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ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Aerial Application Technology Research » Research » Publications at this Location » Publication #356575
Title: Spatial analysis of multispectral and thermal imagery from multiple platforms

Author
item ROUZE, GREGORY - Texas A&M University
item NEELY, HALY - Texas A&M University
item MORGAN, CRISTINE - Texas A&M University
item Yang, Chenghai

Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 9/9/2018
Publication Date: 10/15/2018
Citation: Rouze, G., Neely, H., Morgan, C., Yang, C. 2018. Spatial analysis of multispectral and thermal imagery from multiple platforms. Proceedings of SPIE. 1066404T.

Interpretive Summary: Airborne and satellite remote sensing can potentially be used to model crop characteristics. However, satellite imagery usually exhibit low spatial and temporal resolutions, and manned aircraft imagery, despite improved resolutions, may not be cost-effective. Recent developments in unmanned aerial vehicle (UAV) remote sensing have allowed for imagery at improved spatial resolutions at a fraction of the cost. This study attempted to quantify differences in modeling crop growth variability with UAV imagery compared with satellite and manned aircraft imagery. Initial results showed that UAV data exhibited different spatial variability attributes from manned aircraft and satellite data. These results have implications for predicting agronomic variables such as plant height and yield, indicating that future research should consider standardizing sensors aboard various platforms.

Technical Abstract: Airborne and satellite remote sensing can potentially be used to model crop characteristics. However, satellite imagery usually exhibit low spatial and temporal resolutions, and manned aircraft imagery, despite improved resolutions, may not be cost-effective. Recent developments in UAV remote sensing have allowed for imagery at improved spatial resolutions relative to satellites and at a fraction of the cost relative to manned aircraft. Furthermore, UAVs offer potential advantages over proximal soil sensors (i.e. EM-38) in terms of in-season decision making. However, it is unclear at this point whether these benefits translate to higher quality information. This question has relevance within fields that exhibit contrasting environments, such as soil spatial variability. Therefore, the objectives of this paper were twofold: 1) to quantify improvements in UAV-based plant (cotton) modelling relative to proximal sensing (i.e. EM-38), manned aircraft, and satellites (Landsat 8); and 2) to determine how such modeling can be affected by soil spatial variability. Results indicate that UAVs show higher nugget/sill ratios and larger ranges than manned aircraft and satellites. These results have implications for predicting agronomic variables (i.e. yield, plant height), as well as soil/plant sampling.