Development of a Simplified Radiometric Calibration Framework for Water-Based and Rapid Deployment Unmanned Aerial System (UAS) Operations

Abstract

The current study sets out to develop an empirical line method (ELM) radiometric calibration framework for reducing atmospheric contributions in UAS imagery and for producing scaled remote sensing reflectance imagery. Using a MicaSense RedEdge camera flown on a custom-built octocopter, the research reported herein finds that atmospheric contributions have an important impact on UAS imagery. Data collected over the Lower Pearl River Estuary in Mississippi during five week-long missions covering a wide range of environmental conditions was used to develop and test a simplified ELM radiometric calibration framework designed specifically for the reduction of atmospheric contributions to UAS imagery in studies with limited site accessibility or data acquisition time constraints. The framework was effective in reducing atmospheric and other external contributions to UAS imagery. Unique to the proposed radiometric calibration framework is the radiance to reflectance conversion conducted externally from the calibration equations which allows for the normalization of illumination independent from the time of UAS image acquisition and from the time of calibration equations development. This paper presents the simplified ELM radiometric calibration framework that can be used as a time-effective calibration technique to reduce errors in the UAS imagery.

Calibration to maximize temporal radiometric repeatability of airborne hyperspectral imaging data

“…Use of ELM calibration is probably the most commonly used method of radiometric calibration of airborne remote sensing data (Poncet et al, 2019), and it has been thoroughly reviewed (Smith and Milton, 1999;Baugh and Groeneveld, 2008;Aasen et al, 2018). Several studies have provided important insight into practical use and repeatability of ELM calibration (Che and Price, 1992;Smith and Milton, 1999;Karpouzli and Malthus, 2003;Wang and Myint, 2015;Aasen et al, 2018;Iqbal et al, 2018;Mafanya et al, 2018;Agapiou, 2020;Zarzar et al, 2020). Experimental data from this study showed its considerable sensitivity to time of day (sun parameters and weather variables).…”

“…However, flying at higher altitudes means reduction in spatial resolution and therefore increase in mixed pixels, and that may adversely affect radiometric repeatability. Zarzar et al (2020) acquired drone-based remote sensing data from grey calibration boards at nine altitudes ranging from 4 -244 m, and they developed an ELM calibration framework to maximize radiometric repeatability based on atmospheric correction. The study by Zarzar et al (2020) was based on remote sensing data acquired during multiple flight missions over the course of a full year, and goodness of fit values for three spectral bands were: R 2 -green = 0.77, R 2 -red = 0.79, and R 2 -nir = 0.77).…”

“…Zarzar et al (2020) acquired drone-based remote sensing data from grey calibration boards at nine altitudes ranging from 4 -244 m, and they developed an ELM calibration framework to maximize radiometric repeatability based on atmospheric correction. The study by Zarzar et al (2020) was based on remote sensing data acquired during multiple flight missions over the course of a full year, and goodness of fit values for three spectral bands were: R 2 -green = 0.77, R 2 -red = 0.79, and R 2 -nir = 0.77). Based on number of observations, the fact that the study was conducted over water, and level of temporal replication, obtained goodness of fit values suggest strong performance of the proposed ELM calibration.…”

“…It is indisputable that integration of advanced airborne remote sensing, such as hyperspectral optical sensing, into monitoring and management of crops in agriculture, environmental conservation, and many commercial industry sectors, holds considerable potential (Anderson and Gaston, 2013;Aasen et al, 2018;Assmann et al, 2018;Zarzar et al, 2020). However, widespread adoption of remote sensing technologies is constrained by a critically important challengenon-linear dynamics of weather, solar light intensity, atmospheric composition, scattering, and how effects of these variables are integrated into radiometric calibrations.…”

“…Specific studies have measured and modeled influence of solar irradiance and angle as functions of time of day (King et al, 1997) and effects of atmospheric contributions and drone flight configurations on airborne remote sensing data sets (Kedzierski et al, 2019;Poncet et al, 2019;Zarzar et al, 2020). Radiometric calibration is performed to minimize optical signal noise induced by dynamics of sun parameters and atmospheric conditions, and it can be accomplished via deployment of stationary reference objects, which is referred to as vicarious calibration or empirical line method (ELM) (Smith and Milton, 1999;Karpouzli and Malthus, 2003;Baugh and Groeneveld, 2008;Del Pozo et al, 2014;Wang and Myint, 2015;Aasen et al, 2018;Iqbal et al, 2018;Mafanya et al, 2018;Poncet et al, 2019;Agapiou, 2020;Zarzar et al, 2020). Several articles have provided comprehensive discussions of ELM calibration and listed important underlying assumptions when this radiometric calibration method is used (Smith and Milton, 1999;Baugh and Groeneveld, 2008;Aasen et al, 2018).…”