Simplified field radiometric validation method for UAV acquired spectral imaging

Authors: LITA, BOGDAN - University Of Michigan; Zhang, Huihui; Yemoto, Kevin; Olsen-Mikitowicz, Alexander; ROCK, WILLIAM - University Of Iowa

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 10/29/2019
Publication Date: N/A
Citation: N/A

Interpretive Summary: The use of camera equipped unmanned aircraft systems (UAS), or drones, has emerged as a viable alternative to conventional satellites for acquiring spectral imagery. The main advantage of using a camera on an UAS platform is the acquisition of high spatial resolution multispectral imagery with a pixel size as small as a few centimeters. Another advantage of the UAS imaging is its potential high frequency and the ability to acquire images under full or partial cloud cover. However, there is a need to standardize calibration procedures of small-format cameras and understand the uncertainties in measuring scene reflectance. More importantly, to better understand the effect of climate change to ecological systems or the yield of food crops, there is a need to build repeatable time lapse series using camera equipped UAVs. Since most commercial camera manufacturers publish little information that can be used to infer radiometric performance under solar illumination, a field method is needed. This paper proposes such a method to evaluate camera accuracy, repeatability, and linearity. The method was used in a study performed in a corn field at United States Department of Agriculture (USDA) Agricultural Research Service Limited Irrigation Research Facility near Greeley, CO. The camera used in the study was the UAV mounted Nano-HyperSpec made by Headwall Photonics. The paper will present not only the data acquisition protocol in the field and results, but also the imaging processing workflow, or the step by step process to compare the camera’ inferred scene reflectance with that of a field spectroradiometer, a field validation process typically called “ground truth”. Finally, vegetation indices are calculated from field validated spectral imaging. Results suggesting lower corn leaf nitrogen (N) in areas with reduced fertilizer application will be discussed.

Technical Abstract: The use of camera equipped unmanned aircraft systems (UAS), or drones, has emerged as a viable alternative to conventional satellites for acquiring spectral imagery. The main advantage of using a camera on an UAS platform is the acquisition of high spatial resolution multispectral imagery with a pixel size as small as a few centimeters. Another advantage of the UAS imaging is its potential high frequency and the ability to acquire images under full or partial cloud cover. However, there is a need to standardize calibration procedures of small-format cameras and understand the uncertainties in measuring scene reflectance. More importantly, to better understand the effect of climate change to ecological systems or the yield of food crops, there is a need to build repeatable time lapse series using camera equipped UAVs. Since most commercial camera manufacturers publish little information that can be used to infer radiometric performance under solar illumination, a field method is needed. This paper proposes such a method to evaluate camera accuracy, repeatability, and linearity. The method was used in a study performed in a corn field at United States Department of Agriculture (USDA) Agricultural Research Service Limited Irrigation Research Facility near Greeley, CO. The camera used in the study was the UAV mounted Nano-HyperSpec made by Headwall Photonics. The paper will present not only the data acquisition protocol in the field and results, but also the imaging processing workflow, or the step by step process to compare the camera’ inferred scene reflectance with that of a field spectroradiometer, a field validation process typically called “ground truth”. Finally, vegetation indices are calculated from field validated spectral imaging. Results suggesting lower corn leaf nitrogen (N) in areas with reduced fertilizer application will be discussed.