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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 #395613

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: Form field observations to temporally dynamic roughness retrievals in the corn belt

item WALKER, V.A. - Orise Fellow
item YILDRIM, E. - University Of Iowa
item WALLACE, V. - University Of Iowa
item EICHINGER, W.E. - University Of Iowa
item Cosh, Michael
item HORNBUCKLE, B. - Iowa State University

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/1/2023
Publication Date: 3/15/2023
Citation: Walker, V., Yildrim, E., Wallace, V., Eichinger, W., Cosh, M.H., Hornbuckle, B. 2023. Form field observations to temporally dynamic roughness retrievals in the corn belt. Remote Sensing of Environment. 287. Article e113458.

Interpretive Summary: The roughness of soil can have a significant impact on the ability to remotely sense the earth’s surface at a variety of wavelengths. Microwave remote sensing of soil moisture is particularly impacted and an assessment of roughness conditions is usually assumed to be constant for most remote sensing algorithms. In agricultural domains, this is a false assumption because of tillage and field traffic from tractors or cattle. A temporal analysis of soil roughness and its impact on remote sensing was conducted in 2016 and 2017 in Central Iowa to understand the variance and magnitude of the roughness in a tilled cropland landscape. It was observed that roughness was highest in early spring and late fall, timed with field operations and it was shown to have a significant impact on soil moisture estimation. This study is important for model developers as well as land managers concerned with tillage magnitudes within a domain.

Technical Abstract: Soil surface roughness in the U. S. Corn Belt varies outside of the growing season due to rainfall and farm management activities such as tillage and cultivation. This violates the current assumption by SMAP during soil moisture retrieval that roughness affects on terrestrial emission can be characterized by a static parameter. SMAPVEX16-IA, an extensive field campaign focused on calibration of SMAP retrievals in croplands, presented the opportunity to sample roughness across the network during a period where it is theoretically at a minima. Domain-averaged measurements of rms height and correlation length collected via pinboard, gridboard, and lidar methodologies resulted in a ensemble of model roughness coefficients that, at h = [0.13, 0.97], is significantly rougher than the SMAP assumption in croplands of h = 0.108. Comparing simulated brightness temperatures for the network using the ensemble h to SMAP observations resulted in a best-fit angular sensitivity coefficient of N = -2 for both horizontal and vertical polarizations. h is then retrieved for a four-year study period (excluding growing seasons) via the single channel algorithm from SMAP brightness temperatures and in situ observations of soil moisture and temperature. Roughness retrievals are highly variable in the spring as rainfall events decrease h followed by an increase during soil dry-down; h steadily increases in the fall post-harvest as fields are tilled across the network, Retrievals of h are independent of polarization and demonstrate the potential to simultaneously retrieve soil moisture and model roughness in the absence of crops – negating the need for the too-smooth static h that is currently assumed.