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.

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 Glint Removal Assessment to Estimate the Remote Sensing Reflectance in Inland Waters with Widely Differing Optical Properties

2018 ◽  
Vol 10 (10) ◽  
pp. 1655 ◽  
Author(s):  
Nariane Bernardo ◽  
Enner Alcântara ◽  
Fernanda Watanabe ◽  
Thanan Rodrigues ◽  
Alisson Carmo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Wind Speed ◽  
In Situ Measurements ◽  
Colored Dissolved Organic Matter ◽  
The Third ◽  
Sensing Applications ◽  
Remote Sensing Reflectance ◽  
Remote Sensing Applications

The quality control of remote sensing reflectance (Rrs) is a challenging task in remote sensing applications, mainly in the retrieval of accurate in situ measurements carried out in optically complex aquatic systems. One of the main challenges is related to glint effect into the in situ measurements. Our study evaluates four different methods to reduce the glint effect from the Rrs spectra collected in cascade reservoirs with widely differing optical properties. The first (i) method adopts a constant coefficient for skylight correction (ρ) for any geometry viewing of in situ measurements and wind speed lower than 5 m·s−1; (ii) the second uses a look-up-table with variable ρ values accordingly to viewing geometry acquisition and wind speed; (iii) the third method is based on hyperspectral optimization to produce a spectral glint correction, and (iv) computes ρ as a function of wind speed. The glint effect corrected Rrs spectra were assessed using HydroLight simulations. The results showed that using the glint correction with spectral ρ achieved the lowest errors, however, in a Colored Dissolved Organic Matter (CDOM) dominated environment with no remarkable chlorophyll-a concentrations, the best method was the second. Besides, the results with spectral glint correction reduced almost 30% of errors.

scholarly journals Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

2019 ◽  
Vol 11 (3) ◽  
pp. 230 ◽  
Author(s):  
Tien Pham ◽  
Naoto Yokoya ◽  
Dieu Bui ◽  
Kunihiko Yoshino ◽  
Daniel Friess
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Mangrove Ecosystem ◽  
Carbon Stocks ◽  
Machine Learning Techniques ◽  
Species Structure ◽  
Mangrove Species ◽  
Mitigation And Adaptation ◽  
Sensing Applications ◽  
Wide Range

The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks.

scholarly journals Spatio-Temporal Variability in Bio-Optical Properties of the Southern Caspian Sea: A Historic Analysis of Ocean Color Data

2020 ◽  
Vol 12 (23) ◽  
pp. 3975
Author(s):  
Bonyad Ahmadi ◽  
Mehdi Gholamalifard ◽  
Tiit Kutser ◽  
Stefano Vignudelli ◽  
Andrey Kostianoy
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Attenuation Coefficient ◽  
Temporal Variability ◽  
Caspian Sea ◽  
Ocean Color ◽  
Time Lags ◽  
Chl A ◽  
Remote Sensing Reflectance ◽  
Southern Caspian Sea

Currently, satellite ocean color imageries play an important role in monitoring of water properties in various oceanic, coastal, and inland ecosystems. Although there is a long-time and global archive of such valuable data, no study has comprehensively used these data to assess the changes in the Caspian Sea. Hence, this study assessed the variability of bio-optical properties of the upper-water column in the Southern Caspian Sea (SCS) using the archive of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The images acquired from SeaWiFS (January 1998 to December 2002) and MODIS Aqua (January 2003 to December 2015) satellites were used to investigate the spatial–temporal variability of bio-optical properties including Chlorophyll-a (Chl-a), attenuation coefficient, and remote sensing reflectance, and environmental parameters such as sea surface temperature (SST), wind stress and the El Nino-southern oscillation (ENSO) phenomena at different time lags in the study area. The trend analysis demonstrated an overall increase of 0.3358 mg m−3 in the Chl-a concentration during 1998–2015 (annual increase rate of 0.018 mg m−3 year−1) and four algal blooms and in turn an abnormal increase in Chl-a concentration were occurred in August 2001, September 2005, 2009, and August 2010. The linear model revealed that Chl-a in the northern and middle part of the study area had been influenced by the attenuation coefficient after a one-month lag time. The analysis revealed a sharp decline in Chl-a concentration during 2011–2015 and showed a high correlation with the turbidity and attenuation coefficient in the southern region, while Kd_490nm and remote sensing reflectance did a low one. Generally, Chl-a concentration exhibited a positive correlation with the attenuation coefficient (r = 0.63) and with remote sensing reflectance at the 555 nm (r = 0.111). This study can be used as the basis for predictive modeling to evaluate the changes of water quality and bio-optical indices in the Southern Caspian Sea (SCS).

scholarly journals MOPSMAP v0.9: A versatile tool for modeling of aerosol optical properties

2018 ◽  
Author(s):  
Josef Gasteiger ◽  
Matthias Wiegner
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Aerosol Particles ◽  
Particle Orientation ◽  
Spherical Particles ◽  
Refractive Indices ◽  
Hygroscopic Growth ◽  
Web Interface ◽  
Optical Modeling ◽  
Wide Range

Abstract. The spatiotemporal distribution and characterization of aerosol particles are usually determined by remote sensing and optical in-situ measurements. These measurements are indirect with respect to microphysical properties and thus inversion techniques are required to determine the aerosol microphysics. Scattering theory provides the link between microphysical and optical properties; it is not only needed for such inversions but also for radiative budget calculations and climate modeling. However, optical modeling can be very time consuming, in particular if non-spherical particles or complex ensembles are involved. In this paper we present the MOPSMAP package (modeled optical properties of ensembles of aerosol particles) which is computationally fast for optical modeling even in case of complex aerosols. The package consists of a data set of pre-calculated optical properties of single aerosol particles, a Fortran program to calculate the properties of user-defined aerosol ensembles, and a user-friendly web interface for online calculations. Spheres, spheroids, and a small set of irregular particle shapes are considered over a wide range of sizes and refractive indices. MOPSMAP provides the fundamental optical properties assuming random particle orientation, including the scattering matrix for the selected wavelengths. Moreover, the output includes tables of frequently used properties such as the single scattering albedo, the asymmetry parameter or the lidar ratio. To demonstrate the wide range of possible MOPSMAP applications a selection of examples is presented, e.g., dealing with hygroscopic growth, mixtures of absorbing and non-absorbing particles, the relevance of the size equivalence in case of non-spherical particles, and the variability of volcanic ash microphysics. The web interface is designed to be intuitive for expert and non-expert users. To support users a large set of default settings is available, e.g., several wavelength-dependent refractive indices, climatologically representative size distributions, and a parameterization of hygroscopic growth. Calculations are possible for single wavelengths or user-defined sets (e.g., of specific remote sensing application). For expert users more options for the microphysics are available. Plots for immediate visualization of the results are shown. The complete output can be downloaded for further applications. All input parameters and results are stored in the user’s personal folder so that calculations can easily be reproduced. The MOPSMAP package is available on request for offline calculations, e.g., when large numbers of different runs for sensitivity studies shall be made.

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
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