Calibration of the SMAP Soil Moisture Retrieval Algorithm to Reduce Bias Over the Amazon Rainforest

Kyeungwoo Cho, Robinson Negron-Juarez, Andreas Colliander, Eric G. Cosio, Norma Salinas, Alessandro De Araujo, Jefferey Q. Chambers, Jingfeng Wang

Research output: Contribution to journalArticlepeer-review


Soil moisture (SM) is crucial for the Earth's ecosystem, impacting climate and vegetation health. Obtaining in situ observations of SM is labor-intensive and complex, particularly in remote and densely vegetated regions like the Amazon rainforest. NASA's soil moisture active and passive (SMAP) mission, utilizing an L-band radiometer, aims to monitor global SM. While it has been validated in areas with low vegetation water content (VWC) (< 5 {text{kgm}}{ - 2}), its efficiency in the Amazon, with dense canopies and high VWC (> 10 {text{kgm}}{ - 2}), is limitedly investigated due to scarce in situ measurements. This study assessed and analyzed the SMAP SM retrievals in the Amazon, employing the single-channel algorithm and adjusting vegetation optical depth (τ) and single scattering albedo (ω), two key vegetation parameters. It incorporated in situ SM observations from three old-growth rainforest locations: Tambopata (Southwest Amazon), Manaus (Central Amazon), and Caxiuana (Eastern Amazon). The SMAP SM deviated substantially from the in situ SM. However, calibrating τ and ω values, characterized by a lower τ, resulted in better agreement with the in situ measurements. This study emphasizes the pressing need for innovative methodologies to accurately retrieve SM in high-VWC regions like the Amazon rainforest using SMAP data.

Original languageEnglish
Pages (from-to)8724-8736
Number of pages13
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
StatePublished - 2024


  • Amazon rainforest
  • remote sensing
  • soil moisture (SM )
  • soil moisture active/passive (SMAP)
  • vegetation optical depth (VOD)


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