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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 #371160
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: Evaluating unoccupied aerial systems (UAS) imagery as an alternative tool towards cotton-based management zones

Author
item ROUZE, G. - Texas A&M University
item NEELY, H. - Washington State University
item MORGAN, C. - Soil Health Institute
item Kustas, William - Bill
item WIETHORN, M. - Purdue University

Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/8/2021
Publication Date: 8/10/2021
Citation: Rouze, G., Neely, H., Morgan, C., Kustas, W.P., Wiethorn, M. 2021. Evaluating unoccupied aerial systems (UAS) imagery as an alternative tool towards cotton-based management zones. Precision Agriculture. https://doi.org/10.1007/s11119-021-09816-9.
DOI: https://doi.org/10.1007/s11119-021-09816-9

Interpretive Summary: Since the 1990s, sensor technologies for precision agriculture applications include the use of soil electrical conductivity (ECa) sensors carried by tractors or all-terrain vehicles for mapping soil textural properties used in management zone delineation. Unoccupied aerial system (UAS) imagery may serve as an enhanced tool for management zone delineation. This is because UAS data collection, unlike previous approaches, is relatively flexible when data can be collected, can cover relatively large areas in a short amount of time and provide remote sensing information that maps soil and plant conditions relevant for precision agriculture management. The purpose of this study was to evaluate UAS imagery relevant to ECa data over an irrigated cotton field in terms of their ability to: 1) predict cotton plant height and seed cotton yield, and 2) define cotton management zones based on these traits. The results suggest that UAS imagery of vegetation indices and land surface temperature can offer valuable information for cotton management zone delineation that other precision agricultural techniques such as ECa data are unable to provide.

Technical Abstract: Unoccupied aerial system (UAS) imagery may serve as an alternative tool towards management zone delineation. This is because UAS data collection, unlike previous approaches (e.g. apparent soil electrical conductivity or ECa), is relatively flexible. However, it is unclear how useful UASs can be towards generating management zones, relative to these preexisting techniques. The purpose of this study, therefore, was to evaluate UAS imagery, relative to ECa, in terms of their ability to: 1) predict cotton traits (i.e. height, seed cotton yield), and 2) define cotton management zones based on these traits. Single-season UAS images from multispectral/thermal sensors were collected and processed into Normalized Difference Vegetation Index (NDVI) and radiometric surface temperature (Tr), respectively. Management zones were also delineated using digital camera (RGB) imagery collected at the end of the growing season. RGB management zones were delineated by a novel open boll mapping approach. In-season NDVI and Tr layers were significant (P < 0.01) predictors of canopy height. Additionally, NDVI and Tr maps produced statistically different management zones during flowering and boll filling growth stages (P = 0.001 or less). Open boll layers were all more accurate predictors of cotton seed yield than ECa data - these two layers also produced statistically distinct management zones. ANOVA tests revealed that, given ECa alone, adding UAS information via the RGB open boll map resulted in a significantly different yield prediction model (P < 0.001). These results suggest that UAS imagery can offer valuable information for cotton management zone delineation that other techniques cannot.