used to estimate surface soil moisture over the Boipariguda, Jeypore, and Borigumma sub-watersheds, supported by ground-based soil moisture measurements. A total of 392 in situ soil moisture readings covering multiple soil texture classes were collected during field campaigns using ML3 ThetaProbe. The instrument measures volumetric water content (m<sup>3</sup>/m<sup>3</sup>) with an accuracy of ± 0.01, based on soil-specific calibration. In the present study, Sentinel-1A C-band VV-polarized SAR data were used to estimate surface soil moisture over agricultural croplands and bare soil using a modified Dubois model. The results indicate good agreement between satellite-derived and in situ soil moisture. Across the study area, R<sup>2</sup> values ranges from 0.75 to 0.81, while RMSE values and CDF bias vary between 0.025–0.0425 and 0.019–0.033 m<sup>3</sup>/m<sup>3</sup>, respectively. These findings show that the modified Dubois model is able to retrieve soil moisture over agricultural croplands and bare soil. Therefore, the findings of this study can be useful for irrigation planning, agricultural water management, and soil erosion assessment for a larger area.</p>

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Soil moisture estimation using Sentinel-1 SAR and field observation in the Eastern Ghats region of India

  • 期刊论文

  • 发表日期：

    2026年5月27日

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  • Uday Mandal,
  • M. Madhu,
  • Ramajayam Devarajan,
  • Randhir Kumar,
  • Gopal Kumar,
  • HC Hombegowda,
  • SK Samal,
  • Saswat Kumar Kar,
  • P. Raja,
  • Pyla Sravani,
  • Sitanshu Sekhar Patra

摘要

Surface soil moisture (SSM) plays a vital role in scientific research related to crop management. It is commonly used to monitor crop growth, estimate yields, predict drought conditions, and support agricultural water resource management. This study integrates Synthetic Aperture Radar (SAR) data from Sentinel-1A and multispectral optical imagery from Sentinel-2B. Sentinel-1A SAR data were used to estimate surface soil moisture over the Boipariguda, Jeypore, and Borigumma sub-watersheds, supported by ground-based soil moisture measurements. A total of 392 in situ soil moisture readings covering multiple soil texture classes were collected during field campaigns using ML3 ThetaProbe. The instrument measures volumetric water content (m3/m3) with an accuracy of ± 0.01, based on soil-specific calibration. In the present study, Sentinel-1A C-band VV-polarized SAR data were used to estimate surface soil moisture over agricultural croplands and bare soil using a modified Dubois model. The results indicate good agreement between satellite-derived and in situ soil moisture. Across the study area, R2 values ranges from 0.75 to 0.81, while RMSE values and CDF bias vary between 0.025–0.0425 and 0.019–0.033 m3/m3, respectively. These findings show that the modified Dubois model is able to retrieve soil moisture over agricultural croplands and bare soil. Therefore, the findings of this study can be useful for irrigation planning, agricultural water management, and soil erosion assessment for a larger area.