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Record W3025449201 · doi:10.3390/rs12101587

Field Intercomparison of Radiometer Measurements for Ocean Colour Validation

2020· article· en· W3025449201 on OpenAlexaff
Gavin H. Tilstone, Giorgio Dall’Olmo, Martin Hieronymi, Kevin Ruddick, Matthew Beck, Martin Ligi, Maycira Costa, Davide D’Alimonte, Vincenzo Vellucci, Dieter Vansteenwegen, Astrid Bracher, Sonja Wiegmann, Joel Kuusk, Viktor Vabson, Ilmar Ansko, Riho Vendt, Craig Donlon, Tânia Casal

Bibliographic record

VenueRemote Sensing · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of Victoria
FundersBelgian Federal Science Policy OfficeNatural Environment Research CouncilSight Research UKEuropean Space Agency
KeywordsRadianceRemote sensingRadiometerEnvironmental scienceIrradianceCalibrationZenithRadiometrySea statePhysicsGeologyOptics

Abstract

fetched live from OpenAlex

A field intercomparison was conducted at the Acqua Alta Oceanographic Tower (AAOT) in the northern Adriatic Sea, from 9 to 19 July 2018 to assess differences in the accuracy of in- and above-water radiometer measurements used for the validation of ocean colour products. Ten measurement systems were compared. Prior to the intercomparison, the absolute radiometric calibration of all sensors was carried out using the same standards and methods at the same reference laboratory. Measurements were performed under clear sky conditions, relatively low sun zenith angles, moderately low sea state and on the same deployment platform and frame (except in-water systems). The weighted average of five above-water measurements was used as baseline reference for comparisons. For downwelling irradiance ( E d ), there was generally good agreement between sensors with differences of <6% for most of the sensors over the spectral range 400 nm–665 nm. One sensor exhibited a systematic bias, of up to 11%, due to poor cosine response. For sky radiance ( L s k y ) the spectrally averaged difference between optical systems was <2.5% with a root mean square error (RMS) <0.01 mWm−2 nm−1 sr−1. For total above-water upwelling radiance ( L t ), the difference was <3.5% with an RMS <0.009 mWm−2 nm−1 sr−1. For remote-sensing reflectance ( R r s ), the differences between above-water TriOS RAMSES were <3.5% and <2.5% at 443 and 560 nm, respectively, and were <7.5% for some systems at 665 nm. Seabird-Hyperspectral Surface Acquisition System (HyperSAS) sensors were on average within 3.5% at 443 nm, 1% at 560 nm, and 3% at 665 nm. The differences between the weighted mean of the above-water and in-water systems was <15.8% across visible bands. A sensitivity analysis showed that E d accounted for the largest fraction of the variance in R r s , which suggests that minimizing the errors arising from this measurement is the most important variable in reducing the inter-group differences in R r s . The differences may also be due, in part, to using five of the above-water systems as a reference. To avoid this, in situ normalized water-leaving radiance ( L w n ) was therefore compared to AERONET-OC SeaPRiSM L w n as an alternative reference measurement. For the TriOS-RAMSES and Seabird-HyperSAS sensors the differences were similar across the visible spectra with 4.7% and 4.9%, respectively. The difference between SeaPRiSM L w n and two in-water systems at blue, green and red bands was 11.8%. This was partly due to temporal and spatial differences in sampling between the in-water and above-water systems and possibly due to uncertainties in instrument self-shading for one of the in-water measurements.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.972
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.250
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations55
Published2020
Admission routes1
Has abstractyes

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