Non-parametric methods for soil moisture retrieval from satellite remote sensing data

Tarendra Lakhankar, Hosni Ghedira, Marouane Temimi, Manajit Sengupta, Reza Khanbilvardi, Reginald Blake

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave backscatter interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. In such condition, non-parametric models could be used as possible alternative for better understanding the impact of variables in the retrieval process and relating it in the absence of exact formulation. In this study, non-parametric methods such as neural networks, fuzzy logic are used to retrieve soil moisture from active microwave remote sensing data. The inclusion of soil characteristics and Normalized Difference Vegetation Index (NDVI) derived from infrared and visible measurement, have significantly improved soil moisture retrievals and reduced root mean square error (RMSE) by around 30% in the retrievals. Soil moisture derived from these methods was compared with ESTAR soil moisture (RMSE ~4.0%) and field soil moisture measurements (RMSE ~6.5%). Additionally, the study showed that soil moisture retrievals from highly vegetated areas are less accurate than bare soil areas.

Original languageEnglish
Pages (from-to)3-21
Number of pages19
JournalRemote Sensing
Volume1
Issue number1
DOIs
StatePublished - Mar 2009

Keywords

  • Fuzzy logic
  • Neural network
  • Remote sensing
  • Soil moisture

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