Retrieving soil moisture from simulated brightness temperatures by a neural network

Yuei An Liou, Shou Fang Liu, Wen June Wang

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


We present the retrievals of surface soil moisture (SM) from simulated brightness temperatures by a newly developed error propagation learning back propagation (EPLBP) neural network. The frequencies of interest include 6.9 and 10.7 GHz of the advanced microwave scanning radiometer (AMSR) and 1.4 GHz (L-band) of the soil moisture and ocean salinity (SMOS) sensor. The land surface process/radiobrightness (LSP/R) model is used to provide lime series of both SM and brightness temperatures at 6.9 and 10.7 GHz for AMSRs viewing angle of 55°, and at L-band for SMOS's multiple viewing angles of 0°, 10°, 20°, 30°, 40°, and 50° for prairie grassland with a column density of 3.7 km/m 2. These multiple frequencies and viewing angles allow us to design a variety of observation modes to examine their sensitivity to SM. For example, L-band brightness temperature at any single look angle is regarded as an L-band one-dimensional (1-D) observation mode. Meanwhile, it can be combined with either the observation at the other angles to become an L-band two-dimensional (2-D) or a multiple dimensional observation mode, or with the observation at 6.9 or 10.7 GHz to become a multiple frequency/dimensional observation mode. In this paper, it is shown that the sensitivity of radiobrightness at AMSR channels to SM is increased by incorporating L-band radiobrightness. In addition, the advantage of an L-band 2-D or a multiple dimensional observation mode over an L-band 1-D observation mode is demonstrated.

Original languageEnglish
Pages (from-to)1662-1672
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number8
StatePublished - Sep 2001


  • Brightness temperature
  • Neural network
  • Soil moisture


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