Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurements
Checking the radiometric calibration of satellite hyper-spectral sensors such as the PACE Ocean Color Instrument (OCI) while they operate in orbit and evaluating remote sensing reflectance, the basic variable from which a variety of optical and biogeochemical ocean properties can be derived, require...
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Remote Sensing |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2024.1335627/full |
| _version_ | 1827318709906046976 |
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| author | Jing Tan Robert Frouin Nils Häentjens Andrew Barnard Emmanuel Boss Paul Chamberlain Matt Mazloff Cristina Orrico |
| author_facet | Jing Tan Robert Frouin Nils Häentjens Andrew Barnard Emmanuel Boss Paul Chamberlain Matt Mazloff Cristina Orrico |
| author_sort | Jing Tan |
| collection | DOAJ |
| description | Checking the radiometric calibration of satellite hyper-spectral sensors such as the PACE Ocean Color Instrument (OCI) while they operate in orbit and evaluating remote sensing reflectance, the basic variable from which a variety of optical and biogeochemical ocean properties can be derived, requires measuring upwelling radiance just above the surface (Lw) and downwelling planar irradiance reaching the surface (Es). For this, the current HyperNav systems measure Lw at about 2 nm spectral resolution in the ultraviolet to near infrared, but Es in only four 10 nm wide spectral bands centered on 412, 489, 555, and 705 nm. In this study, the Es data acquired in these spectral bands in clear sky conditions are used to reconstruct via a multi-linear regression model the hyper-spectral Es signal at 0.5 nm resolution from 315 to 900 nm, the OCI spectral range, allowing an estimate of Es at the HyperNav, OCI, and other sensors’ resolutions. After correction of gaseous absorption and normalization by the top-of-atmosphere incident solar flux, the atmospheric diffuse transmittance is expressed as a linear combination of Es measured in those 4 spectral bands. Based on simulations for Sun zenith angles from 0 to 75° and a wide range of (i.e., expected) atmospheric, surface, and water conditions, the Es spectrum is reconstructed with a bias of less than 0.4% in magnitude and an RMS error (RMSE) ranging from 0% to 2.5%, depending on wavelength. The largest errors occur in spectral regions with strong gaseous absorption. In the presence of typical noise on Es measurements and uncertainties on the ancillary variables, the bias and RMSE become −2.5% and 7.0%, respectively. Using a General Additive Model with coefficients depending on Sun zenith angle and aerosol optical thickness at 550 nm improves statistical performance in the absence of noise, especially in the ultraviolet, but provides similar performance on noisy data, indicating more sensitivity to noise. Adding spectral bands in the ultraviolet, e.g., centered on 325, 340, and 380 nm, yields marginally more accurate results in the ultraviolet, due to uncertainties in the gaseous transmittance. Comparisons between the measured and reconstructed Es spectra acquired by the MOBY spectroradiometer show agreement within predicted uncertainties, i.e., biases less than 2% in magnitude and RMS differences less than 5%. Reconstruction can also be achieved accurately with other sets of spectral bands and extended to cloudy conditions since cloud optical properties, like aerosol properties, tend to vary regularly with wavelength. These results indicate that it is sufficient, for many scientific applications involving hyper-spectral Es, to measure Es in a few coarse spectral bands in the ultraviolet to near infrared and reconstruct the hyperspectral signal using the proposed multivariate linear modeling. |
| first_indexed | 2024-04-25T00:06:32Z |
| format | Article |
| id | doaj.art-02d8dfd16e25407c8cb2fec3c8a0ef6e |
| institution | Directory Open Access Journal |
| issn | 2673-6187 |
| language | English |
| last_indexed | 2024-04-25T00:06:32Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Remote Sensing |
