Retrieval of crop biomass and soil moisture from measured 1.4 and 10.65 GHz brightness temperatures

Shou Fang Liu, Yuei An Liou, Wen Jun Wang, Jean Pierre Wigneron, Jann Bin Lee

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


Physically based land surface process/radiobrightness (LSP/R) models may characterize well the relationship between radiometric signatures and surface parameters. They can be used to develop and improve the means of sensing surface parameters by microwave radiometry. However, due to a lack in the skill to properly understand the behavior of the data, a statistical approach is often adopted. In this paper, we present the retrieval of wheat plant water content (PWC) and soil moisture content (SMC) profiles from the measured H-polarized and V-polarized brightness temperatures at 1.4 (L-band), and 10.65 (X-band) GHz by an error propagation learning back propagation (EPLBP) neural network. The PWC is defined as the total water content in the vegetation. The brightness temperatures were taken by the PORTOS radiometer over wheat fields through three month growth cycles in 1993 (PORTOS-93) and 1996 (PORTOS-96). Note that, through the neural network, there is no requirement of ancillary information on the complex surface parameters such as vegetation biomass, surface temperature, and surface roughness, etc. During both field campaigns, the L-band radiometer was used to measure brightness temperatures at incident angles from 0 to 50° at L-band and at an incident angle of 50° at X-band. The SMC profiles were measured to the depths of 10 cm in 1993 and 5 cm in 1996. The wheat was sampled approximately once a week in 1993 and 1996 to obtain its dry and wet biomass (i.e., PWC). The EPLBP neural network was trained with observations randomly chosen from the PORTOS-93 data, and evaluated by the remaining data from the same set. The trained neural network is further evaluated with the PORTOS-96 data.

Original languageEnglish
Pages (from-to)1260-1268
Number of pages9
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number6
StatePublished - Jun 2002


  • Neural network
  • Plant water content
  • Soil moisture


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