Field Validation of UAS-Mounted Multispectral Cameras Against High-Fidelity Airborne Spectrometry

This study evaluates the accuracy and usability of two low-cost uncrewed aerial system (UAS) multispectral sensors—MicaSense RedEdge-M and SlantRange 3p—using a dual-scale comparison framework. Calibrated 3% and 48% reflectance tarps and Analytical Spectral Device (ASD) field spectrometry were used for point-scale absolute validation. The NEON imaging spectrometer (NIS), a well-calibrated airborne hyperspectral sensor, was utilized for a field-scale comparison by spectrally resampling the hyperspectral data to align with the multispectral bands and performing spatial co-registration and aggregation of the multispectral data to a common 1 m grid. Over the 48% tarp, MicaSense Near-infrared (NIR) reflectance was 7.2% below the ASD reference while SlantRange NIR was 7.9% lower, validating previous studies that have observed the underestimation of the NIR band with UAS multispectral sensors. Both sensors overestimated the 3% tarp across all bands when compared to the ASD measurements. Field-scale regressions yielded R2 values above 0.90 for most bands, while NIR slopes were 0.74 for MicaSense and 0.72 for SlantRange. Consistent with these trends, the largest radiometric errors occurred in the NIR region, where both sensors increasingly underestimated high-reflectance surfaces relative to NIS. Median Normalized Difference Vegetation Index (NDVI) differences relative to NIS were −0.027 and −0.064, and these became more negative with an increasing NIS NDVI. Edge buffering reduced MicaSense bias, whereas the SlantRange showed limited edge effects. These results demonstrate that low-cost UAS multispectral imagery can support ecological applications when calibration, reflectance range, view geometry, and ecosystem-specific validation requirements are explicitly considered.

Authors

Institutions

Publication Details

Journal
Remote Sensing
Published
2026-10-29
DOI
https://doi.org/10.3390/rs18213733
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Field Validation of UAS-Mounted Multispectral Cameras Against High-Fidelity Airborne Spectrometry

Tristan Goulden, David Durden, J. Adler, Cedric Hagen et al.
2 links
Remote Sensing
Remote Sensing in Agriculture
article

Field Validation of UAS-Mounted Multispectral Cameras Against High-Fidelity Airborne Spectrometry

Tristan Goulden, David Durden, J. Adler, Cedric Hagen, Joshua A. Roberti, Michael D. SanClements, Michael Wussow
article en

Abstract

This study evaluates the accuracy and usability of two low-cost uncrewed aerial system (UAS) multispectral sensors—MicaSense RedEdge-M and SlantRange 3p—using a dual-scale comparison framework. Calibrated 3% and 48% reflectance tarps and Analytical Spectral Device (ASD) field spectrometry were used for point-scale absolute validation. The NEON imaging spectrometer (NIS), a well-calibrated airborne hyperspectral sensor, was utilized for a field-scale comparison by spectrally resampling the hyperspectral data to align with the multispectral bands and performing spatial co-registration and aggregation of the multispectral data to a common 1 m grid. Over the 48% tarp, MicaSense Near-infrared (NIR) reflectance was 7.2% below the ASD reference while SlantRange NIR was 7.9% lower, validating previous studies that have observed the underestimation of the NIR band with UAS multispectral sensors. Both sensors overestimated the 3% tarp across all bands when compared to the ASD measurements. Field-scale regressions yielded R2 values above 0.90 for most bands, while NIR slopes were 0.74 for MicaSense and 0.72 for SlantRange. Consistent with these trends, the largest radiometric errors occurred in the NIR region, where both sensors increasingly underestimated high-reflectance surfaces relative to NIS. Median Normalized Difference Vegetation Index (NDVI) differences relative to NIS were −0.027 and −0.064, and these became more negative with an increasing NIS NDVI. Edge buffering reduced MicaSense bias, whereas the SlantRange showed limited edge effects. These results demonstrate that low-cost UAS multispectral imagery can support ecological applications when calibration, reflectance range, view geometry, and ecosystem-specific validation requirements are explicitly considered.

Remote SensingVol. 18(21)
Battelle (US), National Ecological Observatory Network (US)
Battelle
Openalex Percentile: Top 16%
Remote Sensing in Agriculture
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.

Laika access coming soon