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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #411080
Research Project: From Field to Watershed: Enhancing Water Quality and Management in Agroecosystems through Remote Sensing, Ground Measurements, and Integrative Modeling

Location: Hydrology and Remote Sensing Laboratory

Title: P- and L-band radiometry retrieval of subsurface soil moisture and temperature profiles

Author
item LI, M - George Washington University
item LANG, R - University Of Washington
item Cosh, Michael

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2024
Publication Date: 6/20/2024
Citation: Li, M., Lang, R.H., Cosh, M.H. 2024. P- and L-band radiometry retrieval of subsurface soil moisture and temperature profiles. IEEE Transactions on Geoscience and Remote Sensing. 62. https://doi.org/10.1109/TGRS.2024.3416988.
DOI: https://doi.org/10.1109/TGRS.2024.3416988

Interpretive Summary: Remotely sensing soil moisture is now regularly estimated with L-band radiometers, but these estimates are limited to the near surface. With the addition of P-band measurements, it may be possible to extend this estimate to depths as great as 60 cm. A field experiment was conducted in Beltsville, MD to test this hypothesis. It was determined that soil moisture could be accurately estimated for depths of 20 cm, but the accuracy decreased significantly when attempted for 60 cm. This study will help guide development of future remote sensing systems.

Technical Abstract: This paper presents the potential use of P and L band passive measurements to determine subsurface soil moisture and temperature. In situ soil data have been collected for the depths (d) of 0-60 cm by the NASA GSFC and USDA researchers during the PLEX19 experiment at Beltsville, MD, USA. With the In situ soil data, a coherent model is used to generate synthetic brightness temperatures at an observation angle of 40 degrees, spanning frequencies of 0.8, 0.9, 1.1, and 1.4 GHz. The synthetic brightness temperatures are used to retrieve the soil moisture and temperature data that are represented by a quadratic function with three unknown coefficients, respectively. The inversion problem is formulated as a least square problem that is solved by the Adaptive Simulated Annealing method. A feature analysis is conducted to assess and quantify the relationships between various linear combinations of radiometer features and the quadratic function coefficients. Feature analysis reveals the 1.4 GHz brightness temperature and the difference in the brightness temperatures at 1.4 GHz and 0.8 GHz that are sensitive to the shapes of the soil moisture and temperature profiles (R2 = 0.62 - 0.97). The regression analysis offers the best-fitted models of function coefficients that serve as the constraints of the unknown coefficients in the inversion problem. The retrieval results demonstrate remarkable accuracy, with a RMSE for soil moisture less than 0.04 cm3/cm3 for d = 20 cm, and less than 0.10 cm3/cm3 for d = 60 cm. For soil temperature, the RMSE is less than 1.35 °C for d = 20 cm, and less than 1.60 °C for d = 60 cm.