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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 #370962
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Multi-scale surface roughness for improved soil moisture estimation

Author
item NEELAM, M. - Goddard Space Flight Center
item COLLIANDER, A. - Jet Propulsion Laboratory
item MOHANTY, B. - Texas A&M University
item Cosh, Michael
item MISRA, S. - Jet Propulsion Laboratory
item JACKSON, T.J. - US Department Of Agriculture (USDA)

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/22/2020
Publication Date: 8/1/2020
Citation: Neelam, M., Colliander, A., Mohanty, B., Cosh, M.H., Misra, S., Jackson, T. 2020. Multi-scale surface roughness for improved soil moisture estimation. IEEE Transactions on Geoscience and Remote Sensing. 58(8):5264-5276. https://doi.org/10.1109/TGRS.2019.2961008.
DOI: https://doi.org/10.1109/TGRS.2019.2961008

Interpretive Summary: Remote sensing of the land surface is often confounded by the small scale surface roughness. The parameterization of the surface into the retrieval model for soil moisture remote sensing specifically has been identified as a potential problem for agricultural remote sensing because the soil is often disturbed by agricultural practices. A study using two sets of field experimentation data was designed to analyze how soil roughness parameterization influences soil moisture estimation errors and also how the effect of roughness is influenced by other land surface variables. It was found that soil texture played a leading role in surface roughness, as did the presence of larger scale topographic relief. It was also observed that vegetation decreased the impact of surface roughness on soil moisture estimate errors. This study will help guide future means of parameterization in the remote sensing community.

Technical Abstract: Surface roughness parameterization plays an important role in passive microwave soil moisture retrieval. This paper proposes a new formulation for estimating surface roughness. The proposed model incorporates the field-scale (micro) roughness, as well as topographic (macro) roughness. The performance of the model is evaluated by inverting the traditional tau-omega model for retrieving soil moisture. The study focuses on the PALS (Passive Active L-band System) radiometer data collected as a part of two Soil Moisture Active Passive Validation Experiments (SMAPVEX) i.e., SMAPVEX12 (humid Manitoba, Canada) and SMAPVEX15 (semi-arid Arizona, USA) with highly different micro-and macro-roughness. The measured surface roughness is observed to increase exponentially with clay fraction. This behavior is minimized with increase in Leaf Area Index (LAI). In the absence of vegetation, the contribution of topography towards surface roughness increases. A higher surface roughness values is estimated for SMAPVEX12, which positively correlate with LAI and clay fraction and negatively correlate with wetness conditions. On the other hand, due to the high topographic variability in SMAPVEX15 region, the contribution of topography (surface curvature) towards total surface roughness is significant. Also, consistently dry soil moisture resulted in high micro roughness for SMAPVEX15. Nevertheless, a total surface roughness estimated for SMAPVEX15 region is less than for SMAPVEX12. The surface roughness formulation presented in this study can be extrapolated to any spatial resolution.