Spectral optimization for constituent retrieval in Case 2 waters II: Validation study in the Chesapeake Bay

Kuchinke, Christopher; Gordon, Howard R.; Harding, Lawrence W.; Voss, Kenneth J.

doi:10.1016/j.rse.2008.11.002

Cited by 40 publications

(16 citation statements)

References 24 publications

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“…In coastal waters, the contribution of the particle backscattering at NIR wavelengths is no longer negligible [16][17][18]. To solve this problem, different AC approaches have been proposed based on: (i) the use of SWIR [17,19] or ultraviolet [20,21] spectral bands; (ii) the spatial homogeneity of the NIR band ratio [13,16,22]; (iii) the spectral shape matching method [23][24][25][26]; or (iv) direct neural networks inversion [27,28] and adding constraints to the GW94 scheme for taking into account a non-zero water-leaving reflectance [29][30][31][32][33][34]. However, the requirement for spectral bands at the NIR and SWIR wavelengths for HRS space missions severely limits these approaches.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric Correction of Multi-Spectral Littoral Images Using a PHOTONS/AERONET-Based Regional Aerosol Model

et al. 2017

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Spatial resolution is the main instrumental requirement for the multi-spectral optical space missions that address the scientific issues of marine coastal systems. This spatial resolution should be at least decametric. Aquatic color data processing associated with these environments requires specific atmospheric corrections (AC) suitable for the spectral characteristics of high spatial resolution sensors (HRS) as well as the high range of atmospheric and marine optical properties. The objective of the present study is to develop and demonstrate the potential of a ground-based AC approach adaptable to any HRS for regional monitoring and security of littoral systems. The in Situ-based Atmospheric CORrection (SACOR) algorithm is based on simulations provided by a Successive Order of Scattering code (SOS), which is constrained by a simple regional aerosol particle model (RAM). This RAM is defined from the mixture of a standard tropospheric and maritime aerosol type. The RAM is derived from the following two processes. The first process involved the analysis of a 6-year data set composed of aerosol optical and microphysical properties acquired through the ground-based PHOTONS/AERONET network located at Arcachon (France). The second process was related to aerosol climatology using the NOAA hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model. Results show that aerosols have a bimodal particle size distribution regardless of the season and are mainly represented by a mixed coastal continental type. Furthermore, the results indicate that aerosols originate from both the Atlantic Ocean (53.6%) and Continental Europe (46.4%). Based on these results, absorbing biomass burning, urban-industrial and desert dust particles have not been considered although they represent on average 19% of the occurrences. This represents the main current limitation of the RAM. An assessment of the performances of SACOR is then performed by inter-comparing the water-leaving reflectance (ρ w ) retrievals with three different AC methods (ACOLITE, MACCS and 6SV using three different standard aerosol types) using match-ups (N = 8) composed of Landsat-8/Operational Land Imager (OLI) scenes and field radiometric measurements. Results indicate consistency with the SWIR-based ACOLITE method, which shows the best performance, except in the green channel where SACOR matches well with the in-situ data (relative error of 7%). In conclusion, the study demonstrates the high potential of the SACOR approach for the retrieval of ρ w . In the future, the method could be improved by using an adaptive aerosol model, which may select the most relevant local aerosol model following the origin of the atmospheric air mass, and could be applied to the latest HRS (Sentinel-2/MSI, SPOT6-7, Pleiades 1A-1B).

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Section: Introductionmentioning

confidence: 99%

Atmospheric Correction of Multi-Spectral Littoral Images Using a PHOTONS/AERONET-Based Regional Aerosol Model

et al. 2017

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“…The spectral optimisation algorithm (SOA) [164,165] employs a simple aerosol model which determines the size of aerosol particles with a Junge power-law distribution. This distribution is a simplification, which enables SOA to achieve optimisation when there are a limited number of spectral bands [223]. This algorithm's performance was demonstrated when it was used for atmospheric correction of turbid waters on a synthetic dataset [224] and on SeaWiFS data in a case study of Chesapeake Bay (Atlantic coast, USA) [223].…”

Section: Spectral Inversion Approachesmentioning

confidence: 99%

Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

et al. 2015

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Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges.

