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ARS Home » Southeast Area » Tifton, Georgia » Southeast Watershed Research » Research » Publications at this Location » Publication #386888

Research Project: Enhancing Water Resources, Production Efficiency and Ecosystem Services in Gulf Atlantic Coastal Plain Agricultural Watersheds

Location: Southeast Watershed Research

Title: Estimating Soil Roughness in Agricultural Fields Using Uncrewed Aerial Systems.

item Tadesse, Haile
item Coffin, Alisa

Submitted to: Proceedings American AGU Chapman Conference on the GIS in the Vadose Zone
Publication Type: Abstract Only
Publication Acceptance Date: 10/6/2021
Publication Date: N/A
Citation: N/A

Interpretive Summary: None.

Technical Abstract: Soil roughness, irregularities of soil surface, is caused by differences in soil texture, structure and land management. It affects infiltration, soil moisture storage, runoff, and soil erosion. Soil roughness is typically measured by contact methods such as pinboard and roller chain. It can also be measured using higher cost laser technologies, e.g. LiDAR. Accounting for soil roughness is a key step in use of synthetic aperture radar for land surface modeling (e.g. crop monitoring). However, acquiring direct measurements of soil roughness is costly and requires access to field sites. Uncrewed aerial systems (UAS) offer a potential alternative for obtaining high resolution soil surface data. The objective of this research is to evaluate the use of UAS in the collection of soil roughness data using Structure-from-Motion (SfM) technologies, comparing soil roughness measurements derived from pinboards with those measured from UAS-borne cameras in a crop field near TyTy, GA, USA (31°30'N, 83°37'W). With the pinboard method, we calculated roughness as the standard deviation of pin heights measured above the level surface of the pinboard base. Pinboard images from three dates (6/23, 07/10, and 7/24/20) at six locations within a peanut field were acquired at 90 (perpendicular) and 0 (parallel) degrees with respect to the tillage direction. UAS data were simultaneously collected using the RGB cameras. Flights were flown with a DJI Mavic2 at 60 m above ground level providing an image resolution of 7cm. Pix4Dmapper software was used for processing and producing surface models, SfM derived point clouds, and other image outputs. The pinboard and UAS data were analyzed using ImageJ, and ArcMap GIS software, respectively. Soil roughness values measured with the pinboard perpendicular and parallel to soil ridges were 2.6 cm and 0.8 cm, respectively. UAS derived soil roughness values measured across, and along soil ridges were 3.9 cm and 1.1 cm, respectively. R-square values of comparisons between UAS and pinboard soil roughness ranged from .02 to .7 depending on date and orientation. This study showed that UAS remote sensing can be applied to analyze soil roughness, but further research is needed to evaluate the utility of this method as the growing season progresses.