Stochastic ensemble methods for multi-SAR-mission soil moisture retrieval

Liujun Zhu, Jeffrey P. Walker, Xiaoji Shen

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The recent and projected investments across the world on radar satellite missions (e.g., Sentinel-1, SAOCOM, BIOMASS and NISAR) provide a great opportunity for operational radar soil moisture mapping with high spatial and temporal resolution. However, there is no retrieval algorithm that can make complementary use of the multi-frequency data from those missions, due to the large uncertainties in observations collected by the different sensors, different validity regions of the forward models, and the fact that inversion algorithms have been designed for specific data sources. In this study, the principle of ensemble learning was introduced to provide two general soil moisture retrieval frameworks accounting for these issues. Instead of trying to find an optimal global solution, multiple soil moisture retrievals (termed sub-retrievals) with moderate performance were first obtained using different channels and/or time instances randomly selected from the available data, with the retrieved ensemble of results being the final output. The ensemble retrievals, taking one existing snapshot method and two multi-temporal methods as the base retrieval algorithms, were evaluated using a synthetic data set with the effectiveness confirmed under various uncertainty sources. An evaluation using the Fifth Soil Moisture Active Passive Experiment (SMAPEx-5) data set showed that the ensemble retrieval outperformed the non-ensemble retrieval in most cases, with a decrease of 0.004 to 0.014 m3/m3 in Root Mean Square Error (RMSE) and an increase of 0.01 to 0.16 in correlation coefficient (R). Weakly biased and correlated sub-retrievals were confirmed to be the basic requirement of an effective ensemble retrieval, being consistent with use of ensemble learning in other applications.

Original languageEnglish
Article number112099
Number of pages12
JournalRemote Sensing of Environment
Publication statusPublished - 15 Dec 2020


  • Ensemble learning
  • Multi-frequency
  • Multi-temporal
  • Soil moisture
  • Synthetic aperture radar

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