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<p>In the last years, more satellites with microwave imagers have been launched, making more observations available to obtain soil moisture estimates globally. China’s endeavour has resulted in the launch of the FengYun (FY) passive microwave observations (FY-3B, C, D) capable of filling in global soil moisture data gaps. In this study, we develop a merged soil moisture dataset at a spatial resolution of 0.15° from the FY series which spans 2011 to present time (2020 in this study) by a merging technique that minimizes mean square error (MSE) using the signal-to-noise ratio of the input parent products. Here, we combine the ascending and descending observations from the three satellite observations to obtain sub-daily estimates. Finally, we averaged the merged sub-daily FY soil moisture into daily estimates and gap-fill it using a deep learning interpolation approach to reconstruct the missing days while preserving the characteristics of the Merged FY data. The results of this study aim to provide datasets that meet challenges in using global satellite soil moisture observations.</p>

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Seamless finer-resolution soil moisture from the synergistic merging of the FengYun-3 satellite series

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月11日

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  • Daniel F. T. Hagan,
  • Seokhyeon Kim,
  • Guojie Wang,
  • Xiaowen Ma,
  • Yifan Hu,
  • Yi Y. Liu,
  • Alexander Barth,
  • Haonan Liu,
  • Waheed Ullah,
  • Isaac K. Nooni,
  • Samuel A. Bhatti

摘要

In the last years, more satellites with microwave imagers have been launched, making more observations available to obtain soil moisture estimates globally. China’s endeavour has resulted in the launch of the FengYun (FY) passive microwave observations (FY-3B, C, D) capable of filling in global soil moisture data gaps. In this study, we develop a merged soil moisture dataset at a spatial resolution of 0.15° from the FY series which spans 2011 to present time (2020 in this study) by a merging technique that minimizes mean square error (MSE) using the signal-to-noise ratio of the input parent products. Here, we combine the ascending and descending observations from the three satellite observations to obtain sub-daily estimates. Finally, we averaged the merged sub-daily FY soil moisture into daily estimates and gap-fill it using a deep learning interpolation approach to reconstruct the missing days while preserving the characteristics of the Merged FY data. The results of this study aim to provide datasets that meet challenges in using global satellite soil moisture observations.