IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Jan 2024)

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

DOI
https://doi.org/10.1109/JSTARS.2024.3388914
Journal volume & issue
Vol. 17
pp. 8724 – 8736

Abstract

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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.

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