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Quantify IOP quality derived from QAA

This study assesses the error and confidence in inverted inherent optical properties (IOPs) using the Quasi-analytical algorithm (QAA), focusing on a550 estimation, error propagation, and quality quantification. Evaluation reveals nuanced impacts of varying a550 and Y values on a440 inversion and IOP estimation reliability. The methodology highlights the interplay between precise parameters in accurately determining IOP qualities per pixel. Findings emphasize the significance of a(λ0) and Y estimation for robust IOP derivations.

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Quantify IOP quality derived from QAA

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  1. Quantify IOP quality derived from QAA ZhongPing Lee1, Robert Arnone2 1Northern Gulf Institute, Mississippi State University, Stennis Space Center, MS 39529; zplee@ngi.msstate.edu 2Naval Research Laboratory, Stennis Space Center, MS 39529

  2. Q: How big is the error (or confidence) of inverted IOPs at each pixel? “Average error of an algorithm is insufficient to describe the error of specific locations.” -- Boss et al We should not expect the same error bars for the three pixels.

  3. The Quasi-analytical algorithm (QAA) Rrs() The three stating points: Y (Lee et al. 2002)

  4. If Rrs is error free, and the relationship between Rrs and {bb,a} is accurate, quality of inverted a&bb by QAA depends on the estimation of a(λ0) and Y.

  5. A “Hydrolight” data set a440 distribution a550 distribution bbp440 distribution Y distribution

  6. QAA inverted a440&bbp440 vs known a440&bbp440 -- when a550 and Y are known exactly. a440inv [m-1] bbp440inv [m-1] bbp440known [m-1] a440known [m-1] Indication: the formulation and scheme can get a closure!

  7. QAA inverted a440 vs known a440 -- impact of imprecise a550; Y is known exactly a550 is 10% higher a550 is 10% lower a440inv [m-1] a440known [m-1] Indication: nearly the same impact to all a440 values.

  8. QAA inverted a440 vs known a440 -- impact of imprecise Y; a550 is known exactly Y is 0.4 higher Y is 0.4 lower a440inv [m-1] a440known [m-1] Indication: nearly the same impact to all a440 values.

  9. QAA inverted a440 vs known a440 -- compounding effects of imprecise Yand imprecisea550 Y is 0.4 higher and a550 is 10% higher Y is known; a550 is 10% higher a440inv [m-1] a440known [m-1] Indication: more errors!

  10. QAA inverted a440 vs known a440 -- compensation between imprecise Yand imprecisea550 Y is 0.4 lower; a550 is 10% higher Both Y and a550 known a440inv [m-1] a440known [m-1] Indication: nearly compensating each other.

  11. Q: 1. How big is the a550 error for different a550? 2. How this error propagates to other IOPs? Error at a550 (εa550) vs estimated a550 εa550 Global ocean a550inv [m-1]

  12. Error at a440 vs compound error of a550 and Y (±0.2, ±0.4) δa440 εa440 Error at a440 Error at a550 (εa550) δ: percentage error at 84th percentile or likelihood range

  13. Error at a410 vs compound error of a550 and Y (±0.2, ±0.4) δa440 εa440 Error at a410 Error at a550 (εa550) δ: percentage error at 84th percentile

  14. Data flow to quantify quality of inverted IOP : Rrs aλ0ελ0εIOP & δIOP Note: No Rrs error considered yet; but can be added when quality measure of Rrs is known.

  15. Conclusion: • Qualities of IOPs derived by QAA, in addition to quality of Rrs, rely on the estimation of a(λ0) and Y. • The qualities at each pixel, measured by projected average error and projected likelihood range, can be quantified by evaluating the error propagation in the QAA process. • Higher qualities for IOPs of oceanic waters, as both a(λ0) and Rrs possess higher reliability.

  16. Acknowledgement: The supported from NASA Ocean Biology and Biogeochemistry Program is greatly appreciated.

  17. aw values dominate the longer wavelengths, so a(λ0) can be estimated reasonably well. a550 [m-1] a440 [m-1]

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