scholarly journals Water remote sensing reflectance from radiative transfer simulations on a global scale of Inherent Optical Properties

2018 ◽  
Author(s):  
Lena Kritten ◽  
Rene Preusker ◽  
Carsten Brockmann ◽  
Tonio Fincke ◽  
Sampsa Koponen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Radiative Transfer ◽  
Spectral Response ◽  
Pure Water ◽  
Atmospheric Correction ◽  
Ocean Colour ◽  
Inherent Optical Properties ◽  
Remote Sensing Reflectance ◽  
Water Constituents

Abstract. The remote-sensing reflectance (Rrs) is in someway an artificial unit, that is constructed in order to contain the spectral colour information of the water body, but to be hardly influenced by the atmosphere above. In ocean colour remotesensing it is the measure to define the optical properties of the water/water constituents. Rrs is the ratio of water-leaving radiance and down-welling irradiance. It is derived from top-of-atmosphere radiance/reflectance measurements through atmospheric correction. A database with Rrs from radiative 5 transfer simulations is capable to serve as a forward model for the retrieval of water constituents. For the present database the Rrs is simulated in dependency of inherent optical properties (IOPs) representing pure water with different salinities and 5 water constituents (Chlorophyll-a-pigment, Detritus, CDOM (coloured dissolved organic matter), a "big" and a "small" scatterer) in a global range of concentrations. The interpolation points for each IOP were chosen in order to reproduce the entire functional relationship between this particular IOP and the corresponding Rrs. The IOPs are varied independently. The data is available for 9 solar, 9 viewing zenith and 25 azimuth angles. The spectral resolution of the data is 1nm, which allows the convolution to any ocean colour sensors’ spectral response function. The data is produced with the radiative transfer code MOMO (Matrix Operator Model), which simulates the full radiative transfer in atmosphere and ocean. The code is hosted at the institute of space sciences at Freie Universität Berlin and is not publicly available. The look-up table (LUT) is available at: doi:10.1594/WDCC/LUT_for_WDC_I (Kritten et al., 2017).

scholarly journals Hybrid Inversion Algorithms for Retrieval of Absorption Subcomponents from Ocean Colour Remote Sensing Reflectance

2021 ◽  
Vol 13 (9) ◽  
pp. 1726
Author(s):  
Srinivas Kolluru ◽  
Surya Prakash Tiwari ◽  
Shirishkumar S. Gedam
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Pure Water ◽  
Decomposition Algorithms ◽  
Sensing Applications ◽  
Remote Sensing Reflectance ◽  
Wide Range ◽  
Water Constituents ◽  
Ocean Colour Remote Sensing ◽  
Constraints Model

Semi-analytical algorithms (SAAs) invert spectral remote sensing reflectance (Rrs(λ), sr−1) to Inherent Optical Properties (IOPs) of an aquatic medium (λ is the wavelength). Existing SAAs implement different methodologies with a range of spectral IOP models and inversion methods producing concentrations of non-water constituents. Absorption spectrum decomposition algorithms (ADAs) are a set of algorithms developed to partition anw(λ), m−1 (i.e., the light absorption coefficient without pure water absorption), into absorption subcomponents of phytoplankton (aph(λ), m−1) and coloured detrital matter (adg(λ), m−1). Despite significant developments in ADAs, their applicability to remote sensing applications is rarely studied. The present study formulates hybrid inversion approaches that combine SAAs and ADAs to derive absorption subcomponents from Rrs(λ) and explores potential alternatives to operational SAAs. Using Rrs(λ) and concurrent absorption subcomponents from four datasets covering a wide range of optical properties, three operational SAAs, i.e., Garver–Siegel–Maritorena (GSM), Quasi-Analytical Algorithm (QAA), Generalized Inherent Optical Property (GIOP) model are evaluated in deriving anw(λ) from Rrs(λ). Among these three models, QAA and GIOP models derived anw(λ) with lower errors. Among six distinctive ADAs tested in the study, the Generalized Stacked Constraints Model (GSCM) and Zhang’s model-derived absorption subcomponents achieved lower average spectral mean absolute percentage errors (MAPE) in the range of 8–38%. Four hybrid models, GIOPGSCM, GIOPZhang, QAAGSCM and QAAZhang, formulated using the SAAs and ADAs, are compared for their absorption subcomponent retrieval performance from Rrs(λ). GIOPGSCM and GIOPZhang models derived absorption subcomponents have lower errors than GIOP and QAA. Potential uncertainties associated with datasets and dependency of algorithm performance on datasets were discussed.

Remote sensing reflectance and inherent optical properties of oceanic waters derived from above-water measurements

1997 ◽  
Author(s):  
Zhongping Lee ◽  
Kendall L. Carder ◽  
Robert G. Steward ◽  
Thomas G. Peacock ◽  
Curtiss O. Davis ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Inherent Optical Properties ◽  
Remote Sensing Reflectance

scholarly journals CAAS: an atmospheric correction algorithm for the remote sensing of complex waters

2012 ◽  
Vol 30 (1) ◽  
pp. 203-220 ◽  
Author(s):  
P. Shanmugam
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Near Infrared ◽  
Atmospheric Correction ◽  
Atmospheric Effects ◽  
Correction Algorithm ◽  
Ocean Colour ◽  
In Situ Data ◽  
Correction Scheme ◽  
The Impact