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“…More recent efforts have concentrated on a variety of non-linear optimization algorithms (for a review see [26]). Despite this switch to more advanced optimization methods, many authors still use simple error, or objective functions based on the sum of squared error (SSE) or mean squared error (MSE) to measure the disparity between measured and modeled r rs during optimization [2,3,25,26,36,37]:…”

Section: Inversion Of Reflectancementioning

confidence: 99%

“…The large geographical coverage of airborne and satellite imaging spectrometers has made them useful for measuring global and regional water quality, especially in the determination of concentrations of water constituents such as phytoplankton, suspended sediment, and colored dissolved organic matter (CDOM) [1][2][3]. However, in tropical regions, frequent cloud cover and unpredictable weather constrain the use of airborne and satellite optical sensors [4].…”

Section: Introductionmentioning

confidence: 99%

“…The term Q has been found to have some wavelength dependence [20], but most approximations assume it is constant over visible wavelengths. Often, the value of Q is taken from the literature and assumed constant, but it can also be approximated as in Gons [11]: Q 2.38 µ ad ; µ ad cos pθ s q ¤ p1 ¡ Fq 0.5F (2) where µ ad is the average cosine of the downwelling light field above the water, θ s is the solar zenith angle, and F is the ratio of diffuse to total irradiance incident on the water surface between 400 and 700 nm. Gons [11] modeled the downwelling cosine assuming the light field was isotropic when the sky was clear or had variable cloud cover and cardioidal when the sky was overcast.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of a Semi-Analytical Algorithm for Multi-Temporal Water Quality Monitoring in Inland Waters with Wide Natural Variability

Bramante

Sin

2015

Remote Sensing

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Current spectrometer design and the increasingly affordable price of field hyperspectral sensors are making feasible their use for water quality monitoring. In this study, we parameterized a semi-analytical algorithm to derive constituent concentrations from field spectroradiometer measurements in ten freshwater reservoirs over two years. In contrast to algorithms parameterized for single airborne or satellite sensor deployments, we optimized the algorithm for robust performance across all reservoirs and for multi-temporal application. Our algorithm produced chlorophyll-a concentration estimates with a root mean squared error (RMSE) of 7.7 mg¤m ¡3 over a range of 4-135 mg¤m ¡3 . The model also produced estimates of total suspended solids (TSS) concentration with an RMSE of 4.0 g¤m ¡3 over a range of 0-25 g¤m ¡3 . Choosing a non-linear objective function during inversion reduced variance of residuals in chlorophyll-a and TSS estimates by 20 and 18 percentage points, respectively. Application of our algorithm to two years of data and over ten study sites allowed us to specify sources of suboptimal parameterization and measure the non-stationarity of algorithm performance, analyses difficult for short or single deployments. Suboptimal parameterization, especially of backscatter properties between reservoirs, was the greatest source of error in our algorithm, accounting for 17%-20% of all error. In only one reservoir was time-dependent error apparent. In this reservoir, decreases in TSS over time resulted in less TSS estimate error due to imperfect model parameterization. For future applications, especially with ground-based sensors, model performance can easily be improved by using non-linear inversion procedures and replicating spectral measurements.

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Spectral optimization for constituent retrieval in Case 2 waters II: Validation study in the Chesapeake Bay

Cited by 40 publications

References 24 publications

Atmospheric Correction of Multi-Spectral Littoral Images Using a PHOTONS/AERONET-Based Regional Aerosol Model

Atmospheric Correction of Multi-Spectral Littoral Images Using a PHOTONS/AERONET-Based Regional Aerosol Model

Sensor Capability and Atmospheric Correction in Ocean Colour Remote Sensing

Optimization of a Semi-Analytical Algorithm for Multi-Temporal Water Quality Monitoring in Inland Waters with Wide Natural Variability

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