| spelling | doaj.art-02d8dfd16e25407c8cb2fec3c8a0ef6e2024-03-14T04:42:28ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872024-03-01510.3389/frsen.2024.13356271335627Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurementsJing Tan0Robert Frouin1Nils Häentjens2Andrew Barnard3Emmanuel Boss4Paul Chamberlain5Matt Mazloff6Cristina Orrico7Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, United StatesScripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, United StatesSchool of Marine Sciences, University of Maine, Orono, ME, United StatesCollege of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, United StatesSchool of Marine Sciences, University of Maine, Orono, ME, United StatesScripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, United StatesScripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, United StatesSea-Bird Scientific, Philomath, OR, United StatesChecking the radiometric calibration of satellite hyper-spectral sensors such as the PACE Ocean Color Instrument (OCI) while they operate in orbit and evaluating remote sensing reflectance, the basic variable from which a variety of optical and biogeochemical ocean properties can be derived, requires measuring upwelling radiance just above the surface (Lw) and downwelling planar irradiance reaching the surface (Es). For this, the current HyperNav systems measure Lw at about 2 nm spectral resolution in the ultraviolet to near infrared, but Es in only four 10 nm wide spectral bands centered on 412, 489, 555, and 705 nm. In this study, the Es data acquired in these spectral bands in clear sky conditions are used to reconstruct via a multi-linear regression model the hyper-spectral Es signal at 0.5 nm resolution from 315 to 900 nm, the OCI spectral range, allowing an estimate of Es at the HyperNav, OCI, and other sensors’ resolutions. After correction of gaseous absorption and normalization by the top-of-atmosphere incident solar flux, the atmospheric diffuse transmittance is expressed as a linear combination of Es measured in those 4 spectral bands. Based on simulations for Sun zenith angles from 0 to 75° and a wide range of (i.e., expected) atmospheric, surface, and water conditions, the Es spectrum is reconstructed with a bias of less than 0.4% in magnitude and an RMS error (RMSE) ranging from 0% to 2.5%, depending on wavelength. The largest errors occur in spectral regions with strong gaseous absorption. In the presence of typical noise on Es measurements and uncertainties on the ancillary variables, the bias and RMSE become −2.5% and 7.0%, respectively. Using a General Additive Model with coefficients depending on Sun zenith angle and aerosol optical thickness at 550 nm improves statistical performance in the absence of noise, especially in the ultraviolet, but provides similar performance on noisy data, indicating more sensitivity to noise. Adding spectral bands in the ultraviolet, e.g., centered on 325, 340, and 380 nm, yields marginally more accurate results in the ultraviolet, due to uncertainties in the gaseous transmittance. Comparisons between the measured and reconstructed Es spectra acquired by the MOBY spectroradiometer show agreement within predicted uncertainties, i.e., biases less than 2% in magnitude and RMS differences less than 5%. Reconstruction can also be achieved accurately with other sets of spectral bands and extended to cloudy conditions since cloud optical properties, like aerosol properties, tend to vary regularly with wavelength. These results indicate that it is sufficient, for many scientific applications involving hyper-spectral Es, to measure Es in a few coarse spectral bands in the ultraviolet to near infrared and reconstruct the hyperspectral signal using the proposed multivariate linear modeling.https://www.frontiersin.org/articles/10.3389/frsen.2024.1335627/fullHyperNavdownwelling planar irradiancemultivariate regressiongeneralized additive modelocean ColorMOBY |
| spellingShingle | Jing Tan Robert Frouin Nils Häentjens Andrew Barnard Emmanuel Boss Paul Chamberlain Matt Mazloff Cristina Orrico Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurements Frontiers in Remote Sensing HyperNav downwelling planar irradiance multivariate regression generalized additive model ocean Color MOBY |
| title | Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurements |
| title_full | Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurements |
| title_fullStr | Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurements |
| title_full_unstemmed | Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurements |
| title_short | Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurements |
| title_sort | reconstructing hyper spectral downwelling irradiance from multi spectral measurements |
| topic | HyperNav downwelling planar irradiance multivariate regression generalized additive model ocean Color MOBY |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2024.1335627/full |
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