TY - JOUR
T1 - Errors associated with atmospheric correction methods for airborne imaging spectroscopy
T2 - Implications for vegetation indices and plant traits
AU - Wang, Ran
AU - Gamon, John A.
AU - Moore, Ryan
AU - Zygielbaum, Arthur I.
AU - Arkebauer, Timothy J.
AU - Perk, Rick
AU - Leavitt, Bryan
AU - Cogliati, Sergio
AU - Wardlow, Brian
AU - Qi, Yi
N1 - Funding Information:
We are thankful for David Scoby from UNL for providing the LAI measurements. We also thank Patrick Rademske for providing additional ATCOR outputs to compare to our RTM results. This work was supported by the European Space Agency “PhotoProxy” funding to J.A.G. This project was also based on research that was partially supported by the Nebraska Agricultural Experiment Station with funding from the Hatch Act (Accession Number 1002649) through the USDA National Institute of Food and Agriculture . The authors acknowledge constructive comments from Y. Ryu, P. Brodrick and three other anonymous reviewers that greatly improved the manuscript.
Funding Information:
We are thankful for David Scoby from UNL for providing the LAI measurements. We also thank Patrick Rademske for providing additional ATCOR outputs to compare to our RTM results. This work was supported by the European Space Agency ?PhotoProxy? funding to J.A.G. This project was also based on research that was partially supported by the Nebraska Agricultural Experiment Station with funding from the Hatch Act (Accession Number 1002649) through the USDA National Institute of Food and Agriculture. The authors acknowledge constructive comments from Y. Ryu, P. Brodrick and three other anonymous reviewers that greatly improved the manuscript.
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/11
Y1 - 2021/11
N2 - Hyperspectral airborne imagery can provide rich information on plant physiological and structural properties at a scale intermediate to that of proximal and satellite remote sensing and has broad applications in assessing ecosystem function and biodiversity. A key processing step of airborne hyperspectral data is the atmospheric correction that compensates for path radiance, aerosol effects and gas absorption to derive an accurate surface reflectance that can be compared across time and space. In practice, routine correction procedures are often customized for various platforms without fully reporting or checking the errors systematically in the atmospheric correction. Such errors can have significant effects on downstream analyses such as vegetation indices or trait retrievals, and not all subsequent analyses are equally affected by the accuracy of reflectance retrievals. In this study, we examined the errors in three types of atmospheric correction methods including a radiative transfer model (RTM), empirical line correction (ELC) and a hybrid method that combines elements of the two via Bayesian inference. Our results revealed that the individual correction methods had different effects on the reflectance retrievals that impacted downstream measurements. Including spectral measurements from ground vegetation targets in addition to painted calibration targets improved the performance of the ELC method. The hybrid method yielded reflectance spectra that most closely matched the spectra of the ground validation data. The errors in vegetation indices differed with the methods, and certain indices (such as PRI) were more affected than indices that rely on stable, broader spectral features (e.g., NDVI). Plant pigment retrievals via partial least squares regression were less sensitive to errors in atmospheric correction. These findings demonstrate that obtaining high-quality, field spectral measurements over well-characterized calibration targets and representative land cover types within the scene is critical for accurate surface reflectance and subsequent downstream products, such as vegetation indices or plant traits.
AB - Hyperspectral airborne imagery can provide rich information on plant physiological and structural properties at a scale intermediate to that of proximal and satellite remote sensing and has broad applications in assessing ecosystem function and biodiversity. A key processing step of airborne hyperspectral data is the atmospheric correction that compensates for path radiance, aerosol effects and gas absorption to derive an accurate surface reflectance that can be compared across time and space. In practice, routine correction procedures are often customized for various platforms without fully reporting or checking the errors systematically in the atmospheric correction. Such errors can have significant effects on downstream analyses such as vegetation indices or trait retrievals, and not all subsequent analyses are equally affected by the accuracy of reflectance retrievals. In this study, we examined the errors in three types of atmospheric correction methods including a radiative transfer model (RTM), empirical line correction (ELC) and a hybrid method that combines elements of the two via Bayesian inference. Our results revealed that the individual correction methods had different effects on the reflectance retrievals that impacted downstream measurements. Including spectral measurements from ground vegetation targets in addition to painted calibration targets improved the performance of the ELC method. The hybrid method yielded reflectance spectra that most closely matched the spectra of the ground validation data. The errors in vegetation indices differed with the methods, and certain indices (such as PRI) were more affected than indices that rely on stable, broader spectral features (e.g., NDVI). Plant pigment retrievals via partial least squares regression were less sensitive to errors in atmospheric correction. These findings demonstrate that obtaining high-quality, field spectral measurements over well-characterized calibration targets and representative land cover types within the scene is critical for accurate surface reflectance and subsequent downstream products, such as vegetation indices or plant traits.
KW - Airborne remote sensing
KW - Atmospheric correction
KW - Imaging spectroscopy
KW - Plant traits
KW - Vegetation indices
UR - http://www.scopus.com/inward/record.url?scp=85113474466&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85113474466&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2021.112663
DO - 10.1016/j.rse.2021.112663
M3 - Article
AN - SCOPUS:85113474466
SN - 0034-4257
VL - 265
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 112663
ER -