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Approach for Propagating Radiometric Data Uncertainties Through NASA Ocean Color Algorithms.
Front Earth Sci (Lausanne). 2019 Jul 18; 7:176.FE

Abstract

Spectroradiometric satellite observations of the ocean are commonly referred to as "ocean color" remote sensing. NASA has continuously collected, processed, and distributed ocean color datasets since the launch of the Sea-viewing Wide-field-of-view Sensor (SeaWiFS) in 1997. While numerous ocean color algorithms have been developed in the past two decades that derive geophysical data products from sensor-observed radiometry, few papers have clearly demonstrated how to estimate measurement uncertainty in derived data products. As the uptake of ocean color data products continues to grow with the launch of new and advanced sensors, it is critical that pixel-by-pixel data product uncertainties are estimated during routine data processing. Knowledge of uncertainties can be used when studying long-term climate records, or to assist in the development and performance appraisal of bio-optical algorithms. In this methods paper we provide a comprehensive overview of how to formulate first-order first-moment (FOFM) calculus for propagating radiometric uncertainties through a selection of bio-optical models. We demonstrate FOFM uncertainty formulations for the following NASA ocean color data products: chlorophyll-a pigment concentration (Chl), the diffuse attenuation coefficient at 490 nm (K d,490), particulate organic carbon (POC), normalized fluorescent line height (nflh), and inherent optical properties (IOPs). Using a quality-controlled in situ hyperspectral remote sensing reflectance (R rs,i) dataset, we show how computationally inexpensive, yet algebraically complex, FOFM calculations may be evaluated for correctness using the more computationally expensive Monte Carlo approach. We compare bio-optical product uncertainties derived using our test R rs dataset assuming spectrally-flat, uncorrelated relative uncertainties of 1, 5, and 10%. We also consider spectrally dependent, uncorrelated relative uncertainties in R rs . The importance of considering spectral covariances in R rs , where practicable, in the FOFM methodology is highlighted with an example SeaWiFS image. We also present a brief case study of two POC algorithms to illustrate how FOFM formulations may be used to construct measurement uncertainty budgets for ecologically-relevant data products. Such knowledge, even if rudimentary, may provide useful information to end-users when selecting data products or when developing their own algorithms.

Authors+Show Affiliations

Go2Q Pty Ltd., Buderim, QLD, Australia. Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United States.Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United States. GESTAR/Universities Space Research Association, Columbia, MD, United States.School of Marine Sciences, University of Maine, Orono, ME, United States.Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United States.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32647655

Citation

McKinna, Lachlan I W., et al. "Approach for Propagating Radiometric Data Uncertainties Through NASA Ocean Color Algorithms." Frontiers in Earth Science, vol. 7, 2019, p. 176.
McKinna LIW, Cetinić I, Chase AP, et al. Approach for Propagating Radiometric Data Uncertainties Through NASA Ocean Color Algorithms. Front Earth Sci (Lausanne). 2019;7:176.
McKinna, L. I. W., Cetinić, I., Chase, A. P., & Werdell, P. J. (2019). Approach for Propagating Radiometric Data Uncertainties Through NASA Ocean Color Algorithms. Frontiers in Earth Science, 7, 176. https://doi.org/10.3389/feart.2019.00176
McKinna LIW, et al. Approach for Propagating Radiometric Data Uncertainties Through NASA Ocean Color Algorithms. Front Earth Sci (Lausanne). 2019 Jul 18;7:176. PubMed PMID: 32647655.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Approach for Propagating Radiometric Data Uncertainties Through NASA Ocean Color Algorithms. AU - McKinna,Lachlan I W, AU - Cetinić,Ivona, AU - Chase,Alison P, AU - Werdell,P Jeremy, PY - 2020/7/11/entrez PY - 2020/7/11/pubmed PY - 2020/7/11/medline KW - bio-optics KW - biogeochemistry KW - ocean color KW - oceanography KW - radiometry KW - remote sensing KW - uncertainties SP - 176 EP - 176 JF - Frontiers in earth science JO - Front Earth Sci (Lausanne) VL - 7 N2 - Spectroradiometric satellite observations of the ocean are commonly referred to as "ocean color" remote sensing. NASA has continuously collected, processed, and distributed ocean color datasets since the launch of the Sea-viewing Wide-field-of-view Sensor (SeaWiFS) in 1997. While numerous ocean color algorithms have been developed in the past two decades that derive geophysical data products from sensor-observed radiometry, few papers have clearly demonstrated how to estimate measurement uncertainty in derived data products. As the uptake of ocean color data products continues to grow with the launch of new and advanced sensors, it is critical that pixel-by-pixel data product uncertainties are estimated during routine data processing. Knowledge of uncertainties can be used when studying long-term climate records, or to assist in the development and performance appraisal of bio-optical algorithms. In this methods paper we provide a comprehensive overview of how to formulate first-order first-moment (FOFM) calculus for propagating radiometric uncertainties through a selection of bio-optical models. We demonstrate FOFM uncertainty formulations for the following NASA ocean color data products: chlorophyll-a pigment concentration (Chl), the diffuse attenuation coefficient at 490 nm (K d,490), particulate organic carbon (POC), normalized fluorescent line height (nflh), and inherent optical properties (IOPs). Using a quality-controlled in situ hyperspectral remote sensing reflectance (R rs,i) dataset, we show how computationally inexpensive, yet algebraically complex, FOFM calculations may be evaluated for correctness using the more computationally expensive Monte Carlo approach. We compare bio-optical product uncertainties derived using our test R rs dataset assuming spectrally-flat, uncorrelated relative uncertainties of 1, 5, and 10%. We also consider spectrally dependent, uncorrelated relative uncertainties in R rs . The importance of considering spectral covariances in R rs , where practicable, in the FOFM methodology is highlighted with an example SeaWiFS image. We also present a brief case study of two POC algorithms to illustrate how FOFM formulations may be used to construct measurement uncertainty budgets for ecologically-relevant data products. Such knowledge, even if rudimentary, may provide useful information to end-users when selecting data products or when developing their own algorithms. SN - 2296-6463 UR - https://www.unboundmedicine.com/medline/citation/32647655/Approach_for_Propagating_Radiometric_Data_Uncertainties_Through_NASA_Ocean_Color_Algorithms L2 - https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/32647655/ DB - PRIME DP - Unbound Medicine ER -
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