Quantifying Temporal Distortions of Artificial UAV Crop Canopy Temperature Measurements

The crop canopy temperature (Tc) measured with fixed-infrared thermometers (IRT’s), provides high-resolution data but only measures small areas of the canopy. Conversely, thermal sensors mounted on UAVs measure Tc over larger areas, overcoming this limitation. However, these measurements may introduce distortions of Tc’s values due to the time of day when they are measured. We measured Tc of a cotton crop over a growing season using fixed-IRTs and compared these measurements to the same data with an assumed time delay of 0.25 - 2 hours. This delay was used as a proxy of Tc values measured with a UAV. The dataset consisted of 7 IRTs measuring 96 values/day over 67 days. Results showed that artificial UAV flight missions resulted in a thermal distortion related to the flight duration. This distortion was applied to detect differences of Tc in seven irrigation treatments. The difference in Tc from the UAV and fixed IRT was affected by the time of day, irrigation treatment, and UAV-flight duration. To identify irrigation treatments, the assumed UAV-Tc produced up to 27% spurious treatment differences relative to the fixed-IRT. Distortion in UAV-Tc was minimal for flights of c data should consider this distortion.

Authors

Institutions

Publication Details

Journal
Advances in Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.4236/ars.2025.142006
Primary Topic
3D Surveying and Cultural Heritage
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantifying Temporal Distortions of Artificial UAV Crop Canopy Temperature Measurements

Andrew W. Young, Timothy S. Goebel, N. Ace Pugh, Robert J. Lascano et al.
Advances in Remote Sensing
3D Surveying and Cultural Heritage
article

Quantifying Temporal Distortions of Artificial UAV Crop Canopy Temperature Measurements

Andrew W. Young, Timothy S. Goebel, N. Ace Pugh, Robert J. Lascano, Paxton Payton, Zhanguo Xin, John E. Stout, J. Robert Mahan
article en

Abstract

The crop canopy temperature (Tc) measured with fixed-infrared thermometers (IRT’s), provides high-resolution data but only measures small areas of the canopy. Conversely, thermal sensors mounted on UAVs measure Tc over larger areas, overcoming this limitation. However, these measurements may introduce distortions of Tc’s values due to the time of day when they are measured. We measured Tc of a cotton crop over a growing season using fixed-IRTs and compared these measurements to the same data with an assumed time delay of 0.25 - 2 hours. This delay was used as a proxy of Tc values measured with a UAV. The dataset consisted of 7 IRTs measuring 96 values/day over 67 days. Results showed that artificial UAV flight missions resulted in a thermal distortion related to the flight duration. This distortion was applied to detect differences of Tc in seven irrigation treatments. The difference in Tc from the UAV and fixed IRT was affected by the time of day, irrigation treatment, and UAV-flight duration. To identify irrigation treatments, the assumed UAV-Tc produced up to 27% spurious treatment differences relative to the fixed-IRT. Distortion in UAV-Tc was minimal for flights of c data should consider this distortion.

Advances in Remote SensingVol. 14(02)
Cropping Systems Research Laboratory (US)
Openalex Percentile: Top 78%
3D Surveying and Cultural Heritage
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Quantifying Temporal Distortions of Artificial UAV Crop Canopy Temperature Measurements — Andrew W. Young, Timothy S. Goebel, et al. · Advances in Remote Sensing (2025) | TGRS Research Map | TGRS