Abstract. The current SeaDAS atmospheric correction algorithm relies on the computation of optical properties of aerosols based on radiative transfer combined with a near-infrared (NIR) correction scheme (originally with assumptions of zero water-leaving radiance for the NIR bands) and several ancillary parameters to remove atmospheric effects in remote sensing of ocean colour. The failure of this algorithm over complex waters has been reported by many recent investigations, and can be attributed to the inadequate NIR correction and constraints for deriving aerosol optical properties whose characteristics are the most difficult to evaluate because they vary rapidly with time and space. The possibility that the aerosol and sun glint contributions can be derived in the whole spectrum of ocean colour solely from a knowledge of the total and Rayleigh-corrected radiances is developed in detail within the framework of a Complex water Atmospheric correction Algorithm Scheme (CAAS) that makes no use of ancillary parameters. The performance of the CAAS algorithm is demonstrated for MODIS/Aqua imageries of optically complex waters and yields physically realistic water-leaving radiance spectra that are not possible with the SeaDAS algorithm. A preliminary comparison with in-situ data for several regional waters (moderately complex to clear waters) shows encouraging results, with absolute errors of the CAAS algorithm closer to those of the SeaDAS algorithm. The impact of the atmospheric correction was also examined on chlorophyll retrievals with a Case 2 water bio-optical algorithm, and it was found that the CAAS algorithm outperformed the SeaDAS algorithm in terms of producing accurate pigment estimates and recovering areas previously flagged out by the later algorithm. These findings suggest that the CAAS algorithm can be used for applications focussing in quantitative assessments of the biological and biogeochemical properties in complex waters, and can easily be extended to other sensors such as OCM-2, MERIS and GOCI.

scholarly journals Evaluation of Weighting Average Functions as a Simplification of the Radiative Transfer Simulation in Vertically Inhomogeneous Eutrophic Waters

2019 ◽  
Vol 9 (8) ◽  
pp. 1635 ◽  
Author(s):  
Kun Xue ◽  
Ronghua Ma
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Radiative Transfer ◽  
Vertical Distribution ◽  
Attenuation Coefficient ◽  
Spectral Variation ◽  
Diffuse Attenuation Coefficient ◽  
Remote Sensing Reflectance ◽  
Vertical Variations ◽  
The Vertical Distribution

Current water color remote sensing algorithms typically do not consider the vertical variations of phytoplankton. Ecolight with a radiative transfer program was used to simulate the underwater light field of vertical inhomogeneous waters based on the optical properties of a eutrophic lake (i.e., Lake Chaohu, China). Results showed that the vertical distribution of chlorophyll-a (Chla(z)) can considerably affect spectrum shape and magnitude of apparent optical properties (AOPs), including subsurface remote sensing reflectance in water (rrs(λ, z)) and the diffuse attenuation coefficient (Kx(λ, z)). The vertical variations of Chla(z) changed the spectrum shapes of rrs(λ, z) at the green and red wavelengths with a maximum value at approximately 590 nm, and changed the Kx(λ, z) from blue to red wavelength range with no obvious spectral variation. The difference between rrs(λ, z) at depth z m and its asymptotic value (Δrrs(λ, z)) could reach to ~78% in highly stratified waters. Diffuse attenuation coefficient of downwelling plane irradiance (Kd(λ, z)) had larger vertical variations, especially near water surface, in highly stratified waters. Three weighting average functions performed well in less stratified waters, and the weighting average function proposed by Zaneveld et al., (2005) performed best in highly stratified waters. The total contribution of the first three layers to rrs(λ, 0−) was approximately 90%, but the contribution of each layer in the water column to rrs(λ, 0−) varied with wavelength, vertical distribution of Chla(z) profiles, concentration of suspended particulate inorganic matter (SPIM), and colored dissolved organic matter (CDOM). A simple stratified remote sensing reflectance model considering the vertical distribution of phytoplankton was built based on the contribution of each layer to rrs(λ, 0−).

scholarly journals A Reflectance Model for Relatively Clear and Turbid Waters

2013 ◽  
Vol 3 (1) ◽  
pp. 325-337
Author(s):  
S. P. Tiwari ◽  
P. Shanmugam ◽  
Y. H. Ahn ◽  
J. H. Ryu
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Suspended Sediments ◽  
Optical Data ◽  
Spectral Curves ◽  
Inherent Optical Properties ◽  
Turbid Waters ◽  
Remote Sensing Reflectance ◽  
South Sea ◽  
Q Factors

Accurate modeling of spectral remote sensing reflectance (Rrs) is of great interest for ocean colour studies in highly turbid and relatively clear waters. In this work a semianalytical model that simulates the spectral curves of remote sensing reflectance of these waters is developed based on the inherent optical properties (IOPs) and f and Q factors. For accommodating differences in the optical properties of the water and accounting for their directional variations, IOPs and f and Q  factors are derived as a function of phytoplankton pigments, suspended sediments and solar zenith angle. Results of this model are compared with in-situ bio-optical data collected at 83 stations encompassing highly turbid/relatively cleared waters of the South Sea of Korea. Measured and modeled remote sensing reflectances agree favorably in both magnitude and spectral shape, with considerably low errors (mean relative error MRE -0.0327; root mean square error RMSE 0.205, bias -0.0727 and slope 1.15 and correlation coefficient R2 0.74). These results suggest that the new model has the ability to reproduce measured reflectance values and has potentially profound implications for remote sensing of complex waters in this region.

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